ALBERT

Description: Over the years, the cellular mobile network has evolved from a wireless plain telephone system to a very complex system providing telephone service, Internet connectivity and many interworking capabilities with other networks. Its air interface performance has increased drastically over time, leading to high throughput and low latency. Changes to the core network, however, have been slow and incremental, with increased complexity worsened by the necessity of backwards-compatibility with older-generation systems such as the Global System for Mobile communication (GSM). In this paper, a new virtualized Peer-to-Peer (P2P) core network architecture is presented. The key idea of our approach is that each user is assigned a private virtualized copy of the whole core network. This enables a higher degree of security and novel services that are not possible in today’s architecture. We describe the new architecture, focusing on its main elements, IP addressing, message flows, mobility management, and scalability. Furthermore, we will show some significant advantages this new architecture introduces. Finally, we investigate the performance of our architecture by analyzing voice-call traffic available in a database of a large U.S. cellular network provider.

Description: The ongoing digital transformation has the potential to revolutionize nearly all industrial manufacturing processes. However, its concrete requirements and implications are still not sufficiently investigated. In order to establish a common understanding, a multitude of initiatives have published guidelines, reference frameworks and specifications, all intending to promote their particular interpretation of the Industrial Internet of Things (IIoT). As a result of the inconsistent use of terminology, heterogeneous structures and proposed processes, an opaque landscape has been created. The consequence is that both new users and experienced experts can hardly manage to get an overview of the amount of information and publications, and make decisions on what is best to use and to adopt. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications and the concerns they target. We supply alignments of shared concepts, identify gaps and give a structured mapping of regarded concerns at each part of the respective reference architectures. Furthermore, the linking of relevant industry standards and technologies to the architectures allows a more effective search for specifications and guidelines and supports the direct technology adoption.

Description: A mixture of red and blue light-emitting diodes (LEDs; at a ratio of 7:3, respectively) were used to analyze the effects of different photosynthetic photon flux densities (PPFDs) (40, 80, and 120 µmol m−2 s−1 hereafter known as LED 40, 80, and 120, respectively) on the micropropagation of Gerbera jamesonii Bolus shoots. The experiment also examined the effect of 6-benzyladenine (BA) in 1, 2.5, and 5 µM concentrations in the media. Biometrical observations and analyses of leaf morphometry and photosynthetic pigment content were conducted. Shoot multiplication increased with an increasing BA concentration. A PPFD of 80 µmol m−2 s−1 and 5 µM BA is suggested as efficient for shoot propagation and economically viable. LED 120 increased the leaf blade area and its width, and circularity and elongation ratios. The intensity of light did not affect the fresh weight, which increased at higher BA concentrations (2.5 and 5 μM). The dry weight content decreased with increasing cytokinin concentration; the greatest content was observed on media with 1 µM BA under PPFD 120 µmol m−2 s−1. LED 80 increased the photosynthetic pigments content in the leaves in comparison to the standard intensity of LED 40. Increased BA concentration raises the content of chlorophyll a.

Description: In this paper, we measure the systemic risk with a novel methodology, based on a “spatial-temporal” approach. We propose a new bank systemic risk measure to consider the two components of systemic risk: cross-sectional and time dimension. The aim is to highlight the “time-space dynamics” of contagion, i.e., if the CDS spread of bank i depends on the CDS spread of other banks. To do this, we use an advanced spatial econometrics design with a time-varying spatial dependence that can be interpreted as an index of the degree of cross-sectional spillovers. The findings highlight that the Eurozone banks have strong spatial dependence in the evolution of CDS spread, namely the contagion effect is present and persistent. Moreover, we analyse the role of the European Central Bank in managing contagion risk. We find that monetary policy has been effective in reducing systemic risk. However, the results show that systemic risk does not imply a policy intervention, highlighting how financial stability policy is not yet an objective.

Description: Service recommendation is one of the important means of service selection. Aiming at the problems of ignoring the influence of typical data sources such as service information and interaction logs on the similarity calculation of user preferences and insufficient consideration of dynamic trust relationship in traditional trust-based Web service recommendation methods, a novel approach for Web service recommendation based on advanced trust relationships is presented. After considering the influence of indirect trust paths, the improved calculation about indirect trust degree is proposed. By quantifying the popularity of service, the method of calculating user preference similarity is investigated. Furthermore, the dynamic adjustment mechanism of trust is designed by differentiating the effect of each service recommendation. Integrating these efforts, a service recommendation mechanism is introduced, in which a new service recommendation algorithm is described. Experimental results show that, compared with existing methods, the proposed approach not only has higher accuracy of service recommendation, but also can resist attacks from malicious users more effectively.

Description: Soil pH is a key factor affecting the growth of blueberries. Understanding the response mechanism of blueberries to different pH values and selecting suitable evaluation indexes are the basis of breeding new blueberry cultivars with high pH tolerances. The effects of different soil pH treatments for 17 months on the plant growth, fruit yield, photosynthetic characteristics, and leaf microelement concentration of Vaccinium ashei Reade ‘Climax’ and V. corymbosum hybrid ‘Chaoyue No. 1′ were studied. Plant height, main stem diameter, branch number per plant, leaf dry weight, stem dry weight, root dry weight, and total dry weight decreased with increasing soil pH. With an increase in soil pH, the first flowering date, 50% flowering date, first ripening date, and 50% ripening date of the two cultivars were postponed, and the flower bud numbers per plant, the floret numbers per bud, and yield per plant showed a downward trend. Moreover, the fruit quality decreased, which was reflected in the increase in the titratable acid content (TA) and the decrease in the total soluble solids content (TSS) and the TSS:TA ratio in the high pH treatment. With increasing soil pH, the chlorophyll content index (CCI), maximal photochemical efficiency of the PSII (Fv/Fm), quantum photosynthetic yield of the PSII (Y(II)) and net photosynthetic rate (Pn) of the two cultivars showed a downward trend, and some microelement concentrations in the leaves were imbalanced. Under high pH treatment, ‘Chaoyue No. 1′ had a relatively higher plant biomass and fruit yield, so it had a stronger tolerance to high pH than ‘Climax’ did. More strongly acidified rhizosphere soil capacity, as well as higher CCI, Fv/Fm, Y(II), and Pn values were the main reasons for the high pH tolerance of ‘Chaoyue No. 1′. Compared with destructive biomass indicators such as plant weight, nondestructive indicators such as CCI, Fv/Fm, and Y(II) can be more valuable indicators for fast and accurate evaluation of blueberry tolerance to high pH at early stages of treatment.

Description: We explore the class of positive integers n that admit idempotent factorizations n = p ¯ q ¯ such that λ ( n ) ∣ ( p ¯ − 1 ) ( q ¯ − 1 ) , where λ is the Carmichael lambda function. Idempotent factorizations with p ¯ and q ¯ prime have received the most attention due to their cryptographic advantages, but there are an infinite number of n with idempotent factorizations containing composite p ¯ and/or q ¯ . Idempotent factorizations are exactly those p ¯ and q ¯ that generate correctly functioning keys in the Rivest–Shamir–Adleman (RSA) 2-prime protocol with n as the modulus. While the resulting p ¯ and q ¯ have no cryptographic utility and therefore should never be employed in that capacity, idempotent factorizations warrant study in their own right as they live at the intersection of multiple hard problems in computer science and number theory. We present some analytical results here. We also demonstrate the existence of maximally idempotent integers, those n for which all bipartite factorizations are idempotent. We show how to construct them, and present preliminary results on their distribution.

Description: Due to its possible utilization in cosmetics, medicine and crop protection, as a valuable alternative to petrochemical-derived products, hemp essential oil is now considered a product with high value added and a promising marketing potential. This experiment was conducted with the aim of evaluating the effect of four different locations of Northern Italy during two years (four environments) and three hemp monoecious varieties on the production and quality of essential oils (EOs) obtained by inflorescences harvested at full flowering of female flowers. The highest inflorescence yield was obtained at Maiano 2017, where a superficial groundwater layer (1.5 m) was present, with values that ranged from 1.69 of Fedora to 2.06 t ha−1 of Futura. EOs production ranged between 3.4 and 4.9 L ha−1, affected mainly by the variety effect. The terpene in EOs, very similar between varieties and environments, was mainly composed of sesquiterpenes (caryophillene and humulene, as the most abundant) rather than monoterpenes (α-pinene, β-myrcene and trans-β-ocimene, in particular). Phytocannabinoids, and in particular cannabidiol (CBD), were not removed from tissues by the steam during hydrodistillation, and if this is confirmed by further experiments, the residual biomass, now considered as waste, could assume significant importance as a source for further utilization.

Description: Google’s Material Design, created in 2014, led to the extended application of floating action buttons (FAB) in user interfaces of web pages and mobile applications. FAB’s roll is to trigger an activity either on the present screen, or it can play out an activity that makes another screen. A few specialists in user experience (UX) and user interface (UI) design are sceptical regarding the usability of FAB in the interfaces of both web pages and mobile applications. They claim that the use of FAB easily distracts users and that it interferes with using other important functions of the applications, and it is unusable in applications designed for iOS systems. The aim of this paper is to investigate by an experiment the quality of experience (QoE) of a static and animated FAB and compare it to the toolbar alternative. The experimental results of different testing methods rejected the hypothesis that the usage and animation of this UI element has a positive influence on the application usability. However, its static and animated utilization enhanced the ratings of hedonic and aesthetic features of the user experience, justifying the usage of this type of button.

Description: Recommender systems are nowadays an indispensable part of most personalized systems implementing information access and content delivery, supporting a great variety of user activities [...]

Description: The role of small bioactive molecules (〈500 Da) in mechanisms improving resource use efficiency in plants under stress conditions draws increasing interest. One such molecule is omeprazole (OMP), a benzimidazole derivative and inhibitor of animal proton pumps shown to improve nitrate uptake and exclusion of toxic ions, especially of chloride from the cytosol of salt-stressed leaves. Currently, OMP was applied as substrate drench at two rates (0 or 10 μM) on hydroponic basil (Ocimum basilicum L. cv. Genovese) grown under decreasing NO3−:Cl− ratio (80:20, 60:40, 40:60, or 20:80). Chloride concentration and stomatal resistance increased while transpiration, net CO2 assimilation rate and beneficial ions (NO3−, PO43−, and SO42−) decreased with reduced NO3−:Cl− ratio under the 0 μM OMP treatment. The negative effects of chloride were not only mitigated by the 10 μM OMP application in all treatments, with the exception of 20:80 NO3−:Cl−, but plant growth at 80:20, 60:40, and 40:60 NO3−:Cl− ratios receiving OMP application showed maximum fresh yield (+13%, 24%, and 22%, respectively), shoot (+10%, 25%, and 21%, respectively) and root (+32%, 76%, and 75%, respectively) biomass compared to the corresponding untreated treatments. OMP was not directly involved in ion homeostasis and compartmentalization of vacuolar or apoplastic chloride. However, it was active in limiting chloride loading into the shoot, as manifested by the lower chloride concentration in the 80:20, 60:40, and 40:60 NO3−:Cl− treatments compared to the respective controls (−41%, −37%, and −24%), favoring instead that of nitrate and potassium while also boosting photosynthetic activity. Despite its unequivocally beneficial effect on plants, the large-scale application of OMP is currently limited by the molecule’s high cost. However, further studies are warranted to unravel the molecular mechanisms of OMP-induced reduction of chloride loading to shoot and improved salt tolerance.

Description: Finite element data form an important basis for engineers to undertake analysis and research. In most cases, it is difficult to generate the internal sections of finite element data and professional operations are required. To display the internal data of entities, a method for generating the arbitrary sections of finite element data based on radial basis function (RBF) interpolation is proposed in this paper. The RBF interpolation function is used to realize arbitrary surface cutting of the entity, and the section can be generated by the triangulation of discrete tangent points. Experimental studies have proved that the method is very convenient for allowing users to obtain visualization results for an arbitrary section through simple and intuitive interactions.

Description: There is a growing body of research that recognizes the potentials of biochar application in agricultural production systems. However, little is known about the effects of biochar, especially hydrochar, on production of containerized seedlings under nursery conditions. This study aimed to test the effects of hydrochar application on growth, quality, nutrient and heavy metal contents, and mycorrhizal association of containerized pine seedlings. The hydrochar used in this study was produced through hydrothermal carbonization of paper mill biosludge at 200 °C. Two forms of hydrochar (powder and pellet) were mixed with peat at ratios of 10% and 20% (v/v) under three levels of applied commercial fertilizer (nil, half and full rates). Application of hydrochar had positive or neutral effects on shoot biomass and stem diameter compared with control seedlings (without hydrochar) under tested fertilizer levels. Analysis of the natural logarithmic response ratios (LnRR) of quality index and nutrient and heavy metal uptake revealed that application of 20% (v/v) hydrochar powder or pellet with 50% fertilizer resulted in same quality pine seedlings with similar heavy metal (Cu, Ni, Pb, Zn and Cr) and nutrient (P, K, Ca and Mg) contents as untreated seedlings supplied with 100% fertilizer. Colonization percentage by ectomycorrhizae significantly increased when either forms of hydrochar were applied at a rate of 20% under unfertilized condition. The results of this study implied that application of proper rates of hydrochar from biosludge with adjusted levels of liquid fertilizer may reduce fertilizer requirements in pine nurseries.

Description: The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of general rules. Multilingual methods have been applied to numerous problems in Natural Language Processing, achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We argue that a multilingual approach can enhance OIE methods as it is ideal to evaluate and compare OIE systems, and therefore can be applied to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.

Description: The tank cascade system (TCS) has been used for over 2000 years for water management in Sri Lanka. Since surface water is limited in the dry zone of Sri Lanka, agricultural production, especially of upland crops, relies on groundwater for irrigation. We sampled 29 wells in the Ulagalla cascade, a prominent TCS near Anuradhapura city in the dry zone of Sri Lanka, in Yala (dry) and Maha (wet) seasons, the two main cropping seasons in Sri Lanka. We evaluated the suitability of groundwater for irrigation using the analytic hierarchy process and geographical information system. Water quality did not vary notably between seasons. However, it deteriorated with the onset of high intensity heavy rain, especially during the Maha season. A water quality zoning map indicated that groundwater in 4% and 96% of the study area is suitable and moderately suitable for irrigation, respectively. Irrigation water quality in tank cascade landscapes and similar environments can be assessed using this methodology and our results.

Description: This paper aims to explore the current status, research trends and hotspots related to the field of infrared detection technology through bibliometric analysis and visualization techniques based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) articles published between 1990 and 2018 using the VOSviewer and Citespace software tools. Based on our analysis, we first present the spatiotemporal distribution of the literature related to infrared detection technology, including annual publications, origin country/region, main research organization, and source publications. Then, we report the main subject categories involved in infrared detection technology. Furthermore, we adopt literature cocitation, author cocitation, keyword co-occurrence and timeline visualization analyses to visually explore the research fronts and trends, and present the evolution of infrared detection technology research. The results show that China, the USA and Italy are the three most active countries in infrared detection technology research and that the Centre National de la Recherche Scientifique has the largest number of publications among related organizations. The most prominent research hotspots in the past five years are vibration thermal imaging, pulse thermal imaging, photonic crystals, skin temperature, remote sensing technology, and detection of delamination defects in concrete. The trend of future research on infrared detection technology is from qualitative to quantitative research development, engineering application research and infrared detection technology combined with other detection techniques. The proposed approach based on the scientific knowledge graph analysis can be used to establish reference information and a research basis for application and development of methods in the domain of infrared detection technology studies.

Description: The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance.

Description: Soils in tropical croplands are becoming degraded because of soil carbon (C) depletion. Local farmers in South India use a specific management of traditional cultivation, i.e., broadcast seeding. However, for sustainable C management, there is no quantitative data on the CO2 flux under this management. Our objectives were to (1) estimate the annual CO2 flux, and (2) evaluate the effect of traditional cultivation management (seeding rate) on the CO2 flux. Our field experiment was conducted in South India, from 2015 to 2017, including two cultivation periods with four cultivation management treatments (traditional cultivation management plot (T), fixed density plot (FD), no thinning plot (NT), and bare plot (B)). The seeding rate in the FD plot was ca. 50% of the T plot. We applied 1.1 Mg C ha−1 farmyard manure just before the experiment as a C input. We found that broadcasting, thinning, and cultivation increased soil moisture, while the CO2 efflux rate showed no significant difference between treatments throughout the experimental period. This indicates that cultivation management did not affect the CO2 flux. The total CO2 fluxes for two years were estimated at 2.2–2.7 Mg C ha−1. Our results indicate that it is necessary to apply larger or more frequent C inputs to prevent C depletion.

Description: Service Level Agreements are employed to set availability commitments in cloud services. When a violation occurs as in an outage, cloud providers may be called to compensate customers for the losses incurred. Such compensation may be so large as to erode cloud providers’ profit margins. Insurance may be used to protect cloud providers against such a danger. In this paper, closed formulas are provided through the expected utility paradigm to set the insurance premium under different outage models and QoS metrics (no. of outages, no. of long outages, and unavailability). When the cloud service is paid through a fixed fee, we also provide the maximum unit compensation that a cloud provider can offer so as to meet constraints on its profit loss. The unit compensation is shown to vary approximately as the inverse square of the service fee.

Description: The rye (Secale cereale L.) 5R chromosome contains some elite genes that can be used to improve wheat cultivars. In this study, a set of 5RKu dissection lines was obtained, and 111 new PCR-based and 5RKu-specific markers were developed using the specific length amplified fragment sequencing (SLAF-seq) method. The 111 markers were combined with the 52 5RKu-specific markers previously reported, and 65 S. cereale Lo7 scaffolds were physically mapped to six regions of the 5RKu chromosome using the 5RKu dissection lines. Additionally, the 5RLKu arm carried stripe rust resistance gene(s) and it was located to the region L2, the same region where 22 5RKu-specific markers and 11 S. cereale Lo7 scaffolds were mapped. The stripe rust resistance gene(s) located in the 5RLKu arm might be new one(s) because its source and location are different from the previously reported ones, and it enriches the resistance source of stripe rust for wheat breeding programs. The markers and the S. cereale Lo7 scaffolds that were mapped to the six regions of the 5RKu chromosome can facilitate the utilization of elite genes on the 5R chromosome in the improvement of wheat cultivars.

Description: Assessing and prescribing fertilizer use is critical to profitable and sustainable coffee production, and this is becoming a priority concern for the Robusta coffee industry. In this study, annual survey data of 798 farms across selected Robusta coffee-producing provinces in Vietnam and Indonesia between 2008 and 2017 were used to comparatively assess the fertilizer management strategies in these countries. Specifically, we aimed to characterize fertilizer use patterns in the key coffee-growing provinces and discuss the potential for improving nutrient management practices. Four types of chemical (NPK, super phosphate, potassium chloride and urea) and two of natural (compost and lime) fertilizers were routinely used in Vietnam. In Indonesia, NPK and urea were supplemented only with compost. Farmers in Vietnam applied unbalanced quantities of chemical fertilizers (i.e., higher rates than recommended) and at a constant rate between years whereas Indonesian farmers applied well below the recommended rates because of poor accessibility and financial support. The overuse of chemical fertilizers in Vietnam threatens the sustainability of Robusta coffee farming. Nevertheless, there is a potential for improvement in both countries in terms of nutrient management and sustainability of Robusta coffee production by adopting the best local fertilizer management practices.

Description: Term translation quality in machine translation (MT), which is usually measured by domain experts, is a time-consuming and expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems often need to be updated for many reasons (e.g., availability of new training data, leading MT techniques). To the best of our knowledge, as of yet, there is no publicly-available solution to evaluate terminology translation in MT automatically. Hence, there is a genuine need to have a faster and less-expensive solution to this problem, which could help end-users to identify term translation problems in MT instantly. This study presents a faster and less expensive strategy for evaluating terminology translation in MT. High correlations of our evaluation results with human judgements demonstrate the effectiveness of the proposed solution. The paper also introduces a classification framework, TermCat, that can automatically classify term translation-related errors and expose specific problems in relation to terminology translation in MT. We carried out our experiments with a low resource language pair, English–Hindi, and found that our classifier, whose accuracy varies across the translation directions, error classes, the morphological nature of the languages, and MT models, generally performs competently in the terminology translation classification task.

Description: Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for livestock producers to use their agricultural lands more productively and intensively within sustainable limits. Breeding perennial ryegrass cultivars that are both productive and persistent is a target of forage breeding programs and will allow farmers to select appropriate cultivars to deliver the highest profitability over the lifetime of a sward. Conventional methods for the estimation of pasture persistence depend on manual ground cover estimation or counting the number of surviving plants or tillers in a given area. Those methods are subjective, time-consuming and/or labour intensive. This study aimed to develop a phenomic method to evaluate the persistence of perennial ryegrass cultivars in field plots. Data acquisition was conducted three years after sowing to estimate the persistence of perennial ryegrass using high-resolution aerial-based multispectral and ground-based red, green and blue(RGB) sensors, and subsequent image analysis. There was a strong positive relationship between manual ground cover and sensor-based ground cover estimates (p 〈 0.001). Although the manual plant count was positively correlated with sensor-based ground cover (p 〈 0.001) intra-plot plant size variation influenced the strength of this relationship. We conclude that object-based ground cover estimation is most suitable for use in large-scale breeding programs due to its higher accuracy, efficiency and repeatability. With further development, this technique could be used to assess temporal changes of perennial ryegrass persistence in experimental studies and on a farm scale.

Description: High input costs combined with multiple management and material inputs have threatened cotton productivity. We hypothesize that this problem can be addressed by a single fertilization at flowering with late sowing in a moderately populated plant stand. Field experiments were conducted to evaluate the cotton biomass accumulation, phosphorus dynamics, and fiber quality under three planting densities (low, 3 × 104; moderate, 6 × 104; and dense, 9 × 104 ha−1) and two cultivars (Zhongmian-16 and J-4B). High planting density had 6.2 and 12.6% larger stems and fruiting nodes m−2, while low density produced a 37.5 and 59.4% maximum height node ratio. Moderate density produced 26.4–15.5%, 24.7–12.6%, and 10.5–13.6% higher biomass accumulation rate at the peak bloom, boll set, and plant removal stages over low and high density in both years, respectively. J-4B produced a higher reproductive organs biomass yield when compared with Zhongmian-16 in both years. This higher biomass formation was due to both the higher average (0.8 VT kg·ha−1·d−1) and maximum (1.0 VM kg·ha−1·d−1) reproductive organ phosphorus uptake, respectively. Plants with low density had 5.3–18.5%, 9.5–15%, and 7.8–12.8% greater length, strength, and micronaire values over moderate and dense plants, respectively. Conclusively, moderate density with J-4B is a promising option for improved biomass, phosphorus acquisition, and fiber quality under a short season.

Description: Three rust diseases namely; stem rust caused by Puccinia graminis f. sp. tritici (Pgt), leaf rust caused by Puccinia triticina (Pt), and stripe rust caused by Puccinia striiformis f. sp. tritici (Pst), are the most common fungal diseases of wheat (Triticum aestivum L.) and cause significant yield losses worldwide including Australia. Recently characterized stripe rust resistance genes Yr51 and Yr57 are effective against pre- and post-2002 Pst pathotypes in Australia. Similarly, stem rust resistance genes Sr22, Sr26, and Sr50 are effective against the Pgt pathotype TTKSK (Ug99) and its derivatives in addition to commercially important Australian pathotypes. Effectiveness of these genes make them good candidates for combining with known pleiotropic adult plant resistance (PAPR) genes to achieve durable resistance against three rust pathogens. This study was planned to transfer rust resistance genes Yr51, Yr57, Sr22, Sr26, and Sr50 into two Australian (Gladius and Livingston) and two Indian (PBW550 and DBW17) wheat cultivars through marker assisted selection (MAS). These cultivars also carry other rust resistance genes: Gladius carries Lr37/Yr17/Sr38 and Sr24/Lr24; Livingston carries Lr34/Yr18/Sr57, Lr37/Yr17/Sr38, and Sr2; PBW550 and DBW17 carry Lr34/Yr18/Sr57 and Lr26/Yr9/Sr31. Donor sources of Yr51 (AUS91456), Yr57 (AUS91463), Sr22 (Sr22/3*K441), Sr26 (Sr26 WA1), and Sr50 (Dra-1/Chinese Spring ph1b/2/3* Gabo) were crossed with each of the recurrent parents to produce backcross progenies. Markers linked to Yr51 (sun104), Yr57 (gwm389 and BS00062676), Sr22 (cssu22), Sr26 (Sr26#43), and Sr50 (Sr50-5p-F3, R2) were used for their MAS and markers csLV34 (Lr34/Yr18/Sr57), VENTRIUP-LN2 (Lr37/Yr17/Sr38), Sr24#12 (Sr24/Lr24), and csSr2 (Sr2) were used to select genes present in recurrent parents. Progenies of selected individuals were grown and selected under field conditions for plant type and adult plant rust responses. Final selections were genotyped with the relevant markers. Backcross derivatives of these genes were distributed to breeding companies for use as resistance donors.

Description: Radar signal processing mainly focuses on target detection, classification, estimation, filtering, and so on. Compressed sensing radar (CSR) technology can potentially provide additional tools to simultaneously reduce computational complexity and effectively solve inference problems. CSR allows direct compressive signal processing without the need to reconstruct the signal. This study aimed to solve the problem of CSR detection without signal recovery by optimizing the transmit waveform. Therefore, a waveform optimization method was introduced to improve the output signal-to-interference-plus-noise ratio (SINR) in the case where the target signal is corrupted by colored interference and noise having known statistical characteristics. Two different target models are discussed: deterministic and random. In the case of a deterministic target, the optimum transmit waveform is derived by maximizing the SINR and a suboptimum solution is also presented. In the case of random target, an iterative waveform optimization method is proposed to maximize the output SINR. This approach ensures that SINR performance is improved in each iteration step. The performance of these methods is illustrated by computer simulation.

Description: Reducing soil tillage can lead to many benefits, but this practice often increases weed abundance and thus the need for herbicides, especially during the transition phase from inversion tillage to non-inversion tillage. We evaluated if subsidiary crops (SCs, e.g., cover crops) can mitigate the effects of non-inversion tillage on weed abundance. Two-year experiments studying SC use, tillage intensity, and nitrogen (N) fertilization level were carried out twice at six sites throughout northern and central Europe. SCs significantly reduced weed cover throughout the intercrop period (−55% to −1% depending on site), but only slightly during the main crops. Overall weed abundance and weed biomass were higher when using non-inversion tillage with SCs compared to inversion tillage without SCs. The effects differed due to site-specific weed pressure and management. With increasing weed pressure, the effect of SCs decreased, and the advantage of inversion over non-inversion tillage increased. N fertilization level did not affect weed abundance. The results suggest that SCs can contribute by controlling weeds but cannot fully compensate for reduced weed control of non-inversion tillage in the transition phase. Using non-inversion tillage together with SCs is primarily recommended in low weed pressure environments.

Description: Chamomile (Matricaria chamomilla L.) is a well-known medicinal plant species in which the products requested from the market are those that are derived from the organic system. The study was conducted to assess the allelopathic effects, as natural herbicides, of two essential oils extracted from oregano (Origanum vulgare L.) and rosemary (Rosmarimum officinalis L.), with the objective of exploring the possibility of their utilization for future weed management. A field experiment was conducted over two seasons, when the infestation of 15 different weed species was detected. Each essential oil was applied at two different concentrations (50% diluted and undiluted), three times during the chamomile crop under an organic farm system. The results demonstrated that the germination of different weed species was affected differently by the type of essential oils and especially by their concentrations. The undiluted oils inhibited most of the germination of several weed species, highlighting a significantly higher percentage of Weed Control Efficiency (WCE) and suggesting the potential to be used as bio-herbicides. Bioherbicidal weed control methods could offer an advantage with respect to hand weeding, particularly from an economic point of view.

Description: In semi-autonomous robot conferencing, not only the operator controls the robot, but the robot itself also moves autonomously. Thus, it can modify the operator’s movement (e.g., adding social behaviors). However, the sense of agency, that is, the degree of feeling that the movement of the robot is the operator’s own movement, would decrease if the operator is conscious of the discrepancy between the teleoperation and autonomous behavior. In this study, we developed an interface to control the robot head by using an eye tracker. When the robot autonomously moves its eye-gaze position, the interface guides the operator’s eye movement towards this autonomous movement. The experiment showed that our interface can maintain the sense of agency, because it provided the illusion that the autonomous behavior of a robot is directed by the operator’s eye movement. This study reports the conditions of how to provide this illusion in semi-autonomous robot conferencing.

Description: Appropriate business processes management (BPM) within an organization can help attain organizational goals. It is particularly important to effectively manage the lifecycle of these processes for organizational effectiveness in improving ever-growing performance and competitivity-building across the company. This paper presents a process discovery and how we can use it in a broader framework supporting self-organization in BPM. Process discovery is intrinsically associated with the process lifecycle. We have made a pre-evaluation of the usefulness of our facts using a generated log file. We also compared visualizations of the outcomes of our approach with different cases and showed performance characteristics of the cash loan sales process.

Description: Correlations between observed data are at the heart of all empirical research that strives for establishing lawful regularities. However, there are numerous ways to assess these correlations, and there are numerous ways to make sense of them. This essay presents a bird’s eye perspective on different interpretive schemes to understand correlations. It is designed as a comparative survey of the basic concepts. Many important details to back it up can be found in the relevant technical literature. Correlations can (1) extend over time (diachronic correlations) or they can (2) relate data in an atemporal way (synchronic correlations). Within class (1), the standard interpretive accounts are based on causal models or on predictive models that are not necessarily causal. Examples within class (2) are (mainly unsupervised) data mining approaches, relations between domains (multiscale systems), nonlocal quantum correlations, and eventually correlations between the mental and the physical.

Description: The establishment and application of a spectral library is a critical step in the standardization and automation of remote sensing interpretation and mapping. Currently, most spectral libraries are designed to support the classification of land cover types, whereas few are dedicated to agricultural remote sensing monitoring. Here, we gathered spectral observation data on plants in multiple experimental scenarios into a spectral database to investigate methods for crop classification (16 crop species) and status monitoring (tea plant and rice growth). We proposed a set of screening methods for spectral features related to plant classification and status monitoring (band reflectance, vegetation index, spectral differentiation, spectral continuum characteristics) that are based on ISODATA and JM distance. Next, we investigated the performance of different machine learning classifiers in the spectral library application, including K-nearest neighbor (KNN), Random Forest (RF), and a genetic algorithm coupled with a support vector machine (GA-SVM). The optimal combination of spectral features and the classifier with the highest classification accuracy were selected for crop classification and status monitoring scenarios. The GA-SVM classifier performed the best, which produced an accuracy of OAA = 0.94, Kappa = 0.93 for crop classification in a complex scenario (crops mixed with 71 non-crop plant species), and promising accuracies for tea plant growth monitoring (OAA = 0.98, Kappa = 0.97) and rice growth stage monitoring (OAA = 0.92, Kappa = 0.90). Therefore, the establishment of a plant spectral library combined with relevant feature extraction and a classification algorithm effectively supports agricultural monitoring by remote sensing.

Description: The capacity of private information retrieval (PIR) from databases coded using maximum distance separable (MDS) codes was previously characterized by Banawan and Ulukus, where it was assumed that the messages are encoded and stored separably in the databases. This assumption was also usually made in other related works in the literature, and this capacity is usually referred to as the MDS-PIR capacity colloquially. In this work, we considered the question of if and when this capacity barrier can be broken through joint encoding and storing of the messages. Our main results are two classes of novel code constructions, which allow joint encoding, as well as the corresponding PIR protocols, which indeed outperformed the separate MDS-coded systems. Moreover, we show that a simple, but novel expansion technique allows us to generalize these two classes of codes, resulting in a wider range of the cases where this capacity barrier can be broken.

Description: Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

Description: The advent of utility computing has revolutionized almost every sector of traditional software development. Especially commercial cloud computing services, pioneered by the likes of Amazon, Google and Microsoft, have provided an unprecedented opportunity for the fast and sustainable development of complex distributed systems. Nevertheless, existing models and tools aim primarily for systems where resource usage—by humans and bots alike—is logically and physically quite disperse resulting in a low likelihood of conflicting resource access. However, a number of resource-intensive applications, such as Massively Multiplayer Online Games (MMOGs) and large-scale simulations introduce a requirement for a very large common state with many actors accessing it simultaneously and thus a high likelihood of conflicting resource access. This paper presents a systematic mapping study of the state-of-the-art in software technology aiming explicitly to support the development of MMOGs, a class of large-scale, resource-intensive software systems. By examining the main focus of a diverse set of related publications, we identify a list of criteria that are important for MMOG development. Then, we categorize the selected studies based on the inferred criteria in order to compare their approach, unveil the challenges faced in each of them and reveal research trends that might be present. Finally we attempt to identify research directions which appear promising for enabling the use of standardized technology for this class of systems.

Description: Crop models are useful tools to evaluate the effects of agricultural management on ecosystem services. However, before they can be applied with confidence, it is important to calibrate and validate crop models in the region of interest. In this study, the Environmental Policy Integrated Climate (EPIC) model was evaluated for its potential to simulate maize yield using limited data from field trials on two maize cultivars. Two independent fields at the Cradock Research Farm were used, one for calibration and one for validation. Before calibration, mean simulated yield was 8 t ha−1 while mean observed yield was 11.26 t ha−1. Model calibration improved mean simulated yield to 11.23 t ha−1 with a coefficient of determination, (r2) = 0.76 and a model efficiency (NSE) = 0.56. Validation with grain yield was satisfactory with r2 = 0.85 and NSE = 0.61. Calibration of potential heat units (PHUs) and soil-carbon related parameters improved model simulations. Although the study only used grain yield to calibrate and evaluate the model, results show that the calibrated model can provide reasonably accurate simulations. It can be concluded that limited data sets from field trials on maize can be used to calibrate the EPIC model when comprehensive experimental data are not available.

Description: Lemon processing generates thousands of tons of residues that can be preserved as flours by thermal treatment to obtain phenolic compounds with beneficial bioactivities. In this study, the effect of different drying temperatures (40, 50, 60, 70, 80, 90, 100 and 110 °C) on the Total Phenolic Content (TPC), antioxidant and antimicrobial activities of phenolic compounds present in Citrus. lemon (L.) Burn f waste was determined. Identification and quantification of phenolic compounds were also performed by UPLC-PDA and UPLC-ESI-MS analysis. Eriocitrin (19.79–27.29 mg g−1 DW) and hesperidin (7.63–9.10 mg g−1 DW) were detected as the major phenolic compounds in the flours by UPLC-PDA and confirmed by UPLC-ESI-MS. Antimicrobial activity determined by Minimum Inhibitory Concentration (MIC) against Salmonella typhimurium, Escherichia coli and Staphylococcus aureus was observed. Accordingly, a stable functional flour as a source of bioactive phenolic compounds obtained from lemon residues at 50 °C may be produced as a value-added product useful in various industrial sectors.

Description: Fusarium Head Blight (FHB, scab) is a destructive fungal disease that causes extensive yield and quality losses in wheat and other small cereals. Biological control of FHB is considered to be an alternative disease management strategy that is environmentally benign, durable, and compatible with other control measures. In this study, to screen antagonistic bacteria with the potential to manage FHB, 113 endophytes were isolated from the stems, leaves, panicles, and roots of wheat. Among them, six strains appeared to effectively inhibit Fusarium graminearum growth and one isolate, XS-2, showed a highly antagonistic effect against FHB. An in vitro antagonistic test of XS-2 on wheat heads confirmed that XS-2 could suppress the disease severity of FHB. The 16S rDNA sequence analysis revealed that XS-2 is a strain of Bacillus amyloliquefaciens. Antagonistic spectrum analyses showed that XS-2 had antagonistic effects against two and four types of cotton and fruit tree pathogens, respectively. The fermentation condition assays showed that glucose and peptone are the most suitable nutrient sources for XS-2, and that the optimal pH value and temperature for fermentation were 7.4 and 28 °C, respectively. Our study indicates that XS-2 has a good antagonistic effect on FHB and lays a theoretical foundation for the application of the strain as a biological agent in the field to control FHB.

Description: In this survey paper, we review various concepts of graph density, as well as associated theorems and algorithms. Our goal is motivated by the fact that, in many applications, it is a key algorithmic task to extract a densest subgraph from an input graph, according to some appropriate definition of graph density. While this problem has been the subject of active research for over half of a century, with many proposed variants and solutions, new results still continuously emerge in the literature. This shows both the importance and the richness of the subject. We also identify some interesting open problems in the field.

Description: China has attained rice sufficiency with the increased use of nitrogen (N) fertilizer, but this has led to serious N pollution. China has the world’s highest use of N with the lowest N use efficiency (NUE). Including livestock production, China’s agriculture sector has surpassed industry as the greatest polluter of water. Using plastic film on raised-beds, combined with improved agronomic practices, can boost rice yield by 50% with 36% less N fertilizer use, 30% higher NUE, and stabilized the yield of 9.75 t ha−1. It also counters the effects of drought and low ambient temperature. A six-year study was conducted combining no-tillage, crop-residue mulch, and plastic cover, alternating organic rice and rapeseed production. All the treatments, fertilized with biogas slurry and rapeseed meal, gave rice yields of 7.0 to 10.7 t ha−1, well above China’s average of 6.5 t ha−1. In this time, soil organic matter increased from 1.6% to 4.2%. In the first four years, the combination of crop-residue mulch with plastic cover had a slightly higher yield than mulch alone. In the fifth and sixth years, the latter treatment surpassed the use of plastic cover with crop-residue mulch. Trials with a biodegradable film show that plastic pollution can be dealt with.

Description: The aim of the study was to determine the Actinobacteria structure in cultivated (C) versus non-cultivated (NC) soils divided into three groups (autogenic, hydrogenic, lithogenic) with consideration its formation process in order to assess the Actinobacteria sensitivity to agricultural soil use and soil genesis and to identify factors affecting their abundance. Sixteen C soil samples and sixteen NC samples serving as controls were taken for the study. Next generation sequencing (NGS) of the 16S rRNA metagenomic amplicons (Ion Torrent™ technology) and Denaturing Gradient Gel Electrophoresis (DGGE) were applied for precise determination of biodiversity. Generally, greater abundance of Actinobacteria in the NC soils relative to the C soils was found. Moreover, it was indicated that the actinobacterial diversity depended on both the soil genesis and the land use; however, this effect directly depended on the particular family and genera. Two factors: redox potential (Eh) and total carbon (TC) seemed to had a significant effect on the diversity of Actinobacteria. More precisely, Actinobacteria from the NC soils displayed a greater affinity for each other and were clearly influenced by Eh, whilst those from the C soils were mostly influenced by TC.

Description: Military named entity recognition (MNER) is one of the key technologies in military information extraction. Traditional methods for the MNER task rely on cumbersome feature engineering and specialized domain knowledge. In order to solve this problem, we propose a method employing a bidirectional long short-term memory (BiLSTM) neural network with a self-attention mechanism to identify the military entities automatically. We obtain distributed vector representations of the military corpus by unsupervised learning and the BiLSTM model combined with the self-attention mechanism is adopted to capture contextual information fully carried by the character vector sequence. The experimental results show that the self-attention mechanism can improve effectively the performance of MNER task. The F-score of the military documents and network military texts identification was 90.15% and 89.34%, respectively, which was better than other models.

Description: This article empirically demonstrates the impacts of truthfully sharing forecast information and using forecast combinations in a fast-moving-consumer-goods (FMCG) supply chain. Although it is known a priori that sharing information improves the overall efficiency of a supply chain, information such as pricing or promotional strategy is often kept proprietary for competitive reasons. In this regard, it is herein shown that simply sharing the retail-level forecasts—this does not reveal the exact business strategy, due to the effect of omni-channel sales—yields nearly all the benefits of sharing all pertinent information that influences FMCG demand. In addition, various forecast combination methods are used to further stabilize the forecasts, in situations where multiple forecasting models are used during operation. In other words, it is shown that combining forecasts is less risky than “betting” on any component model.

Description: Adoption of no-till systems in Eastern Washington has been slow due to the difficulty of managing wheat (Triticum aestivum L.) straw residue and the unknown decomposition potential of cultivars. We hypothesize that by analyzing wheat straw with near-infrared spectroscopy (NIRS), calibration models can be developed to accurately predict fiber and chemical constituents of wheat, determining straw decomposition potential. Straw from a panel of 480 soft winter wheat cultivars adapted to the Pacific Northwest are analyzed for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose, hemicellulose, carbon (C), and nitrogen (N). Using modified partial least squares regression and cross validation techniques, specific environment and broad-based NIRS models are calibrated and predictive ability is validated. R2cal values from broad models are better than the specific models, and are 0.85 (NDF), 0.86 (ADF), 0.65 (ADL), 0.88 (cellulose), 0.42 (hemicellulose), 0.67 (C), and 0.73 (N). The corresponding SEP values are 1.68% (NDF), 1.54% (ADF), 0.62% (ADL), 1.14% (cellulose), 1.11% (hemicellulose), 1.23% (C), and 0.06% (N). A Finch × Eltan breeding population is used to further validate models and prediction accuracies for variety selection within a breeding program scenario. The broad NIRS models prove useful for estimating high and low ranges of NDF, ADF, and cellulose in wheat cultivars which translate into characteristics of slow and fast decomposition potential.

Description: An important problem in machine learning is that, when using more than two labels, it is very difficult to construct and optimize a group of learning functions that are still useful when the prior distribution of instances is changed. To resolve this problem, semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms are combined to form a systematic solution. A semantic channel in G theory consists of a group of truth functions or membership functions. In comparison with the likelihood functions, Bayesian posteriors, and Logistic functions that are typically used in popular methods, membership functions are more convenient to use, providing learning functions that do not suffer the above problem. In Logical Bayesian Inference (LBI), every label is independently learned. For multilabel learning, we can directly obtain a group of optimized membership functions from a large enough sample with labels, without preparing different samples for different labels. Furthermore, a group of Channel Matching (CM) algorithms are developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions in a two-dimensional feature space, only 2–3 iterations are required for the mutual information between three classes and three labels to surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maximization (EM) algorithm is improved to form the CM-EM algorithm, which can outperform the EM algorithm when the mixture ratios are imbalanced, or when local convergence exists. The CM iteration algorithm needs to combine with neural networks for MMI classification in high-dimensional feature spaces. LBI needs further investigation for the unification of statistics and logic.

Description: The inclusion of species with allelopathic activity in crop rotation systems may have benefits for crop management such as weed control, but less is understood about their wider impacts on succeeding crops. The main objective of this study was to investigate the effects of two allelopathic species (spearmint and peppermint) on growth, physiological characteristics, and yield of a following maize crop. Thus, field experiments were carried out at two sites, according to a randomized complete block design, while the examined treatments were: (a) fallow–fallow–maize crop rotation system (FFM), (b) spearmint–spearmint–maize crop rotation system (SSM), and (c) peppermint–peppermint–maize crop rotation system (PPM). Our results indicated that the inclusion of spearmint or peppermint in crop rotation systems negatively affect the growth of maize plants. The highest plant height was recorded for FFM rotation system where no allelopathic species were used. At both sites and for four sampling dates, the aboveground dry biomass was also affected by the implemented crop rotation system. In particular, dry biomass was significantly lower in the PPM and SSM crop rotation systems comparing to the FFM system, whereas there were no significant differences between these two crop rotation systems. Similar to the maize biomass, the highest values of photosynthetic rate, stomatal conductance, and relative chlorophyll content were observed for the FFM rotation system. Additionally, differences in grain yield were observed among the tested crop rotation systems. Grain yield ranged from 10,200 to 13,346 kg ha−1 and from 11,773 to 14,106 kg ha−1 at site A and B, respectively, while it was reduced by 16.54–23.58% and 12.16–17.83% in the SSP and PPM rotation systems comparing to the FFM system. In conclusion, our results indicate that the inclusion of peppermint or spearmint in crop rotation may inhibit plant growth and reduce grain yield of maize as successive crop, an effect that could be attributed to the allelopathic activity of spearmint and peppermint.

Description: The Midwestern U.S. landscape is one of the most highly altered and intensively managed ecosystems in the country. The predominant crops grown are maize (Zea mays L.) and soybean [Glycine max (L.) Merr]. They are typically grown as monocrops in a simple yearly rotation or with multiple years of maize (2 to 3) followed by a single year of soybean. This system is highly productive because the crops and management systems have been well adapted to the regional growing conditions through substantial public and private investment. Furthermore, markets and supporting infrastructure are highly developed for both crops. As maize and soybean production have intensified, a number of concerns have arisen due to the unintended environmental impacts on the ecosystem. Many areas across the Midwest are experiencing negative impacts on water quality, soil degradation, and increased flood risk due to changes in regional hydrology. The water quality impacts extend even further downstream. We propose the development of an innovative system for growing maize and soybean with perennial groundcover to recover ecosystem services historically provided naturally by predominantly perennial native plant communities. Reincorporating perennial plants into annual cropping systems has the potential of restoring ecosystem services without negatively impacting grain crop production and offers the prospect of increasing grain crop productivity through improving the biological functioning of the system.

Description: Rice blast is a serious fungal disease of rice (Oryza sativa L.) that is threatening global food security. It has been extensively studied due to the importance of rice production and consumption, and because of its vast distribution and destructiveness across the world. Rice blast, caused by Pyricularia oryzae Cavara 1892 (A), can infect aboveground tissues of rice plants at any growth stage and cause total crop failure. The pathogen produces lesions on leaves (leaf blast), leaf collars (collar blast), culms, culm nodes, panicle neck nodes (neck rot), and panicles (panicle blast), which vary in color and shape depending on varietal resistance, environmental conditions, and age. Understanding how rice blast is affected by environmental conditions at the cellular and genetic level will provide critical insight into incidence of the disease in future climates for effective decision-making and management. Integrative strategies are required for successful control of rice blast, including chemical use, biocontrol, selection of advanced breeding lines and cultivars with resistance genes, investigating genetic diversity and virulence of the pathogen, forecasting and mapping distribution of the disease and pathogen races, and examining the role of wild rice and weeds in rice blast epidemics. These tactics should be integrated with agronomic practices including the removal of crop residues to decrease pathogen survival, crop and land rotations, avoiding broadcast planting and double cropping, water management, and removal of yield-limiting factors for rice production. Such an approach, where chemical use is based on crop injury and estimated yield and economic losses, is fundamental for the sustainable control of rice blast to improve rice production for global food security.

Description: A study was conducted in an irrigated arid agroecosystem in southwestern USA, to compare two conservation tillage systems (strip tillage (ST) and no-tillage (NT)) to conventional, plow-based tillage (PT) system. Corn silage (Zea mays L.) was planted in this trial. Growth parameters (plant population and height) of corn silage were measured during the season and yield was evaluated at harvest. Soil physical measurements assessed included mean weight diameter of dry aggregates, wet aggregate stability, and penetrometer resistance. While soil biological measurements included total microbial biomass, diversity index (DI), total bacteria biomass, total fungi biomass (TFB), arbuscular mycorrhizae fungi (AMF), and total saprophytes. Results showed that plant population and silage yield at 65% moisture content were not significant with tillage during both trial years. Soil physical parameters were mostly not significant with tillage, while three out of the six biological measurements (DI, TF, and AM) were significant with tillage at p ≤ 0.05. No-tillage had higher DI and TFB than the ST, but not different from PT, while AMF was significantly higher in PT than ST, but not different from NT. The study demonstrates that farmers in the study region can adopt conservation tillage without yield losses during the early years of transition.

Description: In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

Description: The frequency of drought periods influences the yield potential of crops under field conditions. The change in morphology and anatomy of plants has been tested during drought stress under controlled conditions but the change in physiological processes has not been adequately studied in separate studies but needs to be reviewed collectively. This review presents the responses of green peas, snap beans, tomatoes and sweet corn to water stress based on their stomatal behaviour, canopy temperature, chlorophyll fluorescence and the chlorophyll content of leaves. These stress markers can be used for screening the drought tolerance of genotypes, the irrigation schedules or prediction of yield.

Description: A combination of organic and conservation approaches have not been widely tested, neither considering agronomic implications nor the impacts on the environment. Focussing on the effect of agricultural practices on greenhouse gas (GHG) emissions from soil, the hypothesis of this research is that the organic conservation system (ORG+) may reduce emissions of N2O, CH4 and CO2 from soil, compared to an integrated farming system (INT) and an organic (ORG) system in a two-year irrigated vegetable crop rotation set up in 2014, in a Mediterranean environment. The crop rotation included: Savoy cabbage (Brassica oleracea var. sabauda L. cv. Famosa), spring lettuce (Lactuca sativa L. cv. Justine), fennel (Foeniculum vulgare Mill. cv. Montebianco) and summer lettuce (L. sativa cv. Ballerina). Fluxes from soil of N2O, CH4 and CO2 were measured from October 2014 to July 2016 with the flow-through non-steady state chamber technique using a mobile instrument equipped with high precision analysers. Both cumulative and daily N2O emissions were mainly lower in ORG+ than in INT and ORG. All the cropping systems acted as a sink of CH4, with no significant differences among treatments. The ORG and ORG+ systems accounted for higher cumulative and daily CO2 emissions than INT, maybe due to the stimulating effect on soil respiration of organic material (fertilizers/plant biomass) supplied in ORG and ORG+. Overall, the integration of conservation and organic agriculture showed a tendency for higher CO2 emissions and lower N2O emissions than the other treatments, without any clear results on its potential for mitigating GHG emissions from soil.

Description: Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition, the proposed model uses the VGG-16 network for semantic segmentation, which uses a pixel classification layer to predict the categorical label for every pixel in an input image. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. Using the proposed deep learning model, a global accuracy of up to 96.22% is achieved for the prediction of visual saliency.

Description: The skyline query and its variant queries are useful functions in the early stages of a knowledge-discovery processes. The skyline query and its variant queries select a set of important objects, which are better than other common objects in the dataset. In order to handle big data, such knowledge-discovery queries must be computed in parallel distributed environments. In this paper, we consider an efficient parallel algorithm for the “K-skyband query” and the “top-k dominating query”, which are popular variants of skyline query. We propose a method for computing both queries simultaneously in a parallel distributed framework called MapReduce, which is a popular framework for processing “big data” problems. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithm on both real and synthetic datasets.

Description: A generalization of Ding’s construction is proposed that employs as a defining set the collection of the sth powers ( s ≥ 2 ) of all nonzero elements in G F ( p m ) , where p ≥ 2 is prime. Some of the resulting codes are optimal or near-optimal and include projective codes over G F ( 4 ) that give rise to optimal or near optimal quantum codes. In addition, the codes yield interesting combinatorial structures, such as strongly regular graphs and block designs.

Description: The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches.

Description: A crowdsourcing contest is one of the most popular modes of crowdsourcing and is also an important tool for an enterprise to implement open innovation. The solvers’ active participation is one of the major reasons for the success of crowdsourcing contests. Research on solvers’ participation behavior is helpful in understanding the sustainability and incentives of solvers’ participation in the online crowdsourcing platform. So, how to attract more solvers to participate and put in more effort is the focus of researchers. In this regard, previous studies mainly used the submission quantity to measure solvers’ participation behavior and lacked an effective measure on the degree of participation effort expended by a solver. For the first time, we use solvers’ participation time as a dependent variable to measure their effort in a crowdsourcing contest. Thus, we incorporate participation time into the solver’s participation research. With the data from Taskcn.com, we analyze how participation time is affected four key factors including task design, task description, task process, and environment, respectively. We found that, first, for task design, higher task rewards will attract solvers to invest more time in the participation process and the relationship between participation time and task duration is inverted U-shaped. Second, for task description, the length of the task description has a negative impact on participation time and the task description attachment will positively influence the participation time. Third, for the task process, communication and supplementary explanations in a crowdsourcing process positively affect participation time. Fourth, for environmental factors, the task density of the crowdsourcing platform and the market price of all crowdsourcing contests have respectively negative and positive effects on participation time.

Description: Climate change is viewed as a cause in accelerating the rate of invasion by alien species in addition to the globalization of anthropogenic activities. Ecological niche modeling has become an instrument in predicting invasion from natural or invaded ranges to uninvaded ranges based on the presence records of organisms and environmental parameters. This study explored the changes in the distributions of globally noxious alien species (Aegratina adenophora, Ageratum conyzoides, Chromolaena odorata, Lantana camara, Mikania micrantha, and Parthenium hysterophorus) in Bhutan, to provide evidence that even a mountain environment is under the threat of invasion given the change in climatic conditions. With fairly high accuracy, the model results suggest that there will be a potential increase in the areas of invasion among most of the species, except Parthenium hysterophorus, which will experience a northerly shift and decline in distribution. The results also indicate changes in patterns of invasion, some becoming more concentrated toward a given direction, while others become more dispersed over time. This study provides a framework that can be used in the strategic control of the species, future detection surveys, and further research.

Description: Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, for example, in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results together, leading to certain suboptimality from the interaction among different pieces. In other cases, links between different parts may show up in the running time and/or network communications cost, hence the desire to have small cut size. We study a distributed balanced-partitioning problem where the goal is to partition the vertices of a given graph into k pieces so as to minimize the total cut size. Our algorithm is composed of a few steps that are easily implementable in distributed computation frameworks such as MapReduce. The algorithm first embeds nodes of the graph onto a line, and then processes nodes in a distributed manner guided by the linear embedding order. We examine various ways to find the first embedding, for example, via a hierarchical clustering or Hilbert curves. Then we apply four different techniques including local swaps, and minimum cuts on the boundaries of partitions, as well as contraction and dynamic programming. As our empirical study, we compare the above techniques with each other, and also to previous work in distributed graph algorithms, for example, a label-propagation method, FENNEL and Spinner. We report our results both on a private map graph and several public social networks, and show that our results beat previous distributed algorithms: For instance, compared to the label-propagation algorithm, we report an improvement of 15–25% in the cut value. We also observe that our algorithms admit scalable distributed implementation for any number of partitions. Finally, we explain three applications of this work at Google: (1) Balanced partitioning is used to route multi-term queries to different replicas in Google Search backend in a way that reduces the cache miss rates by ≈ 0.5 % , which leads to a double-digit gain in throughput of production clusters. (2) Applied to the Google Maps Driving Directions, balanced partitioning minimizes the number of cross-shard queries with the goal of saving in CPU usage. This system achieves load balancing by dividing the world graph into several “shards”. Live experiments demonstrate an ≈ 40 % drop in the number of cross-shard queries when compared to a standard geography-based method. (3) In a job scheduling problem for our data centers, we use balanced partitioning to evenly distribute the work while minimizing the amount of communication across geographically distant servers. In fact, the hierarchical nature of our solution goes well with the layering of data center servers, where certain machines are closer to each other and have faster links to one another.

Description: Analyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset.

Description: Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this issue, which is particularly critical in light of the possible clinical applications of these predictive models, researchers have developed approaches such as virtual sample generation. Virtual sample generation significantly improves learning and classification performance when working with small samples. The main objective of this study is to evaluate the ability of the proposed virtual sample generation to overcome the small sample size problem, which is a feature of the automated detection of a neurodevelopmental disorder, namely autism spectrum disorder. Results show that our method enhances diagnostic accuracy from 84%–95% using virtual samples generated on the basis of five actual clinical samples. The present findings show the feasibility of using the proposed technique to improve classification performance even in cases of clinical samples of limited size. Accounting for concerns in relation to small sample sizes, our technique represents a meaningful step forward in terms of pattern recognition methodology, particularly when it is applied to diagnostic classifications of neurodevelopmental disorders. Besides, the proposed technique has been tested with other available benchmark datasets. The experimental outcomes showed that the accuracy of the classification that used virtual samples was superior to the one that used original training data without virtual samples.

Description: In this paper, I first review signal detection theory (SDT) approaches to perception, and then discuss why it is thought that SDT theory implies that increasing attention improves performance. Our experiments have shown, however, that this is not necessarily true. Subjects had either focused attention on two of four possible locations in the visual field, or diffused attention to all four locations. The stimuli (offset letters), locations, conditions, and tasks were all known in advance, responses were forced-choice, subjects were properly instructed and motivated, and instructions were always valid—conditions which should optimize signal detection. Relative to diffusing attention, focusing attention indeed benefitted discrimination of forward from backward pointing Es. However, focusing made it harder to identify a randomly chosen one of 20 letters. That focusing can either aid or disrupt performance, even when cues are valid and conditions are idealized, is surprising, but it can also be explained by SDT, as shown here. These results warn the experimental researcher not to confuse focusing attention with enhancing performance, and warn the modeler not to assume that SDT is unequivocal.

Description: Phenolic compounds in basil (Ocimum basilicum) plants grown under a controlled environment are reduced due to the absence of ultraviolet (UV) radiation and low photosynthetic photon flux density (PPFD). To characterize the optimal UV-B radiation dose and PPFD for enhancing the synthesis of phenolic compounds in basil plants without yield reduction, green and purple basil plants grown at two PPFDs, 160 and 224 μmol·m−2·s−1, were treated with five UV-B radiation doses including control, 1 h·d−1 for 2 days, 2 h·d−1 for 2 days, 1 h·d−1 for 5 days, and 2 h·d−1 for 5 days. Supplemental UV-B radiation suppressed plant growth and resulted in reduced plant yield, while high PPFD increased plant yield. Shoot fresh weight in green and purple basil plants was 12%–51% and 6%–44% lower, respectively, after UV-B treatments compared to control. Concentrations of anthocyanin, phenolics, and flavonoids in green basil leaves increased under all UV-B treatments by 9%–18%, 28%–126%, and 80%–169%, respectively, and the increase was greater under low PPFD compared to high PPFD. In purple basil plants, concentrations of phenolics and flavonoids increased after 2 h·d−1 UV-B treatments. Among all treatments, 1 h·d−1 for 2 days UV-B radiation under PPFD of 224 μmol·m−2·s−1 was the optimal condition for green basil production under a controlled environment.

Description: Auxin regulates diverse aspects of growth and development. Furthermore, polar auxin transport, which is mediated by the PIN-FORMED (PIN) and AUXIN1/LIKE-AUX (AUX/LAX) proteins, plays a crucial role in auxin distribution. In this study, six PIN and four AUX/LAX genes were identified in ramie (Boehmeria nivea L.). We used qRT-PCR to characterize and analyze the two gene families, including phylogenetic relationships, intron/exon structures, cis-elements, subcellular localization, and the expression patterns in different tissues. The expression of these genes in response to indole-3-acetic acid (IAA) treatment and drought stress was also assessed; the results indicate that most of the BnAUX/LAX and BnPIN genes were regulated as a result of IAA treatment and drought stress. Our study provides insights into ramie auxin transporters and lays the foundation for further analysis of their biological functions in ramie fiber development and adaptation to environmental stresses.

Description: The genetic architecture and the genetic loci controlling commodity traits in this Hami melon have not been characterized. Multiplexed shotgun genotyping (MSG) was used to genotype an F2 population of 370 Chinese Hami melon progeny. A total of 47,609 single nucleotide polymorphism (SNP) markers were obtained after strict filtering. Thebins were used to construct a genetic linkage map with a total length of 1572.954 cM. Quantitative trait locus (QTL) analysis revealed that fruit color was controlled by one major gene about 2 Mb region on chr09, while exocarp color (EC) was controlled by one major gene about 1.9 Mb on chr04, and skin spotting was controlled by two dominant genes, one in the same region of chr04as the EC QTL and the other in the 1031.05 kb region on chr02. Two major QTLs on chr03 and chr05 were related pleiotropically to several quantitative fruit traits, namely, edge sugar content (ES), center sugar content (CS), fruit weight (FW), and fruit length (FL). A further QTL on chr09 also influenced ES, while five other QTLs affected FL. This study was the first to conduct genetic architecture analysis and QTL mapping in Chinese Hami melon with high-density markers and a large target population.

Description: The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user’s cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session.

Description: The sector of SME is the major force for the national economic and social development. Financial risk is one of the key threats to the activity of small and medium enterprises. The most common manifestation of the financial risk of SMEs is difficulty in financing the business and lack of funds for development. Banks are unwilling to grant loans to such companies. Moreover, it is the rising operating costs that cause shrinking profits, which may result in corporate debt, difficulty in debt repayment, and consequently, high financial risk of these entities. Numerous differences in conducting the activity of small and large enterprises intensify this risk and mean that the model of credit financing for companies is not adjusted to the capabilities and principles of the operation of small enterprises. Therefore, risk management is one of the most important internal processes in small and medium enterprises. The identification of factors that affect the level of financial risk in these entities is therefore crucial. The main objective of this research was to analyze the impact of selected parametric characteristics of the SME sector on the intensity of financial risk they take. This objective was accomplished on the basis of the survey with the participation of Polish SMEs. In order to test the adopted research assumptions, the linear regression model was used with four continuous variables for each type of the identified financial risk. Based on the final research results, the logit model was obtained for the risk of insufficient profits. It was indicated that both the internationalization of the company and the ability to manage risk are the only factors that affect a high level of risk of low income. The article ends with the discussion and the comparison with some previous research in this area.

Description: We consider a two-dimensional ruin problem where the surplus process of business lines is modelled by a two-dimensional correlated Brownian motion with drift. We study the ruin function P ( u ) for the component-wise ruin (that is both business lines are ruined in an infinite-time horizon), where u is the same initial capital for each line. We measure the goodness of the business by analysing the adjustment coefficient, that is the limit of − ln P ( u ) / u as u tends to infinity, which depends essentially on the correlation ρ of the two surplus processes. In order to work out the adjustment coefficient we solve a two-layer optimization problem.

Description: Warm-season turfgrass species prevail in tropical and subtropical areas, but can also be grown in the transition zone. In this case, cold tolerance is a key aspect for germination and successful turfgrass establishment. The germination response to sub-optimal temperatures was investigated for Cynodon dactylon (cvs Jackpot, La Paloma, Transcontinental, Yukon, Riviera), Buchloe dactyloides (cv SWI 2000) and Paspalum vaginatum (cv Pure Dynasty). Four temperature regimes were applied, i.e., 20/30 °C, 15/25 °C, 10/20 °C and 5/15 °C, with a 12:12 h (light:dark) photoperiod. Germination assays were performed twice, with six replicates (Petri dishes) per treatment in each experiment, fifty seeds per dish. The final germinated percentages at last inspection time (FGP) were obtained for each Petri dish and processed by using a generalized linear mixed model (binomial error and logit link). Germination curves were fitted to each Petri dish by using time-to-event methods and germination rates (GR) for the 10th, 20th and 30th percentiles were derived and used to fit a linear thermal-time model. For all cultivars, FGP decreased with decreasing mean daily temperatures. Base temperatures (Tb) ranged between 11.4 °C and 17.0 °C, while the thermal time to obtain 30% germination ranged from 51.3 °C day for SWI 2000 to 144.0 °C day for Pure Dynasty. The estimated parameters were used to predict germination time in the field, considering the observed soil temperatures in Legnaro. The estimated date for the beginning of germination in the field would range from early April for SWI 2000 and Transcontinental to mid-May for Riviera. These results might be used as a practical support for planning spring sowing, which is crucial for successful turfgrass establishment, especially without irrigation.

Description: Terminal drought greatly enhanced Aspergillus flavus Link infection (AI) and aflatoxin contamination (AC) in peanut. Identification of new surrogate traits which have an association with AC may be effective to improve peanut varieties with reduced AI and AC. The objective of this work was to examine the relationships of nutrient uptake and N2-fixation (NF) with AC in peanut in a terminal drought condition. Five peanut varieties were tested in well-watered (WW) and terminal drought (TD) conditions (1/3 available water (AW) from R7 (7th reproductive growth stage; beginning of pod maturity stage)). Data were collected for nutrient uptake (nitrogen, phosphorus, potassium, calcium, magnesium), nodule dry weight (NDW), and NF. AI and AC were also examined. Nutrient uptake, NDW, and NF had negative and significant correlations with AI and AC in the TD condition. Negative and significant correlations of the drought tolerance index of nutrient uptake, NDW, and NF with AI and AC were also observed in the TD condition. The results showed that the ability to maintain nutrient uptake and NF in TD might be a mechanism of tolerance to AI and AC. Moreover, due to their negative impacts on AI and AC, nutrient uptake and NF could be used as selection traits for resistance to AI and AC in peanut in TD.

Description: Red rice (O. sativa) is one the most prevalent and damaging weed problems in direct seeding rice systems worldwide and can cause significant losses in rice productivity and quality. Red rice has been a problem in the United States for decades, and it is a growing problem in Asia, where 90 percent of the global rice production occurs. Unlike for other crops, where genetically engineered (GE) herbicide tolerant varieties are available, to date, Clearfield (CL) and Provisia rice are the only technologies available to selectively control red rice using chemical herbicides in commercial rice fields. We develop a counterfactual scenario without CL rice and ascertain the yield and quality losses due to red rice infestation in the Mid-South of the United States. Our findings suggest that even with the higher costs of CL rice, relative to non-CL rice, that the returns are $0.15, $0.36, and $0.54 more for every dollar invested than non-CL rice with a light, moderate, and heavy initial red rice infestation rate, respectively. These results imply that the higher upfront costs for CL rice are offset by more than proportional higher profits relative to their non-CL rice counterparts.

Description: According to the assumptions of Organisation for Economic Co-operation and Development OECD, the share of biofuels in the global transport sector is estimated to reach 15–23% by 2050. The triticale can be used to produce bioethanol. The appropriate production process should generate as much renewable energy as possible per production unit. Plant production can be carried out in various tillage systems and using appropriate doses of nitrogen fertilization. The objective of this study is to compare the effect of traditional tillage system (TRD) and reduced (RED) tillage technology and nitrogen fertilizer (0, 40, 80, 120 kg N ha−1) on grain and bioethanol yield of spring triticale. The field experiment was performed in the south east of Poland (50°42′ N, 23°15′ E) on medium dystrophic typical brown soil. Based on research and calculations, the TRD system and between 40 and 80 kg ha−1 of N fertilizer are recommended for use in the cultivation of triticale for bioethanol production purposes. Such a variant will ensure a sufficient yield of grain (5.190 and 5.803 t ha−1), starch (3.462 and 3.871 t ha−1) and bioethanol (2487.3 and 2780.7 L ha−1) and good agronomic efficiency of N fertilizer (16.96 and 12.15 L of bioethanol per 1 kg of nitrogen (N) applied). The best ratio of energy efficiency of bioethanol production (EROI—Energy Return on (Energy) Investment or “net energy”) was recorded for the TRD system (1.138:1) and for the N fertilizer at 40 kg N ha−1 (1.144:1).

Description: Since the 2008–2009 financial crisis, banks have introduced a family of X-valuation adjustments (XVAs) to quantify the cost of counterparty risk and of its capital and funding implications. XVAs represent a switch of paradigm in derivative management, from hedging to balance sheet optimization. They reflect market inefficiencies that should be compressed as much as possible. In this work, we present a genetic algorithm applied to the compression of credit valuation adjustment (CVA), the expected cost of client defaults to a bank. The design of the algorithm is fine-tuned to the hybrid structure, both discrete and continuous parameter, of the corresponding high-dimensional and nonconvex optimization problem. To make intensive trade incremental XVA computations practical in real-time as required for XVA compression purposes, we propose an approach that circumvents portfolio revaluation at the cost of disk memory, storing the portfolio exposure of the night so that the exposure of the portfolio augmented by a new deal can be obtained at the cost of computing the exposure of the new deal only. This is illustrated by a CVA compression case study on real swap portfolios.

Description: This paper deals with the Arabic translation taṣawwur in Averroes’ Great Commentary of the term τῶν ἀδιαιρέτων νόησις (“ton adiaireton noesis”, thinking of the indivisibles) in Aristotle’s De anima and the Latin translation from Arabic with (in-)formatio, as quoted by Albertus Magnus [...]

Description: Opportunistic networks are considered as the promising network structures to implement traditional and typical infrastructure-based communication by enabling smart mobile devices in the networks to contact with each other within a fixed communication area. Because of the intermittent and unstable connections between sources and destinations, message routing and forwarding in opportunistic networks have become challenging and troublesome problems recently. In this paper, to improve the data dissemination environment, we propose an improved routing-forwarding strategy utilizing node profile and location prediction for opportunistic networks, which mainly includes three continuous phases: the collecting and updating of routing state information, community detection and optimization and node location prediction. Each mobile node in the networks is able to establish a network routing matrix after the entire process of information collecting and updating. Due to the concentrated population in urban areas and relatively few people in remote areas, the distribution of location prediction roughly presents a type of symmetry in opportunistic networks. Afterwards, the community optimization and location prediction mechanisms could be regarded as an significant foundation for data dissemination in the networks. Ultimately, experimental results demonstrate that the proposed algorithm could slightly enhance the delivery ratio and substantially degrade the network overhead and end-to-end delay as compared with the other four routing strategies.

Description: With digital media, not only are media extensions of their human users, as McLuhan posited, but there is a flip or reversal in which the human users of digital media become an extension of those digital media as these media scoop up their data and use them to the advantage of those that control these media. The implications of this loss of privacy as we become “an item in a data bank” are explored and the field of captology is described. The feedback of the users of digital media become the feedforward for those media.

Description: The main scope of the presented research was the development of an innovative product for the management of city parking lots. Our application will ensure the implementation of the Smart City concept by using computer vision and communication platforms, which enable the development of new integrated digital services. The use of video cameras could simplify and lower the costs of parking lot controls. In the aim of parking space detection, an aggregated decision was proposed, employing various metrics, computed over a sliding window interval provided by the camera. The history created over 20 images provides an adaptive method for background and accurate detection. The system has shown high robustness in two benchmarks, achieving a recognition rate higher than 93%.

Description: Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.

Description: Parameterized complexity theory has led to a wide range of algorithmic breakthroughs within the last few decades, but the practicability of these methods for real-world problems is still not well understood. We investigate the practicability of one of the fundamental approaches of this field: dynamic programming on tree decompositions. Indisputably, this is a key technique in parameterized algorithms and modern algorithm design. Despite the enormous impact of this approach in theory, it still has very little influence on practical implementations. The reasons for this phenomenon are manifold. One of them is the simple fact that such an implementation requires a long chain of non-trivial tasks (as computing the decomposition, preparing it, …). We provide an easy way to implement such dynamic programs that only requires the definition of the update rules. With this interface, dynamic programs for various problems, such as 3-coloring, can be implemented easily in about 100 lines of structured Java code. The theoretical foundation of the success of dynamic programming on tree decompositions is well understood due to Courcelle’s celebrated theorem, which states that every MSO-definable problem can be efficiently solved if a tree decomposition of small width is given. We seek to provide practical access to this theorem as well, by presenting a lightweight model checker for a small fragment of MSO 1 (that is, we do not consider “edge-set-based” problems). This fragment is powerful enough to describe many natural problems, and our model checker turns out to be very competitive against similar state-of-the-art tools.

Description: Tamarix spp. are ecological threats in the Southwest U.S.A. because they displace native vegetation, increase soil salinity, and negatively affect soil microbial communities. After Tamarix L. removal, legacy effects often necessitate restoration to improve ecosystem services of Tamarix-impacted communities. Commercial mycorrhizae fungal inoculation has been recommended to improve restoration success, although inoculation treatments are rarely tested on lesser-known facultative riparian species. Our study asked two questions: (1) Can a commercial mycorrhizal fungal inoculant increase native Baccharis salicifolia (Ruiz & Pav.) Pers. (mule-fat) performance against Tamarix chinensis Lour. (i.e., tamarisk) and is this influenced by tamarisk leaf litter? (2) Is mycorrhizal colonization of mule-fat roots influenced by tamarisk stem density and leaf litter? A greenhouse experiment was performed with mule-fat cuttings in soil collected from a tamarisk monoculture. Treatments were factorial combinations of tamarisk stem densities (0, 1, 2, 3, 4 stems pot−1) with or without mycorrhizal inoculation and tamarisk litter. There were five replications and two greenhouse runs. The total biomass of both species was determined and mule-fat arbuscular mycorrhizal colonization rates were determined via the magnified intersection method. Increasing tamarisk biomass negatively affected mule-fat biomass, but there were interactions with tamarisk biomass, litter and mycorrhizal inoculation, with litter and inoculation increasing mule-fat growth at high tamarisk biomass. Arbuscular mycorrhizal colonization was high in all treatments, yet at higher tamarisk stem densities, inoculation and litter improved colonization. Interestingly, litter did not negatively impact mule-fat as predicted. Moreover, litter and mycorrhizal inoculum interacted with tamarisk to improve mule-fat growth at higher tamarisk biomass, suggesting an opportunity to improve restoration success when in competition with tamarisk.

Description: The productivity of maize in Ethiopia has remained lower than the world average because of several biotic and abiotic factors. Stemborers and poor soil fertility are among the main factors that contribute to this poor maize productivity. A novel cropping strategy, such as the use of push-pull technology, is one of the methods known to solve both challenges at once. A push-pull technology targeting the management of maize stemborers was implemented in the Hawassa district of Ethiopia with the ultimate goal of increased food security among smallholder farmers. This study evaluated farmers’ perception of push-pull technology based on their experiences and observations of the demonstration plots that were established on-farm in Dore Bafano, Jara Gelelcha and Lebu Koremo village of the Hawasa district in 2016 and 2017. This study examined farmers’ perception of the importance of push-pull technology in controlling stemborers and improving soil fertility and access to livestock feed. In both cropping seasons, except for Jara Gelelcha, the maize grain yields were significantly higher in the climate-adapted push-pull plots compared to the maize monocrop plots. The majority (89%) of push-pull technology-practising farmers rated the technology better than their maize production methods on attributes such as access to new livestock feed and the control of stemborer damage. As a result, approximately 96% of the interviewed farmers were interested in adopting the technology starting in the upcoming crop season. Awareness through training and effective dissemination strategies should be strengthened among stakeholders and policymakers for the sustainable use and scaling-up of push-pull technology.

Description: Use of saline water for irrigation is essential to mitigate increasing agricultural water demands in arid and semi-arid regions. The objective of this study is to address the potential of using straw biochar as a soil amendment to promote wheat production under saline water irrigation. A field experiment was conducted in a clay loam soil from eastern China during 2016/2017 and 2017/2018 winter wheat season. There were five treatments: freshwater irrigation (0.3 dS m−1), saline water irrigation (10 dS m−1), saline water irrigation (10 dS m−1) combined with biochar of 10, 20, 30 t ha−1. Saline water irrigation alone caused soil salinization and decreased wheat growth and yield. The incorporation of biochar decreased soil bulk density by 5.5%–11.6% and increased permeability by 35.4%–49.5%, and improved soil nutrient status. Biochar also reduced soil sodium adsorption ratio by 25.7%–32.6% under saline water irrigation. Furthermore, biochar alleviated salt stress by maintaining higher leaf relative water content and lower Na+/K+ ratio, and further enhanced photosynthesis and relieved leaf senescence during reproductive stages, leading to better grain formation. Compared to saline water irrigation alone, biochar application of 10 and 20 t ha−1 significantly increased wheat grain yield by 8.6 and 8.4%, respectively. High dose of biochar might increase soil salinity and limit N availability. In the study, biochar amendment at 10 t ha−1 would be a proper practice at least over two years to facilitate saline water irrigation for wheat production. Long-term studies are recommended to advance the understanding of the sustainable use of straw biochar.

Description: Chokeberry (Aronia melanocarpa L.) contains a large number of bioactive compounds, which make of it a highly nutritional and antioxidant food with anti-cancer and anti-inflammatory properties. It has been recently seen its consumption increased because of its interesting composition and properties as a functional food. The ultrasound-assisted extraction method has been optimized, since it is a simple and fast technique to extract phenolic compounds, as well as anthocyanins, not only separately but also simultaneously. Multiple response has been optimized and demonstrated an effectiveness level similar to the individual ones. This represents a considerable reduction in costs, time and work. The optimal conditions for simultaneous extractions were: 54% methanol as extraction solvent at pH 2.72 and 69.4 °C temperature, 70% amplitude, 0.7 s cycle, and 0.5:18.2 g:mL sample mass/solvent volume ratio. The developed methods showed a high precision level with coefficients of variation lower than 5%. The methods were successfully applied to commercial samples. Additionally, the extraction of other compounds of biological interest, such as organic acids and sugars, was also studied.

Description: In Chile, durum wheat is cultivated in high-yielding Mediterranean environments, therefore breeding programs have selected cultivars with high yield potential in addition to grain quality. The genetic progress in grain yield (GY) between 1964 and 2010 was 72.8 kg ha−1 per year. GY showed a positive and significant correlation with days to heading, kernels per unit ground area and thousand kernel weight. The gluten and protein content tended to decrease with the year of cultivar release. The correlation between the δ13C of kernels and GY was negative and significant (−0.62, p 〈 0.05, for all cultivars; and −0.97, p 〈 0.001, excluding the two oldest cultivars). The yield progress (genetic plus agronomic improvements) of a set of 40–46 advanced lines evaluated between 2006 and 2015 was 569 kg ha−1 per year. Unlike other Mediterranean agro-environments, a longer growing cycle together with taller plants seems to be related to the increase in the GY of Chilean durum wheat during recent decades.

Description: Optimal manure management can maximize agronomic benefits and minimize environmental impacts. Field experiments were conducted in the Pacific Northwest (Vancouver, Canada) to determine how chicken and horse manures that were fall-applied to meet nitrogen crop demand affect soil ammonium (NH4+) and nitrate (NO3−), apparent net mineralization (ANM) and nitrification (ANN), crop biomass and nutrient concentration, and fluxes of nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4). Relative to horse manure, chicken manure increased soil NH4+ by 60-fold, ANM by 2-fold, and ANN by 4-fold. Emissions of N2O (+600%) and CO2 (+45%) were greater and growing season CO2 emissions (−40%) were lower after application of chicken than horse manure. Productivity of cover crop (+30%), legume cover crop (−25%), and squash cash crop (+20%) were affected by chicken relative to horse manure. Overall, fall-applied chicken manure increased yields, N availability, and environmental impacts relative to horse manure.

Description: Let V be a finite set of positive integers with sum equal to a multiple of the integer b . When does V have a partition into b parts so that all parts have equal sums? We develop algorithmic constructions which yield positive, albeit incomplete, answers for the following classes of set V , where n is a given positive integer: (1) an initial interval { a ∈ ℤ + : a ≤ n } ; (2) an initial interval of primes { p ∈ ℙ : p ≤ n } , where ℙ is the set of primes; (3) a divisor set { d ∈ ℤ + : d | n } ; (4) an aliquot set { d ∈ ℤ + : d | n ,   d 〈 n } . Open general questions and conjectures are included for each of these classes.

Description: The blockchain technique is becoming more and more popular due to its advantages such as stability and dispersed nature. This is an idea based on blockchain activity paradigms. Another important field is machine learning, which is increasingly used in practice. Unfortunately, the training or overtraining artificial neural networks is very time-consuming and requires high computing power. In this paper, we proposed using a blockchain technique to train neural networks. This type of activity is important due to the possible search for initial weights in the network, which affect faster training, due to gradient decrease. We performed the tests with much heavier calculations to indicate that such an action is possible. However, this type of solution can also be used for less demanding calculations, i.e., only a few iterations of training and finding a better configuration of initial weights.

Description: The application of blockchain technology to the energy sector promises to derive new operating models focused on local generation and sustainable practices, which are driven by peer-to-peer collaboration and community engagement. However, real-world energy blockchains differ from typical blockchain networks insofar as they must interoperate with grid infrastructure, adhere to energy regulations, and embody engineering principles. Naturally, these additional dimensions make real-world energy blockchains highly dependent on the participation of grid operators, engineers, and energy providers. Although much theoretical and proof-of-concept research has been published on energy blockchains, this research aims to establish a lens on real-world projects and implementations that may inform the alignment of academic and industry research agendas. This research classifies 131 real-world energy blockchain initiatives to develop an understanding of how blockchains are being applied to the energy domain, what type of failure rates can be observed from recently reported initiatives, and what level of technical and theoretical details are reported for real-world deployments. The results presented from the systematic analysis highlight that real-world energy blockchains are (a) growing exponentially year-on-year, (b) producing relatively low failure/drop-off rates (~7% since 2015), and (c) demonstrating information sharing protocols that produce content with insufficient technical and theoretical depth.

Description: The burdens of malnutrition, protein and micronutrient deficiency, and obesity cause enormous costs to society. Crop nutritional quality has been compromised by the emphasis on edible yield and through the loss of biodiversity due to the introduction of high-yielding, uniform cultivars. Heirloom crop cultivars are traditional cultivars that have been grown for a long time (〉50 years), and that have a heritage that has been preserved by regional, ethnic, or family groups. Heirlooms are recognized for their unique appearance, names, uses, and historical significance. They are gaining in popularity because of their unique flavors and cultural significance to local cuisine, and their role in sustainable food production for small-scale farmers. As a contrast to modern cultivars, heirlooms may offer a welcome alternative in certain markets. Recently, market channels have emerged for heirloom cultivars in the form of farmer–breeder–chef collaborations and seed-saver organizations. There is therefore an urgent need to know more about the traits available in heirloom cultivars, particularly for productivity, stress tolerance, proximate composition, sensory quality, and flavor. This information is scattered, and the intention of this review is to document some of the unique characteristics of heirloom cultivars that may be channeled into breeding programs for developing locally adapted, high-value cultivars.

Description: Acid soils and associated Al3+ toxicity are prevalent in Ethiopia where normally Al3+-sensitive durum wheat (Triticum turgidum ssp durum Desf.) is an important crop. To identify a source of Al3+ tolerance, we screened diverse Ethiopian durum germplasm. As a center of diversity for durum wheat coupled with the strong selection pressure imposed by extensive acid soils, it was conceivable that Al3+ tolerance had evolved in Ethiopian germplasm. We used a rapid method on seedlings to rate Al3+ tolerance according to the length of seminal roots. From 595 accessions screened using the rapid method, we identified 21 tolerant, 180 intermediate, and 394 sensitive accessions. When assessed in the field the accessions had tolerance rankings consistent with the rapid screen. However, a molecular marker specific for the D-genome showed that all accessions rated as Al3+-tolerant or of intermediate tolerance were hexaploid wheat (Triticum aestivum L.) that had contaminated the durum grain stocks. The absence of Al3+ tolerance in durum has implications for how Al3+ tolerance evolved in bread wheat. There remains a need for a source of Al3+-tolerance genes for durum wheat and previous work that introgressed genes from bread wheat into durum wheat is discussed as a potential source for enhancing the Al3+ tolerance of durum germplasm.

Description: The availability of big data in agriculture, enhanced by free remote sensing data and on-board sensor-based data, provides an opportunity to understand within-field and year-to-year variability and promote precision farming practices for site-specific management. This paper explores the performance in durum wheat yield estimation using different technologies and data processing methods. A state-of-the-art data cleaning technique has been applied to data from a yield monitoring system, giving a good agreement between yield monitoring data and hand sampled data. The potential use of Sentinel-2 and Landsat-8 images in precision agriculture for within-field production variability is then assessed, and the optimal time for remote sensing to relate to durum wheat yield is also explored. Comparison of the Normalized Difference Vegetation Index(NDVI) with yield monitoring data reveals significant and highly positive linear relationships (r ranging from 0.54 to 0.74) explaining most within-field variability for all the images acquired between March and April. Remote sensing data analyzed with these methods could be used to assess durum wheat yield and above all to depict spatial variability in order to adopt site-specific management and improve productivity, save time and provide a potential alternative to traditional farming practices.

Description: In this study, we address the problem of compaction of Church numerals. Church numerals are unary representations of natural numbers on the scheme of lambda terms. We propose a novel decomposition scheme from a given natural number into an arithmetic expression using tetration, which enables us to obtain a compact representation of lambda terms that leads to the Church numeral of the natural number. For natural number n, we prove that the size of the lambda term obtained by the proposed method is O ( ( slog 2 n ) ( log n / log log n ) ) . Moreover, we experimentally confirmed that the proposed method outperforms binary representation of Church numerals on average, when n is less than approximately 10,000.

Description: Kura clover living mulch (KCLM) systems have been previously investigated for their incorporation into upper Midwestern row crop rotations to provide ecosystem services through continuous living cover. Reductions in soil erosion and nitrate loss to surface and groundwater have been reported, but factors affecting agronomic performance and nutrient management are not well defined. To achieve realized environmental benefits, research must develop agronomic management techniques, determine economic opportunities, and provide management recommendations for row crop production in KCLM systems. Two experiments were conducted in 2017 and 2018 to determine the response to N fertilizer application for maize production in KCLM. The first-year maize experiment followed forage management, and the second-year maize experiment followed maize after forage management. Eight fertilizer N treatments ranging from 0–250 kg N ha−1 were applied to each experiment and grain and stover yields were compared to conventionally managed maize hybrid trials that were conducted nearby. First-year maize did not need fertilizer N to maximize yield and profitability in either growing season, and second-year maize required a fertilizer N rate near local University guidelines for maize following soybean. The net economic return from maize grain and stover in the KCLM averaged over first and second-year maize experiments and 2017 and 2018 growing seasons were $138 ha−1 greater than the conventional comparison.

Description: The agricultural area in the Po Valley is prone to high nitrous oxide (N2O) emissions as it is characterized by irrigated maize-based cropping systems, high amounts of nitrogen supplied, and elevated air temperature in summer. Here, two monitoring campaigns were carried out in maize fertilized with raw digestate in a randomized block design in 2016 and 2017 to test the effectiveness of the 3, 4 DMPP inhibitor Vizura® on reducing N2O-N emissions. Digestate was injected into 0.15 m soil depth at side-dressing (2016) and before sowing (2017). Non-steady state chambers were used to collect N2O-N air samples under zero N fertilization (N0), digestate (D), and digestate + Vizura® (V). Overall, emissions were significantly higher in the D treatment than in the V treatment in both 2016 and 2017. The emission factor (EF, %) of V was two and four times lower than the EF in D in 2016 and 2017, respectively. Peaks of NO3-N generally resulted in N2O-N emissions peaks, especially during rainfall or irrigation events. The water-filled pore space (WFPS, %) did not differ between treatments and was generally below 60%, suggesting that N2O-N emissions were mainly due to nitrification rather than denitrification.

Description: The spectral efficiency of wireless networks can be significantly improved by exploiting spatial multiplexing techniques known as multi-user MIMO. These techniques enable the allocation of multiple users to the same time-frequency block, thus reducing the interference between users. There is ample evidence that user groupings can have a significant impact on the performance of spatial multiplexing. The situation is even more complex when the data packets have priority and deadlines for delivery. Hence, combining packet queue management and beamforming would considerably enhance the overall system performance. In this paper, we propose a combination of beamforming and scheduling to improve the overall performance of multi-user MIMO systems in realistic conditions where data packets have both priority and deadlines beyond which they become obsolete. This method dubbed Reward Per Second (RPS), combines advanced matrix factorization at the physical layer with recently-developed queue management techniques. We demonstrate the merits of the this technique compared to other state-of-the-art scheduling methods through simulations.

Description: In the vehicle routing problem with simultaneous pickup and delivery (VRPSPD), customers demanding both delivery and pickup operations have to be visited once by a single vehicle. In this work, we propose a fast randomized algorithm using a nearest neighbor strategy to tackle an extension of the VRPSPD in which the fleet of vehicles is heterogeneous. This variant is an NP-hard problem, which in practice makes it impossible to be solved to proven optimality for large instances. To evaluate the proposal, we use benchmark instances from the literature and compare our results to those obtained by a state-of-the-art algorithm. Our approach presents very competitive results, not only improving several of the known solutions, but also running in a shorter time.

Description: This paper revisits the spectrally negative Lévy risk process embedded with the general tax structure introduced in Kyprianou and Zhou (2009). A joint Laplace transform is found concerning the first down-crossing time below level 0. The potential density is also obtained for the taxed Lévy risk process killed upon leaving [ 0 , b ] . The results are expressed using scale functions.

Description: An exemplary paradigm of how an AI can be a disruptive technological paragon via the utilization of blockchain comes straight from the world of deep learning. Data scientists have long struggled to maintain the quality of a dataset for machine learning by an AI entity. Datasets can be very expensive to purchase, as, depending on both the proper selection of the elements and the homogeneity of the data contained within, constructing and maintaining the integrity of a dataset is difficult. Blockchain as a highly secure storage medium presents a technological quantum leap in maintaining data integrity. Furthermore, blockchain’s immutability constructs a fruitful environment for creating high quality, permanent and growing datasets for deep learning. The combination of AI and blockchain could impact fields like Internet of things (IoT), identity, financial markets, civil governance, smart cities, small communities, supply chains, personalized medicine and other fields, and thereby deliver benefits to many people.

Description: Although compost and biochar received high attention as growing media, little information is available on the potential of vineyard by-products for the production and use of composted solid digestate (CSD) and biochar (BC). In the present study, two experiments are reported on CSD and BC mixed with commercial peat (CP) for grapevine planting material production. Four doses (0, 10%, 20%, 40% vol.) of CSD and BC were assessed in the first and second experiment, respectively. CSD mixed at a dose of 10% recorded the highest values of shoot dry weight (SDW) and a fraction of total dry biomass allocated to shoot (FTS), both cropping bench-graft and bare-rooted vine. On the other hand, CSD mixed at a dose of 40% displayed the highest values of SDW and FTS, cropping two-year-old vine. BC used at a dose of 10% improved SDW, root dry weight, total dry weight, FTS, shoot diameter, and height on bare-rooted vine. The present study shows that CSD and BC, coming from the valorization of vineyard by-products, can be used in the production of innovative growing media suitable for nursery grapevine production. Further studies are needed to assess the combined applications of CSD and BC in the same growing media.

Description: The application of nitrification inhibitors (NIs) shows promise in prolonging the ammonium presence in soil with beneficial effects for agriculture ecosystems and climate change mitigation. Although the inhibitory effect (IE) of NIs has been studied in the presence of various environmental and edaphic conditions, little is known about the effect of soil nitrification potential (NP) on the effectiveness of NIs. Here, laboratory-scale experiments were conducted to investigate the effect of the variation in soil NP rates, among land-use type and temperature, on the performance of two nitrification inhibitors, dicyandiamide (DCD) and 3,4-dimethypyrazole phosphate (DMPP), at four NI application rates imposed upon eight cropland and non-cropland soils. We found that the IE of DCD and DMPP were organized according to soil NP rates. Nevertheless, NP was lower in non-cropped soil than in cropped systems, and DMPP-based inhibition was higher than DCD. The IE of both NIs decreased with NP and the amount of NI required to achieve an IE ≈ 50%, was significantly reduced for soils that exhibited the lowest NP rates, especially for DMPP. However, the temperature did not appear to have a major influence on IE of both DCD and DMPP, demonstrating the potential of NIs to inhibit nitrification for a wider temperature range, dependent on the NI application rate. Our findings provide evidence that change in soil NP rate has important influences on the efficacy of NI which required great consideration for N-fertilizer optimization with the application of nitrification inhibitors.