Network Mapping Research Articles

Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping

We introduce an optimal sensor placement method using convolutional neural networks for ensemble-based data assimilation. The proposed method utilizes the gradient-weighted class activation mapping of the convolutional neural networks to identify important regions for assimilation processes. It is achieved by using the initial ensemble of samples for data assimilation as training data to construct a convolutional neural network-based surrogate model. In doing so, the method can estimate optimal sensor locations in an a priori manner, allowing for sensor placement before conducting data assimilation processing. Moreover, the gradient-weighted class activation mapping is used to alleviate the effect of error accumulation during the backpropagation process through global averaging. Further, these observation sensors are incorporated to reconstruct mean turbulent flow fields based on the ensemble Kalman method. The proposed optimal sensor placement method is applied to two flow applications with complex geometries, i.e., flows around periodic hills and an axisymmetric body of revolution. Both cases demonstrate that the proposed method can significantly reduce the number of sensors without sacrificing the accuracy of the reconstructed flow field.

Mapping global research trends: Nutrition associations with nonalcoholic fatty liver disease - a Scopus bibliometric analysis

BACKGROUND Several bibliometric analyses have been carried out to identify research hotspots and trends in nonalcoholic fatty liver disease (NAFLD) research. Nonetheless, there are still significant knowledge gaps that must be filled to advance our understanding of and ability to treat NAFLD. AIM To evaluate, through bibliometric and visual analysis, the current status of related research, related research frontiers, and the developmental trends in the field of diet and NAFLD. METHODS We retrieved publications about diet and NAFLD published between 1987 and 2022 from Scopus. Next, we used VOSviewer 1.6.20 to perform bibliometric analysis and visualization. RESULTS We found a total of 1905 studies, including 1637 (85.93%) original articles and 195 (10.24%) reviews, focused on the examination of NAFLD and its correlation with diet that were published between 1987 and 2022. Among the remaining five types of documents, 38 were letters, notes, editorials, meeting minutes, or brief surveys, representing 1.99% of the total documents. The countries with the most publications on this topic were China (n = 539; 28.29%), followed by the United States (n = 379; 19.90%), Japan (n = 133; 6.98%), and South Korea (n = 127; 6.6%). According to the citation analysis, the retrieved papers were cited an average of 32.3 times and had an h -index of 106, with 61014 total citations. The two main clusters on the map included those related to: (1) Inflammation and oxidative stress; and (2) Dietary interventions for NAFLD. CONCLUSION This was the first study to use data taken from Scopus to visualize network mapping in a novel bibliometric analysis of studies focused on diet and NAFLD. After 2017, the two domains that received the most attention were “dietary interventions for NAFL”’ and “‘inflammation and oxidative stress implicated in NAFLD and its correlation with diet.” We believe that this study provides important information for academics, dietitians, and doctors, and that additional research on dietary interventions and NAFLD is warranted.

RNA-seq reveals differentially expressed lncRNAs and circRNAs and their associated functional network in HTR-8/Svneo cells under hypoxic conditions

Placental hypoxia is hazardous to maternal health as well as fetal growth and development. Preeclampsia and intrauterine growth restriction are common pregnancy problems, and one of the causes is placental hypoxia. Placental hypoxia is linked to a number of pregnancy illnessesv. To investigate their potential function in anoxic circumstances, we mimicked the anoxic environment of HTR-8/Svneo cells and performed lncRNA and circRNA studies on anoxic HTR-8/Svneo cells using high-throughput RNA sequencing. The miRNA target genes were predicted by integrating the aberrant expression of miRNAs in the placenta of preeclampsia and intrauterine growth restriction, and a ceRNA network map was developed to conduct a complete transcriptomic and bioinformatics investigation of circRNAs and lncRNAs. The signaling pathways in which the genes were primarily engaged were predicted using GO and KEGG analyses. To propose a novel explanation for trophoblastic organism failure caused by lncRNAs and circRNAs in an anoxic environment.

Alzheimer's Disease: Combination Therapies and Clinical Trials for Combination Therapy Development.

Alzheimer's disease (AD) is a complex multifaceted disease. Recently approved anti-amyloid monoclonal antibodies slow disease progression by approximately 30%, and combination therapy appears necessary to prevent the onset of AD or produce greater slowing of cognitive and functional decline. Combination therapies may address core features, non-specific co-pathology commonly occurring in patients with AD (e.g., inflammation), or non-AD pathologies that may co-occur with AD (e.g., α-synuclein). Combination therapies may be advanced through co-development of more than one new molecular entity or through add-on strategies including an approved agent plus a new molecular entity. Addressing add-on combination therapy is currently urgent since patients on anti-amyloid monoclonal antibodies may be included in clinical trials for experimental agents. Phase 1 information must be generated for each agent in combination drug development. Phase 2 and Phase 3 of add-on therapies may contrast the new molecular entity, the approved agent as standard of care, and the combination. More complex development programs including standard or modified combinatorial designs are required for co-development of two or more new molecular entities. Biomarkers are markedly affected by anti-amyloid monoclonal antibodies, and these effects must be anticipated in add-on trials. Examining target engagement biomarkers and comparing the magnitude and sequence of biomarker changes in those receiving more than one therapy, compared with those on monotherapy, may be informative. Using network-based medicine approaches, computational strategies may identify rational combinations using disease and drug effect network mapping.

Effects and Mechanism of Morinda Officinalis How on Metabolism-Associated Fatty Liver Disease.

This study aims to investigate the effects and mechanism of Morinda Officinalis How (MOH), a lianoid shrub with potential therapeutic properties, on Metabolism- Associated Fatty Liver Disease (MAFLD). bjective: The objective of this study was to construct a MOH-MAFLD network prediction model and explore the effect of MOH on MAFLD and its underlying mechanism in vivo. Screening of MAFLD targets was performed using the DisGeNET database. Venny database was used to establish the MOH-MAFLD interaction network map, while the STRING database was applied to assess the Protein-Protein Interaction (PPI) network. The central target gene was screened using Gene Ontology (GO) function analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. GO function enrichment analysis revealed that MOH affected MAFLD through apoptosis and estrogen-related pathways. KEGG pathway enrichment and PPI network analysis indicated that MOH might mitigate MAFLD by reducing apoptosis and improving lipid metabolism. Additionally, 6 weeks of MOH treatment in rats decreased caspase-3 levels and increased Bcl-2, Estrogen receptor α(Esr1), and JUN proteins, thus ameliorating MAFLD progression. MOH could delay the progression of MAFLD by affecting estrogen-related pathways, reducing cell stress, and inhibiting apoptosis.

Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network

In order to solve the problem of insufficient end positioning accuracy due to factors such as gravity and material strength during the inverse solution process of a large hydraulic robotic arm, this paper proposes an inverse solution algorithm based on an adaptive spider wasp optimization (ASWO) optimized back propagation (BP) neural network. Firstly, the adaptability of the SWO algorithm is enhanced by analyzing the phase change in population fitness and dynamically adjusting the trade-off rate, crossover rate, and population size in real time. Then, the ASWO algorithm is used to optimize the initial weights and biases of the BP neural network, effectively addressing the problem of the BP neural network falling into local optima. Finally, a neural network mapping relationship between the actual position of the robotic arm’s end-effector and the corresponding joint values is established to reduce the influence of forward kinematic errors on the accuracy of the inverse solution. Experimental results show that the average positioning error of the robotic arm in the XYZ direction is reduced from (91.3, 87.38, 117.31) mm to (18.16, 24.67, 27.21) mm, significantly improving positioning accuracy by 80.11%, 71.78%, and 76.81%, meeting project requirements.

Understanding the Historic Center by Using Network Analysis with Mental Mapping Method: The Case Study of Amasra, Turkey

The history of the Amasra city center dates back 3000 years with its Fortress and City Walls, which are on the UNESCO temporary heritage list, and the important historical buildings around it. However, the ongoing controversy on Amasra’s conservation plan has revealed the difficulties of developing an approach that understands the historic fabric. In this context, the social relations of the urban images in Amasra, which is located on an archeological site and has an urban identity with its historical buildings, need to be analyzed. The aim of this study is to present an evaluation model that analyzes the network map of the places that create the urban image in the historic center of Amasra using visual methods. The urban images that form the historical identity in Amasra were mapped using the mental mapping method and developed using network analysis on the Graph Commons platform. The network analysis includes cluster and centrality metrics, which helps to understand social relationships in the network map according to its algorithm. The main focus area and central actors were explored with the simulation obtained by using this proposed model. The study results showed that the evaluation model is effective in understanding the formation mechanism of the historical center of the social networks between the architectural structures in Amasra. This study contributes to development in terms of enhancing historical identity and revitalizing the city images with tourism by evaluating the spatial structure of Amasra.

Aeolian sand challenges in desert rail infrastructures, overview of Iran’s experience and advancement

The processes of wind erosion and the movement of dunes have destructive effects on the transportation infrastructure with an emphasis on railway lines. Consequently, the construction and maintenance costs of desert railways will increase under the influence of sand sediment movement. In severe conditions, passing trains can also be affected, compromising their safety and comfort. However, the passage of railway infrastructures through desert areas prone to aeolian sand is inevitable. This research discusses the main challenges encountered by railways in Iran's deserts, including two ultimate and serviceability limit states that affect civil works, track superstructure, rolling stock, and signaling systems. It also examines the mitigating measures implemented in Iran based on the source, transport, or deposition areas of sand dunes. Moreover, the performance of Iran's desert railways has been classified based on wind sand activities. According to the distribution map of Iran's railway network, sand accumulation in the main desert lines has been classified into four levels: very severe, severe, moderate, and low. The research also presents the experience of implementing and monitoring the hump slab track system, which has proven to be a sustainable solution for addressing the dune problem with suitable efficiency over a period of six months.

Agent-based modelling of polarized news and opinion dynamics in social networks: a guidance-oriented approach

Abstract In the contemporary social network landscape, opinion polarization has emerged as a prominent challenge, sparking concerns about the effective guidance of news sentiment and mitigation of opposing opinions. This is particularly pertinent in the intricate web of social networks, where complexity reigns supreme. Addressing this pivotal issue, this article introduces a news opinion guidance approach grounded in motif recognition. To accurately mirror real-world social networks, we have crafted an agent-based model that simulates polarized news propagation. This model encompasses diverse media agents and user agents, meticulously replicating the news dissemination process within the network. In our quest to unveil the underlying structures of social networks, we have enhanced the Augmented Multiresolution Network approach, incorporating multi-dimensional node attributes for more nuanced clustering and network mapping. This refinement enables us to pinpoint potential motif regions with greater precision. Leveraging these insights, we introduce a triangular motif-based opinion guidance strategy aimed at shaping opinion distribution by bolstering the influence of nodes within these motifs. Once the pertinent motifs are identified, we undertake simulation experiments that reveal the remarkable efficacy of our motif recognition-driven guidance strategy. Notably, it reduces opinion polarization by a substantial 74% compared to scenarios without guidance strategies. This research offers a fresh perspective on crafting personalized and targeted news sentiment guidance strategies. It presents a versatile and potent computational framework for understanding and managing polarization phenomena in social networks, carrying profound theoretical and practical ramifications.

Scientific Production on Leadership in Social Entrepreneurship: Co-Word Analysis and Network Mapping

Scientific Production on Leadership in Social Entrepreneurship: Co-Word Analysis and Network Mapping

Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks

The catastrophic consequences of lithium-ion battery (LIB) accidents have attracted high attention from society and industry. Accordingly, risk analysis is indispensable for the risk prevention and control of LIBs. Nevertheless, it is difficult to establish a physics-informed risk analysis model due to the complex material characteristics and aging mechanisms of LIBs. Meanwhile, the data-driven approach requires historical information of LIBs and does not merely rely on knowledge of the internal mechanisms of LIBs. This study proposes a method integrating the physics-informed Bayesian network (BN) (i.e., mapping from fault tree) and data-driven BN (i.e., learning from data) to conduct risk analysis of LIBs. First, we establish physics-informed and data-driven BNs. Subsequently, we bridge physics-informed and data-driven BNs to establish a Bayesian network for risk analysis of LIB accidents. Second, we set up safety barriers in the system, including detectors, emergency response, and firefighting facilities. Third, we evaluate the performance of safety barriers. We validate the proposed model using data from LIBs in air transportation. The results indicate that safety barriers can reduce the accidental risk of LIBs. Eventually, we propose suggestions for the risk control of LIBs in air transportation. This study is supposed to provide theoretical basis for the risk prevention and control of LIB accidents.

An interim exploratory proteomics biomarker analysis of a phase 2 clinical trial to assess the impact of CT1812 in Alzheimer's disease

CT1812 is a novel, brain penetrant small molecule modulator of the sigma-2 receptor (S2R) that is currently in clinical development for the treatment of Alzheimer's disease (AD). Preclinical and early clinical data show that, through S2R, CT1812 selectively prevents and displaces binding of amyloid beta (Aβ) oligomers from neuronal synapses and improves cognitive function in animal models of AD. SHINE is an ongoing phase 2 randomized, double-blind, placebo-controlled clinical trial (COG0201) in participants with mild to moderate AD, designed to assess the safety and efficacy of 6 months of CT1812 treatment. To elucidate the mechanism of action in AD patients and pharmacodynamic biomarkers of CT1812, the present study reports exploratory cerebrospinal fluid (CSF) biomarker data from 18 participants in an interim analysis of the first set of patients in SHINE (part A). Untargeted mass spectrometry-based discovery proteomics detects >2000 proteins in patient CSF and has documented utility in accelerating the identification of novel AD biomarkers reflective of diverse pathophysiologies beyond amyloid and tau, and enabling identification of pharmacodynamic biomarkers in longitudinal interventional trials. We leveraged this technique to analyze CSF samples taken at baseline and after 6 months of CT1812 treatment. Proteome-wide protein levels were detected using tandem mass tag-mass spectrometry (TMT-MS), change from baseline was calculated for each participant, and differential abundance analysis by treatment group was performed. This analysis revealed a set of proteins significantly impacted by CT1812, including pathway engagement biomarkers (i.e., biomarkers tied to S2R biology) and disease modification biomarkers (i.e., biomarkers with altered levels in AD vs. healthy control CSF but normalized by CT1812, and biomarkers correlated with favorable trends in ADAS-Cog11 scores). Brain network mapping, Gene Ontology, and pathway analyses revealed an impact of CT1812 on synapses, lipoprotein and amyloid beta biology, and neuroinflammation. Collectively, the findings highlight the utility of this method in pharmacodynamic biomarker identification and providing mechanistic insights for CT1812, which may facilitate the clinical development of CT1812 and enable appropriate pre-specification of biomarkers in upcoming clinical trials of CT1812.

Systematic mapping of organism-scale gene-regulatory networks in aging using population asynchrony

In aging, physiologic networks decline in function at rates that differ between individuals, producing a wide distribution of lifespan. Though 70% of human lifespan variance remains unexplained by heritable factors, little is known about the intrinsic sources of physiologic heterogeneity in aging. To understand how complex physiologic networks generate lifespan variation, new methods are needed. Here, we present Asynch-seq, an approach that uses gene-expression heterogeneity within isogenic populations to study the processes generating lifespan variation. By collecting thousands of single-individual transcriptomes, we capture the Caenorhabditis elegans "pan-transcriptome"-a highly resolved atlas of non-genetic variation. We use our atlas to guide a large-scale perturbation screen that identifies the decoupling of total mRNA content between germline and soma as the largest source of physiologic heterogeneity in aging, driven by pleiotropic genes whose knockdown dramatically reduces lifespan variance. Our work demonstrates how systematic mapping of physiologic heterogeneity can be applied to reduce inter-individual disparities in aging.

Mapping pedagogies of education for sustainable development in Scottish higher education institutions

Sustainable Development Goal (SDG) target 4.7 promotes education for sustainable development (ESD), urging higher education institutions (HEIs) to prepare students to address global sustainability challenges. Currently, the target indicators do not assess how ESD is enacted in HEIs, making ESD provision a black box in achieving SDG 4.7. This article proposes an analytical tool based on actor–network theory (ANT) to ‘unpack’ the black box of ESD pedagogies in HEIs. ANT allows a closer look at the web of human and non-human actors and their interrelations, visually mapping how ESD pedagogical practices are enacted. Drawing on public sustainability reports and ESD-related web pages of eight Scottish HEIs, we employed a hybrid data analysis approach to produce such a map. The aggregated actor–network map reveals clusters such as university sustainability bodies, ESD-related programs, curriculum elements, co-curricular training, and sustainability awards. Also, Scottish HEI reports and web pages tend to emphasise and quantify what is taught regarding sustainability issues and not elaborate on how ESD is taught. This way, ESD pedagogical practices, connecting students, educators, and curriculum to produce transformative learning, are missing in university reporting. Making those practices visible would provide further solid evidence of Scottish HEIs’ contribution to SDG 4.7.

Generative artificial intelligence: Educational reflections from an analysis of scientific production

Generative Artificial Intelligence (AI-Gen) in education is a challenge for educational administrations due to the benefits and risks of its use. This research seeks to analyse the scientific production of AI-Gen in the educational field. The method used was a bibliometric analysis of descriptive and quantitative documents from the Web of Science database. The VOSviewer statistical programme was used to identify the keyword clusters and to create the network map. The results of this study ascertain that 2023 is the birth of AI-Gen research in education and has since produced a total of 178 papers, where Australia heads the ranking of countries with studies on this subject matter. The ChatGPT phenomenon materialises as a focus of study in the majority of cited research. The study includes seven keyword clusters, where the IA-Gen cluster which is related to terms such as challenges, risks and opportunities are highlighted. Following the discussion, the main conclusion of the study denotes that further research is required on the use of AI-Gen during the teaching-learning process in several educational stages to prevent plagiarism and to obtain a comprehensive understanding of this technology as an educational resource.

Exploring the Concept of Cultural Fit and its Impact on Positive Organizational Outcomes

Purpose The concept of cultural fit, also known as "person-organization fit", has gained significant attention within the organizational culture literature. This study aims to establish the foundations of cultural fit and its influence on positive outcomes at the organizational level. By employing bibliometrics and network mapping analysis techniques, this research sheds light on the interplay between cultural fit and positive organizational outcomes. Further research is necessary to explore the contextual factors and mechanisms that influence the relationship between cultural fit and performance. Design/methodology/approach This paper makes use of bibliometrics and cutting-edge network mapping analysis techniques. Findings This paper sheds light upon the interplays between cultural fit and a set of positive organizational outcomes while providing a systematic review, which facilitates the task for new researchers interested in delving into this field. Limitations and Implications The available literature on "cultural fit" is considerable but fairly limited as it focuses mainly on individual and operational performance, leaving out such important aspects as financial performance and strategic outcomes. Additionally, the limitations could encompass the quality and quantity of articles available for analysis, as well as the fact that the obtained results solely assess publications indexed in the selected databases, potentially lacking a comprehensive comprehension of the research subject.As an implication, the study suggests that a positive relationship exists between cultural fit and performance. Cultural fit can be explored from different perspectives and applied in various situations. Further research is needed to understand the mechanisms and contextual factors that influence the relationship between cultural fit and performance. Originality The scientific literature lacks until the moment of a rigorous systematic review with which to ascertain whether this paradigmatic concept contributes to or leads firms to attain superior performance.

Novel insights into discoloration of crude pine oleoresin during storage: Browning mechanisms via untargeted metabolomics

Quality deterioration due to undesirable discolorations in crude pine oleoresin during storage is a major problem limiting its subsequent processing applications. To reveal the essence of color changes, the browning phenomenon and comprehensive mechanism of crude pine oleoresin during storage were investigated in depth using an approach based on high-resolution mass spectrometry metabolomics. Crude pine oleoresin was stored with sterile, deionized, tap, and pond water for 30, 60, 90, 120, 150, and 180 days, respectively. The changes in browning substrate were detected by tracking analysis protein, reducing sugar, ascorbic acid, and total phenol content, browning was evaluated through colorimeter measurement. The metabolism of crude pine oleoresin in different storage times was uncovered that total color difference (ΔE) increased with storage time, and the browning of crude pine oleoresin soaked in tap water was greatest while soaked in deionized water exhibited the mildest. The content of reducing sugars, proteins, and total phenols increased with storage time, whereas the ascorbic acid content tended to increase and then decrease. Furthermore, the metabolomics analyses showed that 1,194 and 979 differentially metabolized compounds were found between days 120 and days 150, and days 120 and days 180. The metabolite profiles in the water samples changed as crude pine oleoresin storage time increased. Most carbohydrate content decreased, and the phenols and organic acids content increased. Metabolic network mapping linking differentially metabolized compounds by mapping different biosynthetic pathways proved that caffeoylquinic acid and catechin play a key role in the browning of crude pine oleoresin during storage: caffeoylquinic acid forms black iron complexes with iron, and catechin is oxidized to catechol quinone, which eventually produces a yellow dimer. This study will provide a scientific basis for developing or choosing more effective methods for controlling the discoloration of crude pine oleoresin and other similar products during storage.

Electric network and Hirota type 4-simplex maps

Bazhanov-Stroganov (4-simplex) maps are set-theoretical solutions to the 4-simplex equation, namely the fourth member of the family of n-simplex equations, which are fundamental equations of mathematical physics. In this paper, we develop a method for constructing Bazhanov-Stroganov maps as extensions of tetrahedron maps which are set-theoretical solutions to the Zamolodchikov tetrahedron (3-simplex) equation. We employ this method to construct birarional Bazhanov-Stroganov maps which boil down to the famous electric network and Hirota tetrahedron maps at a certain limit.

Primary Sjogren syndrome - A bibliometric analysis via CiteSpace.

This study employs CiteSpace software to analyze the research status, hotspots, and trends of primary Sjogren syndrome (pSS). Relevant publications from 1999 to 2023 were searched in the Web of Science Core Collection (WoSCC) set, followed by generating a network map using CiteSpace software to identify top authors, institutions, countries, keywords, journals, references, and research trends. A total of 3564 valid articles were included in this study. The People Republic of China had the highest number of articles (n = 524), while the University of Bergen emerged as the institution with the highest publication count (n = 94). Mariette X was identified as the author with the most publications (n = 67), whereas Vitali C received recognition as the most cited author (n = 1706). Annals of Rheumatic Diseases stood out as the journal with the highest citation count (n = 2530). Notably, an article published in the Annals of Rheumatic Diseases in 2017 garnered significant attention by being cited a remarkable 304 times. The bibliometric analysis reveals that key areas of research in pSS encompass investigating pathogenesis; advancing and applying targeted biological agents; and establishing treatment and diagnostic standards.

Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI

Deep learning has emerged as a highly effective and precise method for classifying images. The presence of plant diseases poses a significant threat to food security. However, accurately identifying these diseases in plants is challenging due to limited infrastructure and techniques. Fortunately, the recent advancements in deep learning within the field of computer vision have opened up new possibilities for diagnosing plant pathology. Detecting plant diseases at an early stage is crucial, and this research paper proposes a deep convolutional neural network model that can rapidly and accurately identify plant diseases. Given the minimal variation in image texture and color, deep learning techniques are essential for robust recognition. In this study, we introduce a deep, explainable neural architecture specifically designed for recognizing plant diseases. Fine-tuned deep convolutional neural network is designed by freezing the layers and adjusting the weights of learnable layers. By extracting deep features from a down sampled feature map of a fine-tuned neural network, we are able to classify these features using a customized K-Nearest Neighbors Algorithm. To train and validate our model, we utilize the largest standard plant village dataset, which consists of 38 classes. To evaluate the performance of our proposed system, we estimate specificity, sensitivity, accuracy, and AUC. The results demonstrate that our system achieves an impressive maximum validation accuracy of 99.95% and an AUC of 1, making it the most ideal and highest-performing approach compared to current state-of-the-art deep learning methods for automatically identifying plant diseases.