ALBERT

Description: Festivals are experiential products heavily depending on the recommendations of previous visitors. With the power of social media growing, understanding the antecedents of positive electronic word-of-mouth (eWOM) intentions of festival attendees is immensely beneficial for festival organizers to better promote their festivals and control negative publicity. However, there is still limited research regarding eWOM intentions in the festival context. Thus, this study aims to fill such a gap by investigating the relationships among festival attendees’ enjoyment seeking motivation, perceived value, visitor satisfaction, and eWOM intention in a local festival setting. Additionally, the moderating role of gender was tested as it is one of the most important demographic variables to show individual differences in behavioral intentions. The results of structural equation modeling showed a positive effect of enjoyment seeking motivation on perceived value, visitor satisfaction, and eWOM intention. Moreover, gender differences in eWOM intention and a full mediating effect of visitor satisfaction between perceived value and eWOM intention for female respondents were revealed. The findings of this study extend the existing festival literature and provide insights for strategically organizing and promoting festivals to generate more positive eWOM which can be utilized as an effective marketing tool and a feedback channel.

Description: Access and use of seed of improved varieties of groundnut among farmers can improve farmers’ livelihoods and contribute to the potential of crop production in Tanzania. This paper analyzes factors underpinning the adoption of improved groundnut varieties among farmers to pave the way for upscaling quality seed used for increased production and commodity business in farming communities. A four-stage stratified sampling was used to collect data from 300 groundnut farmers in seven agro-ecological zones through individual interviews. Secondary data were collected from the literature and the Tanzania Agricultural Research Institute at Naliendele centre (TARI–Naliendele). Descriptive statistics and Probit regression model were used for data analysis. The empirical results showed that Johari 1985, Pendo 1998, Naliendele 2009, Mnanje 2009, Mangaka 2009 and Nachi 2015, are the main six improved groundnut varieties used by farmers, with Pendo 1998 having the highest adoption rate (17.1%). In the grain market, four varieties, namely Pendo 1998, Mnanje 2009, Nachi 2015 and Johari 1985, were observed to be highly preferred by grain off-takers. Furthermore, among the adopted improved varieties, Nachi 2015, is observed to be the most consistent high yielding variety, ranging from 1100 kg/ha to 1500 kg/ha in all agro-ecological zones. A farmer’s decision to adopt new varieties is affected by age and gender, farmer group membership, availability of improved seed and seed cost. Overall, male farmers are more likely to adopt improved varieties of groundnut than female farmers. The implications of these findings are also discussed, in particular in the area of policy support.

Description: Green bonds are similar to conventional bonds but are specifically earmarked to raise money to finance climate or environmental projects. There have been anecdotes of green bonds being priced tighter than similar conventional bonds by the same issuers. Our survey of academic literature indicates that most papers show the yield of a green bond is lower than that of the equivalent conventional bond at issuance (also known as green premium or greenium). However, green bond pricing studies by Climate Bonds Initiative produce mixed results. The conflicting results are likely explained by differences in sample selections, time periods, methodologies, and the properties of the respective issuing entity and the bond. In addition, we examine investment returns from select green bond funds and green bond indexes. The assets under management of those funds are still small and they underperform their benchmark indexes.

Description: Information and communication technologies transform modern education into a more available learning matrix. One of the unexplored aspects of open education is the constant communicative interaction within the student group by using social media. The aim of the study was to determine principal functions of student-led communication in the educational process, the method for assessing its strong points and the disadvantages disrupting traditional learning. For the primary study of the phenomenon, we used methods that made it possible to propose approaches to further analysis. Netnography is the main research method defining the essence and characteristics of the student-led peer-communication. In our research, we applied data visualization, analytical and quantitative methods and developed a set of quantitative indicators that can be used to assess various aspects of student communication in chats. The elaborated visual model can serve as a simple tool for diagnosing group communication processes. We revealed that online group chats perform a support function in learning. They provide constant informational resource on educational and organizational issues and create emotional comfort. Identified features serve to define shortcomings (e.g., lack of students’ readiness to freely exchange answers to assignments) and significant factors (e.g., underutilized opportunities for self-organization) that exist in the modern system of higher education.

Description: Sugarcane is an important industrial crop because it is the major source of white sugar. It is also one of the crops for the alcohol and biofuel industries. Disease-causing organisms can significantly decrease the productivity of sugarcane plants and sugar quality. Among the disease-causing organisms, Colletotrichum falcatum Went causes the most significant economic loss (5–50%) in the sugarcane production due to red rot disease. This loss results in only 31% sugar recovery. It is reported that C. falcatum can kill sugarcane plants. Currently, there is no sustainable way of preventing red rot disease from spreading in sugarcane plantations. Many popular sugarcane varieties are no longer used in sugarcane cultivation because of their susceptibility to C. falcatum. The objectives of this manuscript were to: (i) summarize existing approaches for the early detection of red rot disease and controlling techniques of red rot disease in the field and laboratory and (ii) assess red rot disease control effectiveness so as to propose better methods for mitigating the spread C. falcatum. If our proposition is adopted or practiced, it could significantly contribute to the mitigation of C. falcatum infection in the sugarcane industry. This could enable achieving sustainable cultivation of sugarcanes to guarantee the sustainability of the sugar industry in the tropics and the subtropics.

Description: The transition from intensive conventional agriculture to sustainable agriculture has become a global priority. This is due to the need for environmentally friendly agriculture to ensure sufficient food for a rapidly growing population. The bioeconomy is essential to progress in the field of sustainable agriculture. It contributes to the conservation of biological resources through circular and comprehensive management. The bioeconomy prioritizes the reduction and reuse of materials and products. The focus of this study is the use of biodegradable/compostable raffia in protected horticultural crops in the Province of Almería (Spain). The analysis and evaluations, based on an extensive literature review and information given by stakeholders, determined that biodegradable raffia use significantly improves the management of residual biomass in Almería. However, biodegradable raffia is only used in a small percentage of crops even though it is a sustainable alternative and profitable for farmers. The economic analysis incorporates the higher cost of recycling non-biodegradable raffia.

Description: Sustainable agricultural production is endangered by several ecological factors, such as drought, extreme temperatures, excessive salts, parasitic ailments, and insect pest infestation. These challenging environmental factors may have adverse effects on future agriculture production in many countries. In modern agriculture, conventional crop-breeding techniques alone are inadequate for achieving the increasing population’s food demand on a sustainable basis. The advancement of molecular genetics and related technologies are promising tools for the selection of new crop species. Gene pyramiding through marker-assisted selection (MAS) and other techniques have accelerated the development of durable resistant/tolerant lines with high accuracy in the shortest period of time for agricultural sustainability. Gene stacking has not been fully utilized for biotic stress resistance development and quality improvement in most of the major cultivated crops. This review emphasizes on gene pyramiding techniques that are being successfully deployed in modern agriculture for improving crop tolerance to biotic and abiotic stresses for sustainable crop improvement.

Description: The aim of the study was to evaluate the changes in glomalin-related soil proteins (GRSP) content, microbial diversity and soil physical quality depending on the type of soil measures of soil improvement and changes in soil health. The study was based on a 100-year stationary field microplot experiment where the soil profiles were collected with preserving the natural soil horizons. The microplot experiment was carried out on eight different soil types: Brunic Arenosol (Dystric I), Rendzic Leptosol, Fluvic Cambisol, Haplic Cambisol (Eutric), Gleyic Phaeozem, Brunic Arenosol (Dystric II), Haplic Cambisol (Eutric II) and Haplic Cambisol (Dystric). These soils are the most common types of agricultural soils in Poland. Relatively significant correlations with the soil quality, physical parameters and the glomalin-related soil proteins have been found. The study determined the total GRSP (T-GRSP) and easily extractable GRSP (EE-GRSP) levels in soils as well as the soil physical quality index and soil’s microbial biodiversity. The GRSP depended on the type of soil and correlated with S-Index and also was responsible for the unique chemical and physical properties of soils. Soils characterized by the highest T-GRSP content belonged to the group of very good and good soil physical quality characterized also by high biological activity, for which there were strong correlations with such parameters as dehydrogenase activity (DHA), microbial biomass content (MBC), microbial nitrogen content (MBN) and total bacteria number (B). The highest T-GRSP content and higher microbial diversity were found in Gleyic Phaeozem, Rendzic Leptosol and Fluvic Cambisol. The T-GRSP and EE-GRSP content were additionally correlated with the number of AMF spores. Very poor and poor soil physical quality according to S-Index characterized Brunic Arenosol (Dystric I) and Haplic Cambisol (Dystric). This research indicates that a specific edaphone of soil microorganisms and GRSP content may be of great importance when assessing a soil’s quality and improvements in soil health. The abundance of glomalin-producing fungi significantly affects the quality of the soil. This effect is particularly important for agricultural soils are threatened by ongoing land degradation.

Description: Leaf rust (LR) and stem rust (SR) pose serious challenges to wheat production in Kazakhstan. In recent years, the susceptibility of local wheat cultivars has substantially decreased grain yield and quality. Therefore, local breeding projects must be adjusted toward the improvement of LR and SR disease resistances, including genetic approaches. In this study, a spring wheat segregating population of Pamyati Azieva (PA) × Paragon (Par), consisting of 98 recombinant inbred lines (RILs), was analyzed for the resistance to LR and SR at the seedling and adult plant-growth stages. In total, 24 quantitative trait loci (QTLs) for resistance to rust diseases at the seedling and adult plant stages were identified, including 11 QTLs for LR and 13 QTLs for SR resistances. Fourteen QTLs were in similar locations to QTLs and major genes detected in previous linkage mapping and genome-wide association studies. The remaining 10 QTLs are potentially new genetic factors for LR and SR resistance in wheat. Overall, the QTLs revealed in this study may play an important role in the improvement of wheat resistance to LR and SR per the marker-assisted selection approach.

Description: Due to the growing success of neural machine translation (NMT), many have started to question its applicability within the field of literary translation. In order to grasp the possibilities of NMT, we studied the output of the neural machine system of Google Translate (GNMT) and DeepL when applied to four classic novels translated from English into Dutch. The quality of the NMT systems is discussed by focusing on manual annotations, and we also employed various metrics in order to get an insight into lexical richness, local cohesion, syntactic, and stylistic difference. Firstly, we discovered that a large proportion of the translated sentences contained errors. We also observed a lower level of lexical richness and local cohesion in the NMTs compared to the human translations. In addition, NMTs are more likely to follow the syntactic structure of a source sentence, whereas human translations can differ. Lastly, the human translations deviate from the machine translations in style.

Description: The emergence and outbreak of the novel coronavirus (COVID-19) had a devasting effect on global health, the economy, and individuals’ daily lives. Timely diagnosis of COVID-19 is a crucial task, as it reduces the risk of pandemic spread, and early treatment will save patients’ life. Due to the time-consuming, complex nature, and high false-negative rate of the gold-standard RT-PCR test used for the diagnosis of COVID-19, the need for an additional diagnosis method has increased. Studies have proved the significance of X-ray images for the diagnosis of COVID-19. The dissemination of deep-learning techniques on X-ray images can automate the diagnosis process and serve as an assistive tool for radiologists. In this study, we used four deep-learning models—DenseNet121, ResNet50, VGG16, and VGG19—using the transfer-learning concept for the diagnosis of X-ray images as COVID-19 or normal. In the proposed study, VGG16 and VGG19 outperformed the other two deep-learning models. The study achieved an overall classification accuracy of 99.3%.

Description: In this work, we demonstrate how the blockchain and the off-chain storage interact via Oracle-based mechanisms, which build an effective connection between a distributed database and real assets. For demonstration purposes, smart contracts were drawn up to deal with two different applications. Due to the characteristics of the blockchain, we may still encounter severe privacy issues, since the data stored on the blockchain are exposed to the public. The proposed scheme provides a general solution for resolving the above-mentioned privacy issue; that is, we try to protect the on-chain privacy of the sensitive data by using homomorphic encryption techniques. Specifically, we constructed a secure comparison protocol that can check the correctness of a logic function directly in the encrypted domain. By using the proposed access control contract and the secure comparison protocol, one can carry out sensitive data-dependent smart contract operations without revealing the data themselves.

Description: Healthcare facilities are constantly deteriorating due to tight budgets allocated to the upkeep of building assets. This entails the need for improved deterioration modeling of such buildings in order to enforce a predictive maintenance approach that decreases the unexpected occurrence of failures and the corresponding downtime elapsed to repair or replace the faulty asset components. Currently, hospitals utilize subjective deterioration prediction methodologies that mostly rely on age as the sole indicator of degradation to forecast the useful lives of the building components. Thus, this paper aims at formulating a more efficient stochastic deterioration prediction model that integrates the latest observed condition into the forecasting procedure to overcome the subjectivity and uncertainties associated with the currently employed methods. This is achieved by means of developing a hybrid genetic algorithm-based fuzzy Markovian model that simulates the deterioration process given the scarcity of available data demonstrating the condition assessment and evaluation for such critical facilities. A nonhomogeneous transition probability matrix (TPM) based on fuzzy membership functions representing the condition, age and relative deterioration rate of the hospital systems is utilized to address the inherited uncertainties. The TPM is further calibrated by means of a genetic algorithm to circumvent the drawbacks of the expert-based models. A sensitivity analysis was carried out to analyze the possible changes in the output resulting from predefined modifications to the input parameters in order to ensure the robustness of the model. The performance of the deterioration prediction model developed is then validated through a comparison with a state-of-art stochastic model in contrast to real hospital datasets, and the results obtained from the developed model significantly outperformed the long-established Weibull distribution-based deterioration prediction methodology with mean absolute errors of 1.405 and 9.852, respectively. Therefore, the developed model is expected to assist decision-makers in creating more efficient maintenance programs as well as more data-driven capital renewal plans.

Description: Organic amendments can reportedly sustain and increase lowland rice productivity in smallholder systems. Few studies have assessed locally-available substrates in hydrologically variable floodplain environments. We investigated the effects of green and farmyard manures on rice yields, and total soil C and N in the Kilombero floodplain, Tanzania. At both the fringe and the middle positions, five treatments were applied in 2016 and 2017, comprising (1) non-amended control, (2) farmyard manure, (3) pre-rice legumes, (4) post-rice legumes and (5) a combination of green and farmyard manures. Residual treatment effects were assessed in 2018 when rice plots were uniformly non-amended. Depending on the year and the position, organic amendments increased rice grain yields by 0.7–3.1 Mg ha–1 above the non-amended control. Sole green and farmyard manure applications had similar effects on grain yield, while a combination of green and farmyard manure led to a significant increase in grain yield above both the control and sole applications of organic amendments in both years. The contribution from biological N2 fixation by legumes ranged from 4 to 61 kg N ha–1. Despite partial N balances being mostly negative, we observed positive residual effects on the yield of the non-amended rice in the third year. Such effects reached up to 4 Mg ha−1 and were largest with post-rice legumes, sole or combined with farmyard manure. Irrespective of the position in the floodplain, manures significantly increased soil C and N contents after two years, hence enhancing soil fertility and resulting in increased rice grain yields. Comparable benefits may be obtained along the hydrological gradients of other large river floodplains of the region and beyond.

Description: The aim of this study was to analyse the genetic and phenotypic differentiation of bacteria isolated from root nodules of Trifolium rubens, a rare clover species. The symbiotic compatibility of selected isolates was investigated using two agronomically important plants, red clover (Trifolium pratense L.) and pea (Pisum sativum L.). A high genetic diversity of analysed strains was shown using the Enterobacterial Repetitive Intergenic Consensus Polymerase Chain Reaction (ERIC-PCR) method. Most of the strains showed a high similarity to R. leguminosarum in the 16S rRNA sequence. Two strains were identified as Agrobacterium spp. There was a strong positive correlation between fresh clover weight and the number of root nodules and a statistically significant increase in the number of the root nodules in selected strains compared to the negative control. Phenotypic tests and BIOLOG analysis showed the ability of the analysed strains to grow in different experimental conditions (pH = 6–10, 0.5–3% NaCl, different carbon and nitrogen sources), which makes them suitable for application as an agronomically important factor.

Description: Ecuador Andean purple corn (Zea mays L.) was subjected to a germination process at 15–40 °C for 24–168 incubation hours. Purple corn protein concentrates (PCPCs) were obtained by alkaline extraction at pH 8.0 and pH 10.0, followed by an isoelectric precipitation process at pH 4.0, pH 5.0 and pH 6.0. Proteins and phenolic content of PCPCs was calculated. PCPC antioxidant properties were determined by the ferric-reducing antioxidant power (FRAP) in vitro method and by the 2,2-azinobis, 3-ethyl-benzothiazoline-6-sulfonic acid, (ABTS) in vitro method. Andean purple corn seeds were able to germinate under the germination conditions tested in this study. The higher percentage of germination was of 63.33% at 168 h/25 °C. The PCPCs protein profile was characterized for the presence of six bands with molecular weights of 14.50 kDa, 20.12 kDa, 25.18 kDa, 41.85 kDa, 59.59 kDa, and 65.87 kDa. Germinated PCPC presented a high TPC content with ranges of 605.71–1820.00 mg gallic acid equivalents (GAE)/g PCPC dry weight (DW), germinated PCPC/72 h/25 °C presented a higher value of 1820.00 mg GAE/g PCPC, DW. All germinated PCPCs samples assayed presented strong antioxidant activity when measured by the ABTS and FRAP methods. Germinated PCPC/144 h/35 °C presented high antioxidant activity by ABTS with 804.35 µmol Trolox equivalents (TE)/g PCPC DW and germinated PCPC/144 h/30 °C presented a high value by the FRAP method, 987.83 µmol TE/g PCPC DW.

Description: Weedy rice is one of the most common weeds in rice cultivation in many rice areas throughout the world and it is able to cause significant yield reductions. Weedy rice is characterized by a high biological diversity that permits different populations to be identified on the basis of their morphological and physiological traits. This variability contributes to its success in different environments and allows different abiotic stresses, which are intensified by climate change, to be faced. Taller plants, enhanced tillering, seed shattering and the presence of red pericarp, variable hull coloration and awn morphology, linked to a deeper seed dormancy, are some of the traits that help weedy rice to spread in changing environments. The higher phenotypic plasticity and genetic variability of weedy rice make it more able to cope with temperature variations, intermittent water availability, soil salinity, drought conditions and increased CO2 concentrations than cultivated rice. As these abiotic stresses will become more frequent in the future, weedy rice competitiveness may be higher, with a spread of infestations. Thus, the control of weedy rice should be based on an integration of different preventive and agronomic techniques, a sensible use of herbicides and the use of suitable rice varieties.

Description: Testing Fusarium resistance to ear rots in maize requires a well-supported methodology and tests for toxin responses. In this study, commercial hybrids were tested for resistance to Fusarium graminearum, Fusarium culmorum, and Fusarium verticillioides (kernel and silk channel), as well as their toxin response. One third of the hybrids tested showed a similar resistance or susceptibility to the three pathogens and their toxin response, but there is no proof for their genetic background being the same or different. The performance of the remaining hybrids was highly variable and supports the idea of different genetic regulation. The mean ear rot severity of the kernel resistance was doubled compared with the silk channel resistance data. The ear rot and toxin tests displayed significant positive correlations, verifying the decisive role of resistance in toxin regulation. Several hybrids, termed toxigenic hybrids, showed significant extra toxin production, indicating an additional food safety risk. The toothpick method gave more reliable results and a better differentiation of genotypes. The resistance to different Fusarium spp. in a specific growing region should be analyzed separately in independent resistance tests. Through this, the food safety risks could be better identified. Susceptible hybrids should not be used for commercial production.

Description: The harmonic closeness centrality measure associates, to each node of a graph, the average of the inverse of its distances from all the other nodes (by assuming that unreachable nodes are at infinite distance). This notion has been adapted to temporal graphs (that is, graphs in which edges can appear and disappear during time) and in this paper we address the question of finding the top-k nodes for this metric. Computing the temporal closeness for one node can be done in O(m) time, where m is the number of temporal edges. Therefore computing exactly the closeness for all nodes, in order to find the ones with top closeness, would require O(nm) time, where n is the number of nodes. This time complexity is intractable for large temporal graphs. Instead, we show how this measure can be efficiently approximated by using a “backward” temporal breadth-first search algorithm and a classical sampling technique. Our experimental results show that the approximation is excellent for nodes with high closeness, allowing us to detect them in practice in a fraction of the time needed for computing the exact closeness of all nodes. We validate our approach with an extensive set of experiments.

Description: The study’s objective was to evaluate the influence of thinning on the quality and regularity of yield of ‘Regina’ cherries grown on a dwarf Gisela 5 rootstock. The experiments were conducted in the years 2009–2012 in Western Poland. Trees were thinned using a chemical agent, ammonium thiosulphate (ATS), at doses of 20 g ATS L−1, 30 g ATS L−1 and 40 g ATS L−1, and by hand, and the results of chemical and hand thinning were compared with those obtained for unthinned trees. The course of weather conditions in winter and during flowering had a significant impact on yield. Chemical thinning is known to be an effective method to regulate fruiting of pome trees, but it can also be successful if applied to cherry trees, as reflected by the alternate bearing index, which was the lowest after applying ATS at a dose of 40 g. ATS treatment improved fruit quality in full crop years. Thinning resulted in, among other things, larger and darker fruit and higher content of total soluble solids and titratable acidity. Treatment with 40 g ATS L−1 significantly stimulated the vegetative growth of trees.

Description: Computer programmers require various instructive information during coding and development. Such information is dispersed in different sources like language documentation, wikis, and forums. As an information exchange platform, programmers broadly utilize Stack Overflow, a Web-based Question Answering site. In this paper, we propose a recommender system which uses a supervised machine learning approach to investigate Stack Overflow posts to present instructive information for the programmers. This might be helpful for the programmers to solve programming problems that they confront with in their daily life. We analyzed posts related to two most popular programming languages—Python and PHP. We performed a few trials and found that the supervised approach could effectively manifold valuable information from our corpus. We validated the performance of our system from human perception which showed an accuracy of 71%. We also presented an interactive interface for the users that satisfied the users’ query with the matching sentences with most instructive information.

Description: Since 2006, an increasing number of French vineyards have chosen to convert to organic farming. One major change in vineyard practices includes replacing chemical pesticides with copper and sulfur-based products in line with Council Regulation (EC) No. 834/2007. This change can make overall management and pest and disease control more difficult and potentially lead to yield losses. From 2013 to 2016, a network of 48 vineyard plots, in southern France, under conventional management and in conversion to organic farming were monitored throughout the three-year conversion phase to investigate the grapevine phytosanitary management of four major pests and diseases and variations in control efficiency. The severity of downy and powdery mildew, grape berry moths, and Botrytis bunch rot were assessed and linked to the protection strategy. The findings showed that pests and diseases were controlled in the third year of conversion at similar efficiency levels as in conventional farming. However, the first two years of conversion were a transitional and less successful period during which higher incidences of cryptogamic diseases were observed. This demonstrates a need for winegrowers to receive more in-depth technical advice and support, especially on pest and disease control, during this critical transition period.

Description: The sugarcane (Saccharum spp. L.) mechanical harvesting system leaves a large amount of straw mulch on the soil surface. The straw mulch may affect soil conditions, root regrowth, and sugarcane yield. Thus, this study assessed the response of sugarcane root system growth and stalk yield to different rates of straw removal. An experiment was conducted in a Rhodic Kandiudox with sand clay loam texture to test the impact of four rates of straw removal: no removal (18.9 Mg ha−1 of dry mass); moderate removal (8.7 Mg ha−1); high removal (4.2 Mg ha−1) and total removal on sugarcane root system and stalk yield. Higher concentrations of roots (60%) were found in the first 40 cm of soil. Moderate straw removal resulted in higher root mass (3.6 Mg ha−1) and stalk production (23 Mg ha−1 of dry mass). However, no straw removal reduced root mass by 20% (105 Mg ha−1). Through regression analysis, it was estimated that retaining between 8.5 and 13 Mg ha−1 of straw resulted in the highest root mass and stalk yield. Managing straw removal to retain a moderate amount enables producers to sustain suitable soil conditions for sugarcane root growth and stalk production while providing straw for industrial use.

Description: Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the extraction of useful information responsible for controlling crop yield. Four ML algorithms, namely linear regression (LR), elastic net (EN), k-nearest neighbor (k-NN), and support vector regression (SVR), were used to predict potato (Solanum tuberosum) tuber yield from data of soil and crop properties collected through proximal sensing. Six fields in Atlantic Canada including three fields in Prince Edward Island (PE) and three fields in New Brunswick (NB) were sampled, over two (2017 and 2018) growing seasons, for soil electrical conductivity, soil moisture content, soil slope, normalized-difference vegetative index (NDVI), and soil chemistry. Data were collected from 39–40 30 × 30 m2 locations in each field, four times throughout the growing season, and yield samples were collected manually at the end of the growing season. Four datasets, namely PE-2017, PE-2018, NB-2017, and NB-2018, were then formed by combing data points from three fields to represent the province data for the respective years. Modeling techniques were employed to generate yield predictions assessed with different statistical parameters. The SVR models outperformed all other models for NB-2017, NB-2018, PE-2017, and PE-2018 dataset with RMSE of 5.97, 4.62, 6.60, and 6.17 t/ha, respectively. The performance of k-NN remained poor in three out of four datasets, namely NB-2017, NB-2018, and PE-2017 with RMSE of 6.93, 5.23, and 6.91 t/ha, respectively. The study also showed that large datasets are required to generate useful results using either model. This information is needed for creating site-specific management zones for potatoes, which form a significant component for food security initiatives across the globe.

Description: Targeted genome editing using CRISPR-Cas9 has been widely adopted as a genetic engineering tool in various biological systems. This editing technology has been in the limelight due to its simplicity and versatility compared to other previously known genome editing platforms. Several modifications of this editing system have been established for adoption in a variety of plants, as well as for its improved efficiency and portability, bringing new opportunities for the development of transgene-free improved varieties of economically important crops. This review presents an overview of CRISPR-Cas9 and its application in plant genome editing. A catalog of the current and emerging approaches for the implementation of the system in plants is also presented with details on the existing gaps and limitations. Strategies for the establishment of the CRISPR-Cas9 molecular construct such as the selection of sgRNAs, PAM compatibility, choice of promoters, vector architecture, and multiplexing approaches are emphasized. Progress in the delivery and transgene detection methods, together with optimization approaches for improved on-target efficiency are also detailed in this review. The information laid out here will provide options useful for the effective and efficient exploitation of the system for plant genome editing and will serve as a baseline for further developments of the system. Future combinations and fine-tuning of the known parameters or factors that contribute to the editing efficiency, fidelity, and portability of CRISPR-Cas9 will indeed open avenues for new technological advancements of the system for targeted gene editing in plants.

Description: The use of Light Emitting Diode (LED) lights in microscale vegetable production is more and more widespread. In this context, the effect of light spectrum on photosynthesis, growth, shoot yield, pigment content, and nutritional status of einkorn seedlings (Triticum monococcum L. ssp. monococcum), germinated and grown in a nutrient solution, was investigated. Plants were subjected to six different LED light treatments, all having a photon flux density (PFD) of 200 μmol m−2 s−1. Two light treatments were monochromatic (red or blue), three dichromatic (blue and red in the proportion), and one of a wider spectrum (selected as a control). All the light treatments affected the morphological, biochemical, and nutritional status of einkorn seedlings. Overall, the dichromatic treatments were the most effective in stimulating biomass production, CO2 assimilation, and evapotranspiration, as well as contents in chlorophyll a and b and carotenoids, and additionally nitrogen, phosphorous, manganese, iron, and zinc. These results are of relevance for the beneficial effects of dichromatic LED treatments in maximizing einkorn shoot yield and nutritional values, and in limiting energy consumption in indoor cultivation.

Description: Lentil (Lens culinaris Medikus) is one of the most important cool season food legume crops grown in many countries. Seeds are typically rich in protein, fiber, prebiotic carbohydrates and minerals, such as iron and zinc. With changing climate and variability, the lentil crop faces frequent droughts and heat stress of varying intensity in its major production zones. In the present study, a set of 162 lentil accessions selected through the Focused Identification of Germplasm Strategy (FIGS) were screened for tolerance to heat stress and combined heat-drought stresses under field conditions at two contrasting locations, namely Marchouch and Tessaout in Morocco. The results showed a significant genotypic variation for heat tolerance and combined heat-drought tolerance among the accessions at both locations. Based on the heat tolerance index (HTI), accessions, namely ILL 7833, ILL 6338 and ILL 6104, were selected as potential sources of heat tolerance at Marchouch, and ILL 7814 and ILL 8029 at Tessaout. Using the stress tolerance index (STI), ILL 7835, ILL 6075 and ILL 6362 were identified as the most tolerant lines (STI 〉 1) at Marchouch, and ILL 7814, ILL 7835 and ILL 7804 (STI 〉 1) at Tessaout, under the combined heat-drought stress conditions. Accession ILL 7835 was identified as a good source of stable tolerance to heat stress and combined heat-drought stress at both locations.

Description: Background: Health benefits from physical activity (PA) can be achieved by following the WHO recommendation for PA. To increase PA in inactive individuals, digital interventions can provide cost-effective and low-threshold access. Moreover, gamification elements can raise the motivation for PA. This study analyzed which factors (personality traits, app features, gamification) are relevant to increasing PA within this target group. Methods: N = 808 inactive participants (f = 480; m = 321; age = 48 ± 6) were integrated into the analysis of the desire for PA, the appearance of personality traits and resulting interest in app features and gamification. The statistical analysis included chi-squared tests, one-way ANOVA and regression analysis. Results: The main interests in PA were fitness (97%) and outdoor activities (75%). No significant interaction between personality traits, interest in PA goals, app features and gamification were found. The interest in gamification was determined by the PA goal. Participants’ requirements for features included feedback and suggestions for activities. Monetary incentives were reported as relevant gamification aspects. Conclusion: Inactive people can be reached by outdoor activities, interventions to increase an active lifestyle, fitness and health sports. The study highlighted the interest in specific app features and gamification to increase PA in inactive people through an app.

Description: This paper studies the effect of variance swap in hedging volatility risk under the mean-variance criterion. We consider two mean-variance portfolio selection problems under Heston’s stochastic volatility model. In the first problem, the financial market is complete and contains three primitive assets: a bank account, a stock and a variance swap, where the variance swap can be used to hedge against the volatility risk. In the second problem, only the bank account and the stock can be traded in the market, which is incomplete since the idiosyncratic volatility risk is unhedgeable. Under an exponential integrability assumption, we use a linear-quadratic control approach in conjunction with backward stochastic differential equations to solve the two problems. Efficient portfolio strategies and efficient frontiers are derived in closed-form and represented in terms of the unique solutions to backward stochastic differential equations. Numerical examples are provided to compare the solutions to the two problems. It is found that adding the variance swap in the portfolio can remarkably reduce the portfolio risk.

Description: This paper investigates the clustering or dependency of extremes in financial returns by estimating the extremal index value, in which smaller values of the extremal index correspond to more clustering. We apply the interval estimator method to determine the extremal index for a range of threshold values in the developed and emerging markets from 2007–2017. The indices we used to represent developed markets are from France, Germany, Italy, Japan, USA, UK, Spain, and Sweden. For the emerging markets, we use indices from China, Brazil, India, Malaysia, Russia, Saudi Arabia, and Portugal. The results show that clustering occurs in the emerging and developed markets under several threshold values. This study will shed light on the dependency structure of financial returns data and the proprieties of the extremes returns. Moreover, understanding clustering of extremes in these markets can help investors reduce the exposure to extreme financial events, such as the financial crisis.

Description: Economic and environmental concerns limit peat use for substrate production, promoting interest in alternative materials. Hence, in this study, 16 substrates were obtained by mixing, in a factorial combination, eight substrates with different ratios of peat, rice hulls (RH), and anaerobic digestion residues (ADR) and two types of RH: whole (WRH) or ground RH (GRH). Substrates were physically and chemically characterized and then tested as potting substrates for Pelargonium peltatum ‘Ville de Paris’ and Rosa × hybrida ‘La Sevillana’ production. Physical characteristics worsened the increasing RH content. This problem was partly solved using GRH and adding ADR to the substrates. As for chemical characteristics, RH increased P and K, reducing cation exchange capacity, NO3-N, and Ca, thus causing a possible nutritional imbalance. ADR addition increased all nutrients, restoring the nutritional balance. Geranium and rose plants were negatively affected by an increasing rate of RH. In both species, the use of GRH improved the considered parameters, whereas ADR improved some parameters but only in geranium. It was possible to partly substitute peat with 33% RH, but GRH plus ADR is necessary for geranium production, and facultative for rose. The multiple regression method and principal component analysis appear to be useful tools to understand which substrate parameters, and to what extent, influence the growth of ornamental plants.

Description: This paper presents a study related to human psychophysiological activity estimation based on a smartphone camera and sensors. In recent years, awareness of the human body, as well as human mental states, has become more and more popular. Yoga and meditation practices have moved from the east to Europe, the USA, Russia, and other countries, and there are a lot of people who are interested in them. However, recently, people have tried the practice but would prefer an objective assessment. We propose to apply the modern methods of computer vision, pattern recognition, competence management, and dynamic motivation to estimate the quality of the meditation process and provide the users with objective information about their practice. We propose an approach that covers the possibility of recognizing pictures of humans from a smartphone and utilizes wearable electronics to measure the user’s heart rate and motions. We propose a model that allows building meditation estimation scores based on these parameters. Moreover, we propose a meditation expert network through which users can find the coach that is most appropriate for him/her. Finally, we propose the dynamic motivation model, which encourages people to perform the practice every day.

Description: We study numerical algorithms for reflected anticipated backward stochastic differential equations (RABSDEs) driven by a Brownian motion and a mutually independent martingale in a defaultable setting. The generator of a RABSDE includes the present and future values of the solution. We introduce two main algorithms, a discrete penalization scheme and a discrete reflected scheme basing on a random walk approximation of the Brownian motion as well as a discrete approximation of the default martingale, and we study these two methods in both the implicit and explicit versions respectively. We give the convergence results of the algorithms, provide a numerical example and an application in American game options in order to illustrate the performance of the algorithms.

Description: The recent literature concerning globalizing regional development has placed significant emphasis on the Global Production Network (GPN 2.0). GPN 2.0 in economic geography emphasizes that regional growth is caused by a shift in the strategic coupling mode from a low to high level. In addition, GPN 2.0 regards firm-level value capture trajectories as key analytical object, rather than the interactive relationships among scalar and divergent actors in GPN 1.0. To provide a better understanding of causal linkages between the GPNs and uneven regional development in the background of globalization and to test the applicability of GPN 2.0 analysis framework, the paper analyzed 62 Korean-invested automotive firms in Jiangsu Province, China. In order to explore the value capture trajectories of lead firms in the GPNs, the authors applied K-means clustering method to quantitatively analyze the local supply networks of lead firms from organizational and spatial dimensions. Then, comparisons were made between strategic coupling modes of GPNs and regional development in North and South Jiangsu. This study found obvious similarities within these two regions but obvious differences between them in terms of value capture trajectories. We observed that North Jiangsu is currently in the stage of “structural coupling”, whereas South Jiangsu is in the stage of “functional coupling.” Thus, this article argues that spatial settings such as regional assets and autonomy are key factors influencing uneven economic development. This research may provide a crucial reference for the regional development of Jiangsu, China.

Description: Drought is a major abiotic stress factor limiting cotton yield. It is important to identify the genotypes that can conserve water under drought stress conditions and improve yield. The objective of the current study was to evaluate cotton genotypes for water conservation traits, i.e., high FTSW (Fraction of Transpirable Soil Water) threshold for transpiration. Plants utilize water slowly by declining transpiration at high FTSW and conserving soil water, which can be used by the plant later in the growing season to improve yield. Fifteen cotton varieties were selected based on their differences in transpiration response to elevated vapor pressure deficit (VPD) to study drought responses. Two pot experiments were carried out in the greenhouse to determine the FTSW threshold for the transpiration rate as the soil dried. A significant variation (p 〈 0.01) in the FTSW threshold values for transpiration decline was observed, ranging from 0.35 to 0.60 among cotton cultivars. Genotypes with high FTSW thresholds also displayed low transpiration under well-watered conditions. Further studies with four selected genotype contrasts in FTSW threshold values for transpiration showed differences (p 〈 0.05 to 0.001) in gas exchange parameters and water potentials. This study demonstrated that there are alternate traits among the cotton genotypes for enhancing soil water conservation to improve yield under water-limited conditions.

Description: Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

Description: High order convective Cahn-Hilliard type equations describe the faceting of a growing surface, or the dynamics of phase transitions in ternary oil-water-surfactant systems. In this paper, we prove the well-posedness of the classical solutions for the Cauchy problem, associated with this equation.

Description: Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by Sphaerotheca macularis (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (〉8 km h−1), leaf overlapping, leaf angle, and presence of spider mite disease during field testing.

Description: We present a statistical model which can be employed to understand the contagion dynamics of the COVID-19, which can heavily impact health, economics and finance. The model is a Poisson autoregression of the daily new observed cases, and can reveal whether contagion has a trend, and where is each country on that trend. Model results are exemplified from some observed series.

Description: The Vietnamese economy has increased at high speed over the transformation decades; however, most recent studies on the economic growth of this country used the Cobb-Douglas or CES (Constant Elasticity of Substitution) production functions, which are unable to explore the relationship between the elasticity of capital-labour substitution and development process, and hence, are not relevant to accessing a dynamic economic system. For that reason, this study is conducted to specify an unrestricted VES (Variable Elasticity of Substitution) production function in a one-sector growth model of Vietnam, highlighted by two characteristics: successful transition from plan to market and rapid progress. The VES is given preference over the CES and the Cobb-Douglas having the elasticity of substitution between capital and labour varying with economic development. By employing a Bayesian nonlinear regression through MCMC methods, the study reported the following findings: (1) the above-unity variable elasticity of capital-labour substitution in an aggregate unrestricted VES function specified for Vietnam shows that the model generates the possibility of endogenous economic growth; (2) the capital share tends to increase, while the labour share faces a downward trend along with the development of Vietnam; (3) the VES is empirically proven through a Bayes factor test to be superior to the CES and Cobb-Douglas for analysis of the growth process of Vietnam, an emerging transition economy.

Description: Coatings offer a means to control nanoparticle (NP) size, regulate dissolution, and mitigate runoff when added to crops through soil. Simultaneously, coatings can enhance particle binding to plants and provide an additional source of nutrients, making them a valuable component to existing nanoparticle delivery systems. Here, the surface functionalization of metal and metal-oxide nanoparticles to inhibit aggregation and preserve smaller agglomerate sizes for enhanced transport to the rooting zone and improved uptake in plants is reviewed. Coatings are classified by type and by their efficacy to mitigate agglomeration in soils with variable pH, ionic concentration, and natural organic matter profiles. Varying degrees of success have been reported using a range of different polymers, biomolecules, and inorganic surface coatings. Advances in zwitterionic coatings show the best results for maintaining nanoparticle stability in solutions even under high salinity and temperature conditions, whereas coating by the soil component humic acid may show additional benefits such as promoting dissolution and enhancing bioavailability in soils. Pre-tuning of NP surface properties through exposure to select natural organic matter, microbial products, and other biopolymers may yield more cost-effective nonagglomerating metal/metal-oxide NPs for soil applications in agriculture.

Description: As Web applications become more and more complex, the development costs are increasing as well. A Model Driven Architecture (MDA) approach is proposed in this paper since it simplifies modeling, design, implementation, and integration of applications by defining software mainly at the model level. We adopt the The Unified Modeling Language (UML), as modeling language. UML provides a set of diagrams to model structural and behavioral aspects of the Web applications. Automatic translation of UML diagrams to the Object-Oriented code is highly desirable because it eliminates the chances of introducing human errors. Moreover, automatic code generation helps the software designers delivering of the software on time. In our approach, the automatic transformations across the MDA’s levels are based on meta-models for two of the most important constructs of UML, namely Use Cases and classes. A proprietary tool (called xGenerator) performs the transformations up to the Java source code. The architecture of the generated Web applications respects a variant of the well-known Model-View-Controller (MVC) pattern.

Description: According to the recommendations of the World Health Organization (WHO), due to the increased risk of cardiovascular disease, the daily consumption of table salt should be reduced. To avoid the health consequences of iodine deficiency, it is necessary to include alternative food sources of this trace element in the human diet. One of the most effective ways of improving nutrition is the biofortification of crops with minerals and vitamins. The purpose of this study was to determine the influence of iodine biofortification (potassium iodate/KIO3/, 5-iodosalicylic acid/5-ISA/and 3.5-diiodosalicylic acid/3.5-diISA/) on the chemical composition of lettuce (Lactuca sativa L. capitata) cv. ‘Melodion’. Plants were cultivated in a hydroponic system NFT (Nutrient Film Technique). We compared the effect of iodine fertilization on the basic chemical composition, fatty acid profile, macro- and micronutrients, content of sugars, nitrogenous compounds, chlorides, and iodine compounds. The results obtained in this research indicate that the application of iodine compounds has an influence on changes of concentration of iodine and other compounds in the treated samples. In lettuce, the main fatty acid was linolenic acid; however, fertilization with iodine did not affect the fatty acid profile in plants, except for concentrations of myristic and arachidic acids. We also found that iodine fortification has positive effects on concentrations of some micro- and micronutrients. Moreover, the application of 3.5-diISA decreased the concentration of nitrates as compared to control and other treatments. Therefore, it may be postulated that the production of lettuce fortified with iodosalicylates is worthy of consideration due to the fact that it may be a good source of iodine and other compounds in the human diet.

Description: Carbonization of cage layer chicken manure (CLCM) can improve its bio-recalcitrance which might improve nitrogen (N) bioavailability in soil. However, temperature(s) to exhibit appropriate variations in the chemical makeup of the manure during carbonization in order to achieve this objective is unknown. In this study, we investigated the alterations in chemical compositions, surface functionalities, and N speciation initiated by different carbonization temperatures (350, 500, and 650 °C) and the effects of these alterations on N bioavailability in soil. The objective was to identify suitable temperature condition(s) for the conversion of CLCM into a carbonized product of appropriate bio-recalcitrance that is capable of improving N bioavailability in soil more than the un-carbonized CLCM. The results showed an increased bio-recalcitrance of the manure with increasing carbonization temperatures due to drastic changes in the chemical makeup and accumulation of heterocyclic aromatic N structures. Subsequently, these alterations in the chemical makeup and state of the organic N species in the manure affected N bioavailability in soil. Notably, N bioavailability of CLCM and benefits on plants were improved when soil was supplied with CLCM that was altered at 350 °C. With these observations, we concluded that alterations in chemical and surface structural compositions and N speciation at 350 °C are optimum for instituting the required bio-recalcitrance to CLCM in order to improve N bioavailability in soil for plants.

Description: Medicago truncatula (barrel medic) and Pisum sativum subsp. elatius (wild pea) accessions originating from variable environmental conditions in the Mediterranean basin were used to study physical seed dormancy (PY) release. The effect of soil burial on PY release was tested on 112 accessions of medic and 46 accessions of pea over the period of 3 months in situ at three common gardens (Hungary, Spain and Greece) from 2017 through 2019. PY release after soil exhumation followed by experimental laboratory germination of remaining dormant seeds (wet, 25 °C, 21 days) were related to the environmental conditions of the common garden and macroclimatic variables of the site of origin of the accessions. Higher PY release was observed in buried seeds under humid rather than under dry and hot environments. Exposure of remaining dormant seeds to experimental laboratory conditions increased total PY release up to 70% and 80% in barrel medic and wild pea, respectively. Wild pea showed higher phenotypic plasticity on PY release than barrel medic, which had higher bet-hedging within-season. Wild pea showed lower bet-hedging among-season (PY 〈 10%) in relation to precipitation than barrel medic, which was more conservative (PY ≈ 20%). Observed variability suggests that these species have the capability to cope with ongoing climate change.

Description: It is critical for organizations to self-assess their Industry 4.0 readiness to survive and thrive in the age of the Fourth Industrial Revolution. Thereon, conceptualization or development of an Industry 4.0 readiness model with the fundamental model dimensions is needed. This paper used a systematic literature review (SLR) methodology with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and content analysis strategy to review 97 papers in peer-reviewed academic journals and industry reports published from 2000 to 2019. The review identifies 30 Industry 4.0 readiness models with 158 unique model dimensions. Based on this review, there are two theoretical contributions. First, this paper proposes six dimensions (Technology, People, Strategy, Leadership, Process and Innovation) that can be considered as the most important dimensions for organizations. Second, this review reveals that 70 (44%) out of total 158 total unique dimensions on Industry 4.0 pertain to the assessment of technology alone. This establishes that organizations need to largely improve on their technology readiness, to strengthen their Industry 4.0 readiness. In summary, these six most common dimensions, and in particular, the dominance of the technology dimension provides a research agenda for future research on Industry 4.0 readiness.

Description: This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion.

Description: Soybean (Glycine max L. Merrill) is one of the most important crops worldwide. In several European countries such as Germany, soybean cultivation is not traditional and as such remains to be optimized. One option to increase soybean production is to adapt and improve the seeding system. To investigate the effects of different seeding systems on seed yield, seed protein, and oil content, plant characteristics were assessed using a two-factorial (two seeding systems and four cultivars) field trial over two years (2017–2018) at two locations in southeast Germany. The seeding systems were drill (row spacing 14 cm) and precision seeding (row spacing 28 cm), and the cultivars were Viola 000, Lissabon 000, ES Mentor 00, and Orion 00. Depending on the seeding system, a seed yield of 3.8 t ha−1 dry matter (DM), 40.9% protein content (DM), and 18.8% oil content (DM) was achieved by drilling, and 3.6 t ha−1 yield (DM), 40.1% protein content (DM), and 19.1% oil content (DM) with precision seeding (average across four cultivars, two locations, and two years). No significant effects of the seeding system on all seed and plant characteristics were observed. As drilling and precision seeding did not affect the seed yield, seed protein, oil contents, and plant characteristics of soybean in this study, farmers are able to choose the seeding system which fits best into their farms and is economically most viable.

Description: Fractal’s spatially nonuniform phenomena and chaotic nature highlight the function utilization in fractal cryptographic applications. This paper proposes a new composite fractal function (CFF) that combines two different Mandelbrot set (MS) functions with one control parameter. The CFF simulation results demonstrate that the given map has high initial value sensitivity, complex structure, wider chaotic region, and more complicated dynamical behavior. By considering the chaotic properties of a fractal, an image encryption algorithm using a fractal-based pixel permutation and substitution is proposed. The process starts by scrambling the plain image pixel positions using the Henon map so that an intruder fails to obtain the original image even after deducing the standard confusion-diffusion process. The permutation phase uses a Z-scanned random fractal matrix to shuffle the scrambled image pixel. Further, two different fractal sequences of complex numbers are generated using the same function i.e. CFF. The complex sequences are thus modified to a double datatype matrix and used to diffuse the scrambled pixels in a row-wise and column-wise manner, separately. Security and performance analysis results confirm the reliability, high-security level, and robustness of the proposed algorithm against various attacks, including brute-force attack, known/chosen-plaintext attack, differential attack, and occlusion attack.

Description: In the last decade, there has been a surge in interest in connected and automated vehicles (CAVs) and related enabling technologies in the fields of communication, automation, computing, sensing, and positioning [...]

Description: We consider a rather general problem of nonparametric estimation of an uncountable set of probability density functions (p.d.f.’s) of the form: f ( x ; r ) , where r is a non-random real variable and ranges from R 1 to R 2 . We put emphasis on the algorithmic aspects of this problem, since they are crucial for exploratory analysis of big data that are needed for the estimation. A specialized learning algorithm, based on the 2D FFT, is proposed and tested on observations that allow for estimate p.d.f.’s of a jet engine temperatures as a function of its rotation speed. We also derive theoretical results concerning the convergence of the estimation procedure that contains hints on selecting parameters of the estimation algorithm.

Description: The lockdown was crucial to stop the COVID-19 pandemic in Italy, but it affected many aspects of social life, among which traditional live science cafés. Moreover, citizens and experts asked for a direct contact, not relying on mass-media communication. In this paper, we describe how the Florence and Rome science cafés, contacted by citizens and experts, either directly or through the Florence science shop, responded to these needs by organizing online versions of traditional face-to-face events, experiencing high levels of participation. The science café methodology was also requested by a high school that needed to conclude an engagement experience with students and their families. We also report the results of a survey about the satisfaction of this new methodology with respect to the old one.

Description: Medical disputes that result in medical compensation and losses affect the financial management and sustainable operational risks of enterprises. Employee training plays an important role in the sustainable growth of human resource management and also can help avoid any potential risks to enterprises’ operating revenue. Based on data of a company, this study’s model aims to establish a mathematical model to find the most suitable decision variables in order to provide decision-making analysis and judgment of a company’s individual economic behaviors. From the second-order differential modeling method, where the functional training time of the aesthetic medicine industry (including medical errors/dispute incidences, functional training costs, and medical benefits) links to a specific functional relationship, the optimal decision-making model and evaluation criteria for the proportion of this training time under the concept of sustainable management can be constructed. The method proposed herein reduces medical errors or disputes, strengthens risk and financial management, provides customers with the best service quality, and serves as the basis for decision-making evaluation of the maximum benefits of sustainable operations.

Description: Process-based crop models are advantageous for the identification of management strategies to cope with both temporal and spatial variability of sugarcane yield. However, global optimization of such models is often computationally expensive. Therefore, we performed global sensitivity analysis based on Gaussian process emulation to evaluate the sensitivity of cane dry weight to trait parameters implemented in the Agricultural Productions System Simulator (APSIM)-Sugar model under selected environmental and management conditions in Khon Kaen (KK), Thailand. Emulators modeled 30 years, three soil types and irrigated or rainfed conditions, and emulator performance was investigated. rue, green_leaf_no, transp_eff_cf, tt_emerg_to_begcane and cane_fraction were identified as the most influential parameters and together they explained more than 90% of total variance on the simulator output. Moreover, results indicate that the sensitivity of sugarcane yield to the most influential parameters is affected by water stress conditions and nitrogen stress. Our findings can be used to improve the efficiency and accuracy of modeling and to identify appropriate management strategies to address temporal and spatial variability of sugarcane yield in KK.

Description: This research presents a machine vision approach to detect lesions in liver ultrasound as well as resolving some issues in ultrasound such as artifacts, speckle noise, and blurring effect. The anisotropic diffusion is modified using the edge preservation conditions which found better than traditional ones in quantitative evolution. To dig for more potential information, a learnable super-resolution (SR) is embedded into the deep CNN. The feature is fused using Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) with a pre-trained deep CNN model. Moreover, we propose a Bayes rule-based informative patch selection approach to reduce the processing time with the selective image patches and design an algorithm to mark the lesion region from identified ultrasound image patches. To train this model, standard data ensures promising resolution. The testing phase considers generalized data with a varying resolution and test the performance of the model. Exploring cross-validation, it finds that a 5-fold strategy can successfully eradicate the overfitting problem. Experiment data are collected using 298 consecutive ultrasounds comprising 15,296 image patches. This proposed feature fusion technique confirms satisfactory performance compared to the current relevant works with an accuracy of 98.40%.

Description: QR (quick response) Codes are one of the most popular types of two-dimensional (2D) matrix codes currently used in a wide variety of fields. Two-dimensional matrix codes, compared to 1D bar codes, can encode significantly more data in the same area. We have compared algorithms capable of localizing multiple QR Codes in an image using typical finder patterns, which are present in three corners of a QR Code. Finally, we present a novel approach to identify perspective distortion by analyzing the direction of horizontal and vertical edges and by maximizing the standard deviation of horizontal and vertical projections of these edges. This algorithm is computationally efficient, works well for low-resolution images, and is also suited to real-time processing.

Description: A soil tillage system adjusted to the soil type and agro-ecological conditions, together with the optimal nutrient supply, is a prerequisite for high maize (Zea mays L.) yield. However, there is little knowledge about the influence of soil tillage and nitrogen (N) rates on maize grain quality. A study was initiated in 1978 in Zemun Polje, Belgrade, Serbia, on the chernozem soil type. The effects of three N rates—0, 180 and 240 kg N ha−1—within three tillage practices—no-tillage, reduced and conventional tillage—and their influence on grain yield (GY) and the content of proteins, starch, oil, total tocopherols and carotenoids, phytic phosphorus (Phy), glutathione (GSH), phenols and inorganic P (Pi) in maize grain were analysed in the period 2016–2018. Differences in maize GY were 2.57 and 3.01 t ha−1 for reduced- or no-tillage, respectively, in comparison to conventional tillage. Conventional tillage and higher N rates contributed to the increase in maize yield and the grain nutritional quality with regard to the increased level of proteins, GSH and Phy. On the other hand reduced inputs were beneficial only for grain quality, in relation to the increase in tocopherols and phenols.

Description: ×Amarine hybrids are attractive ornamental geophytes grown for cut flower production. Their cultivation is limited due to lesser flowering percentages and lesser bulb weight gain. To optimize the growth and propagation of geophytes, plant growth regulators (PGRs) are used, but so far none have been tested in ×Amarine. We investigated the effect of gibberellic acid (GA3; 50, 100, and 200 mg dm−3) and methyl jasmonate (MeJA; 100, 500, and 1000 µmol dm−3) on growth, flowering, bulb yield, and select physiological parameters of ×A. tubergenii “Zwanenburg”. PGRs were applied as foliar sprays on the 70th and 77th day after planting. GA3 treatment at 200 mg dm−3 exhibited the greatest leaf number, leaf length, bulb weight, daughter bulb number, CO2 assimilation intensity, greenness index, total sugars, and total protein content in bulbs. GA3 application at 100 and 200 mg dm−3 accelerated flowering and at 50 and 100 mg dm−3 significantly increased the bulb flowering percentage. MeJA at all tested concentrations prolonged anthesis time and reduced the bulb flowering percentage. GA3 at all concentrations and MeJA at 500 and 1000 µmol dm−3 stimulated daughter bulbs formation. GA3, especially at 200 mg dm−3 can improve anthesis and increase ×A. tubergenii “Zwanenburg” bulb yield.

Description: Smart agriculture is an evolving trend in the agriculture industry, where sensors are embedded into plants to collect vital data and help in decision-making to ensure a higher quality of crops and prevent pests, disease, and other possible threats. One of the most critical pests of palms is the red palm weevil, which is an insect that causes much damage to palm trees and can devastate vast areas of palm trees. The most challenging problem is that the effect of the weevil is not visible by humans until the palm reaches an advanced infestation state. For this reason, there is a pressing need to use advanced technology for early detection and prevention of infestation propagation. In this project, we have developed an IoT-based smart palm monitoring prototype as a proof-of-concept that (1) allows monitoring palms remotely using smart agriculture sensors, (2) contribute to the early detection of red palm weevil infestation. Users can use web/mobile applications to interact with their palm farms and help them in getting early detection of possible infestations. We used an industrial-level IoT platform to interface between the sensor layer and the user layer. Moreover, we have collected data using accelerometer sensors, and we applied signal processing and statistical techniques to analyze collected data and determine a fingerprint of the infestation.

Description: The aim of this experiment was to evaluate the effect of fertilizing processing tomato by coupling the green manuring of fall-winter cover crops with fertigation in spring-summer. In a two-year experiment, seven fertilization treatments were compared: green manuring of pure barley (B100) and pure vetch (V100) sown at 100% of their ordinary seeding rates, green manuring of a barley-vetch mixture at a ratio of 75:25 of their own seed rates (B75V25), fertigation with drip irrigation at a rate of 200 kg ha−1 of nitrogen (N) (Fert_N200), fertigation combined with B100 and B75V25 at a N rate complementary to 200 kg N ha−1 (B100 + Fert and B75V25 + Fert, respectively), and an unfertilized control (N0) with no cover crops for green manuring prior to tomato transplanting or fertigation. The Fert_N200 treatment resulted in maximum tomato N uptake, growth and yield, but caused high N leaching, especially during the no-cover fall-winter period, as was also the case for N0. The V100 treatment promoted quite good tomato N status and yield, but did not reduce N leaching. The B100 and B75V25 treatments reduced N leaching but decreased tomato N uptake, growth and yield. The B100 + Fert and B75V25 + Fert treatments reduced N leaching, likely increased soil N stock, and facilitated optimal tomato N nutrition and maximum yields. Combining fertigation with green manuring of cover crops composed of pure grass or grass-legume mixtures appears to be a very effective and environmentally sound practice for fertilizing high N-demanding spring-summer crops like processing tomato.

Description: Pesticides play a crucial role in regulating crop production by reducing crop losses and increasing crop yield and quality. However, they may threaten surface and groundwater, a phenomenon occurring at global scale, potentially causing environmental damage and prohibition of water use or high treatment costs for drinking water. Assessing spatially-defined aquifer vulnerability to pesticide is then important, as it may allow defining agricultural areas where pesticides should be used following well-defined agronomic practices/limitations. In this study, after a brief review of recent studies on aquifer vulnerability assessment to pesticide, we applied the Vulnerability Index method to the agricultural area of the Municipality of San Giuliano Terme (Pisa, Italy) in order to focus on the data needs and discuss the reliability of this method (as an example of index-overlay methods). The proposed method needs a relatively small number of parameters compared to other more complex ones. Despite a such a small number of parameters, some were not easily available in our case study. Thus, some assumptions were made. This led to vulnerability maps with reduced reliability, no validation with groundwater samples, and little practical use. This means that to produce robust but static vulnerability assessments, large datasets are needed. In turn, the cost of data gathering may be high. The value of these data may, however, be increased, and the cost better justified if the analyses are based on process-based or advanced statistical methods. While the future for vulnerability assessment methods is the use of process-based/advanced statistical methods, index-overlay methods, as a preliminary step for process-based simulation analysis, may still provide initial and relatively quick insights on potential leaching of pesticides. This in turn may support extension services in delivering timely and relevant advices on the use of such pesticides to farmers and owners of plant nurseries and greenhouses.

Description: The middle and lower reaches of the Yangtze River (MLYR) plain represent the second-largest wheat producing area in China; the winter wheat-rice system is one of the main planting systems in this region. The use of the agricultural production system simulator (APSIM)-wheat model to simulate wheat production potential and evaluate the impact of future climate change on wheat production in this region is of great importance. In this study, the adaptability of the APSIM-wheat model in the MLYR was evaluated based on observational data collected in field experiments and daily meteorological data from experimental stations in Wuhan, Jingmen, and Xiangyang in Hubei province. The results showed significant positive relationships between model-predicted wheat growth duration from sowing to anthesis and maturity and the observed values, with coefficients of determination (R2) in ranges of 0.90–0.97 and 0.93–0.96, respectively. The normalized root-mean-square error (NRMSE) of the simulated growth durations and measured values were lower than 1.6%, and the refined index of agreement (dr-values) was in the range of 0.74–0.87. The percent mean absolute relative error (PMARE) was cited here as a new index, with a value below 1.4%, indicating that the model’s rating was excellent. The model’s performance in terms of grain yield and above-ground biomass simulation was also acceptable, although it was not as good as the growth periods simulation. The R2 value was higher than 0.75 and 0.72 for the simulation of grain yield and biomass, respectively. The indices NRMSE and PMARE were lower than 19.8% and 19.9%, and the dr-value was higher than 0.71. According to our results, APSIM-wheat was an effective and accurate model for simulating the phenology and yield production processes of wheat in the MLYR, and the results also provided a theoretical basis and technical support for further research on the yield potential of wheat-rice rotation planting systems with clarification of the key factors limiting the yield gap in this region.

Description: Certain phosphorous solubilizing (PSB) and phosphorous mineralizing (PMB) bacteria may improve plant growth by improving nutrient availability. The aim of this work was to evaluate the effect of inoculation with two Bacillus spp. strains, 12A and 25A, on wheat seedlings growth. To this aim, a durum and a bread wheat genotype were grown under controlled conditions in a low P compost medium to evaluate: (i) the effect of the bacterial isolates on plant growth and root system architecture; (ii) the expression of two key genes indicative of the P-starvation response and phosphate (Pi) uptake, TaIPS1 and TaPHT1.6-B1. The results showed that 12A Bacillus sp. significantly increased root length, surface area and biomass. Furthermore, an enhanced shoot dry weight and P content were observed. This might be explained by the capacity of strain 12A to produce indole-3-acetic acid (IAA) in addition to P mineralizing and P solubilizing capability. No effect on plant growth was observed for 25A strain. The semi-quantitative gene expression analysis showed an overall lower expression of TaIPS1 in the inoculated plants and highest expression of TaPHT1.6-B1 in 12A inoculated plants. This suggests that Pi-responsive genes might be useful molecular indicators for the effectiveness of PSB and PMB.

Description: Geophytes are an increasingly widely used group of plants. Its high biodiversity, however, significantly impacts the cultivation process. Therefore, every new insight into the behavioral aspects of each single species is crucial for production. This study, for the first time, aims at contributing to a better understanding of the interplay between the bulb size potential of Narcissus poeticus and the pattern of bulb lifting time to find the optimal combination of these factors in obtaining the best quality propagation material. Twelve bulb size groups were examined. Plants were planted in the open air in two locations of Central East Europe and lifted in three terms: immediately and two and four weeks after flowering. Bulb fresh and dry weight, as well as leaf, root and total plant dry weight, increased with growing bulb size and delayed the time of lifting. The bulb weight increase indicator was twice as high for bulbs lifted in the third date compared to those lifted earlier. Its value was the highest for the smallest bulbs. Bulb shape ratio gradually decreased together with the increase of bulb weight and later lifting term. Principal components analysis allowed singling out two principal components that accounted for 86.58% of total variability.

Description: Biochar soil amendment to agricultural systems can reduce nitrogen (N) leaching; however, application to agricultural nitrogen treatment systems has not been extensively explored. The objective of this study was to assess the impact on N leaching in soils receiving repeated N applications which may be observed in agricultural treatment systems. In this study, 400 °C, 700 °C, and oxidized 700 °C corncob biochar was amended to sandy loam soil columns at 5% (wt/wt) to assess the impacts to N cycling following repeated synthetic N applications. Columns received weekly applications of either organic N (ORG-N), ammonium (NH4+-N), or nitrate (NO3−-N) and the N effluent, gaseous emissions, and soil N retention was measured. Biochar produced at 400 °C significantly reduced N leaching compared to control columns by 19% and 15% for ORG-N and NH4+-N, respectively, with application concentrations similar to silage bunker runoff. For NO3−-N applications, 700 °C biochar significantly reduced leaching by 25% compared to the controls. The primary mechanism reducing N effluent for biochar amended columns was enhanced soil retention of ORG-N and NO3−-N. Biochar surface chemistry analysis measured an increase in oxygenated functional groups and cationic minerals on the biochar surface, which likely enhanced retention through cationic bridging or the development of an organomineral layer on the biochar surface. Results indicated biochar amendment to agricultural treatment systems receiving N runoff may reduce the risk of N leaching.

Description: Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh.

Description: This study uses the BEKK-GARCH model to examine the return-and-volatility spillover between the world-leading markets (USA and China) and four emerging Latin American stock markets over the global financial crisis of 2008 and the crash of the Chinese stock market of 2015. Regarding return spillover, our findings reveal a unidirectional return transmission from Mexico to the US stock market during the global financial crisis. During the crash of the Chinese stock market, the return spillover is found to be unidirectional from the US to the Brazil, Chile, Mexico, and Peru stock markets. Moreover, the results indicate a unidirectional return transmission from China to the Brazil, Chile, Mexico, and Peru stock markets during the global financial crisis and the crash of the Chinese stock market. Regarding volatility spillover, the results show the bidirectional volatility transmission between the US and the stock markets of Chile and Mexico during the global financial crisis. During the Chinese crash, the bidirectional volatility transmission is observed between the US and Mexican stock markets. Furthermore, the volatility spillover is unidirectional from China to the Brazil stock market during the global financial crisis. During the Chinese crash, the volatility spillover is bidirectional between the China and Brazil stock markets. Lastly, a portfolio analysis application has been conducted.

Description: Fifth generation (5G) is a new generation mobile communication system developed for the growing demand for mobile communication. Channel coding is an indispensable part of most modern digital communication systems, for it can improve the transmission reliability and anti-interference. In order to meet the requirements of 5G communication, a dual threshold self-corrected minimum sum (DT-SCMS) algorithm for low-density parity-check (LDPC) decoders is proposed in this paper. Besides, an architecture of LDPC decoders is designed. By setting thresholds to judge the reliability of messages, the DT-SCMS algorithm erases unreliable messages, improving the decoding performance and efficiency. Simulation results show that the performance of DT-SCMS is better than that of SCMS. When the code rate is 1/3, the performance of DT-SCMS has been improved by 0.2 dB at the bit error rate of 10 − 4 compared with SCMS. In terms of the convergence, when the code rate is 2/3, the number of iterations of DT-SCMS can be reduced by up to 20.46% compared with SCMS, and the average proportion of reduction is 18.68%.

Description: The EU’s Common Agricultural Policy (CAP 2014–2020) on soil management points to the combination of sustainable food production with environmental protection, reduction of CO2 emissions, and safeguarding of soil biodiversity. In this study, three farms (in the Emilia-Romagna region), managed with both conventional and conservation practices (the last ones with and without sub-irrigation systems), were monitored from 2014 to 2017 to highlight the impact of different crops and soil managements on soil arthropods, in terms of abundance, composition, and soil biological quality (applying QBS-ar index). To do this, linear mixed models were performed, whereas arthropods assemblages were studied through PERMANOVA and SIMPER analysis. Soil communities varied among farms, although most differences were found among crops depending on management practices. Nonetheless, conservation systems and a wider reduction in anthropogenic practices provided better conditions for soil fauna, enhancing QBS-ar. Moreover, arthropod groups responded to soil practices differently, highlighting their sensitivity to agricultural management. Community assemblages in corn and wheat differed between managements, mainly due to Acari and Collembola, respectively. In conservation management, wheat showed the overall greatest abundance of arthropods, owing to the great number of Acari, Collembola, and Hymenoptera, while the number of arthropod groups were generally higher in crop residues of forage.

Description: We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for 1 〈 t 〈 T /4, where t is the number of milliseconds in a simulated exposure frame and T is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras.

Description: Excess of cadmium (Cd) in soil leads to a number of adverse effects which challenge agricultural production. Aims of this study were to investigate variations in Cd content in leaves (Zea mays L.) of different maize genotypes and to detect effects of Cd on photosynthesis through chlorophyll a fluorescence. Pot vegetation experiments with four maize genotypes and four different soil Cd levels were repeated for two years. Chlorophyll fluorescence, photosynthetic pigments and inductively coupled plasma (ICP) analysis for ear-leaf Cd and zinc (Zn) and soil Cd were carried out. Significant differences between genotypes were found for leaf Cd, where higher Cd soil concentrations resulted in higher Cd leaf concentrations. Cd uptake into maize leaves increased with increasing Cd levels in soil, which was genotype-dependent and higher and lower Cd accumulating groups were formed. Changes in chlorophyll fluorescence caused by elevated Cd levels in soil were mostly visible as changes in dissipation energy, yields of primary photosystem II photochemistry and electron transport. Decrease of reaction centers per antenna chlorophyll and increased variable fluorescence at J step (VJ) resulted in decrease of performance indexes in the highest Cd concentration. Decreases in chlorophyll fluorescence parameters suggest reduced functionality of reaction centers and problems in re-oxidation of primary quinone acceptor (QA).

Description: The collection and processing of personal data offers great opportunities for technological advances, but the accumulation of vast amounts of personal data also increases the risk of misuse for malicious intentions, especially in health care. Therefore, personal data are legally protected, e.g., by the European General Data Protection Regulation (GDPR), which states that individuals must be transparently informed and have the right to take control over the processing of their personal data. In real applications privacy policies are used to fulfill these requirements which can be negotiated via user interfaces. The literature proposes privacy languages as an electronic format for privacy policies while the users privacy preferences are represented by preference languages. However, this is only the beginning of the personal data life-cycle, which also includes the processing of personal data and its transfer to various stakeholders. In this work we define a personal privacy workflow, considering the negotiation of privacy policies, privacy-preserving processing and secondary use of personal data, in context of health care data processing to survey applicable Privacy Enhancing Technologies (PETs) to ensure the individuals’ privacy. Based on a broad literature review we identify open research questions for each step of the workflow.

Description: A failure of the EPSPS-inhibiting herbicide glyphosate to control several populations of Hordeum murinum subsp. leporinum (or H. murinum) occurred in southern Spain after more than fifteen applications in both crop (olive, orchards, and citrus) and non-crop (dry areas, roadsides and ditches) areas. Eight out of 18 populations studied were resistant (R) to glyphosate with R factors higher than four based on GR50. These populations also had the highest values of LD50 and the lowest levels of shikimic acid accumulation. Two adjuvants tested increased glyphosate efficacy in both susceptible (S) and R populations thanks to better spray foliar retention. Moreover, PS I-, PS II-, and ACCase-inhibiting herbicides, in pre- or post-emergence, proved to be the best chemical alternatives with different sites of action (SoA) to control both S and glyphosate-R populations. This study represents the first report worldwide of glyphosate resistance in H. murinum found in very different crop and non-crop areas from southern Spain. To design chemical strategies to implement integrated weed management programs for glyphosate-R H. murinum, both adjuvants and herbicides with alternative SoA as well as application timings should be considered.

Description: No-tillage has been used for mitigating wind erosion on the Southern High Plains US for decades. This study investigated the effects of tillage and nitrogen (N) fertilizer timing on cotton lint yield, fiber quality, and seed N content during a three-year transition from conventional tillage (CT) to a no-tillage system both with a wheat (Triticum aestivum) cover crop (NTW) and without a cover crop (NT). Lint yield was different between tillage systems within each year with the NTW system producing greater lint yield than the CT system in the second and third year of the transition period. The concentration of cotton seed N was not different within years, although it was decreased in the no N added control in the third year. Cotton fiber strength was increased in the NTW system compared to the CT system in the second year of the study. However, the CT system produced increased fiber strength compared to the other two systems in 2018 and is likely the result of late-season weather conditions. It was determined that implementing a NTW system may increase lint yield within the first few years and has no effect on most fiber quality parameters, especially in environmentally challenging conditions.

Description: Herbicide resistance in weeds restricts control options, thereby escalating economic loss and threatening agricultural sustainability in cereal production. Field evaluation of the crop performance, competitive traits, and consequent weed suppressive potential of 13 commercial winter wheat (Triticum aestivum L.) cultivars was performed in central NSW Australia with a focus on the evaluation and modelling of above-ground interactions. In 2015 and 2016, replicated field trials were established with genetically diverse commercial wheat genotypes under moderate to low rainfall conditions in Wagga Wagga (572 mm) and Condobolin (437 mm) New South Wales, respectively. The heritage cultivar Federation and a commercial cultivar of winter cereal rye (Secale cereale L.) were included as known weed-suppressive controls. Crop and weed growth, as well as early vigour, leaf area index, and photosynthetically active radiation, were monitored at various crop phenological stages including early growth, vegetative, flowering, grain fill, and harvest. Significant differences between wheat cultivar and location were observed for crop biomass, early vigour, leaf area index, weed number, weed biomass, canopy architecture, and yield in both 2015 and 2016. Differences in weed establishment were largely impacted first by rainfall and season and secondly by crop architecture (i.e., height, size, canopy) and phenology (i.e., growth stages). Early vigour and early canopy closure were instrumental in suppressing weed establishment and growth. Cultivar performance and competition with weeds were also clearly influenced by both environmental factors and genotype, as evidenced by differences in early cultivar performance, yield, and weed suppression by season and location. Specifically, Federation, Condo, and Janz wheat cultivars were superior performers in terms of weed suppression in both locations and years; however, Federation produced up to 55% lower yield than recently introduced cultivars. Partial least squares (PLS) regression was performed to develop a predictive linear model for weed competition in commercial wheat cultivars based on weed dry biomass as the response variable and selected aboveground crop canopy traits as predictors. In 2015, the model differed in accordance with crop growth stage, but the impact of predictors on weed biomass at both locations was not significant. In 2016, under local above average rainfall conditions, the model showed a significant negative correlation (p 〈 0.001) of most predictors on weed biomass (r2 = 0.51 at Condobolin, r2 = 0.62 at Wagga Wagga), suggesting the most influential factors in reducing weed numbers and establishment as crop vigour, biomass, and height. Our results indicate the establishment of competitive wheat cultivars in the absence of post-emergent herbicides resulted in a two to five-fold increased weed suppression over less suppressive genotypes, without significant yield penalties. Therefore, cultivar choice constitutes a cost-effective and sustainable weed management tool, particularly when weed pressure is significant.

Description: Plant biostimulants have been increasingly used in agriculture. Plant biostimulants have been mostly investigated regarding their stress attenuation and only a few reports have brought information on their effects on fruit quality, which is related to appearance, chemical and physical attributes. Due to their organic nature, they have a sustainable appeal, attending the demand of consumers worried about the beneficial effects of healthier foods. This review turns evident the little information available on this matter. As fruits have differentiated metabolism, from pollination to harvest and after-harvesting, it is suggested that further research should focus separately on each stage. Thus, research should aim at quality formation, when the fruits are still attached to the plant, and shelf life extension, after harvesting.

Description: Sorghum is the world’s fifth major cereal in terms of production and acreage. It is expected that its growth will be affected by the increase in air temperature, an important component of global climate change. Our objective was to use the Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model to (a) evaluate the impact of climate warming on forage and grain sorghum production in Argentina and (b) to analyze to what extent yield changes were associated with changes in water or nitrogen stress days. For model calibration, we used previous information related to the morpho-physiological characteristics of both sorghum types and several soil parameters. We then used multiyear field data of sorghum yields for model validation. Yield simulations were conducted under three possible climate change scenarios: 1, 2, and 4 °C increase in mean annual temperature. ALMANAC successfully simulated mean yields of forage and grain sorghum: root mean square error (RMSE): 2.6 and 1.0 Mg ha−1, respectively. Forage yield increased 0.53 Mg ha−1, and grain yield decreased 0.27 Mg ha−1 for each degree of increase in mean annual temperature. Yields of forage sorghum tended to be negatively associated with nitrogen stress (r = −0.94), while grain sorghum yield was negatively associated with water stress (r = −0.99). The information generated allows anticipating future changes in crop management and genetic improvement programs in order to reduce the yield vulnerability.

Description: Based on 243 current valid distribution records for six wild strawberry species in China and data on 20 environmental variables, the geographical distributions of and potentially suitable areas for the wild strawberry species in Yunnan Province (China) under the current climate scenario were explored using the MaxEnt model and ArcGIS software, and major environmental variables affecting their geographical distributions were evaluated. In addition, the spatio-temporal dynamic patterns of the suitable areas for the six wild strawberry species in Yunnan Province in the 2050s and 2070s under the two climate models of RCP2.6 and RCP8.5 were predicted. Under the current climate scenario, the six wild strawberry species have suitable areas in Yunnan Province, which were mainly distributed in the high-altitude and low-temperature regions in the northwest and northeast, such as Diqing and Zhaotong. In addition, the average size of the highly suitable area for diploid wild strawberry species was greater than that for tetraploid species. Under the future climate scenarios, the average size of the highly suitable area for diploid species showed a tendency to expand, while that of tetraploid species showed a tendency to shrink. Altitude was a critical variable affecting the distribution of tetraploid species. Under the two future climate models of RCP2.6 and RCP8.5, the suitable areas for wild strawberry species shifted to the regions of high latitude, high altitude, and low temperature. In addition, the average distance in the shift of the suitable area for tetraploid strawberry species was greater than that for the suitable area for diploid strawberry species. The above results provide valuable information for the management and protection of the germplasm resources of Fragaria.

Description: Soil salinization due to climate change and intensive use of water and soil is increasing exponentially. Cucurbitaceae species are cultivated worldwide and the identification of salinity tolerant genotypes to be used as rootstock or scion for securing yield stability in salt affected agricultural areas is a research priority. In the present greenhouse study, we assessed the response to salinity (0 mM a non-salt control and 150 mM NaCl dissolved in the nutrient solution) in the seedlings of 30 genotypes of cucurbits grown in a floating hydroponic system. The species tested included 16 genotypes of Cucumis melo L. (CM1-16), 6 Citrullus vulgaris Schrad. (CV1-6), 2 interspecific hybrids of Cucurbita maxima Duch. × Cucurbita moschata Duch. (CMM-R1 and 2), 4 bottle gourd (Lagenaria siceraria (Molina) Standl. (LS1-4)), 1 Cucurbita moschata Duch. (CMO51-17), and 1 luffa (Luffa cylindrica Mill. (LC1)) species. Results highlighted different morphological and physiological traits between the species and genotypes and a different response to salt stress. We identified C. maxima × C.moscata interspecific hybrid CMM-R2, melon genotypes CM6, CM7, CM10, and CM16 together with watermelon genotypes CV2 and CV6 and bottle gourd LS4 as salt tolerant genotypes and possible candidates as salt resistant rootstock to be introduced in grafting programs.

Description: The trehalose-6-phosphate synthase (TPS) gene family plays important roles in conferring plant stress resistance, but a comprehensive analysis of the gene family is lacking for sugarcane (Saccharum spp. hybrids). The objective of this study is to document functional classification, evolutionary characterization, and expression profiling of sugarcane TPS gene (ScTPS) family. Nine putative ScTPS genes were identified and assigned to two distinct classes based on gene structure and phylogeny. Phylogenetic analysis showed that 31 TPS genes from Arabidopsis, rice and sugarcane could be divided into five distinct clades, suggesting that there were at least five orthologous groups in monocot and dicot plants. Evolution analysis of TPS genes revealed that TPS family members appeared to have undergone strong negative selection. The strength of the selective pressure differed in most clades, especially Class I TPS genes, experienced significantly stronger negative selection pressure than Class II TPS genes. There were also cis-regulatory elements related to phytohormones and abiotic stresses. Additionally, ScTPS genes were found to exhibit divergent expression in response to simulated drought, salinity, and ABA stresses. Since ScTPS genes function in sugarcane adaptation to environmental stimuli, it might be used as a molecular marker in screening sugarcane germplasm for increased stress resistance.

Description: Microscopic crowd simulation can help to enhance the safety of pedestrians in situations that range from museum visits to music festivals. To obtain a useful prediction, the input parameters must be chosen carefully. In many cases, a lack of knowledge or limited measurement accuracy add uncertainty to the input. In addition, for meaningful parameter studies, we first need to identify the most influential parameters of our parametric computer models. The field of uncertainty quantification offers standardized and fully automatized methods that we believe to be beneficial for pedestrian dynamics. In addition, many methods come at a comparatively low cost, even for computationally expensive problems. This allows for their application to larger scenarios. We aim to identify and adapt fitting methods to microscopic crowd simulation in order to explore their potential in pedestrian dynamics. In this work, we first perform a variance-based sensitivity analysis using Sobol’ indices and then crosscheck the results by a derivative-based measure, the activity scores. We apply both methods to a typical scenario in crowd simulation, a bottleneck. Because constrictions can lead to high crowd densities and delays in evacuations, several experiments and simulation studies have been conducted for this setting. We show qualitative agreement between the results of both methods. Additionally, we identify a one-dimensional subspace in the input parameter space and discuss its impact on the simulation. Moreover, we analyze and interpret the sensitivity indices with respect to the bottleneck scenario.

Description: Weed management is a challenge for farmers worldwide, and the problem is exacerbated by the spread of weed herbicide resistance. Simulation models that combine population dynamics and genetics are valuable tools for predicting the impact of competing management options on weed density, allele frequency, and phenotypic resistance levels. The new R package PROSPER provides functions for the forward simulation of weed population dynamics on a field scale, the selection of individuals according to their sensitivity to herbicides, and the recombination of alleles during reproduction. Objects are provided to enter and save model parameters in a clear structure, and to save output data for further processing in R. The basic functions are extensible with R code. PROSPER combines individual-based population dynamics with monogenic or polygenic diploid inheritance and flexible selection pressure. Stochasticity can be included at all model steps. Two examples of the population dynamics of two annual weed species with herbicide resistance are presented. All parameters and the models are available in PROSPER. In addition to simulation, PROSPER is intended for sharing and publishing population dynamic parameters and models, which is easily done thanks to R.

Description: Bacterial blight is a common disease found in the rice-growing regions in the Korean peninsula. Identification of the gene network involved against Xanthomonas oryzae pv. oryzae Korean race K2 in popular japonica cultivars is essential in underpinning the molecular mechanism of resistance. A microarray of two popular Korean japonica rice cultivars, a bacterial blight susceptible Dongjin and resistant Jinbaek, was performed to investigate the transcripts of inducible genes at 48 h post-inoculation. A total of 771 differentially expressed genes were identified in Jinbaek, whereas 298 were found in Dongjin. The resistance observed in Jinbaek is likely participated by genes with predicted functions in transmembrane perception, intracellular signal transduction, and transcription activity. Moreover, the remarkable involvement of numerous WRKY proteins signifies orchestration of defense signals via robust cellular reprogramming, which leads to resistance. To discover genes essential to bacterial blight resistance in Jinbaek, 13 highly expressed genes encoding different protein classes were cloned and overexpressed in rice. Although none of the overexpression plants exhibited resistance comparable to Jinbaek, four candidate genes, including one twin-arginine translocation pathway signal (LOC_Os01g45640.1), one cytochrome p450 (LOC_Os09g10340.1), and two uncharacterized expressed protein (LOC_Os08g26230.4, LOC_Os09g04310.1) conferred partial resistance. However, of these four genes, only p450s have been reported to play an important role in the synthesis of plant defense compounds. These findings revealed the complexity of key immune signaling conduits critical to mounting a full defense against Xanthomonas. oryzae pv. oryzae race K2 in japonica rice.

Description: Prunus spinosa L. is a shrub that produces berries—sloes—with a high content of phenolic compounds and anthocyanins. Numerous consumer products are made from sloes, such as “Pacharán” liquors or jams. For these reasons, it is crucial to design accurate analytical methods for the extraction of these bioactive compounds in order to promote their consumption and to improve our health. In this study, an ultrasound-assisted extraction method was developed to extract phenolic compounds and anthocyanins from sloes. Several process variables (methanol composition, pH, temperature, cycle, amplitude, and sample–solvent ratio) were optimized based on a Box–Behnken design. The most influential parameters for the extraction of total phenolic compounds were the cycle, the percentage of methanol, and pH of the extraction solvent, with 0.2 s, 67%, and pH 7 as their optimal values, respectively. For total anthocyanins, all parameters were influential, with 44% methanol at pH 2, 48 °C, 0.2 s cycles, 30% amplitude, and 1.5 g/20 mL ratio as the optimal values for their corresponding variables. Both methods showed high recoveries, short time use, and high precision (RSD 〈 5%). In addition, the sloe liqueur “Pacharán” and sloe jams were analyzed to demonstrate the applicability of these methods for the extraction of the real matrix.

Description: The use of chatbots in news media platforms, although relatively recent, offers many advantages to journalists and media professionals and, at the same time, facilitates users’ interaction with useful and timely information. This study shows the usability of a news chatbot during a crisis situation, employing the 2020 COVID-19 pandemic as a case study. The basic targets of the research are to design and implement a chatbot in a news media platform with a two-fold aim in regard to evaluation: first, the technical effort of creating a functional and robust news chatbot in a crisis situation both from the AI perspective and interoperability with other platforms, which constitutes the novelty of the approach; and second, users’ perception regarding the appropriation of this news chatbot as an alternative means of accessing existing information during a crisis situation. The chatbot designed was evaluated in terms of effectively fulfilling the social responsibility function of crisis reporting, to deliver timely and accurate information on the COVID-19 pandemic to a wide audience. In this light, this study shows the advantages of implementing chatbots in news platforms during a crisis situation, when the audience’s needs for timely and accurate information rapidly increase.

Description: Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Hashtags, hyperlinked words in tweets, are widely used for tweet topic classification, retrieval, and clustering. Hashtags are used widely for analyzing tweet sentiments where emotions can be classified without contexts. However, regardless of the wide usage of hashtags, general tweet topic classification using hashtags is challenging due to its evolving nature, lack of context, slang, abbreviations, and non-standardized expression by users. Most existing approaches, which utilize hashtags for tweet topic classification, focus on extracting hashtag concepts from external lexicon resources to derive semantics. However, due to the rapid evolution and non-standardized expression of hashtags, the majority of these lexicon resources either suffer from the lack of hashtag words in their knowledge bases or use multiple resources at once to derive semantics, which make them unscalable. Along with scalable and automated techniques for tweet topic classification using hashtags, there is also a requirement for real-time analytics approaches to handle huge and dynamic flows of textual streams generated by Twitter. To address these problems, this paper first presents a novel semi-automated technique that derives semantically relevant hashtags using a domain-specific knowledge base of topic concepts and combines them with the existing tweet-based-hashtags to produce Hybrid Hashtags. Further, to deal with the speed and volume of Big Data streams of tweets, we present an online approach that updates the preprocessing and learning model incrementally in a real-time streaming environment using the distributed framework, Apache Storm. Finally, to fully exploit the batch and stream environment performance advantages, we propose a comprehensive framework (Hybrid Hashtag-based Tweet topic classification (HHTC) framework) that combines batch and online mechanisms in the most effective way. Extensive experimental evaluations on a large volume of Twitter data show that the batch and online mechanisms, along with their combination in the proposed framework, are scalable, efficient, and provide effective tweet topic classification using hashtags.

Description: Standard (Lomb-Scargle, likelihood, etc.) procedures for power-spectrum analysis provide convenient estimates of the significance of any peak in a power spectrum, based—typically—on the assumption that the measurements being analyzed have a normal (i.e., Gaussian) distribution. However, the measurement sequence provided by a real experiment or a real observational program may not meet this requirement. The RONO (rank-order normalization) procedure generates a proxy distribution that retains the rank-order of the original measurements but has a strictly normal distribution. The proxy distribution may then be analyzed by standard power-spectrum analysis. We show by an example that the resulting power spectrum may prove to be quite close to the power spectrum obtained from the original data by a standard procedure, even if the distribution of the original measurements is far from normal. Such a comparison would tend to validate the original analysis.

Description: Zinc (Zn) is a nutrient that regulates many vital functions of the human body, hence the demand for Zn in the balanced daily nutrition has to be covered. Priming of seeds with Zn for Zn-enriched sprout production can be considered a good alternative to artificial supplementation in a modern diet. Hence, the aim of our study was to determine the exposure level of Zn bringing neither risk for humans nor growth inhibition of enriched broccoli, pea and sunflower sprouts. Seeds treated with 0, 10, 20 and 30 μg mL−1 ZnSO4 responded in a differentiated way to Zn. Pea seed germination and sprout growth was diminished by 30 μg mL−1 ZnSO4, but for sunflower sprouts this Zn level resulted in the highest fresh mass and largest hypocotyls. Zn content in sprouts greatly increased in a dose-dependent manner, mostly in broccoli (up to 25 times) and peas (up to 4 times), and to a lesser extent (up to 120%) for sunflowers. Free radical scavenging activity was usually decreased. Considering the potential non-carcinogenic risk of sprouts consumption estimated by the hazard analysis, the safest exposure level for seed priming will be 10 μg mL−1 ZnSO4. It was confirmed by random amplification of polymorphic DNA analyses indicating no DNA variations in sprouts treated with 10 μg mL−1 ZnSO4 compared to the control.

Description: Wild blueberry is a native plant managed as a crop in northeastern North America. Fruit drop in wild blueberry, Vaccinium angustifolium Aiton, was studied over 6 years (1996–1999, 2008 and 2011). Three hypotheses were tested regarding fruit drop mechanisms from plants (clones) sampled in the field: (1) proportional fruit drop; (2) constant or uniform fruit drop; and (3) pollen or resource limitation. In addition, the temporal rate of fruit drop was measured between the end of pollination (petal fall) and fruit maturity just prior to harvest (2008 and 2011). The dynamics of fruit drop were different in the two years. The role of pollination intensity in fruit drop was also assessed. Evidence for both proportional fruit drop and resource limitation at the clone level was found but only proportional drop at the field level. Low levels of seed set are associated with fruit drop. In a four-year study (1996–1999), fruit drop ranged from 23.3 to 49.4% of the total set fruit. Partitioning the variance in fruit drop between years, fields and clones (plant genotypes) showed that the highest variation is between stems within clones (37.4%) and clones (plant genotype) within fields (35.4%), followed by between fields (19.8%) and between years (7.4%).

Description: Nitrogen fertilization is indispensable in increasing wheat crop productivity but, in order to achieve maximum profitable production and minimum negative environmental impact, improving nitrogen use efficiency (NUE) should be considered. The aim of this study was to evaluate the nitrogen use efficiency (NUE) in a long-term wheat–maize–soybean rotation system with the final purpose of increasing the overall performance of the wheat cropping system. Research was undertaken at the Agricultural Research Development Station Turda (ARDS Turda), located in Western Transylvania Plain, Romania. The experimental field was carried out at a fixed place during seven wheat vegetation seasons. The plant material consisted of a wheat variety created by the ARDS Turda (Andrada), one variety of maize (Turda 332) and one variety of soybean (Felix). The experiment covered two planting patterns: wheat after maize and wheat after soybean and five levels of nitrogen fertilization (control-unfertilized, fertilization with 0—control plot, 30, 60, 90 and 120 kg N ha−1 y−1). The following indices were assessed: NUE (nitrogen use efficiency), N uptake and PFP (partial factor productivity). The results of the present study suggest that reduced N-fertilization doses could improve N uptake and utilization for both planting patterns.

Description: Toward strong demand for very high-speed I/O for processors, physical performance growth of hardware I/O speed was drastically increased in this decade. However, the recent Big Data applications still demand the larger I/O bandwidth and the lower latency for the speed. Because the current I/O performance does not improve so drastically, it is the time to consider another way to increase it. To overcome this challenge, we focus on lossless data compression technology to decrease the amount of data itself in the data communication path. The recent Big Data applications treat data stream that flows continuously and never allow stalling processing due to the high speed. Therefore, an elegant hardware-based data compression technology is demanded. This paper proposes a novel lossless data compression, called ASE coding. It encodes streaming data by applying the entropy coding approach. ASE coding instantly assigns the fewest bits to the corresponding compressed data according to the number of occupied entries in a look-up table. This paper describes the detailed mechanism of ASE coding. Furthermore, the paper demonstrates performance evaluations to promise that ASE coding adaptively shrinks streaming data and also works on a small amount of hardware resources without stalling or buffering any part of data stream.

Description: When highly automated driving is realized, the role of the driver will change dramatically. Drivers will even be able to sleep during the drive. However, when awaking from sleep, drivers often experience sleep inertia, meaning they are feeling groggy and are impaired in their driving performance―which can be an issue with the concept of dual-mode vehicles that allow both manual and automated driving. Proactive methods to avoid sleep inertia like the widely applied ‘NASA nap’ are not immediately practicable in automated driving. Therefore, a reactive countermeasure, the sleep inertia counter-procedure for drivers (SICD), has been developed with the aim to activate and motivate the driver as well as to measure the driver’s alertness level. The SICD is evaluated in a study with N = 21 drivers in a level highly automation driving simulator. The SICD was able to activate the driver after sleep and was perceived as “assisting” by the drivers. It was not capable of measuring the driver’s alertness level. The interpretation of the findings is limited due to a lack of a comparative baseline condition. Future research is needed on direct comparisons of different countermeasures to sleep inertia that are effective and accepted by drivers.

Description: An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can take place in two routes. On the one hand, we can first truncate the available data to retiree ages and then produce mortality forecasts based on a partial age-range model. On the other hand, with all available data, we can first apply a full age-range model to produce forecasts and then truncate the mortality forecasts to retiree ages. We investigate the difference in modeling the logarithmic transformation of the central mortality rates between a partial age-range and a full age-range model, using data from mainly developed countries in the Human Mortality Database (2020). By evaluating and comparing the short-term point and interval forecast accuracies, we recommend the first strategy by truncating all available data to retiree ages and then produce mortality forecasts. However, when considering the long-term forecasts, it is unclear which strategy is better since it is more difficult to find a model and parameters that are optimal. This is a disadvantage of using methods based on time-series extrapolation for long-term forecasting. Instead, an expectation approach, in which experts set a future target, could be considered, noting that this method has also had limited success in the past.

Description: Pre-harvest application of prohydrojasmon (PDJ) or abscisic acid (ABA) induces the red color in fruits that were exposed to sunlight at the orchard. In this large-scale work, we evaluated the effect of two different pruning techniques of ‘Kent’ mango orchards, one leading to opening the orchard canopy to expose as much fruit as possible to sunlight, while the second pruning leads to square-shaped trees and subsequently reduces the amount of sunlight reaching the fruit. These two pruning methods were combined with preharvest spraying with prohydrojasmon (PDJ) or abscisic acid (ABA) using two different types of sprayers, i.e., regular and air-jet sprayer. Pruning the canopy of the orchards to open and closed trees exposed 80% or 30% of fruits to sunlight, respectively. Both of the application with air-jet and regular sprayers effectively covered the fruit without causing fruit detachment and damage to yield. Both the phytohormones (PDJ and ABA) application treatments induced red blush skin, red intensity, anthocyanin, and flavonoids, particularly in fruit grown outside the tree canopy in both open and closed trees. PDJ and ABA treatments exhibited marginally reduced acidity than the untreated control, while the brix was not affected much by any of the treatments. Besides these, exposure to sunlight and PDJ treatment also reduced postharvest decay and increased chlorophyll degradation and yellowing in comparison to the controls. This study promoted applicative evidence about the positive effects of exposure to sunlight, prohydrojasmon (PDJ), and abscisic acid (ABA) on red color development without compromising the mango fruit’s quality.

Description: Among the rust diseases, leaf rust of wheat caused by Puccinia triticina, is the most prevalent worldwide and causes significant yield losses. This study aimed to determine the genomic location of loci that control adult plant resistance (APR) to leaf rust in the pre-Green Revolution landrace accession, Aus27506, from the “Watkins Collection”. An Aus27506/Aus27229-derived F7 recombinant inbred line (RIL) population was screened under field conditions across three cropping seasons and genotyped with the iSelect 90K Infinium SNP bead chip array. One quantitative trait loci (QTL) on each of the chromosomes 1BL, 2B and 2DL explained most of the leaf rust response variation in the RIL population, and these were named QLr.sun-1BL, QLr.sun-2B and QLr.sun-2DL, respectively. QLr.sun-1BL and QLr.sun-2DL were contributed by Aus27506. QLr.sun-1BL is likely Lr46, while QLr.sun-2DL appeared to be a new APR locus. The alternate parent, Aus27229, carried the putatively new APR locus QLr.sun-2B. The comparison of average severities among RILs carrying these QTL in different combinations indicated that QLr.sun-2B does not interact with either of the other two QTL; however, the combination of QLr.sun-1BL and QLr.sun-2DL reduced disease severity significantly. In planta fungal quantification assays validated these results. The RILs carrying QLr.sun-1BL and QLr.sun-2DL did not differ significantly from the parent Aus27506 in terms of resistance. Aus27506 can be used as a source of adult plant leaf rust resistance in breeding programs.

Description: Text annotation is the process of identifying the sense of a textual segment within a given context to a corresponding entity on a concept ontology. As the bag of words paradigm’s limitations become increasingly discernible in modern applications, several information retrieval and artificial intelligence tasks are shifting to semantic representations for addressing the inherent natural language polysemy and homonymy challenges. With extensive application in a broad range of scientific fields, such as digital marketing, bioinformatics, chemical engineering, neuroscience, and social sciences, community detection has attracted great scientific interest. Focusing on linguistics, by aiming to identify groups of densely interconnected subgroups of semantic ontologies, community detection application has proven beneficial in terms of disambiguation improvement and ontology enhancement. In this paper we introduce a novel distributed supervised knowledge-based methodology employing community detection algorithms for text annotation with Wikipedia Entities, establishing the unprecedented concept of community Coherence as a metric for local contextual coherence compatibility. Our experimental evaluation revealed that deeper inference of relatedness and local entity community coherence in the Wikipedia graph bears substantial improvements overall via a focus on accuracy amelioration of less common annotations. The proposed methodology is propitious for wider adoption, attaining robust disambiguation performance.

Description: The problem posed by complex, articulated or deformable objects has been at the focus of much tracking research for a considerable length of time. However, it remains a major challenge, fraught with numerous difficulties. The increased ubiquity of technology in all realms of our society has made the need for effective solutions all the more urgent. In this article, we describe a novel method which systematically addresses the aforementioned difficulties and in practice outperforms the state of the art. Global spatial flexibility and robustness to deformations are achieved by adopting a pictorial structure based geometric model, and localized appearance changes by a subspace based model of part appearance underlain by a gradient based representation. In addition to one-off learning of both the geometric constraints and part appearances, we introduce a continuing learning framework which implements information discounting i.e., the discarding of historical appearances in favour of the more recent ones. Moreover, as a means of ensuring robustness to transient occlusions (including self-occlusions), we propose a solution for detecting unlikely appearance changes which allows for unreliable data to be rejected. A comprehensive evaluation of the proposed method, the analysis and discussing of findings, and a comparison with several state-of-the-art methods demonstrates the major superiority of our algorithm.