Deeper understanding of biological processes, disease mechanisms, and prospective therapeutic targets can be attained by Gene Expression Network Analysis (GENA), which offers a potent framework for revealing the intricate regulatory mechanisms controlling gene expression. GENA is a computational method used to understand the intricate interactions and relationships between the genes in a biological system. It entails using network theory, statistical analysis, and gene expression data to pinpoint functional modules, regulatory linkages, and important genes or pathways involved in a particular biological process or illness. This chapter broadly outlines the principles and practice of GENA, to an novice reader and outlines a simple method of performing a GENA using online Rice gene expression datasets available in various websites.

: Biological networks such as protein interaction networks, gene regulation networks and metabolic pathways are examples of complex networks which are large graphs with small-world and scale-free properties. Analysis of these networks has a profound effect on our understanding the origins of life, health and disease states of organisms, and diagnose diseases to aid the search for remedial processes. In this review, we describe main analysis methods of biological networks using graph theory by first defining main parameters such as clustering coefficient, modularity and centrality. We then survey fundamental graph clustering methods and algorithms followed by the network motif search algorithms with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function carried out by that small network and discovering such a function provides an insight to the overall function of the organism. Lastly, we review network alignment algorithms that achieve to find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor and finding such relationship may help researchers derive ancestral relationships and predict the future evolution of organisms to enable designing new drugs. We conclude by the current challenging areas of biological network analysis using graph theory and parallel processing for high performance analysis

This research aims to produce a network structure in the Integrated Urban Farming Program in Bandung City to map the policy actors involved in it as an effort to support food security in the city. This research uses a mixed method with an exploratory sequential strategy involving policy actors from the government, private sector, academia, community, and mass media. To obtain the network structure in integrated urban farming to determine the most important actors, this research uses a social network analysis approach by utilizing the Gephi application. The network structure is based on four dimensions, namely centrality. The results of this study confirm that the actor who has the most connections (degree centrality), as well as the one who holds the best communication control (betweenness centrality), is Parahyangan Catholic University (Academic). While the actor who plays the most important role (eigenvector) is at the lower level of the government Sub-District and Urban Village. This research is useful for explaining the importance of the position of actors in the urban agriculture policy network which is the key to the success of a program.

Recently, the Integrated Air Defense System (IADS) has become vital for the defense system as the military defense system is vital for national security. Placing Integrated Air Defense System batteries among locations to protect locations assets is a crucial problem because optimal solutions are needed for interceptor missiles to intercept attacker missiles for maximizing protection of assets across locations or places. In this research, the procedures of using network algorithms along with developing several network algorithms are going to be demonstrated to develop a model for sequential development of seven network securing strategies of placing Surface to Air Missile (SAM) batteries to maximize the protection of assets across locations (based on given asset values) by generating optimal solutions through computation to destroy maximum attacker missiles by using minimum interceptor missiles with given intercept probability. This network securing strategies can be implemented not only for Integrated Air Defense System (IADS) planning but also Counter Air (CA) planning as Integrated Air Defense System (IADS) is conducted with defensive counter air supported by attack operations in offensive counter air.

By framing the problem of determining the state of Schrodinger’s cat as a problem of searching for Hidden Markov Model (HMM), this work shows that Schrodinger’s cat is dead with stationary probabilities P(Cat = Dead) = 1 and P(Cat = Alive) = 0 consistently in various observation-based modelling of the HMM.

Graphs are largely used in computer science to model relations, or associations, among entities the compose complex systems. More recently, they found a broad field of application in bioinformatics and medical informatics supporting modelling, analysis of many systems. The applications span from representing interactions among molecules within cells, to model the functions of the brains. In order to support research, algorithms from graph theory that are able to extract knowledge from network should be coupled to efficient visualisation techniques. Although the importance of such topic, we retain that many challenges should be faced in the futures, such as automated pathway and network layout to better match biologists’ needs, the developing of a standard able to give more and better choices to represent nodes, edges finally, the developing of automated mechanism able to improve the network navigation methods that help users manage large and complex networks with the goal to improve the usability.

The development of transversal competences, in interaction with professional competences, is essential for individuals to adapt to change and live meaningful and productive lives. The aim of this study was to explore the mutual interaction of six transversal competences—civic, digital, entrepreneurial, global, innovation, and research, as well as their respective sub-competences—and propose possible approaches to how this dynamic of interaction can be applied to facilitate the development of transversal competences. A sample of students (N = 1575) from 21 universities in Latvia completed a self-assessment survey evaluating multiple behavioural indicators for each transversal competence and sub-competence. The results revealed that civic competence was evaluated significantly lower than other competences, and, for most transversal competences, sub-competences associated with active and systematic collaboration and networking received lower evaluations. A network analysis showed that the community involvement sub-competence of the civic competence and the initiative and critical thinking sub-competences of the innovation competence had the strongest links to the other measured sub-competences on average. The results suggest that the development of initiative and cooperation-and-collaboration-related and network-building skills are likely to have a systematic positive effect on the development of a wider set of transversal competences and the professional autonomy of students in the long term.

A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classification tasks. Control flow and term representations are widely adopted, but provide only a partial view of program semantics. This study is an empirical analysis of the networks composing malicious binaries in order to provide a complete representation of the structural properties of a program. This is accomplished by the measurement of structural properties of program networks in a malicious binary executable dataset. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. By providing an increase in feature resolution within labeled datasets of executable programs we provide a quantitative basis to interpret the results of classifiers trained on CFG graph features. An increase in feature resolution allows for the structural properties of program classes to be analyzed for patterns as well as their component parts. By capturing a complete picture of program graphs we can enable theoretical solutions for the mapping a program's operational semantics to its structure.

This study examined the last four decades of the existing academic literature related to the environmental impacts of using cover crops in agricultural production systems. Data were collected from the Web of Science database, resulting in a sample of 3,246 peer-reviewed articles published between 1980 and 2021. We combined two advanced scientometrics analysis software (i.e., CiteSpace 6.0.R1 and Gephi 0.9.2) to identify the trajectory of the literature, hotspots, and frontiers. We developed authorship-, institution-and country-levels networks to examine academic cooperation over the last forty years. Our findings revealed that the number of peer-reviewed outputs documenting the environmental effects of cover crops has consistently increased, with a notable rise in publications between 2015 and 2021. Eighteen salient research topics were identified in the literature, including winter cover crops' effects on soil health, cover crops' effects on nitrous oxide emissions, and the relationship between cover crops and nitrate leaching. Based on the citation-clustering analysis, the trajectory of the literature may be divided into three stages. Studies in Stage 1_A (1980-2000) mainly assessed the role of cover crops in nitrogen management. In Stage 1_B (2001-2010), the research evaluated the impact of using different cover crop mixtures on farming systems. In Stage 2 (2011-2021), studies primarily addressed the environmental impacts of cover crops, particularly their effects on physical and chemical soil properties. Finally, the countries with the most outputs were the United States, Brazil, and Spain. The U.S. Department of Agriculture-Agricultural Research Service was the main contributor to the literature on the environmental impacts of cover crops.

Formation in soccer is among the most important tactical choices for a successful match.Herein, the simulations of 420000 match-plays have been performed varying the formation, the number of opponents that are actively pressing the team, the speed of the opponents in attempting a pass interception. Dribbling has been neglected. The match-play ends either with a successful series of passes from a central back to the line of the strikers or with the opponents that steal the ball. In this work, I demonstrate that 3-4-3 formation, which is among the most employed formations, relates to the highest probability of success.

Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behavior. Suicidal behavior is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behavior, but we do not yet understand how their interaction increases the risk for suicidal behavior. A new approach called network analysis can help us better understand this process as it allows to visualize and quantify complex association between many different symptoms or risk factors. A network analysis of data contain information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.

A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3,283 abstracts). Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. It is suggested that all disci-plines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.

This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in pre-pandemic but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks in pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level.

Over the years, network analysis became a promising strategy to analyze complex system, i.e. systems composed by a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists to uncover complex relationships and identify key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we model omics, diseases and drugs data together through the multilayer network representation. Then, we mined the multilayer network with a community detection algorithm by obtaining top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected form the selected genes. The results show that the genes forming the top community have the multiple roles through different pathways.

One of the most serious concerns about mega event-related changes to small cities is how to effectively utilize newly developed public attractions after the Olympic Games. Making connections with existing local amenities and forming attraction networks can be effective strategies for continuing a city’s revitalization. However, despite the expected benefits, attraction network research shows that these benefits often fail to materialize. With the case of Gangneung, a 2018 winter Olympic hosting city, this study investigated visitation patterns to nineteen selected attractions using network analysis. The results indicate that the most influential nodes are located on the northern coast, the eastern coast, and in the south downtown area, those nodes being the central locations where the strongest of connections are made. New attractions such as the Olympic Park and Walwha Linear Park were rather isolated. While seasonal and periodic variations, visitors’ residences, and destination choice attitudes had a significant effect on visitation patterns, the attraction networks, modes of transportation, proximity to region, and type similarity were not significant factors in the forming of visitation patterns. The results make a methodological contribution to tourist behavior and network research. In addition, beyond individual attraction development, the results provide practical implications in regard to networking and cooperation between multiple attractions using temporal and spatial strategies such as management/investment prioritizing, travel route development, and program scheduling.

Phylostratigraphic analysis is a way to look anew on phylogenetic data in the evolutionary aspect. It allows counting the evolutionary age based on the analysis of genes, their orthologs and finding the last common ancestor. We performed phylostratigraphic analysis of Arabidopsis thaliana genes associated with several types of abiotic stresses (heat, cold, water-related, light, osmotic, salt, and oxidative) determined by the Gene Ontology annotation. Comparison of the distributions of ages of genes associated with stresses of different type has shown the heat stress to involve older genes while the light stress – younger genes. At the same time, all types of stress are characterized by a significantly higher proportion of old genes (common to all eukaryotes) compared to the whole set of A.thaliana genes. This can be explained by the involvement of basic molecular processes in plant cells into the stress response. Reconstruction and graphical analysis of the gene network of the heat stress educed several clusters associated with different response functions. Some of these clusters contain only ancient genes. The results obtained show that the phylostratigraphic analysis reveals the fundamental features of the organization of gene networks and their evolution.

Distance Learning has become the new standard, especially after the pandemic and due to the technological advances, that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. This work aims to propose a model that can capture the students' behaviors based on the clickstream data associated with the discussion forum and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis (SNA) as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of SNA, differences can be spotted in the communicational patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communicational approach.

Using SNA techniques, the study examined H2020 forestry projects. An adjacency matrix was created using the CORDIS data collection, and it was then utilized to depict the network of project members. Then, different network indicators were computed. Several statistical techniques (maximum likelihood, Kolmogorov-Smirnov test, moments, bootstrapping) were employed to do a goodness-of-fit analysis on the frequencies of the degrees to confirm scale-freedom or randomness in the search for significant distributions in network research. Additionally, the small-world aspect was investigated. The findings demonstrate that while the number of project participations by project participants follows a power distribution, the distribution of project participants’ degrees reflects various effects. As a result, the scale-freedom that has been emphasized in many scientific investigations is not evident. The network indicators demonstrate that the network is not clearly small-world.

Evacuation is the primary and most important preparedness initiative to save people from the devastating effects of a cyclone. For proper evacuation, a route is specifically chosen that will play a role during cyclones. In this study, Geographic information system has been applied for evacuation route planning for Mirzaganj Union, Patuakhali, Bangladesh. The study is based on questionnaire surveys; direct observation and literature review on the existing sheltering system and develops an evacuation route using the shortest distance analysis. This study provides us an idea how we can manage our population to find out suitable nearby shelters and the limitations that we should focused on. The result shows that only 12% of the total population gets shelter opportunity with the existing official shelter capacity by identifying evacuation route with the shortest distance and the shortest time needed. Shortest distance and time have been calculated for each cyclone shelters. If we consider the low vulnerable infrastructures as shelter, 75% of the total population can be accommodated. This study demonstrates the utilization of an ideal evacuation route planning in the coastal parts of Bangladesh.

Background. Upper respiratory infections (URIs) remains as significant cause of morbidity in children. Evidence on efficacy of probiotics to prevent URIs in children is increasing. This systematic review was assembled to analyze evidence about the efficacy of probiotics to reduce duration of upper respiratory infections in ambulatory children. Methods. Randomized controlled trials (RCTs) comparing probiotics vs. placebo to prevent URIs, published between 2001 and 2016 were considered. Quality evaluation was evaluated using CONSORT. Standard mean difference (SMD) or risk ratio (RR) was calculated. Network Meta-Analysis (NMA), using a random effect model was assembled. Results. 31 RCTs were evaluated and 20 studies were included with 3,635 children randomized to probiotics and 3,433 to placebo. Lactobacillus reuteri [SMD -0.56 CI95% (-0.72 to -0.41), p 0.0001] and Lactobacillus acidophillus [SMD -0.33 CI95% (-0.60 to -0.06), p 0.01] were superior to placebo to reduce duration of URIs. L. rhamnosus GG showed tendency [SMD -0.14 CI95% (-0.28 to 0.0), p 0.048]. On the network forest plot L. reuteri showed preventive equivalence when was compared to L. rhamnosus GG, L. casei and BB12. Conclusions. Lactobacillus reuteri, Lactobacillus rhamnosus GG and Bifidobacterium BB12 are evidence-based alternatives to be considered to prevent URIs in children.

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maxi-mally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7,560 proteins related to GBM disease and 3,860 proteins associated with signaling pathways involved in GBM. By integrating the results of the aforementioned analyses and performing centrality analysis again, eleven key genes involved in GBM disease were identi-fied. ProteomicsDB or Gliovis databases were used for determining the gene expression in normal or tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of iden-tified genes, miRNAs, and metabolites associated with GBM disease was done using ExpressAna-lyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was done through the pathway, joint pathway, and network analyses. The results of this study identified 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in the GBM disease development. To establish early diagnostic methods and plan personalized GBM treatment strategies, the identified top genes-miRNAs and metabolite signatures can be targeted.

We are currently witnessing a strong global transition towards new, more sustainable models of development and consumption. This transition is both activating and highlighting a series of discrepancies between the various actors in agri-food marketing systems, including the institutions that govern regulatory and trade aspects. These discrepancies highlight how, on a global level, the agri-marketing system is unable to provide adequate responses to the new principles of sustainability that are increasingly expected by civil society. This inability is often linked to the phenomena of opportunism, information asymmetries and lock-in effects (resulting from the slowness or inability of some sectors or actors to evolve technologically and culturally). The control of all these factors is often ineffective and often involves high transaction costs . These conditions lead the global market into an ongoing state of failure, creating structural holes, in which new forms of exchange are born, in response to the principles mentioned above. We identify these as nested markets: hybrid market forms that, thanks to new information technologies, create a new form of proximity that transcends the classical concept of physical proximity. This new proximity directly links producers and consumers in relationships based mainly on two substantial values: reciprocity and reputation. Both of these values significantly reduce transaction costs which today play an increasing role in determining the form and repetition of exchanges. This condition is difficult to interpret from classical and neo-classical economic theories. To do so it is necessary to use a multidisciplinary approach. In this article we combine neo-institutional theory and network analysis to interpret phenomena that are increasingly emerging in all areas of the world. Using these two theories, and case studies from Brasil, North America, China and Europe, we try to highlight how the market is not only the place where the price of products and of their quantities are formed. Market is a complex social spaces, where more-or-less stable relationships are formed, based on the values of reciprocity and reputation that determine behaviour and codes of conduct aimed at containing opportunism and encouraging sustainable, autonomous, forms of self-regulation. These relationships are also the driving forces that ensure the ongoing continuation of the exchanges and the diffusion and preservation of knowledge over time and space. These relationships represent the beating heart of nested markets and their dynamic ability to adapt to the continuous changes that occur in the socio-economic and environmental context in which they are rooted. In this way sustainability is built as an expression of the co-production between different actors (producers, consumers, institutions) and natural resources.

Communication is a key method of expressing one's thoughts and opinions. Amongst many modalities, speech and writing are the most powerful and common forms of human communication. Analysing what and how people think has inherently been an interesting and progressive research domain. This includes bimodal sentiment analysis which is an emerging area in natural language processing (NLP) and has received a great deal of attention in recent years in a variety of areas including social opinion mining, health care, banking, and so on. At present, there are limited studies on bimodal conversational sentiment analysis as it proves to be a challenging area given the complex nature of the way humans express sentiment cues across various modalities. To address this gap, a comparison of the performance of multiple data modality models has been conducted on the MELD dataset, a widely-used dataset for benchmarking sentiment analysis within the research community. Our work then demonstrates the results of combining acoustic and linguistic representations. Lastly, our proposed neural network-based ensemble learning technique is employed over six transformer and deep learning-based models, achieving a State-Of-The-Art (SOTA) accuracy.

Abstract: Background: Pain management is one of the main parts of treatments for Temporomandibular Disorders (TMDs). However, there is still a lack of high-quality evidence that compare the overall effects of these non-surgical treatments. The objective of this systematic review is to identify the most potential treatment protocol in dealing with pain caused by TMDs through a mixed comparison of interventions based on network meta-analysis. Methods: A systematic review and network meta-analysis of studies identified by searching PubMed, Embase, Medline, Ovid, and CINAHL. All the included studies should have characteristics that: (1) participants with TMDs of any age; (2) non-surgical treatments; (3) score of Visual Analog Scale (VAS) as the outcome measure; (4) randomized controlled trials. The Cochrane Bias Assessment Toll was used to assess the bias, the CINeMA website was applied to rate the confidence of evidence, and ADDIS software was used to conduct the network meta-analysis. Results: 46 studies were included in this review. The agreement between authors reached a kappa value of 0.78. The results of the network meta-analysis showed that wearable therapy devices are more likely to be the best choice for reducing the pain of patients with myogenic TMDs, whereas a combination of platelet-rich plasma injection and wearable therapy devices is more likely to be the best choice for reducing the pain in a long term after treatment for patients with mix-type TMDs. Moreover, the application of therapy equipment has the most potential in reducing pain in a long term after treatment for patients with articular TMDs. Discussion: Wearable devices have a great potential for pain syndrome caused by TMDs, the mechanism might come from a biomechanical perspective. However, the overall confidence rating of evidence is low. Studies with high quality are still needed in the future. Other: The PROSPERO Registration Number of this systematic review is CRD42021253442.

Weight gain affects about 10-20% of patients after bariatric surgery. It is a phenomenon that is difficult to understand and to intervene due to its complexity and etiological heterogeneity. In the present study, we investigated, from a network analysis perspective, the associations between weight regain, psychological, sociodemographic factors and physical activity in patients undergoing bariatric surgery. The sample consisted of 124 patients, of both sexes, aged 39 ± 9.1 years, who had undergone surgical intervention for more than 18 months. After voluntary consent, respondents answered questionnaires and instruments directly on the Google Forms platform. The results indicated that weight gain was negatively associated with the items of depression, anxiety and stress, binge eating and with the dimensions of the personality questionnaire (negative affectivity -0.182; detachment -0.078; antagonism -0.107; disinterest - 0.198 and psychoticism -0.158). The centrality indicators revealed that the characteristics of disinterest and negative affectivity and most of the items on the depression, anxiety and stress scale had a greater expected influence (values ​​from 1,043 to 1,502), indicating that these are the most sensitive variables to intervention and who need more attention from health professionals.

Shrinking farm size and fragile farm resources pose a significant challenge to the sustainability of small-scale farms. Efficient resource utilization in small-scale farms is crucial to achieving farm sustainability through endogenous mechanisms. However, the precise mechanisms to integrate physical resources to achieve farm sustainability are not very clear yet. By capturing the interaction among farm resources as a network phenomenon, we identify the discrete resource interactions (RIs) in different types of small-scale farms of Indian Sundarbans, which are associated with higher farm sustainability. Thirty-two linkages, 11 reciprocal linkages, 22 triads, and three ‘core elements’ that occurred and cooccurred on highly sustainable farms are found to be critical in achieving farm sustainability. Using the properties of resource interaction networks as explanators of farm sustainability, we anticipate that sustainability in small-scale farms can be achieved by strategically creating new RIs on the farm. However, there may be limitations to such achievement depending on the nature of RI and type of farm. The analytical approach helps to understand the structural basis of sustainability in small-scale farms, and this approach can be used to achieve farm sustainability through the strategic integration of existing farm resources in the smallholder systems.

Manual review of the extensive literature covering nutrition-based lifestyle interventions to promote healthy cognitive ageing has proved educative, however, data-driven techniques can better account for the large size of the literature (tens of thousands of potentially relevant publications to date) and interdisciplinary nature of where relevant publications may be found. In this study we present a new way to map the literature landscape focusing on nutrition-based lifestyle interventions to promote healthy cognitive ageing. We applied a combination of citation network analysis and text mining to map out the existing literature on nutritional interventions and cognitive health. Results indicated five overarching clusters of publications, which could be further deconstructed into a total of 35 clusters. These could be broadly distinguished by focus on lifespan stages (e.g. infancy versus older age), and specificity regarding nutrition (e.g. narrow focus on iodine deficiency versus broad focus on weight gain). Rather than concentrating into a single cluster, interventions were present throughout the majority of the research. We conclude that a data-driven map of the nutritional intervention literature can benefit the design of future interventions, by highlighting topics and themes that could be synthesized across currently disconnected clusters of publications.

Health differences across socioeconomic strata have always pointed out that poorer and minorities have higher mortality and morbidity than richer and majorities. This difference is exacerbated for particular populations such as the victims of ongoing armed conflicts, who are also much harder to quantify due to the conflict itself. This study applies network analysis to a combination of three large administrative records for the health system and mortality records in the province of Risaralda (Colombia) between 2011 and 2016. It produces the most common causes of morbi-mortality for both victims of violence and the poorest inhabitants of Risaralda, defined as those who qualify as recipients of subsidies from the Colombian welfare program called SISBEN in the categories of highest need. Both populations show high morbidity frequencies for non-communicable diseases such as Type II diabetes, hypertension, and hyperglyceridaemia, mostly associated with exposure to unhealthy lifestyles. Additionally, the mortality outcomes reflect the different lifestyles and medical treatments of both subpopulations. While the poorest replicate the same causes identified for morbidity, the victims of armed conflict die of additional causes including Type II diabetes, which reflects the even worse conditions they face.

In 2023, the technological landscape witnessed an unprecedented surge of transformative innovations, including Web 3.0 and machine learning, poised to revolutionize various aspects of our daily lives. At the forefront of this revolution stands artificial intelligence (AI), which has evolved from a concept once confined to science fiction into an indispensable force reshaping industries on a global scale. Notably, recent years have seen remarkable strides in AI functionalities, particularly in machine learning, computer vision, and natural language processing. These advancements have empowered AI to generate diverse interactions and media autonomously. This study ultimately provides invaluable insights into the dynamic intersection of AI and Advertising, con-tributing to a deeper comprehension of this evolving field. This study conducts a comprehensive review of Artificial Intelligence within the advertising domain, employing network analysis. By meticulously examining the evolution of AI through co-occurrences of terms, keywords, and co-authorship analysis, we aim to unveil the relationship of AI & Advertising through the lens of Generative models to identify pivotal trends, core concepts, and seminal studies that have profoundly influenced the industry. The analysis charts the trajectory leading to the emergence of Generative Artificial Intelligence (GenAI), a transformative development poised to revolutionize advertising practices and consumer engagement. In tandem with these objectives, the results highlight vital trends, emphasizing the growing prominence machine learning tools and techniques such as deep learning techniques and advanced natural language processing methods like word2vec, GANs, and more in shaping the future of advertising practices.

We advance binational link-tracing sampling design, an innovative data collection methodology for sampling from transnational social fields, i.e., transnational networks embedding migrants and non-migrants. This paper shows the practical challenges of such a design, the representativeness of the samples and the qualities of the resulted networks. We performed 303 face-to-face structured interviews on sociodemographic variables, migration trajectories and personal networks of people living in a Romanian migration sending community (Dâmbovița) and in a migration receiving Spanish town (Castellón), simultaneously in both sites. Inter-connecting the personal networks, we built a multi-layered complex network structure embedding 4,855 nominated people, 5,477 directed ties (nominations) and 2,540 edges. Results indicate that the participants’ unique identification is a particularly difficult challenge, the representativeness of the data is not optimal (homophily on observed attributes was detected in the nomination patterns), and the relational and attribute data allow to explore the social organization of the Romanian migrant enclave in Castellón, as well as its connectivity to other places. Furthermore, we provide methodological suggestions for improving link-tracing sampling from transnational networks of migration. Our research contributes to the emerging efforts of applying social network analysis to the study of international migration.

This study aimed to identify changes in visitor behavior and visitor interest in healing forests before and after the COVID-19 pandemic. The study used text mining analysis techniques to identify changes in visitation behavior over time, divided into three periods: pre-COVID-19 (January 1 to December 31, 2019), during the COVID-19 pandemic (November 1, 2020 to October 31, 2022), and post-COVID-19 (November 1, 2022 to October 31, 2023 ). After the COVID-19 outbreak, healing forest use behavior did not regress to pre-COVID-19 levels. Activity-based keywords such as "hiking," "trekking," and "walking" stood out as the main drivers of this change in behavior. Therefore, related authorities must examine the scalability of the functions, services, and programs of healing forests from a general healing space to a space for leisure and tourism. These findings will contribute to the development of future marketing strategies and programs for healing forests.

Applying the improved social network analysis method and the idea of rolling window regression, this paper explores the impact of HSR network on the quality of urbanization and its dynamics. Based on a sample of 273 cities in China over 2009–2019, we find that the high-speed railway network has an increasing positive effect on the quality of urbanization, which proves the existence of network effect. The empirical results further show that this effect is closely related to the cov-erage rate of high-speed rail network. In addition, heterogeneity analysis reveals that urban ag-glomeration cities are the main beneficiaries. Academically, our study provides a plausible expla-nation and evidence from network size differences for the two conflicting views of the HSR effect. Practically, we also propose some important policy implications for countries in different high-speed rail network construction stages.

The fusion of "innovation theory" and "ecology" gave birth to a large number of studies on "innovation ecology", which mainly studies how to build an industrial ecological chain at the regional level, focusing on self-evolution, achieving ecological balance, and enabling the regional economy to take the path of sustainable innovation. This type of research borrows a lot of concepts from ecology, and very vividly describes the competition and cooperation relationships formed by various agents in the innovation system, laying a good foundation for qualitative analysis of the inherent dynamics of innovation development. However, many studies focus on the analogous description of ecosystems and economic systems, lacking scientifically and rigorously quantitative empirical research as a support. This paper uses network-based indicators such as degree, cluster coefficient, and betweenness centrality to measure the function and position of high-tech enterprises in the Z-Park in a business environment, so as to clarify the socio-economic meaning of the topological structure of the regional innovation system. On this basis, it provides theoretical references for regional innovation development and sustainable development policy formulation.

With the deepening of the diversification and openness of financial system, financial vulnerability, as an endogenous attribute of financial system, becomes an important measurement of financial security. Based on network analysis, we introduce network curvature indicator improved by Copula entropy as an innovative metric of financial vulnerability. Compared with previous network curvature analysis method, CE-based curvature proposed in this paper can measure market vulnerability and systematic risk with significant advantages.

Social network analysis is extensively employed for the examination of relationships and networks among actors across diverse disciplines and thematic research domains. Nevertheless, the utilization of this approach in agriculture and natural resources, particularly pertaining to watershed management, has been relatively limited. The objective of this study is to conduct an analysis of the social interaction and networks of Cidanau Watershed Management (CWM) stakeholders and measure the nodes of units as individuals or organizations in relation to the role and influence of each other through a quantitative approach. The results of the data analysis show that Fkdc has a high degree of centrality and centrality betweenness. This shows that Fkdc has a strong influence and controls CWM network interaction. Conversely, Hs (households) have a low degree of centrality, and centrality betweenness values have a low influence and role in actors’ networks. According to the closeness centrality parameter, it is observed that both Households (Hs) and Universities (Univ) have exhibited the shortest geodesic distance in relation to other stakeholders within the network, thus indicating their highest level of closeness. It means that households and universities had interdependency and were controlled by others. The lowest centrality of Fkdc means that Fkdc has independence of influence and high control access over others. There are four CWM network clusters based on total link strength. Fkdc demonstrates outstanding leadership qualities, enjoys a strong reputation, and possesses a high degree of popularity within the network. Furthermore, Fkdc serves as a positive central node that effectively brings together the interests of all actors, fostering a collective commitment towards shared goals and mutual rewards. In the CWM case, Fkdc is a sustainable watershed organization that acts as a central network body and plays a fundamental role as an intermediary between networks and interactions. Ultimately, with regards to the viewpoint of CWM, the decentralization of watersheds aimed to facilitate wider public engagement by establishing Fkdc as a central network entity and assuming a crucial role as a medium for the dissemination of information, exchange of ideas, and the exertion of influence among its members.

This study probes the probabilistic features of major fire hazards in enclosed spaces to establish their importance to the occurrence of fires onboard ro-ro passenger ships and in turn to raise effective operational countermeasures. Distinct from the previous studies, the present research employs Bayesian Network (BN) analysis to determine the probabilities of fire hazards more effectively. The findings of the research include five critical basic events (BE) identified namely, vehicle engine fire (fuel system fault), vehicle electric fire (electrical equipment defect or short circuit), used car electrical fire, reefer units electrical fire (electrical appliances defects or short circuit), and cargo spontaneous combustion. Additionally, the risk of fire for lithium - ion battery powered vehicles is also highlighted in the process of BN analysis, which prompts the authors to propose preventive measures for mitigating the possibility of fire occurrence on this type of electric vehicles. It is hoped that these measures can be essential justifications for establishing relevant rules regarding carrying LIB vehicles in enclosed spaces on international level.

Limited accessibility in some parts of Indonesia causes difficulties in logistic distribution, especially for emergency and medical supplies. Emerging drone cargo technology is a potential solution to improve logistic distribution in those areas. However, implementing drone cargo technology involves unknown risks, both in technical and non-technical aspects. Since data on drone operations in Indonesia is limited, a new method is explored to build a Bayesian Network (BN) model for risk analysis of drone crashes in Indonesia’s outermost and underdeveloped areas. The method optimizes the modelling process, in which significant risk factors are selected based on three drone operator companies’ experiences, which include wind speed, rain intensity, and system component failure or malfunction. Real wind speed and rain probability data are then implemented in the model. The operator’s data shows that wind speed contributes to drone crashes, which can be appropriately modelled in the BN model. The model produced a probability of safe operation of 94.1%, comparable to the annual operator’s data. The result shows that most operations are safe, with a minimum case of crashing and no case of harming human life.

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contain rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Over this, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

Lignocellulosic fibers are widely applied as composite reinforcement due to their properties. The thermal degradation behavior determines the maximum temperature in which the fiber can be applied without significant mass loss. It is possible to determine these temperatures using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined and more detailed characteristics of the material are obtained. However, every curve obtained at a distinct heating rate demands material, cost, and time. Methods to predict thermogravimetric curves can be very useful in the materials science field and in this sense mathematical approaches are powerful tools if well employed. For this reason, in the present study, curaua TG curves were obtained at three different heating rates (5, 10, 20, and 40 °C.min-1) and Vyazovkin kinetic parameters were obtained. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM). Curves at any heating rate between the minimum and maximum experimental heating rates were obtained with high reliability. Finally, Vyazovkin kinetic parameters were tested again with the new curves showing similar kinetic parameters from the experimental ones. In conclusion, due to the capability to learn from the own data, ANN combined with SRM seems to be an excellent alternative to predict TG curves that do not test experimentally, opening the range of applications.

The diagnosis of papillary thyroid carcinoma has always been a concerned and challenging issue and it is very important and meaningful to have a definite diagnosis before the operation. In this study, we tried to use an artificial intelligence algorithm instead of medical statistics to analyze the genetic fingerprint from gene chip results to identify papillary thyroid carcinoma. We trained 20 artificial neural network models with differential genes and other important genes related to the cell metabolic cycle as the list of input features, and apply them to the diagnosis of papillary thyroid cancer in the independent validation data set. The results showed that when we used the DEGs and all genes lists as input features the models got the best diagnostic performance with AUC=98.97% and 99.37% and the accuracy were both 96%. This study revealed that the proposed artificial neural network models constructed with genetic fingerprints could achieve a prediction of papillary thyroid carcinoma. Such models can support clinicians to make more accurate clinical diagnoses. At the same time, it provides a novel idea for the application of artificial intelligence in clinical medicine.

The present study aims to analyze scientific production about technological cooperation networks in biotechnology sector, based on bibliometrics network analysis. We used the Gephi software to identify groups of partnerships, proving that the cooperation relationship is a practice used, re-sulting technological development. Findings identify that in the path for new resources that com-plement skills and competences universities are an important player in this cooperative ecosystem, and The United States is a large hub, with an extensive network of global cooperation. A strong role of its researchers in the publication of scientific articles in cooperation is highlighted. This study contributes to the advancement of the discussion about cooperative networks, as well as to the development of policies aimed at the biotechnology sector advancements

Background: At present, the treatments for patients with advanced lung cancer focus on chemotherapy, targeted therapy, immunotherapy, or a combination of multiple treatments. Purpose: The main purpose of this study is to compare the various chemotherapy-based combination therapies and find the best one for patients with advanced lung cancer. Methods: Based on database (PubMed, EMBASE and Medline) for randomized controlled trials of advanced lung cancer with combination therapy from 2008 to 2020, we searched literatures with overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and adverse as outcome indicators and established a Bayesian mesh meta-analysis for multiple treatment strategies. Then, we combined the results of four outcome indicators to find out the best chemotherapy-based combination therapy strategy for patients with advanced lung cancer, further, we tried to screen out the best drugs of which were commonly used now. Results: It contained a total of 51 studies, including five combination therapies: Chemotherapy/Chemotherapy plus placebo (CT), chemotherapy plus one targeted therapy drug (CT+T), chemotherapy plus two targeted therapy drugs (CT+T+T), chemotherapy combined with immunotherapy (CT+I) or chemotherapy combined with biotherapy (CT+B). In terms of four outcome indicators, CT+I showed the best therapeutic benefits. In the comparison of immunotherapy drugs, pembrolizumab showed the best effect. Conclusion: Our results showed that, among the multiple chemotherapy-based combination therapy strategies, chemotherapy combined with immunotherapy is the best choice for patients with advanced lung cancer, and pembrolizumab combined with chemotherapy has the best effect.

Poa crymophila is a perennial, cold-tolerant, native grass species, widely distributed to the Qing-hai-Tibet Plateau. However, the molecular mechanism behind the cold stress tolerance and role of key regulatory genes and pathways of P. crymophila are poorly understood yet. Therefore, the present study investigated the physiological and transcriptome responses of P. crymophila’s roots, stems, and leaves under cold stress to explore the molecular mechanism of cold tolerance. Results of the present study suggested that cold stress significantly changed the physiologic characteristics of roots, stems, and leaves of P. crymophila. In addition, the transcriptome results showed that 4434, 8793, and 14942 differentially expressed genes (DEGs) were identified in roots, stems, and leaves, respectively; however, 464 DEGs were commonly identified in these three tissues. The Gene On-tology (GO) results showed that a large numbers of DEGs were significantly enriched in the pho-tosynthetic related categories in leaves. In addition, the “response to stimulus” category was sig-nificantly enriched in roots and stems. The Kyoto Encyclopedia of Genes and Genomes (KEGG) results showed that DEGs involved in “phenylpropanoid biosynthesis” were significantly enriched in roots, “photosynthesis” and “circadian rhythm-plant” pathways significantly enriched in stems and leaves, starch and sucrose metabolism, and galactose metabolism were pathways significantly enriched in three tissues. Weighted gene coexpression network analysis (WGCNA) identified Hub genes involved in P. crymophila cold response. This study provides new insights into the molecular mechanisms underlying the cold tolerance of P. crymophila belowground and aboveground tissues. In addition, specific genes involved in Ca2+ signaling, ROS scavenging system, hormones, circadian clock, photosynthesis, and transcription factors (TFs) were identified in P. crymophila. The identi-fication of key genes may provide valuable resources for further functional genomic and breeding studies.

The economy is a complex system, and the interactions between different agents are still not easy to quickly see-through. This complexity should reflect in a spatial dimension; in this way, tracking the tradeoffs opens a new window to the nexus of place and flow. Due to the fact, the economic systems often go through transitions and end up in another state, and this evolution is embedded in cities as the new motor of paradigm shift. To adequately represent and study these dynamics, we aim to develop an integrated method based on network analysis science and geographic economy synthesis to detect a multiscale navigator to track the transition from regional to the local level. This paper seeks to explore the specialization of regional clusters and their innovative behaviour in a particular lagging region, hence unfolding the innovation ecosystem to the smallest granularity then simulating the emergence phase of this complex system. First, our findings reveal that the local scale is relevant to start a bottom-up planning approach on policy implementation. Second, the global challenges could be addressed on a regional scale if we investigate the local complexity to unfold the innovation flow over its complex ecosystem and lead the knowledge as a critical element for inclusive transition, most probably into cities. Finally, the innovation network is an existing fact which can translate as a host for prosperity; In this line of reasoning, we intend to spatialize the track of the innovation flow to achieve transition hotspots and respond adequately to upcoming world concerns.

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. The study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 keypoints using an open-source tool called DeepLabCut, which helped accurately mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the Long Short-Term Memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior were over 90%.

Understanding the impact of drought stress on Arabica coffee physiology and metabolism is essential in the pursuit of developing drought-resistant varieties. In this study, we explored the physiological and metabolite changes in coffee genotypes exhibiting varying degrees of tolerance to drought—namely, the relatively tolerant Ca74110 and Ca74112, and the sensitive Ca754 and CaJ-19—under well-watered conditions and during terminal drought stress periods over two-time intervals (0 and 60 days following the onset of stress). The Metabolites profiling conducted uncovered significant associations between growth and the physiological characteristics of coffee genotypes with distinct drought tolerance behaviors. Initially, no marked differences were observed among genotypes or treatments. However, at the 60-day post-drought onset, notable increases in shoot growth, biomass, CO2 assimilation, pigments, and various physiological parameters were evident, particularly in the relatively tolerant genotypes. Metabolite profiling revealed elevations in glucose, maltose, amino acids, organic acids, and decreases in other metabolites, more pronounced in drought-tolerant genotypes. These alterations were more pronounced in the drought-tolerant genotypes, indicating a correlation with enhanced compatible solutes and energy-associated metabolites crucial for drought tolerance mechanisms. This research introduces GC-MS based metabolome profiling to Ethiopian coffee, shedding light on the intricate responses to drought stress and paving the way for the potential development of drought-resistant coffee seedlings in intensified agro-ecological zones.

While most published research on COVID-19 focused in a few countries and especially during the second wave of the pandemic and the vaccination period, we turn to the first wave (march-May 2020) to examine sentiment and emotions expressed by Twitter users in Greece. We use computational methods combining opinion mining and the application of a modified emotion lexicon with Social Network Analysis to explore the subtleties behind an overarching negative sentiment. Our analysis has shown that popular sentiment isn’t stable nor monolithic, since underlying emotions may be a better predictor of attitudes during a crisis.

Variation in metabolite profiles of under high salinity and nitrogen constraint conditions are the key issues of study at the present. To investigate the optimum NaCl and NaNO3 concentration and the corresponding metabolic pathways of astaxanthin accumulation in H. pluvialis, a 26-day batch culture experiment of H. pluvialis treated under the variable concentration gradients of NaCl and NaNO₃ were conducted. The results indicated that 7.5g•L^-1 and nitrogen-deficient were the optimum NaCl and NaNO₃ concentration for the astaxanthin accumulation according to single factor experiments respectively, under which the highest contents of astaxanthin accumulated in H. pluvialis reached up to 7.51mg•L^-1 and 5.60mg•L^-1. A total of 132 metabolites were analysed using LC-MS/MS technique, among which 30 differential metabolites with statistical significance were highlighted. Subsequently, 18 and 10 differential metabolic pathways in the high salinity and nitrogen-deficient treatments with statistic significance were enriched and annotated respectively. Furthermore, the metabolic networks related to the astaxanthin accumulation were constucted based on the crucial pathways and metabolites. Moreover, the major carbon metabolism in H. pluvialis under the high salinity and nitrogen deficient stress were presented, which exhibited the characteristics in the identified metabolites changes under the different stress conditions. The results did not only demonstrate the differential metabolic networks and the distinct changes in the metabolites involved in the major carbon metabolism, related to the astaxanthin accumulation in H. pluvialis under the different stress factors, but provided the regulation targets for astaxanthin production in practice.

The aims of this paper can be traced back to goals number seven, twelve and thirteen introduced by Agenda 2030, namely "affordable and clean energy", “responsible consumption and production” and "climate action”, respectively. This is due to the fact that the present work supports decarbonization processes acting in a direct way on the electricity consumption achieved by plants pertaining to the waste industry; in fact, this study aims at the realization of a machine learning model for predicting the energy consumption achieved by a mechanical biological treatment (MBT) waste plant located in central Italy, given the distribution of the entering waste. This model is implemented in MATLAB. The model can be used to tune the distribution of the entering waste in order to adapt the plant energy consumption to the capability of the energy sources and can serve as a play-ground model for other energy transformation plants. The results of the study, which feed the literature on the application of artificial intelligence to real industrial plants, could be used to determine energy efficiency actions that could be incorporated into Property’s strategic planning.

Most of the literature on the optimization problem of transportation in supply chain networks (SCN) considered fixed costs (FC). However, in the practical applications of this problem it is important to study the effect of FC. This paper examines the impact of fixed cost increase as well as the variable cost on the optimization of a Two-Stage SCN. To do so, two mathematical models for mixed-integer nonlinear programming are developed. The first model optimizes fixed and variable costs in addition to the opening cost, whereas the second model only optimizes variable and opening costs. To evaluate the effect of considering FC on the optimization supply chain problem, four groups of instances are randomly generated, and solved using Lingo. The results of the two models are compared utilizing the average percentage deviation. In addition, sensitivity analysis is performed to determine the impact of changes in opening and variable costs on the considered optimization problem. The computational results and the sensitivity analysis show that the first model with minimized FC outperforms the second model, which does not consider the FC in minimization and FC affect the optimization.

This study analysed through the perspective of networks the association between physical activity (PA), sedentary behaviour (SB), and cardiovascular risk factors in overweight low-income schoolchildren from the perspective of complex systems. Methods: The sample consisted of 41 overweight children between 6 and 10 years old (56% girls). PA and SB were analysed by accelerometer, and the measures of cardiovascular factor risk were: anthropometric and hemodynamic measurements, body fat, lipid profile and glucose, cardiorespiratory fitness (CRF), left ven-tricular mass (LVM), and Sokolov-Lyon. Network analysis with Boot-strap-1000 was performed to analyze the association between PA, SB and the cardiovascular risk factors. Results: Moderate to vigorous PA was positive related with Waist circumference (WC; 0.499), HDL-C (0.307), and CRF (0.276), and negatively associated with BMI (-0.251) and Fat (-0.341). For SB, positive associations were seen with WC (0.326), CRF (0.296), LVM (0.250) and Sokolov (0.215). Adicionalment, the centrality indicators highlighted WC as the most important variable in the network. Conclu-sion: Interventions that aim to mitigate the harmful effects of obesity on cardiovascular risk factors in overweight children should consider WC as an important variable in of system.

At the end of 2018, a high school student asked a question in Zhihu community, claiming that he had proved Goldbach's conjecture. The problem caused an explosive reaction and a large number of users participated in the discussion. And has caused the widespread influence. On January 1, 2019, the questioner issued his "proof". His proof was soon proved wrong. The heated discussion caused by the incident contains a lot of information of social science analysis value. Therefore, we follow up the event in the first time and build a time series dataset for the event. Taking the questioner's "proof" as the dividing line, all the answers, comments, sub comments and user information of writing these texts before and after two days were recorded. This series of temporal information can reflect the dynamic features of the interaction between user opinions, and the impact of exogenous shocks (proof release) on community opinions. The dataset can be used not only for the demonstration of various social network analysis algorithms, but also for a series of natural language processing tasks such as fine-grained sentiment analysis for long texts, as well as multimodal tasks combining natural language processing and social network analysis. This paper introduces the characteristics and structure of the dataset, shows the visualization effect of social network, and uses the dataset train the benchmark model of emotion analysis.

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders worldwide. Among the symptoms of MDD, sleep disturbance such as insomnia is prominent and the first reason patients may seek professional help. However, the underlying pathophysiology of this comorbidity is still elusive. Recently, genome-wide association studies (GWAS) have begun to unveil the genetic background of several psychiatric disorders, including MDD and insomnia. Identifying the shared genomic risk loci between comorbid psychiatric disorders could be a valuable strategy to understand their comorbidity. This study seeks to identify the shared genes and biological pathways between MDD and insomnia based on their shared genetic variants. First, we performed a meta-analysis based on the GWAS summary statistics of MDD and insomnia obtained from Psychiatric Genomics Consortium and UK Biobank, respectively. Next, we associated shared genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, a total of 719 shared genes were identified. Over half (51%) of them are protein-coding genes. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification, sensory perception, and immunologic signatures. We also identified druggable targets using a network approach. Together these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid MDD and insomnia symptoms.

Studies on human mobility have a long history with increasingly strong connections to multi-disciplines across social science, environmental science, information and technology, computer science, engineering, and health science. However, what is lacking in the current research is a summary of studies on human mobility to identify the evolutional pathway and future research directions. To address this gap, we conduct a systematic review of human mobility-related studies published from 1990 to 2020. Drawing on the selected publications retrieved from the Web of Science, we conduct a bibliometric analysis and network visualisation by CiteSpace and VOSviewer on publication years and numbers, authors and their countries and afflictions, citations, topics, abstracts, keyword, and journals. Our findings show that human mobility-related studies have become increasingly interdisciplinary and multi-dimensional, enhanced by the involvement of multi-source big data, the development of technologies, the innovation of modelling approaches, and the novel applications in various areas. We also summarise four future research directions proposed by top cited authors and mobility studies, in terms of data sources, modelling methods, applications, and technologies. We advocate in-depth research on human mobility to address real-world problems and contribute to social good as promising future orientations through integrating multi-source big data and advanced modelling methods facilitated by artificial intelligence, and machine and deep learning.

This study aims to understand the global environment of COVID-19 management and guide future policy directions after the pandemic crisis. To this end, we analyzed a series of the World Economic Forum’s COVID-19 response reports through text mining and network analysis. These reports, written by experts in diverse fields, discuss multidimensional changes in socioeconomic situations, various problems created by those changes, and strategies to respond to national crises. Based on 3,897 refined words drawn from a morphological analysis of 26 reports (as of the end of 2020), this study analyzes the frequency of words, the relationships among words, the importance of specific documents, and the connection centrality through text mining. In addition, network analysis helps develop strategies for sustainable response to and management of national crises through identifying clusters of words with a similar structural equivalence.

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image. (2) region adjacency graph conversion. (3) graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements such as walls, doors, and symbols as well as spatial elements such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph, which is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users’ interactions. Initial findings show the soundness of the approaches based on this new metric with respect to the state-of-the-art model, and its ability to generate a denser graph, revealing crucial nodes that participated in the retweet propagation. Reliable retweet graph generation will enable a better understanding of the diffusion path of a specific tweet.

Hydrogen is useful as a fuel and could be produced by a variety of means. One approach uses artificial photosynthesis where energy from sunlight powers the splitting of water into hydrogen and oxygen. But, biological methods for producing hydrogen has emerged strongly over the past decades. In particular, specific microorganisms could use different substrates to produce hydrogen at differing yields. Such fundamental discoveries with industrial applications thus motivated the use of metabolic engineering approaches and methodologies in enhancing biological hydrogen production through a series of enzyme over-expression, pathway debottlenecking, and gene deletion. However, such approaches heavily rely on the selection of an appropriate microbial chassis for biohydrogen production. With the proper strain in hand, use of alternative substrates may engender greater hydrogen productivities. But learning from the bioprocessing field, co-culture of two compatible microorganisms have been sought after for improving biohydrogen production. In addition, thermophilic microbes may also be useful candidates for exploiting hydrogen production from composting. Future outlook in the field looks into filling our gaps in understanding of the metabolic network that feeds into hydrogen production in different organisms. But, more importantly, problems such as reduced growth rate in engineered microbes point to fundamental issues with using genetically engineered microorganisms for improved biohydrogen production, to which clever bioprocess engineering may yield solutions.

Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively.

Electric energy has become essential nowadays not only for the daily life of each of us but also for the economy of different countries. The dissemination of geographic information plays an important role in national development as it facilitates communication between managers, investors, and consumers in this sector. Since the management of electricity network data was previously done in Tunisia based on paper maps and plans, the purpose of this article is to present a case of planning based on GIS, Web, and 3D Web GIS, which would have significant positive consequences on this sector from a technical and financial sides with an improvement in customer satisfaction and the creation of an intelligent electricity network which will be a real decision-making tool. This work draws up an inventory of the network MV (Medium Voltage)/LV (Low Voltage) of the region of Medjez El Bab which routes electricity to the big centers of consumption with access to MV/LV subscribers. The analysis of the network's impedance allowed carrying out different scenarios to optimize performance and obtain more realistic routes. Many thematic maps were produced as part of this project (Slope map, Land use map, map of the MV voltage domains, map of the MV/LV transformer stations power, etc.). A three-dimensional virtual city has been developed to visualize the graphical and attribute data for the study area. A Web and 3D Web GIS applications that allows the publication of the interactive maps on the Web as well as the database information have been developed to offer users the possibility of consulting the produced products by internet. Finally, a website related to the study was developed.

In the face of escalating cybercriminal sophistication, an innovative approach to network anomaly detection has been pursued in this longitudinal study, integrating computational data analytics in a geographic, organizational, and behavioral context. A data-driven scoring mechanism was employed to systematically analyze and correlate source countries of IP addresses and organization-associated Autonomous System (AS) Numbers (ASN) with network anomalies. Significant correlations between certain countries, specific organizations, and high behavior scores were identified through the data analytics. An increase in connection requests was also found to be linked with elevated behavior scores. Validated by cross-validation techniques, these findings emphasize the necessity for continuous model recalibration. The transformative role of integrative data analytics in cybersecurity is underscored, paving the way for the development of more sophisticated, context-aware anomaly detection systems. Specifically, the analysis underscores the need for organizations to adopt a proactive and adaptive approach to cybersecurity that can keep pace with the evolving threat landscape.

Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation of healthy cities. Latterly, there has been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces with an aim towards pioneering research in Urban Studies. However, in informal areas, open-data access tends to lack adequate and updated information, making it difficult to use AI methods. Hence, we propose a methodology for restricted open data collection and preparation for future use in machine learning or spatial data science models for similar areas. To that extent, we examine two peripheral and low-income neighborhoods in Quito, Ecuador, La Bota and Toctiuco, to analyze their public spaces, urban green areas, points of interest, and road networks, and how they address healthy cities criteria. We develop an original methodological approach that combines an index of proximity, accessibility, quantity, and quality of these spaces with geospatial and network analysis techniques. Results indicate that the connectivity and structure of these spaces are centralized and nodal, representing exclusion and segregation. This work provides insights on potential healthy spaces, and information to urban planners and policymakers in decision-making for healthy urban infrastructure.

This study focuses on understanding how global auto parts companies manage innovation by analyzing keywords in CEO messages from their reports. The study involved three key phases: (1) extraction of innovation-related keywords emphasized in CEO messages, (2) comparison of innovation-related keyword usage between high-performing (upper group) and low-performing (lower group) companies, and (3) analysis of the characteristics of innovation management via internal meaning and the structural interpretation of keywords. A comparison between the upper and lower groups revealed distinct differences in the emphasized keywords. The mapping of open innovation success factors, based on keywords extracted according to eigenvector centrality, identified Provision of Resources and Governance as central to innovation management in the upper group. These findings serve as a benchmark for establishing the direction, vision, and strategy of innovation management in the lower group.

This research looks into the evolving dynamics of ransomware, shifting from conventional encryption-based attacks to sophisticated data exfiltration strategies. Employing the Bidirectional Encoder Representations from Transformers (BERT) model, the study analyzes network traffic patterns to detect ransomware activities, offering new insights into their covert operations. The findings emphasize the need for advanced AI tools in cybersecurity, highlighting the significance of adapting and innovating defense strategies to counter the changing landscape of ransomware threats. The study contributes to a deeper understanding of ransomware evolution and underscores the importance of integrating AI in cybersecurity practices.

This study investigates the intricate relationship between Venezuelan migration and urban growth in Colombia from 2018 to 2021. The study employs remote sensing data and social network metrics to uncover migration patterns and their impact on urban expansion. The methodology consists of three stages. Firstly, nighttime satellite imagery is used to analyze year-over-year urban growth in Colombia. Secondly, social network data estimates Venezuelan migration, overcoming challenges of underreporting and informal border crossings. Lastly, an econometric analysis explores the quantitative link between Venezuelan migration and urban growth, integrating socioeconomic variables to address endogeneity. The findings reveal the complex interplay of Venezuelan migration, socioeconomic factors, and urban growth. The study outlines remote sensing analysis, introducing the Anthropogenic Footprint Expansion Index (AFEI) to quantify urban growth. Facebook API data estimates migration trends and explores socioeconomic impacts on urban expansion. The analysis uncovers migration, poverty, aging, and urban population proportion as key factors affecting Colombia's urban landscape. Furthermore, the research underscores how Venezuelan migration affected short-term urban expansion pre- and post-COVID-19. Migration had a notable effect before the pandemic, but this influence waned afterward. The study highlights migration's short-term nature and emphasizes age demographics' role in medium-term dynamics.

Our understanding of the diversity of life on our planet and the possibility of finding or sustaining life elsewhere in the universe plays a central role in supporting human space settling and exploration. Astrobiology and its outcomes require a multidisciplinary and comprehensive approach, in which the microbial, geological, chemical, astronomical, and physical domains of research are interlinked. An example of the applications of astrobiology and space microbiology is the use of extremophiles for in-situ resource utilization through biomining and bioleaching. To better understand the multidisciplinary research landscape in this area, we have quantitatively reviewed the global scientific literature on astrobiology with a focus on biomining and bioleaching through bibliometric network analysis, investigating patterns and trends in its development over time. The network analysis of the keywords co-occurrence highlights different connecting and overlapping clusters, illustrating the multidisciplinary character of astrobiology. Temporal analyses show a recent focus on topics related to microbiology and geomicrobiology, emphasizing the role that these fields will play in future astrobiology research. In conclusion, astrobiology, biomining, and bioleaching research are timely responding to the identification of these techniques as tools for biotechnological applications, expected to play a crucial role in long-term human space exploration.

In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

COVID-19 is a well-known respiratory disease spreading worldwide since January 2020, causing many deaths and massive pressure on health systems. This pandemic’s appearance compromised the health services’ sustainability and quality as many procedures were postponed or even canceled out, with an expected increase of adverse events like nosocomial infections, in-hospital deaths, and the worsening of the patients’ clinical status. Therefore, assessing whether the healthcare providers’ performance has changed is paramount. In this case, we evaluated the public hospitals in Portugal as the leading secondary health care providers in the country, which in turn has been highly affected by the pandemic. To this end, we developed a network Data Envelopment Analysis model serially relating efficiency and effectiveness. Using 2,795 observations from public hospitals and 18 variables, we observed consistent drops in efficiency when the pandemic started, followed by a recovery to levels above the pre-pandemic ones. Regarding effectiveness, we observed a positive trend during the evaluated period. In short, these results allow us to conclude that, apart from the great resilience of public healthcare providers, the Portuguese State was not prepared for a pandemic like COVID-19, but still the actions taken (including massive vaccination) were beneficial.

Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.

Background: Three drug-combinations, ipilimumab-nivolumab (Ipi-Nivo), pembrolizumab-axitinib (Pembro-Axi) and avelumab-axitinib (Ave-Axi), have received regulatory approvals in USA and Europe for the treatment of metastatic renal cell carcinoma with clear cell component (mRCC). However, no head-to-head comparison data are available to identify the best option. Therefore, we aimed to compare these new treatments in the first-line setting. Methods: We conducted a systematic search in Pubmed, the Cochrane library and clinicaltri-al.gov website from January 2015 to October 2019, for any randomized controlled trials of treatment-naïve patients with mRCC. The process was performed according to PRISMA guide-lines. We performed a Bayesian network meta-analysis with two different approaches. The out-comes for analysis were overall survival, progression-free survival, and objective response rate. Results: Our search identified 3 published phase 3 randomized clinical trials (2835 patients). In the contrast-based model, Ave-Axi (SUCRA: 83%) and Pembro-Axi (SUCRA: 80%) exhibited the best ranking probabilities for PFS. For OS, Pembro-Axi (SUCRA: 96%) was the most pref-erable option against Ave-Axi and Ipi-Nivo. Objective response rate analysis showed Ave-Axi as the best (SUCRA= 94%) and Pembro-Axi as second best option. In the parametric models, risk of progression was comparable for Ave-Axi and Ipi-Nivo, whereas Pembro-Axi exhibited a lower risk during the first 6 months of treatment and a higher risk afterward. Furthermore, Pembro-Axi exhibited a net advantage in terms of OS over the two other regimens, while Ave-Axi was the least preferable option. Conclusions: Overall evidences suggested pembrolizumab plus axitinib may be the best option.

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common one has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

This paper investigates women’s empowerment and their active involvement in governance aimed at promoting sustainable development. The study focuses on examining the roles and processes of co-creation of nature-based solutions (NBS) for water management in Milan. Three research directions were explored: 1) to determine gender-based roles within local governance; 2) to detect semantic groups of NBS based on stakeholders' gender; c) to analyze gender-based linguistic and conceptual patterns of NBS. To achieve our objectives, we converted a sample of audio-video interviews of local stakeholders into textual data along with metadata indicating speakers' roles and gender. Descriptive statistics were applied to explore the governance's roles. We employed a combination of textual analysis and network analysis to identify thematic groups of NBS, and applied a word embedding approach to uncover linguistic patterns indicative of semantic sub-structures. The findings revealed significant demographic diversity. Male stakeholders perceive sustainable development as a process driven by the circular economy and the coordination of territorial authorities, while female stakeholders envision a transformative approach rooted in science, culture, and collaboration. The semantic sub-structure analysis highlighted a tendency among male governance to emphasize specialized skills, whereas female governance placed greater emphasis on problem definition rather than resolution.

The research presented in this paper examines a new proposed approach for identifying safety improvement sites on rural highways. Unlike conventional approaches, the proposed approach does not require crash history, but rather utilizes classified variables for traffic volume, geometric features, and roadside characteristics that do not require access to exact data or extensive technical expertise. The research validates the performance of the proposed approach using field data from a large sample of rural two-lane highway segments in the state of Oregon including traffic, roadway, and crash data. Using observed crash history as a reference, the performance of the proposed ap-proach was compared to two of the well-established methods in practice, namely: the Empirical Bayes (EB) and the Potential for Safety Improvement (PSI) methods. The study results suggest that, using crash density for highway segments, the performance of the proposed method was lower than that of the EB and PSI methods. This is despite the high R-square value of the predictive model used in the proposed method. However, when using crash frequencies for highway segments, the per-formance of the proposed method was found comparable to the well-established EB and PSI methods.

Chronic Low Back Pain (CLBP), defined as pain persisting for at least 12 weeks, is a significant public health issue, with prevalence intensifying due to an ageing global population, amassing approximately 619 million cases in 2020 and projected to escalate to 843 million by 2050. Given the multitude of therapeutic modalities available for CLBP, this research sought to ascertain their respective efficacies in ameliorating pain and disability. We specifically scrutinized Multidisciplinary Biopsychosocial Rehabilitation (MBR) among adults enduring non-specific low back pain for over 12 weeks, excluding acute pain conditions and pre-study surgical interventions. MBR is an approach encompassing various modalities, including exercise, behavioral therapies, educational programs, and work conditioning aspects, necessitating a comprehensive Network Meta-Analysis (NMA) to fully grasp the extensive scope of its impact. The constructed network comprised diverse MBR modalities (behavioral, educational, and work conditioning) alongside exercise therapy (ET), minimal intervention, and usual care, serving as controls with pain and disability as outcomes. Through the analysis of 4,619 initially identified studies, 93 were included, encompassing a total of 8,059 participants. The NMA conducted substantiated that both ET and MBR modalities were notably effective in alleviating CLBP, with education-oriented MBR emerging as the most efficacious for pain mitigation and behavior-focused MBR for disability reduction. Nevertheless, the discerned differences amongst the treatments were minimal and uncertain, highlighting that no modality was definitively superior to the others. The analysis revealed low global and local inconsistency within the network, underscoring the reliability of the observed outcomes. Given the intricate nature of CLBP, embodying various facets, our findings advocate for a combined therapeutic approach to optimize treatment efficacy. Nonetheless, the presence of high study heterogeneity and potential biases necessitates cautious interpretation of the results, prompting future research.

In South Korea, demolition waste (DW) management has become increasingly significant owing to the rising number of old buildings. Effective DW management requires an efficient approach that accurately quantifies and predicts the generation of DW (DWG) of various types, which necessitates access to the required information or technology capable of achieving this. Hence, we developed an artificial intelligence-based model that predicts the generation of ten DW types, specifically from buildings in redevelopment areas. We used an artificial neural network algorithm with < 10 neurons in the hidden layer to derive individual input variables and optimal hyperparameters for each DW type. All DWG prediction models achieved an average validation and test prediction performance (R²) of 0.970 and 0.952, respectively, with their ratios of percent deviation ≥ 2.5, ver-ifying them as excellent models. Moreover, a Shapley additive explanations analysis revealed that DWG was most impacted by the floor area for all the DW types, with a positive correlation with DWG. Conversely, other factors showed either a positive or negative correlation with DWG de-pending on the DW type. The study findings will enable demolition companies and local gov-ernments in making informed decisions for efficient DW management and resource allocation by accurately predicting the generation of various types of DW.

This paper presents a scenario evolution model for maritime accidents using Bayesian networks (BN) to predict the most likely causes of specific types of maritime incidents. The BN nodes encompass accident type, life loss contingency, accident severity, the quarter and time period of the accident, and the type and gross tonnage of the ships involved. We analyzed 5,660 global maritime accidents from 2005 to 2020. Using Netica software, we constructed a Tree Augmented Network (TAN) model, accounting for interdependencies among risk influencing factors. We validated the results through sensitivity analysis and historical accident records. Forward causal inference and reverse diagnostic inference were then performed on each node variable to investigate the accident development trend and evolution process under predetermined conditions. The findings indicate that the model can effectively predict the likelihood of various accident scenarios under specific conditions, as well as the extrapolation of accident consequences. Forward causal reasoning reveals that general cargo ships with a gross tonnage of 1-18,500 t are most likely to experience collision, grounding, and stranding accidents in the first quarter. Reverse diagnostic reasoning indicates that during early morning hours, container ships, general cargo ships, and chemical ships with a tonnage of 1-18,500 t are less likely to involve life loss in the event of collision accidents.

The environment is seldom optimal for plant growth and changes in abiotic and biotic signals, including temperature, water availability, radiation and pests, induce plant responses to optimise survival. The New Zealand native plant species and close relative to Arabidopsis thaliana, Pachycladon cheesemanii grows under environmental conditions that are unsustainable for many plant species. Here we compare the responses of both plant species to different stressors (low temperature, salt and UV-B radiation) to help understand how P. cheesemanii can grow in such harsh environments. The stress transcriptomes were then determined and comparative transcriptome and network analyses discovered similar and unique responses within species between different stresses, and between the two plant species. A number of widely studied plant stress processes were highly conserved in A. thaliana and P. cheesemanii. However, in response to cold stress, Gene Ontology terms related to glycosinolate metabolism were only enriched in P. cheesemanii. Salt stress was associated with alteration of the cuticle and proline biosynthesis in A. thaliana and P. cheesemanii, respectively. Anthocyanin production may be a strategy to cope with UV-B radiation stress in P. cheesemanii only. These results allowed us to construct broad stress response pathways in A. thaliana and P. cheesemanii and identify possible novel plant strategies that help mitigate environmental stress.

The treatment of insulin-dependent diabetes mellitus is characterized by artificial supplementation of pancreatic β-cell ability to regulate sugar levels in the blood. Even though various insulin analogs are crucial for reasonable glycemic control, understanding the dynamic mechanism of the insulin analogs may help to improve the best-protracted insulin analog to assist people with Type 1 Diabetes (T1D) to live comfortably while maintaining tight glycemic control. Here we present the high-resolution crystal structure of NN304, known as insulin detemir, to 1.7 A resolution at cryogenic temperature. We computationally further investigated our crystal structure's monomeric-dimeric conformation and dynamic profile by comparing it with a previously available detemir structure (PDB ID: 1XDA). Our structure (PDB ID: 8HGZ) obtained at elevated pH provides a distinct alternative dimeric conformation compared to the previous structure, suggesting it might induce an intermediate state in the dissociation pathway of the insulin detemir’s hexamer:dihexamer equilibrium. Combined with orientational cross-correlation analysis by Gaussian Network Model (GNM), this alternate oligomeric conformation offers the distinct cooperative motions of a protracted insulin analog that has not been previously observed.

Social network analysis (SNA) is an effective method for characterizing networks from various specific perspectives. Global trade contracted sharply in 2020 owing to the COVID-19 pandemic, and growth is expected to be lower than the pre-pandemic trends. This study takes countries worldwide as the primary unit of analysis and uses different procedures of social network analysis (SNA), including network density, centrality, and core-periphery structure, and applies them to the field of trade in electronic integrated circuit products (ICPT). In addition to static and descriptive analysis, this study also uses of tools such as Ucinet 6.732 for visual analysis, visual analysis, and more convenient and precise display of the network structure of ICPT. The important countries play a central role in determining the overall structural features. The core-periphery structure in the network from both spatial and temporal perspectives, data on electronic integrated circuit products for the period 2015-2021 were used in SNA. This study found that the ICPT network became denser from 2015 to 2021 but declined and picked up in 2018 and 2020. Moreover, the core-peripheral structure exists in the ICPT network. The main core network countries are Taiwan, Hong Kong, Singapore, mainland China, Malaysia, Japan, the Philippines, Thailand, Vietnam, and other Asian countries, including the United States and Germany.

Surrogate model based optimization method is widely-used to accelerate the design and optimization process~\cite{marler2004survey}. The input of regression model used in the surrogate model are numbers, which requires users to parametrize the geometries. In this paper, a new parameterization-free surrogate model is introduced and its corresponding uncertainty quantification and sensitivity analysis method are discussed. The input of new surrogate model methods is surface mesh of simulation domain. \Gls{gnns} is used to extract geometric information, and \Gls{cnns} is used to predict contours. This framework bypasses parameterization,as a consequence, uncertainties introduced by manual parameterization is reduced. However, such changes compared with conventional surrogate model methods impose great challenge on uncertainties quantification and sensitivity analysis. Uncertainties quantification in this paper means the error bar of prediction results, which is calculated by Gaussian Process Regression method in current surrogate method. In this paper, a new quantification method achieved by Kullback-Leibler divergence (KLD) is introduced. And the sensitivity analysis is conducted by Automatic Differentiation, which gives a Jacobian matrix of inputs. The method and analysis mentioned above are demonstrated by a low-pressure steam turbine rotator and its exhaust system.

This study aims to develop a hybrid approach based on backpropagation Artificial Neural Network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using Aerosol Optical Depth (AOD) and several climatic indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using Motor Vehicle Emission Simulator (MOVES), the measured climatic variables and AOD were obtained from the California Irrigation Management Information System (CIMIS), and NASA’s Moderate Resolution Spectroradiometer (MODIS). The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150, and precipitation was determined to be the most important independent variable. Coefficient of determination ( ), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can accurately predict the PM2.5 concentration and can be used to forecast future trends.

The underground injection and geological sequestration of carbon dioxide (CO₂) is a promising approach for reducing the levels of CO₂ in the atmosphere. To mitigate the leakage of CO₂ resulting from natural fracture networks in rock masses, the sequestration process is commonly accompanied by the injection of reactive Portland cement representing a coupled hydro-chemo-mechanical process. In this work, a numerical approach based on the unified pipe network method (UPM) is presented that considers the coupled permeation and diffusion processes with chemical precipitation. Most input parameters are derived from the published literature. The proposed approach is validated through a comparison with analytical solutions, which are applied to simulate the CO₂ sequestration process in a fractured rock mass. The results indicate that the long-term sequestration effect, which is highly influenced by the fracture distribution, can be captured effectively by the model. Consequently, the presented approach can assist engineers in properly designing the arrangement of boreholes and determining the concentration of the grouting material.

The study concentrates on researching possibilities of using computer image analysis and neural modeling in order to assess selected quality discriminants of spray-dried chokeberry powder. The aim of the paper is quality identification of chokeberry powders on account of their highest dying power, the highest bioactivity as well as technologically satisfying looseness of powder. The article presents neural models with vision technique backed up by devices such as digital camera as well as electron microscope. Reduction in size of input variables with PCA has influence on improving the processes of learning data sets, thus increasing effectiveness of identifying chokeberry fruit powders included in digital pictures, which is shown in the results of the conducted research. The effectiveness of image recognition are presented by classifying abilities as well as low Root Mean Square Error (RMSE), for which the best results are achieved with typology of network type Multi-Layer Perceptron (MLP). The selected networks type MLP are characterized by the highest degree of classification at 0.99 and RMSE at 0.11 at most at the same time.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

Traffic shaping is a critical task in software-defined -IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic shaping approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and Multi-arm Bandit algorithms to dynamically optimize traffic shaping policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a Multi-arm Bandit algorithm to optimize traffic shaping policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic shaping methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic shaping in SDNs, enabling efficient resource management and QoS assurance

This paper outlines a tool developed with the purpose of creating a simple configurable emulated network environment that can be used in cyber defence exercises. Research has been conducted into the various related subject areas: cyber defence exercises, network threats, network emulation, network traffic replay, network topologies, and common network services. From this research a requirements specification was produced to encapsulate the features required to create this tool. A network, containing many of the aspects researched, was designed and implemented using Netkit-NG to act as a blueprint for the tool and to further knowledge in the construction of an emulated network. Following this the tool was developed and tested to ensure requirements were met.

Abstract: Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

In recent years, frequent occurrences of low temperature disasters due to global climate change have significantly impacted the normal growth of crops. Research has indicated that inorganic selenium (which is more toxic) can not only enhance the effects of low temperature stress but also increase selenium content on plants; however, there is a scarcity of studies investigating the impacts of organic selenium and nano-selenium on crops. In order to investigate whether organic selenium and nano-selenium can enhance low-temperature tolerance and increase selenium content in pak choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’), different concentrations (0, 5, 10, 20 mg L-1) of exogenous selenium were applied in this experiment to assess their effects on plant growth, nutritional quality and antioxidant properties. RNA-Seq technology was used to sequence the transcriptome of the leaves. Based on the transcriptome data, a Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct a network that associates with physiological traits related to stress resistance. Two highly correlated gene co-expression modules were identified, and within them, nine hub genes associated with endocytosis, antioxidant stress, absorption, transport, and metabolism of selenium were discovered. The results indicated that the beneficial effects on yield and total selenium content under low temperature were attributed to (1) protection of photosynthetic pigments for enhancing photosynthetic capacity by the up-regulation of LHca2, LHcb1, LHca1, LHcb4 in KEGG pathway: photosynthesis-antenna proteins; (2) activation of antioxidant system for efficient ROS homeostasis such as SOD, POD and CAT by the genes such as Superoxide dismutase, Monodehydroascorbate and (3) selenium absorption by endocytosis, seleninum transportation by ABC transporter gene family and selenium metabolism related genes such as Cysteine synthase, Glutaredoxin. These findings provide a foundation for further investigation into the molecular mechanisms underlying the effects of organic selenium and nano-selenium on cyanochloride production at low temperatures.

Background: This study presents a graph-based and purely structural analysis to detect echo chambers on Twitter. Echo chambers are a concern as they can spread misinformation and reinforce harmful stereotypes and biases in social networks. Methods: The study recorded the German-language Twitter stream over two months, recording about 180.000 accounts and their interactions. The study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. Results: The purely structural detection approach was able to identify an echo chamber (red community) that was focused on a few topics with a triad of Anti-Covid, right-wing populism, and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community was much more heterogeneous and showed "normal" communication interaction patterns. Conclusions: The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship.

Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach as proactive knowledge-based technique to estimate the FP based maintenance planning with subjective information from domain experts. However, the classical-FTA still suffers from uncertainty and static structure limitations which poses a substantial dilemma in predicting FP. To deal with the uncertainty issues, a Fuzzy-FTA (FFTA) model was developed by statistical analysing the effective attributes such as experts' trait impacts, scales variation and, assorted membership and defuzzification functions. Besides, a Bayesian Network (BN) theory was conducted to overcome the static limitation of classical-FTA. The results of FFTA model revealed that the changes in decision attributes were not statistically significant on FP variation while BN model considering conditional rules to reflect the dynamic relationship between events had more impact on predicting FP. After all, the integrated FFTA-BN model was used in the optimization model to find the optimal maintenance intervals according to estimated FP and the total expected cost. As a practical example, the proposed model was implemented in a semi-automatic filling system in an automotive production line. The outcomes could be useful for upgrading the availability and safety of complex equipment in manufacturing systems.

The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV-1-host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV-host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV-human protein-protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies may represent a basis for developing new anti-HIV therapeutic strategies such as new small-molecule drugs preventing infection of CD4+ cells and viral replication, effective vaccines, "shock and kill" and "block and lock" approaches to cure latent infection.

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and, Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used Average Similarity, Computational Time, Association Rule with Apriori algorithm, and Clustering Performance Evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies-Bouldin Index.

Social and spatial characteristics of a population often interact to influence health outcomes, suggesting a need to jointly analyze both to offer useful insights in community health. However, researchers have used either social or spatial analyses to examine community-based health issues and inform intervention programs. We propose a combined socio-spatial analytic approach to develop a social network with spatial weights and a spatial statistic with social weights, and apply them to an ongoing study of mental and physical well-being of rural Latino immigrants in North Florida, USA. We demonstrate how this approach can be used to calculate measures, such as social network centrality, support contact dyads, and spatial kernel density based on a health survey data. Findings reveal that the integrated approach accurately reflected interactions between social and spatial elements, and identified community members (who) and locations (where) that should be prioritized for community-based health interventions.

With advances in next-generation sequencing(NGS) technologies, large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer mechanism is to identify the driver genes from the mutation genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profilings and copy number variation(CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.

Climate change-induced temperature fluctuations pose a significant threat to crop production, particularly in the Southern Hemisphere. This study investigates the transcriptome and physiological responses of rapeseed to post-flowering temperature increases, providing valuable insights into the molecular mechanisms underlying rapeseed tolerance to heat stress. Two rapeseed genotypes, Lumen and Solar, were assessed under control and heat stress conditions in field experiments conducted in Valdivia, Chile. Results showed that seed yield and seed number were negatively affected by heat stress, with genotype-specific responses. Lumen exhibited a 9.3% average seed yield reduction, while Solar showed a 28.7% reduction. RNA-seq analysis of siliques and seeds revealed tissue-specific responses to heat stress, with siliques being more sensitive to temperature stress. Hierarchical clustering analysis identified distinct gene clusters reflecting different aspects of heat stress adaptation in siliques, with a role for protein folding in maintaining silique development and seed quality under high temperature conditions. In seeds, three distinct patterns of heat-responsive gene expression were observed, with genes involved in protein folding and response to heat showing genotype-specific expression. Gene coexpression network analysis revealed major modules for rapeseed yield and quality, as well as the trade-off between seed number and seed weight. Overall, this study contributes to understanding the molecular mechanisms underlying rapeseed tolerance to heat stress and can inform crop improvement strategies targeting yield optimization under changing environmental conditions.

Wireless Sensor networks (WSNs) are gaining more and more research interest due to their ability to monitor large areas more or less independent. However, their reliability is an important issue, since their system reliability is influenced from a hardware, a data and an energetic point of view due to different factors like loading conditions, signal attenuation and battery lifetime. Regarding these issues, the positions of sensor nodes have to be chosen correctly to maximize the system reliability, which has to be considered during the development of new products equipped with WSNs. In this paper, an approach to estimate a WSNs system reliability during the development phase based on the analysis of the measurements on the example of strain measurements in finite elements (FE) models is presented. For that, the part under consideration is divided into regions with similar strains by the use of a Region Growing Algorithm (RGA). Afterwards, the WSN’s configuration is analyzed regarding its reliability in accordance to the data paths and measurement redundancy due to the sensor positions in the found measuring regions. This methodology is tested on an exemplary WSN configuration at an aircraft wing box under bending load and was found to work very well regarding the estimation of the hardware perspective on the system reliability. Therefore, the methodology and the developed algorithm show potentials for optimizations in sensor node positions for better reliability results.

Social media platforms are not serving only as tools of communication rather they are acting like intricate systems managing our interactions. A major setback to our interactions occur when the flow of information in these networks gets distorted through polarization. It resembles a society where every debate turns into divide. Our research focuses exclusively on quantifying polarization as well as solving this issue head-on. In order to accurately gauge polarization, we’ve developed a new method that takes into consideration majority of its influencing factors. This helps us in correctly quantifying the polarization value of a given network and then by proposing it as an optimization problem we are attempting to maximally decrease polarization while staying within the set budget. We test our method on synthetic and real-world data sets, and find out that polarization is declining, and diversity is increasing. This proves that our recommendation engine has a penchant for finding the ideal connections between the nodes to start a dialogue. Our research presents a successful tactic to quantifying and reducing polarization in social media networks. The results indicate that our novel metric and intervention technique are effective in dismantling echo chambers, thereby promoting diversity in these networks.

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry and science related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, rational design of combination therapies and the global response to the Covid19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human focused, systems-based approach to drug discovery and research.

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks like difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared to manual spectrum analysis. In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labelling a high variety of frames in different environments is too challenging, an artificial data generation pipeline has been developed. The data set consists of 20 000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enables the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples.