ALBERT

Beschreibung: With the increasingly close relations between cities in China, it is of great significance to explore the regular characteristics of the intercity connection. Through Tencent’s population migration heat and Baidu map big data, this paper analyzes the regular characteristics of the relations between complex cities based on such index as the rich node propensity index, preference level index, and relative heat index and also investigates the influence of geographical proximity factors on the external relations of different cities. The research has the following results. Firstly, the relations between cities have obvious club characteristics. The rich nodes tend to connect with the rich nodes, while the nonrich nodes tend to connect with the nonrich nodes. Secondly, the connection between cities has the effect of hierarchical proximity, and cities mainly establish spatial connections with cities of the same level and adjacent level. Thirdly, the relations between cities also have the effect of geographical proximity, and the degree of influence of geographical proximity in low-level cities is greater than that in high-level cities. Fourthly, the external connection mode of high-level cities is to establish close contact with high-level cities adjacent to the level, with strong attraction to low-level cities adjacent to the location at the same time. The low-level cities are closely related to the high-level cities adjacent to the location and other cities of geographical proximity or adjacent level. This study helps to further understand the complex characteristics and laws of intercity connections and urban networks.

Beschreibung: Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate results are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired objectives and their real-time implementation as compared to other algorithms.

Beschreibung: As the externalized carrier of intrinsic value, value decision-making is an important factor affecting the social value system. As an old Chinese saying goes, “A friend in need is a friend indeed,” crisis environment provides the background for the conflicts of multiple values, while individual social value orientation (SVO) determines the ranking of the value states. This paper defined the SVO types by means of Slider Measure method on the basis of environment description, constructed a decision-making game model in accordance with SVO differences, and finally analysed the mechanism of people’s decision-making. Taking the epidemic situation as the background, this paper conducted an empirical analysis with the sample of college students. The results showed that the most SVO types of college students were prosocial orientation, followed by individualistic orientation, altruistic orientation, and competitive orientation. In the crisis environment, individual SVO type and decision-making constituted a mapping relationship. There was an equilibrium point in the decision of prosocial orientation, and the dominant decision of altruistic orientation or individualistic orientation is relatively stable.

Beschreibung: To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.

Beschreibung: People are the most important factors of economy and the primary carriers of social culture. Cross-border migration brings economic and cultural impacts to the origin and destination and is also a key to reflect the international relations of related countries. In fact, the migration relationships of countries are complex and multilateral, but most traditional migration models are bilateral. Network theories could provide a better description of global migration to show the structure and statistical characteristics more clearly. Based on the estimated migration data and disparity filter algorithm, the networks describing the global multilateral migration relationships have been extracted among 200 countries over fifty years. The results show that the global migration networks during 1960–2015 exhibit a clustering and disassortative feature, implying globalized and multipolarized changes of migration during these years. The networks were embed into a Poincaré disk, yielding a typical and hierarchical “core-periphery” structure, which is associated with angular density distribution, and has been used to describe the “multicentering” trend since 1990s. Analysis on correlation and evolution of communities indicates the stability of most communities, yet some structural changes still exist since 1990s, which reflect that the important historical events are contributable to regional and even global migration patterns.

Beschreibung: Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result in insufficient training of long-tail entities. However, unstructured text encoding entities and relational knowledge can be obtained anywhere in large quantities. Word vectors of entity names estimated from the unlabelled raw text using natural language model encode syntax and semantic properties of entities. Yet since these feature vectors are estimated through minimizing prediction error on unsupervised entity names, they may not be the best for knowledge graphs. We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge representation. Experiments on Freebase show that our method can rely less on the labelled data and outperforms the baselines when the labelled data is relatively less. Especially, it is applicable to zero-shot scenario.

Beschreibung: Portfolio investment is adopted by the venture capital to diversify those risks involved in project selection, investing or operating so that the venture capitalist can expect a relatively stable income and lower financing risks. Based on the design of portfolio investment contract with unlimited funds developed by Kanniainen and Keuschnigg, and Inderst et al., this article makes a modification and presents a model given the limitation of funds available for the venture capitalist. It is demonstrated that the marginal benefit of efforts paid by the entrepreneurs exceeds the marginal cost, given the limitation of funds available, which will conduce to a high-level engagement of the entrepreneurs. Thus, by adopting the design of renegotiation contract, the venture capitalist can manage to stimulate the entrepreneurs to make efforts, which is to result in moral hazard reduction.

Beschreibung: Huge data on the web come from discussion forums, which contain millions of threads. Discussion threads are a valuable source of knowledge for Internet users, as they have information about numerous topics. The discussion thread related to single topic comprises a huge number of reply posts, which makes it hard for the forum users to scan all the replies and determine the most relevant replies in the thread. At the same time, it is also hard for the forum users to manually summarize the bulk of reply posts in order to get the gist of discussion thread. Thus, automatically extracting the most relevant replies from discussion thread and combining them to form a summary are a challenging task. With this motivation behind, this study has proposed a sentence embedding based clustering approach for discussion thread summarization. The proposed approach works in the following fashion: At first, word2vec model is employed to represent reply sentences in the discussion thread through sentence embeddings/sentence vectors. Next, K-medoid clustering algorithm is applied to group semantically similar reply sentences in order to reduce the overlapping reply sentences. Finally, different quality text features are utilized to rank the reply sentences in different clusters, and then the high-ranked reply sentences are picked out from all clusters to form the thread summary. Two standard forum datasets are used to assess the effectiveness of the suggested approach. Empirical results confirm that the proposed sentence based clustering approach performed superior in comparison to other summarization methods in the context of mean precision, recall, and F-measure.

Beschreibung: This paper considers the two-player location game in a closed-loop market with quantity competition. Based on the Cournot and Hotelling models, a circle model is established for a closed-loop market in which two players (firms) play a location game under quantity competition. Using a two-stage (location-then-quantity) pattern and backward induction method, the existence of subgame-perfect Nash equilibria is proved for the location game in the circle model with a minimum distance transportation cost function. In addition, sales strategies are proposed for the two players for every local market on the circle when the players are in the equilibrium positions. Finally, an algorithm for simulating the competitive dynamics of the closed-loop market is designed, and two numerical simulations are provided to substantiate the effectiveness of the obtained results.

Beschreibung: A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.

Beschreibung: In this paper, a delayed control strategy for a class of nonlinear underactuated fourth-order systems is developed. The proposal is based on the implementation of the tangent linearization technique, differential flatness, and a study of the σ-stabilization of the characteristic equation of the closed-loop system. The tangent linearization technique allows obtaining a local controllability property for the analyzed class of systems. Also, it can reduce the complexity of the global control design, through the use of a cascade connection of two second-order controllers instead of designing a global controller of the fourth-order system. The stabilizing behavior of the delayed controller design is supported by the σ-stability criterion, which provides the controller parameter selection to reach the maximum exponential decay rate on the system response. To illustrate the efficiency of the theoretical results, the proposal is experimentally assessed in two cases of study: a flexible joint system and a pendubot.

Beschreibung: Recently, knowledge graph embedding methods have attracted numerous researchers’ interest due to their outstanding effectiveness and robustness in knowledge representation. However, there are still some limitations in the existing methods. On the one hand, translation-based representation models focus on conceiving translation principles to represent knowledge from a global perspective, while they fail to learn various types of relational facts discriminatively. It is prone to make the entity congestion of complex relational facts in the embedding space reducing the precision of representation vectors associating with entities. On the other hand, parallel subgraphs extracted from the original graph are used to learn local relational facts discriminatively. However, it probably causes the relational fact damage of the original knowledge graph to some degree during the subgraph extraction. Thus, previous methods are unable to learn local and global knowledge representation uniformly. To that end, we propose a multiview translation learning model, named MvTransE, which learns relational facts from global-view and local-view perspectives, respectively. Specifically, we first construct multiple parallel subgraphs from an original knowledge graph by considering entity semantic and structural features simultaneously. Then, we embed the original graph and construct subgraphs into the corresponding global and local feature spaces. Finally, we propose a multiview fusion strategy to integrate multiview representations of relational facts. Extensive experiments on four public datasets demonstrate the superiority of our model in knowledge graph representation tasks compared to state-of-the-art methods.

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

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

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

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

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

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

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

Beschreibung: A multimode resource-constrained project scheduling problem (MRCPSP) may have multifeasible solutions, due to its nature of targeting multiobjectives. Given the NP-hard MRCPSP and intricate multiobjective algorithms, finding the optimized result among those solutions seems impossible. This paper adopts data envelopment analysis (DEA) to evaluate a series of solutions of an MRCPSP and to find an appropriate choice in an objective way. Our approach is applied to a typical MRCPSP in practice, and the results validate that DEA is an effective and objective method for MRCPSP solution selection.

Beschreibung: Based on the oligopoly game theory and the intellectual property rights protection policy, we investigate the complex dynamical behaviors of a mixed duopoly game with quadratic cost. In the new system, a few parameters are improved by considering intellectual property rights protection and the stability conditions of the Nash equilibrium point are discussed in detail. A set of the two-dimensional bifurcation diagrams is demonstrated by using numerical modeling, and these diagrams show abundant complex dynamical behaviors, such as coexistence of attractors, different bifurcation, and fractal structures. These dynamical properties can present the long-run effects of strengthening intellectual property protection.

Beschreibung: Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model’s emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset.

Beschreibung: Social networks are a complex system that members communicate, create new connections or destroy existing connections, and further deliver major impacts on each member’s life. Given the spread of the Internet and increased academic pressure, sedentary and prescreen behaviors are very common among adolescents; meanwhile, sports behaviors are gradually decreasing. This situation has had an adverse effect on health. This paper used a questionnaire survey to investigate the influence of social networks on adolescent sports behavior, including the intermediary role of social efficacy and moderating effect of self-presentation. The questionnaire survey was conducted on 568 students from 6 high schools in Shaanxi, Henan, and Shandong Provinces. After this, factor analysis and weighted least squares method were used for the empirical test. Based on theoretical and empirical analysis, this paper found the following: (1) Social networks of adolescents have obvious positive predictive effects on their sports behavior. A single online social network and an offline social network, instrumental network, emotional network, and mixed network have obvious positive predictive effects on adolescent sports behaviors. However, under the influence of multiple types of social networks, an offline social network has a negative predictive effect, while a mixed network has effects that are not as obvious. (2) Social efficacy plays an intermediary role in the relationship between social network and adolescent sports behavior. (3) The moderating effect of self-presentation is not significant.

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

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

Beschreibung: Height measurement for moving pedestrians is quite significant in many scenarios, such as pedestrian positioning, criminal suspect tracking, and virtual reality. Although some existing height measurement methods can detect the height of the static people, it is hard to measure height accurately for moving pedestrians. Considering the height fluctuations in dynamic situation, this paper proposes a real-time height measurement based on the Time-of-Flight (TOF) camera. Depth images in a continuous sequence are addressed to obtain the real-time height of the pedestrian with moving. Firstly, a normalization equation is presented to convert the depth image into the grey image for a lower time cost and better performance. Secondly, a difference-particle swarm optimization (D-PSO) algorithm is proposed to remove the complex background and reduce the noises. Thirdly, a segmentation algorithm based on the maximally stable extremal regions (MSERs) is introduced to extract the pedestrian head region. Then, a novel multilayer iterative average algorithm (MLIA) is developed for obtaining the height of dynamic pedestrians. Finally, Kalman filtering is used to improve the measurement accuracy by combining the current measurement and the height at the last moment. In addition, the VICON system is adopted as the ground truth to verify the proposed method, and the result shows that our method can accurately measure the real-time height of moving pedestrians.

Beschreibung: Since China’s first stock index futures, China Securities Index 300 (CSI300) stock index futures were published in 2010, and China’s stock index futures market is now in a period of rapid development and play a key role in price discovery. During 2014 to 2015, China’s stock index futures market fluctuated abnormally, and the overuse of high-frequency trading (HFT) strategies in the stock index futures market was blamed as the main reason of the abnormal volatility. To lower down market fluctuation, the regulatory institute then announced a series of trade restriction policy to prevent the overuse of HFT behaviour. However, until now, the impact of such trade restriction policy for HFT remains uncertain. To tackle this issue, based on minute-level HFT data from the CSI 300 index futures market, this paper aims to investigate the relationship between HFT and the exogenous liquidity risk and how HFT affects China’s stock index futures market on its liquidity using the liquidity-adjusted value at risk (LVaR) model. The findings indicate that HFT improves the return of the liquidity provider and reduces the exogenous liquidity risk significantly.

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

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

Beschreibung: Community is the core spatial unit for evaluating sustainable development. However, single data and method seem inadequate for conducting a scientific, effective, and innovative sustainable evaluation of complex community units. In this study, we perform a sustainable-oriented land use scheme using multisource remote sensing, machine learning, and object-based postclassification refinement. Furthermore, we assess the sustainability of the traffic community by data-driven and combined housing, ecosystem services, and landscape configuration. The results indicated that (1) the relationship between housing, ecosystem services, and landscape pattern has obvious synergistic effects, although with dissimilar importance in different sustainability levels. High sustainability level is intensely coordinated with landscape configuration, medium sustainability level is more affected by ecosystem services, and low sustainability level is more related to housing. (2) Community sustainability presents a significant spatial distribution. The communities of high sustainability level are mainly located in both sides of the Pearl River and emerging urban areas, while those of medium sustainability level are distributed sporadically in the study area and those of low sustainability level are concentrated in old towns. (3) Community transformation cannot be accomplished at one step. Along with the continuous optimization of landscape configuration, the priority should be given to housing reconstruction and improvement of ecosystem services further. We provide scientific and effective data-based evidence for urban decision-makers by integrating the advantages of the Earth Observation System and multifactor analysis.

Beschreibung: Practically, supply disruption may lead production process to entirely halt (completely disrupted) or the output to differ in the order size (partially disrupted), which makes it more difficult for the retailer to satisfy stochastic market demand. Under the circumstance, the retailer is likely to procure products from two suppliers to effectively alleviate the demand-supply mismatches. Thus, under supply disruption and stochastic demand, this paper develops both backup sourcing and simultaneous sourcing (SS) strategies to analyze the retailer’s performance, where backup sourcing includes wholesale price priority (WPP) and supply reliability priority (SRP). Specifically, (1) under WPP, when the selling price is relatively lower (higher), the retailer is suggested to activate the reliable backup supplier after the realization of supply disruption (demand uncertainty). (2) Under SRP, two scenarios including minor disruption and major disruption can be identified, where the retailer’s order quantity from the reliable (unreliable) supplier under minor disruption scenario is more (less) than that under major. (3) Finally, this paper systematically compares the retailer’s preferences among WPP, SRP, and SS via theoretical results and numerical examples. That is, when the unreliable supplier is more likely to work normally or shortage cost (selling price) is relatively lower, the retailer prefers SPR regarding the unreliable supplier as backup sourcing due to its lower wholesale price and acceptable supply disruption. Otherwise, the retailer is inclined to WPP regarding the reliable supplier as backup sourcing for ensuring all market demand to be satisfied. In addition, unless the emergency prices of two suppliers are extremely higher, backup sourcing strategies could perform better than simultaneous sourcing strategy.

Beschreibung: This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

Beschreibung: Single image super-resolution (SISR) is a traditional image restoration problem. Given an image with low resolution (LR), the task of SISR is to find the homologous high-resolution (HR) image. As an ill-posed problem, there are works for SISR problem from different points of view. Recently, deep learning has shown its amazing performance in different image processing tasks. There are works for image super-resolution based on convolutional neural network (CNN). In this paper, we propose an adaptive residual channel attention network for image super-resolution. We first analyze the limitation of residual connection structure and propose an adaptive design for suitable feature fusion. Besides the adaptive connection, channel attention is proposed to adjust the importance distribution among different channels. A novel adaptive residual channel attention block (ARCB) is proposed in this paper with channel attention and adaptive connection. Then, a simple but effective upscale block design is proposed for different scales. We build our adaptive residual channel attention network (ARCN) with proposed ARCBs and upscale block. Experimental results show that our network could not only achieve better PSNR/SSIM performances on several testing benchmarks but also recover structural textures more effectively.

Beschreibung: With the gestation and development of new technologies, new products, new formats, and new models, venture capital investment, as one of the most important forms of open innovation in large companies, plays an increasingly important role in the innovation of mature large companies and entrepreneurial enterprises. To deal with the complex and dynamic environment, the niche of Corporate Venture Capital (CVC) ecological community is investigated from the perspective of the innovation ecosystem. By analyzing the innovation of CVC ecological community with the use of the logistic expansion model, this paper analyzes the stability of evolution game through the replicator dynamic equation and discusses ten parameters of niche state. In the end, we conclude that there are four optimization strategies in the coevolution of major corporations and entrepreneurial firms, namely, niche separation, niche expansion, niche K-R, and niche alliance.

Beschreibung: Multiobject detection tasks in complex scenes have become an important research topic, which is the basis of other computer vision tasks. Considering the defects of the traditional single shot multibox detector (SSD) algorithm, such as poor small object detection effect, reliance on manual setting for default box generation, and insufficient semantic information of the low detection layer, the detection effect in complex scenes was not ideal. Aiming at the shortcomings of the SSD algorithm, an improved algorithm based on the adaptive default box mechanism (ADB) is proposed. The algorithm introduces the adaptive default box mechanism, which can improve the imbalance of positive and negative samples and avoid manually set default box super parameters. Experimental results show that, compared with the traditional SSD algorithm, the improved algorithm has a better detection effect and higher accuracy in complex scenes.

Beschreibung: The nonlinear resonant responses, mode interactions, and multitime periodic and chaotic oscillations of the cantilevered pipe conveying pulsating fluid are studied under the harmonic external force in this research. According to the nonlinear dynamic model of the cantilevered beam derived using Hamilton’s principle under the uniformly distributed external harmonic excitation, we combine Galerkin technique and the method of multiple scales together to obtain the average equation of the cantilevered pipe conveying pulsating fluid under 1 : 3 internal resonance and principal parametric resonance. Based on the average equation in the polar form, several amplitude-frequency response curves are obtained corresponding to the certain parameters. It is found that there exist the hardening-spring type behaviors and jumping phenomena in the cantilevered pipe conveying pulsating fluid. The nonlinear oscillations of the cantilevered pipe conveying pulsating fluid can be excited more easily with the increase of the flow velocity, external excitation, and coupling degree of two order modes. Numerical simulations are performed to study the chaos of the cantilevered pipe conveying pulsating fluid with the external harmonic excitation. The simulation results exhibit the existence of the period, multiperiod, and chaotic responses with the variations of the fluid velocity or excitation. It is found that, in the cantilevered pipe conveying pulsating fluid, there are the multitime nonlinear vibrations around the left-mode and the right-mode positions, respectively. We also observe that there exist alternately the periodic and chaotic vibrations of the cantilevered pipe conveying pulsating fluid in the certain range.

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

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

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

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

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

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

Beschreibung: Ventilator is important equipment for mines as it safeguards the lives under the shaft and ensures other equipment’s proper functioning by providing fresh air. Therefore, how to effectively control the ventilator system becomes more significant. In order to acquire the commonly used model and control strategy for ventilator systems, a new universal ventilator model is established based on the blast capacity differential pressure in the ventilating duct and the ventilator motor model. Then, an adaptive Chebyshev neural network (ACNN) controller is proposed to effectively control the ventilator system where the unknown load torque and the unknown disturbance caused by the complex environment under the shaft are approximated by the Chebyshev neural network (CNN). Afterwards, an appropriate Lyapunov function candidate is designed to guarantee the stability of the proposed controller and the closed-loop ventilator system. Finally, the ACNN controller has been demonstrated to be effective in terms of validity and precision for the new proposed ventilator model through the simulations.

Beschreibung: Nonlinear evolution equations widely describe phenomena in various fields of science, such as plasma, nuclear physics, chemical reactions, optics, shallow water waves, fluid dynamics, signal processing, and image processing. In the present work, the derivation and analysis of Lie symmetries are presented for the time-fractional Benjamin–Bona–Mahony equation (FBBM) with the Riemann–Liouville derivatives. The time FBBM equation is reduced to a nonlinear fractional ordinary differential equation (NLFODE) using its Lie symmetries. These symmetries are derivations using the prolongation theorem. Applying the subequation method, we then use the integrating factor property to solve the NLFODE to obtain a few travelling wave solutions to the time FBBM.

Beschreibung: The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.

Beschreibung: There is a large amount of information and maintenance data in the aviation industry that could be used to obtain meaningful results in forecasting future actions. This study aims to introduce machine learning models based on feature selection and data elimination to predict failures of aircraft systems. Maintenance and failure data for aircraft equipment across a period of two years were collected, and nine input and one output variables were meticulously identified. A hybrid data preparation model is proposed to improve the success of failure count prediction in two stages. In the first stage, ReliefF, a feature selection method for attribute evaluation, is used to find the most effective and ineffective parameters. In the second stage, a K-means algorithm is modified to eliminate noisy or inconsistent data. Performance of the hybrid data preparation model on the maintenance dataset of the equipment is evaluated by Multilayer Perceptron (MLP) as Artificial Neural network (ANN), Support Vector Regression (SVR), and Linear Regression (LR) as machine learning algorithms. Moreover, performance criteria such as the Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used to evaluate the models. The results indicate that the hybrid data preparation model is successful in predicting the failure count of the equipment.

Beschreibung: The optical images collected by remotely operated vehicles (ROV) contain a lot of information about underwater (such as distributions of underwater creatures and minerals), which plays an important role in ocean exploration. However, due to the absorption and scattering characteristics of the water medium, some of the images suffer from serious color distortion. These distorted color images usually need to be enhanced so that we can analyze them further. However, at present, no image enhancement algorithm performs well in any scene. Therefore, in order to monitor image quality in the display module of ROV, a no-reference image quality predictor (NIPQ) is proposed in this paper. A unique property that differentiates the proposed NIPQ metric from existing works is the consideration of the viewing behavior of the human visual system and imaging characteristics of the underwater image in different water types. The experimental results based on the underwater optical image quality database (UOQ) show that the proposed metric can provide an accurate prediction for the quality of the enhanced image.

Beschreibung: Identification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community structures, that is, more links within communities and fewer links between different communities, which ignore the rich diversity of community regularities in real networks. In addition, the node attributes that provide rich semantics information of communities and networks can facilitate in-depth community detection of structural information. In this paper, we propose a novel unified Bayesian generative model to detect generalized communities and provide semantic descriptions simultaneously by combining network topology and node attributes. The proposed model is composed of two closely correlated parts by a transition matrix; we first apply the concept of a mixture model to describe network regularities and then adjust the classic Latent Dirichlet Allocation (LDA) topic model to identify community semantically. Thus, the model can detect broad types of network structure regularities, including assortative structures, disassortative structures, and mixture structures and provide multiple semantic descriptions for the communities. To optimize the objective function of the model, we use an effective Gibbs sampling algorithm. Experiments on a number of synthetic and real networks show that our model has superior performance compared with some baselines on community detection.

Beschreibung: This work investigates a decentralized state feedback scheme of neural network control for an interconnected system. The completely unknown associated terms are estimated directly by the neural structure. A modified approach is proposed to deal with the state feedback format. By combining the Lyapunov function and backstepping technology together, an adaptive decentralized controller is established, and we can construct the boundedness of all signals in the closed-loop structure through the controller, which can drive the formation of a given reference signal. In the end, the effectiveness of the presented strategy is referred to a simulation example.

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

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

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

Beschreibung: A novel four-dimensional energy, economic, and environmental (3E) under energy reduction constraints chaotic system is proposed. The acquisition of environmental quality data is the key to this paper. During the course of the study, we used Bayesian estimation algorithm to calibrate the environmental quality. Based on the official data, the Levenberg–Marquardt backpropagation neural network method was optimized by genetic algorithm to effectively identify the parameters in the 3E system. The research results show that although increasing energy reduction inputs can improve environmental quality, the effect on energy intensity and overall stability of the system is not obvious. When polluting input in the ecological environment system affects its maximum capacity, the environmental system collapses (i.e., the ecological system can no longer purify the environment through the self-circulation process and will eventually die out). Therefore, it is necessary to correctly grasp the ecological environment protection and the relationship between economic developments and explore synergies to promote ecological priorities and green development new ideas.

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

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

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

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

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

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

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

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

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

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

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

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

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

Beschreibung: Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.

Beschreibung: In the development of technology for smart cities, the installation and deployment of electronic motor vehicle registration identification have attracted great attention in terms of smart transportation in recent years. Vehicle velocity measurement is one of the fundamental data collection efforts for motor vehicles. The velocity detection using electronic registration identification of motor vehicles is constrained by the detection algorithm, the material of the automobile windshield, the placement of the decals, the installation method of the signal reader, and the angle of the antenna. The software and hardware for electronic motor vehicle registration identification produced in the standard manner cannot meet the accuracy of velocity detection for all scenarios. Based on the actual application requirements, we propose a calibration method for the numerical output of the automobile velocity detector based on edge computing of the optimized multiple reader/writer velocity values and based on a particle swarm-optimized radial basis function (RBF) neural network. The proposed method was tested on a two-way eight-lane road, and the test results showed that it can effectively improve the accuracy of velocity detection using electronic registration identification of motor vehicles. Compared with the actual velocity, 87.12% of all the data samples had an error less than 5%, and 91.76% of the data samples for vehicles in the center lane had an error less than 5%. By calibrating the electronic vehicle velocity based on the registration identification, the accuracy of velocity detection in different application environments can be improved. Moreover, the method can establish an accurate foundation for application in traffic flow management, environmental protection, traffic congestion fee collection, and special vehicle traffic management.

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

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

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

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

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

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

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

Beschreibung: In this work, we solve the uncertain unmanned aerial vehicle smooth landing problem over a moving platform, assuming that the aircraft position relative to the platform and its acceleration is always measurable. The landing task is carried out by an output-feedback robust controller, together with a repulsive force. The robust controller controls the nominal model, accomplishes the needed tracking trajectory, and counteracts the unknown uncertainties. To assure that the aircraft is always above the platform, we include a repulsive force that only works in a small vicinity of the platform. To estimate the relative aircraft velocity and platform acceleration, we use a supertwisting-based observer, assuring finite-time convergence of these signals. This fact allowed us to design the feedback state stabilizer independently of the observer design (in accordance with the separation principle). We confirmed the effectiveness of our control approach by convincing numerical simulations.

Beschreibung: As a green and renewable energy source, photovoltaic power is of great significance for the sustainable development of energy and has been increasingly exploited. The photovoltaic controller is the key component of a photovoltaic power generation system, and its central technology is the maximum power point tracking technology. In this paper, a mathematical model of photovoltaic cells is firstly established, the output characteristics of photovoltaic cells are analyzed, the main factors that affect the output efficiency of photovoltaic cells are obtained, and it is proved that the most important factor that affects the output power is the light intensity. Therefore, in the design, the maximum power point of the photovoltaic cell is tracked by the control algorithm and can maximize the use of photovoltaic output power fast charging. The key to the design of a photovoltaic controller is the design of control algorithm. So, an improved fuzzy control algorithm is proposed to overcome the shortcomings of the traditional maximum power point tracking (MPPT) algorithm. The algorithm can consider tracking both speed and convergence, but the algorithm requires high input and output fuzzy domain parameters, and although the tracking speed is fast, the stability of convergence is poor. For the limitation of fuzzy control algorithm, considering the property of the Versoria function, an MPPT design method for an intelligent controller based on the Versoria variable step algorithm is further proposed. According to the output characteristics of photovoltaic cells, three parameters, α, β, and γ, are set to solve the tracking speed and tracking stability. In order to reduce the static error, a genetic factor is proposed to sum up the historical error to effectively improve the tracking stability. The simulation results show that the algorithm can track the maximum power point quickly and has good tracking speed and stability. This algorithm can be used in engineering practice effectively.

Beschreibung: Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process.

Beschreibung: With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision.

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

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

Beschreibung: This paper presents a multi-innovation stochastic gradient parameter estimation algorithm for dual-rate sampled state-space systems with d-step time delay by the multi-innovation identification theory. Considering the stochastic disturbance in industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. The difficulty of identification is that the information vector in the identification model contains the unknown states. The proposed algorithm uses the state estimates of the observer instead of the state variables to realize the parameter estimation. The simulation results indicate that the proposed algorithm works well.

Beschreibung: In the present paper, sufficient conditions ensuring the complete controllability for a class of semilinear fractional nonlocal evolution systems with finite delay in Banach spaces are derived. The new results are obtained under a weaker definition of complete controllability we introduced, and then the Lipschitz continuity and other growth conditions for the nonlinearity and nonlocal item are not required in comparison with the existing literatures. In addition, an appropriate complete space and a corresponding time delay item are introduced to conquer the difficulties caused by time delay. Our main tools are properties of resolvent operators, theory of measure of noncompactness, and Mönch fixed point theorem.

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

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

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

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

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

Beschreibung: Solution multiplicity of natural ventilation in buildings is of much importance for personnel safety and ventilation design. In this paper, a new mathematical model of buoyancy pressure ventilation for two vertically connected open cavities is presented. Compared with the previous published papers studying two vertically connected open cavities with equal heights and hot source E2 

Beschreibung: Considering the multienergy structure of the electricity market and supply-side competition reform in China, a dual-channel and multiproduct supply chain model is constructed. There are three players in the game model: new energy company and traditional energy company provide energy for the market and the State Grid at the same time. The State Grid is a retailer who buys electricity from two companies and supplies to the market after converting and transmitting the power. Three companies can invest in grid management for saving energy and reducing losses. The energy loss rate is an exponential function of line loss investment. Through the bifurcation graph, Lyapunov exponent, and the basin of parameter, the complex characteristics of the investment market are analyzed. It is interesting to find the Grazing–Hopf bifurcation which usually occurs in nonlinear circuits. The mixed expectation of bounded rationality and the naive expectation is conducive to suppressing the bifurcation and chaos of the market. When external shocks occur, the control model has good robustness.

Beschreibung: With the energy consumption increasing, the coal supply in China has been becoming tight, which has made it difficult for thermal power generation in Yunnan Province. Making full use of local inferior coal and biomass resources in Yunnan can remedy the lack of fuel in power plants. In this paper, an oxygen-rich atmosphere thermogravimetric experiment was performed for a blended sample of Xiaolongtan lignite, Yiliang tobacco rod, and Fuyuan bituminous coal. The combustion characteristics of the mixed fuel under several key operating parameters (i.e., mass ratios, oxygen concentration, and heating rates) were studied. The response surface methodology was used to determine the optimal blending ratio of the three fuels. The results show that the ignition and burnout temperature of coal decrease and the combustion time diminishes with the enrichment of oxygen. The optimal oxygen concentration in the practical application is around 30%. The activation energy and preexponential factor increase with the enlargement of oxygen concentration. Such complex evaluation and optimization approach ensure the stable operation of thermal power plant production.

Beschreibung: This paper mainly investigates the projection synchronization of complex chaotic systems with both uncertainty and disturbance. Using the linear feedback method and the uncertainty and disturbance estimation- (UDE-) based control method, the projection synchronization of such systems is realized by two steps. In the first step, a linear feedback controller is designed to control the nominal complex chaotic systems to achieve projection synchronization. An UDE-based controller is proposed to estimate the whole of uncertainty and disturbance in the second step. Finally, numerical simulations verify the feasibility and effectiveness of the control method.

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

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

Beschreibung: A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.