Search

Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.

To prevent the overaggressive treatment of axillary lymph nodes (ALNs) in breast cancer, it is necessary to develop a convenient analysis method that accurately and comprehensively reflects whether ALNs are metastatic or nonmetastatic. We retrospectively analyzed data from patients who underwent surgery for breast cancer at the Weifang Hospital of Traditional Chinese Medicine between January 2019 and June 2023. Binary logistic regression analysis was used to predict the metastasis status of ALNs. The developmental data set included 531 patients (January 2019–June 2023). The validation set included separate data points (n = 178, January 2019–June 2023). Multivariate analysis revealed that positive findings on breast physical examination, ultrasound grades of ALNs, lymphovascular invasion, and Her-2 status had significant predictive value for metastatic ALNs. Based on these findings, a 5-grade risk scoring system and 3-level management recommendations were developed. The risk of metastasis ranged from 11.25 to 93.46%, which was positively correlated with an increase in risk grade. The areas under the curve of the development and validation sets were 0.895 and 0.865, respectively. Ultimately, a convenient, accurate and comprehensive web-based predictive model was constructed using various breast cancer clinical, imaging and pathological criteria to stratify ALNs according to the metastasis probability. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the psychometric characteristics of the Social Phobia Inventory (SPIN) by employing network analysis, confirmatory factor analysis, and the Polytomous Rasch Model. A cross-sectional data set was collected comprising 1,530 participants, with 959 being women and 571 being men. The Bootstrap Exploratory Graph Analysis unveiled the presence of two dimensions, with Items 17, 15, 5, 14, 6, and 9 exhibiting the highest strength centrality index. Notably, the Network Comparison Test indicated no differences in Network Invariance and global strength between the networks of women and men. Furthermore, the confirmatory factor analysis results demonstrated that the two extracted dimensions displayed an acceptable goodness of fit. In addition, the reliability coefficient values were acceptable, exceeding the threshold of 0.70. The Rasch analysis results suggested an overall fit, but some items exhibited overlap, suggesting their potential removal. Furthermore, it was recommended to develop new items to address gaps between existing items, particularly for measuring the lower levels of Social Anxiety Disorder. In conclusion, these findings provide robust evidence supporting the reliability and validity of the SPIN as a tool for measuring Social Anxiety Disorder in Algeria. [ABSTRACT FROM AUTHOR]

The introduction of mobile interaction technology has fundamentally transformed the delivery of tourism services, enabling travelers to access real-time information, engage in online interactions, and enjoy personalized services through smart devices. This innovative consumption model not only enhances tourist engagement and satisfaction but also drives innovation and transformation in the tourism industry. However, despite existing studies exploring the impact of technology on tourism, most research focuses primarily on the technology itself, lacking a systematic analysis of its deeper implications for reshaping consumption patterns, enhancing industry resilience, and addressing market uncertainties. Current studies are predominantly centered on applications related to big data and consumer behavior analysis, often overlooking how to measure the resilience of tourism consumption patterns through specific indicators and failing to investigate the long-term sustainability of consumption models transformed by interactive technologies. To address these research gaps, this study introduces a novel perspective by constructing a mobile interaction network model, defining resilience indicators for tourism consumption patterns, and systematically evaluating them using a comprehensive assessment approach. This study aims to provide a new theoretical framework for the digital transformation of the tourism industry and practical guidance for enhancing industry resilience and improving risk resistance capabilities. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Method Results Conclusion The transdiagnostic and network model approaches to psychopathology have emerged as an alternative to taxonomic systems. The main goal was to examine the network structure of the transdiagnostic dimensions of emotional disorders in adolescents with subclinical emotional symptoms. In addition, cross‐sectional network analyses were conducted to investigate the relationships between transdiagnostic dimensions, psychopathology, and socio‐emotional adjustment.The sample consisted of a total of 476 adolescents from 85 schools from different provinces in Spain with subthreshold anxiety and depression. The mean age was 13.77 years (SD = 1.43, range: 10–18 years), 73.9% were female. Several questionnaires assessing transdiagnostic dimensions of emotional disorders, mental health difficulties, self‐esteem, perceived social support, sense of belonging at school, and prosocial behaviour were used.A strong interrelation was found between the transdiagnostic variables, except with the positive temperament dimension. The dimensions with the greatest weight were those of traumatic re‐experiencing, intrusive cognitions, avoidance, neuroticism, and depressed mood. Negative relationships were found between the transdiagnostic dimensions of emotional disorders and the protective factors included in the study. Stability analyses indicated that the networks were accurately estimated.The present paper points to the value of conceptualising emotional disorders from a transdiagnostic and network model perspective. In addition, the work provides new insights into the nature of the relationships between transdiagnostic dimensions of emotional disorders, and the role played by risk and protective factors. [ABSTRACT FROM AUTHOR]

For decades, schizotypy has proved its relevance as a useful concept for understanding schizophrenia spectrum disorders. In this paper, schizotypy was considered from a novel perspective, the network approach which conceptualizes mental disorders as dynamic systems, emerging from symptom associations. The aims of this study were to determine central symptoms (those most connected with others), communities (subgroups of symptoms that are more connected among themselves) and bridges (symptoms that connect communities) in a network of schizotypy symptoms. The sample consisted of 775 students, (Mage=20.49, SD = 2.35), 83.9% females. Schizotypal Personality Questionnaire–Brief Revised (SPQ-BR) was used as a measure of schizotypy using "state of the art" network suites. The network analysis returned a sparse, stable network with 7 communities. The most central symptom and the strongest bridge was the sense of eccentricity. Some symptoms related to interpersonal problems were intensively connected with each other, suggesting the importance of this domain for the dynamics of schizotypy. The symptom predictability was substantially higher than in the previous literature and explained 41% variance. The theoretical and practical implications of the obtained results were discussed, as well as their meaning for the future perspective of schizotypy construct. [ABSTRACT FROM AUTHOR]

This study aims to assess pre-post change of 439 patients undergoing a multicomponent treatment (psychodynamic psychotherapy complemented with other treatment components) using a novel network methodology targeting symptoms comorbidity. Patients were recruited from seven clinical sites in the Czech Republic. First, the effectiveness of the treatment was assessed traditionally as a pre-post change in wellbeing, depression, and anxiety using a Bayesian mixed model. The Bayesian factors of time effect (pre-post comparison) on the three outcomes indicate evidence in favor of hypotheses suggesting psychotherapy effectiveness. Second, a network analysis of individual items measuring all three outcomes (Gaussian Graphical Model) was conducted to compare baseline and post-treatment patients' networks in global edge strength, the topography of the network, the centrality of nodes, and the clique percolation. The network density represented by global edge strength was not affected by the treatment. Nevertheless, the network structure changed in a more qualitative manner into a clearer and separated set of node communities, potentially showing a reduction in comorbidity. The central position of the depression node community in the patients' self-reported outcome assessment network was replaced by anxiety and wellbeing node communities after treatment. [ABSTRACT FROM AUTHOR]

A prominent challenge for managing migratory species is the development of conservation plans that accommodate spatiotemporally varying distributions throughout the year. Migratory networks are spatially‐explicit models that incorporate migratory assignment and seasonal abundance data to define patterns of connectivity between stages of the annual cycle. These models are particularly useful for widespread application because different types of migratory data can be used to quantify individual and population‐level movement across the annual cycle of migratory species. While there are clear benefits of combining migratory assignment and abundance data for the development of conservation strategies, there is a concurrent need for corresponding user‐friendly software to facilitate the integration of these data for conservation.Here, we present mignette (migratory network tools ensemble), an R package for developing migratory network models to estimate network connectivity among migratory populations. We demonstrate the functionality of mignette with three empirical examples that highlight the use of different types of tracking data for migratory assignment.mignette facilitates the modelling of migratory networks by providing R functions to: (1) define breeding and nonbreeding nodes, (2) assemble abundance and assignment data and (3) model the migratory network. Additionally, mignette provides R functions to visualize modelled migratory networks.With increasing availability of migratory assignment and abundance data, mignette represents a valuable tool for developing effective conservation strategies for migratory species. [ABSTRACT FROM AUTHOR]

Pedestrian movement has always been one of the main concerns for urban planning and design, but it has become more important within the sustainable development agenda, as walking is crucial to reducing urban emissions and fostering liveable cities. Therefore, urban planners need to take pedestrian movement into consideration as part of the workflow of planning and designing cities. This study outlines a comprehensive workflow tailored for urban planners. It proposes a hybrid model that integrates an agent-based model, which simulates the micro-scale movement of pedestrians in outdoor urban environments, with a network model, which predicts the aggregated pedestrian flows on a macro-scale. The hybrid model is applied to a pedestrian precinct in the city centre of Gothenburg, Sweden, and is compared to real-world measurements. The reasonable agreement between the simulation results and the real-world data supports the reliability of the proposed workflow, underscoring the model's ability to statistically predict pedestrian movement on a large scale and individually on a local scale. Furthermore, the model enables the analysis of flow distributions and movement restrictions and facilitates the analysis of different design scenarios and specific pedestrian behaviour. This functionality is valuable for urban design and planning practice, contributing to the optimisation of pedestrian flow dynamics. [ABSTRACT FROM AUTHOR]

Magnetoreception, the ability to sense magnetic fields, is widespread in animals but remains poorly understood. The leading model links this ability in migratory birds to the photo-activation of the protein cryptochrome. Magnetic information is thought to induce structural changes in cryptochrome via a transient radical pair intermediate. This signal transduction pathway has been the subject of previous all-atom molecular dynamics (MD) simulations, but insights were limited to short timescales and equilibrium structures. To address this, we developed a non-equilibrium coarse-grained MD simulation approach, exploring cryptochrome’s photo-reduction over 20 replicates of 20 µs each. Our results revealed significant structural changes across the protein, with an overall time constant of 3 µs. The C-terminal (CT) region responded on a timescale of 4.7 µs, followed by the EEE-motif, while the phosphate binding loop (PBL) showed slower dynamics (9 µs). Network analysis highlighted direct pathways connecting the tryptophan tetrad to the CT, and distant pathways involving the EEE and PBL regions. The CT-dynamics are significantly impacted by a rearrangement of tryptophan residues in the central electron transfer chain. Our findings underscore the importance of considering longer timescales when studying cryptochrome magnetoreception and highlight the potential of non-equilibrium coarse-grained MD simulations as a powerful tool to unravel protein photoactivation reactions.

Abstract Background Major Depressive Disorder (MDD) poses a significant global health challenge, with symptom presentation potentially varying between adolescents and adults. Adolescence is a critical period marked by heightened vulnerability to interpersonal stresses, yet the impact of these stresses on the structure of depressive symptoms is not well understood. Recognizing the cultural nuances in how depression manifests among adolescents is crucial. To this end, this paper employs a network analysis approach, utilizing a comprehensive symptom checklist from the Multidimensional Depression Assessment Scale (MDAS). Our study investigates the role of interpersonal symptoms within the broader cluster of emotional, cognitive, and somatic symptoms and explores variations in adolescent groups in four Asian and European regions. Methods We recruited a diverse sample of 6,348 adolescents aged 12 to 18 from Hong Kong, Taiwan, the UK, China, and the Netherlands using the Qualtrics platform. Employing the Gaussian Graphical Model, we established a network model of depressive symptoms as measured by the MDAS, segregating the sample into Asian and European regions to examine the interconnections between them. The study focused on identifying central symptom nodes and comparing the network structures between the two groups. Results The analysis identified feeling worthless, low energy, being a burden to others, and low mood as central symptoms of depression. Notably, there were significant differences in the connections between depressive symptoms among Asian (Hong Kong, China and Taiwan) and European (UK and the Netherlands) adolescents, highlighting cultural variations in how interpersonal symptoms interact with emotional, cognitive, and somatic symptoms. Conclusion This study is pioneering in applying network analysis to include interpersonal symptoms in examining depression among a diverse adolescent population. It demonstrates that interpersonal symptoms are integral to the central features of depressive symptoms. Furthermore, our findings suggest that, compared to their UK and Dutch peers, interpersonal symptoms in Asian adolescents are uniquely connected to other symptom clusters, reflecting distinct cultural patterns. Limitations: The study engaged a broad community sample; however, future research could benefit from including a larger sample size to allow for a more detailed analysis of a greater number of symptom nodes.

The All-pairs shortest path problem (ALL-SPP) aims to find the shortest path joining all the vertices in a given graph. This study proposed a new optimal method, Dhouib-matrix-ALL-SPP (DM-ALL-SPP) to solve the ALL-SPP based on column-row navigation through the adjacency matrix. DM-ALL-SPP is designed to generate in a single execution the shortest path with details among all-pairs of vertices for a graph with positive and negative weighted edges. Even for graphs with a negative cycle, DM-ALL-SPP reported a negative cycle. In addition, DM-ALL-SPP continues to work for directed, undirected and mixed graphs. Furthermore, it is characterized by two phases: the first phase consists of adding by column repeated (n) iterations (where n is the number of vertices), and the second phase resides in adding by row executed in the worst case (n∗log(n)) iterations. The first phase, focused on improving the elements of each column by adding their values to each row and modifying them with the smallest value. The second phase is emphasized by rows only for the elements modified in the first phase. Different instances from the literature were used to test the performance of the proposed DM-ALL-SPP method, which was developed using the Python programming language and the results were compared to those obtained by the Floyd-Warshall algorithm.

Background: Major Depressive Disorder (MDD) poses a significant global health challenge, with symptom presentation potentially varying between adolescents and adults. Adolescence is a critical period marked by heightened vulnerability to interpersonal stresses, yet the impact of these stresses on the structure of depressive symptoms is not well understood. Recognizing the cultural nuances in how depression manifests among adolescents is crucial. To this end, this paper employs a network analysis approach, utilizing a comprehensive symptom checklist from the Multidimensional Depression Assessment Scale (MDAS). Our study investigates the role of interpersonal symptoms within the broader cluster of emotional, cognitive, and somatic symptoms and explores variations in adolescent groups in four Asian and European regions. Methods: We recruited a diverse sample of 6,348 adolescents aged 12 to 18 from Hong Kong, Taiwan, the UK, China, and the Netherlands using the Qualtrics platform. Employing the Gaussian Graphical Model, we established a network model of depressive symptoms as measured by the MDAS, segregating the sample into Asian and European regions to examine the interconnections between them. The study focused on identifying central symptom nodes and comparing the network structures between the two groups. Results: The analysis identified feeling worthless, low energy, being a burden to others, and low mood as central symptoms of depression. Notably, there were significant differences in the connections between depressive symptoms among Asian (Hong Kong, China and Taiwan) and European (UK and the Netherlands) adolescents, highlighting cultural variations in how interpersonal symptoms interact with emotional, cognitive, and somatic symptoms. Conclusion: This study is pioneering in applying network analysis to include interpersonal symptoms in examining depression among a diverse adolescent population. It demonstrates that interpersonal symptoms are integral to the central features of depressive symptoms. Furthermore, our findings suggest that, compared to their UK and Dutch peers, interpersonal symptoms in Asian adolescents are uniquely connected to other symptom clusters, reflecting distinct cultural patterns. Limitations: The study engaged a broad community sample; however, future research could benefit from including a larger sample size to allow for a more detailed analysis of a greater number of symptom nodes. [ABSTRACT FROM AUTHOR]

In existing schemes, there has been no analysis or steps built for balancing data integrity and energy consumption of clusters in the network, and providing security in WSN is difficult. In this research work, to overcome these troubles, we have used a Query-based Location-Aware Secure Multicast Routing for Wireless Sensor Networks (QLAMSR) is proposed to attain energy efficiency and security. There are three modules involved here. The first module is about the network model and the system overview to obtain more network lifetime. In the second module, an advanced encryption standard and RC6 algorithms is used here to provide authentication and data integrity. In the third module, efficient energy routes are established and demonstrated to illustrate network reliability. Node trust and reliable route energy efficiency are calculated to produce efficient data integrity through the idea of a fuzzy decision model. The proposed protocol is simulated with the Network Simulator tool (NS 2.34) to analyze the network performance metrics and finally its shown that proposed QLAMSR attains route energy efficiency by 39–92% and data integrity rate by 18–89(packets/sec). [ABSTRACT FROM AUTHOR]

This work aims at comparing the ability of 7 modeling approaches to simulate the pyrolysis kinetics of spruce wood, wheat straw, swine manure, miscanthus and switchgrass. Measurements were taken using a thermogravimetric analyzer (TGA) with 4 heating rates comprised between 5 and 30 K min−1. The obtained results were processed using 3 isoconversional methods (Kissinger–Akahira–Sunose (KAS), Ozawa–Flynn–Wall (OFW) and Friedman), 1-step and 3-step Kissinger models, as well as an advanced fitting method recently proposed by Bondarchuk et al. [1] (Molecules 28:424, 2023, https://doi.org/10.3390/molecules28010424). Seventeen reaction models were considered to derive rate constant parameters, which were used to simulate the variation of the fuel conversion degree α as a function of the temperature T . To complement this benchmarking analysis of the modeling approaches commonly used to simulate biomass pyrolysis, a network model, the bio-CPD (chemical percolation devolatilization), was additionally considered. The suitability of each model was assessed by computing the root-mean-square deviation between simulated and measured α = f (T) profiles. As highlights, the model-free methods were found to accurately reproduce experimental results. The agreement between simulated and measured data was found to be higher with the Friedman model, followed by the KAS, FWO, 3-step, and 1-step Kissinger models. As for the bio-CPD, it failed to predict measured data as well as the above-listed models. To conclude, although it was less efficient than the Friedman, KAS or OFW models, the fitting approach from Bondarchuk et al. [1] (Molecules 28:424, 2023, https://doi.org/10.3390/molecules28010424) still led to satisfactory results, while having the advantage of not requiring the selection of a reaction model a priori. [ABSTRACT FROM AUTHOR]

There are profound methodological challenges facing microdosing research. One way we can address some of these methodological issues is by understanding how psilocybin microdosing fits in the broader existential context of people’s lives. We recruited participants who underwent psilocybin microdosing on their own and consented to being monitored for harm mitigation purposes. We combined momentary ecological assessment and detailed retrospective interviews. Participants reported loosening of mental structures (i.e., less intense strength of thoughts, tangential stream of consciousness), increased salience of external stimuli (varyingly associated with greater interest in otherwise mundane activities, as well as sensory overload), an increase in flexible cognition, a decrease in stable cognition, and various ego-dystonic contents. Highly structured environments were conducive to positive appraisal of experiences and vice versa. Momentary ecological assessment and retrospective interviews yielded differing accounts of microdosing experience. We relate our findings to stable and flexible cognition, as well as the notion of salience. We point out the necessity for systematic mixed methods studies to better characterize the lived experience of taking low doses of psilocybin. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Economia e Sociologia Rural is the property of Sociedade Brasileira de Economia e Sociologia Rural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The All-pairs shortest path problem (ALL-SPP) aims to find the shortest path joining all the vertices in a given graph. This study proposed a new optimal method, Dhouib-matrix-ALL-SPP (DM-ALL-SPP) to solve the ALL-SPP based on column-row navigation through the adjacency matrix. DM-ALL-SPP is designed to generate in a single execution the shortest path with details among all-pairs of vertices for a graph with positive and negative weighted edges. Even for graphs with a negative cycle, DM-ALL-SPP reported a negative cycle. In addition, DMALL-SPP continues to work for directed, undirected and mixed graphs. Furthermore, it is characterized by two phases: the first phase consists of adding by column repeated (n) iterations (where n is the number of vertices), and the second phase resides in adding by row executed in the worst case (n*log(n)) iterations. The first phase, focused on improving the elements of each column by adding their values to each row and modifying them with the smallest value. The second phase is emphasized by rows only for the elements modified in the first phase. Different instances from the literature were used to test the performance of the proposed DM-ALL-SPP method, which was developed using the Python programming language and the results were compared to those obtained by the Floyd-Warshall algorithm. [ABSTRACT FROM AUTHOR]

In this study, a hybrid undesirable network data envelopment analysis model is developed for assessing the efficiency of social care systems. Studies investigating the performance of social care activities have mostly focused on a single social care type. To date, no study has proposed an integrated framework for assessing social care performance. To address this gap in the literature, the developed model incorporates four divisions for a comprehensive performance measurement: disability care, child and youth care, women care, and elderly care. In the model, the government's expenditure on social care is the initial input, and the efficiency of social care activities in the aforementioned four divisions is evaluated in the second stage. Social care facilities and social care workers serve as the nonradial input and radial input, respectively. Care recipients and those affected by violence serve as the desirable output and undesirable output, respectively, in the second stage. The empirical results have several practical implications for Taiwan's social care system. [ABSTRACT FROM AUTHOR]

This study models a firm's investors as connected via a social network and examines how an appropriate investor relations (IR) strategy can maximize information flow through this network. When IR can initially reach only a few investors, information flows faster when IR targets highly connected investors through, for example, direct contact. When IR can initially reach many investors, information flows faster from untargeted broadcast-type communications that reach random investors. Turning to empirically measurable outcomes of information flow, we show that the time series of bid-ask spreads have a hump-shaped pattern, first increasing and then decreasing. Faster information flows cause a sharper, but briefer, bid-ask spread spike. Such results provide a rigorous framework for thinking about how IR activities drive stock liquidity, and unite several observed empirical regularities into an investor social network-based framework for IR activities, a research area where descriptive empirical findings have outpaced theory. [ABSTRACT FROM AUTHOR]

Aquatic invasive species (AIS) are one of the greatest threats to the functioning of aquatic ecosystems worldwide. Once an invasive species has been introduced to a new region, many governments develop management strategies to reduce further spread. Nevertheless, managing AIS in a new region is challenging because of the vast areas that need protection and limited resources. Spatial heterogeneity in invasion risk is driven by environmental suitability and propagule pressure, which can be used to prioritize locations for surveillance and intervention activities. To better understand invasion risk across aquatic landscapes, we developed a simulation model to estimate the likelihood of a waterbody becoming invaded with an AIS. The model included waterbodies connected via a multilayer network that included boater movements and hydrological connections. In a case study of Minnesota, we used zebra mussels (Dreissena polymorpha) and starry stonewort (Nitellopsis obtusa) as model species. We simulated the impacts of management scenarios developed by stakeholders and created a decision‐support tool available through an online application provided as part of the AIS Explorer dashboard. Our baseline model revealed that 89% of new zebra mussel invasions and 84% of new starry stonewort invasions occurred through boater movements, establishing it as a primary pathway of spread and offering insights beyond risk estimates generated by traditional environmental suitability models alone. Our results highlight the critical role of interventions applied to boater movements to reduce AIS dispersal. [ABSTRACT FROM AUTHOR]

Pedestrian movement has always been one of the main concerns for urban planning and design, but it has become more important within the sustainable development agenda, as walking is crucial to reducing urban emissions and fostering liveable cities. Therefore, urban planners need to take pedestrian movement into consideration as part of the workflow of planning and designing cities. This study outlines a comprehensive workflow tailored for urban planners. It proposes a hybrid model that integrates an agent-based model, which simulates the micro-scale movement of pedestrians in outdoor urban environments, with a network model, which predicts the aggregated pedestrian flows on a macro-scale. The hybrid model is applied to a pedestrian precinct in the city centre of Gothenburg, Sweden, and is compared to real-world measurements. The reasonable agreement between the simulation results and the real-world data supports the reliability of the proposed workflow, underscoring the model’s ability to statistically predict pedestrian movement on a large scale and individually on a local scale. Furthermore, the model enables the analysis of flow distributions and movement restrictions and facilitates the analysis of different design scenarios and specific pedestrian behaviour. This functionality is valuable for urban design and planning practice, contributing to the optimisation of pedestrian flow dynamics.

Abstract Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for identifying spatial domains in spatial transcriptomics data with joint learning. We demonstrate that spatial domains can be precisely characterized and discriminated by the topological structure of cell networks, facilitating identification and interpretability of spatial domains, which outperforms state-of-the-art baselines. Furthermore, we prove that network model offers an effective and efficient strategy for integrative analysis of spatial transcriptomics data from various platforms.

Objective The embedded granularity of target minerals refers to their particle size and distribution in ore, which directly influences the design and effectiveness of ore beneficiation processes. Therefore, the measurement of embedded granularity of target minerals is a crucial task in process mineralogy research. In traditional process mineralogy research, the main method for observing and analysing ore samples is to use a polarized light microscopy. However, this approach generally suffers from problems such as long processing times, susceptible to subjective factors, and difficulties in achieving automation and large-scale applications. To overcome these limitations, this paper proposes a method for detecting the embedded granularity of metal minerals under the microscope based on based on deep learning. Methods This method takes specimens from the Shuichang magnetic ore deposit in Tangshan City, Hebei Province as the object. We take photographs under the reflected light conditions using a polarized light microscope to create a dataset, and design a mineral recognition network model based on the Deeplabv3+ network. Thereby it enables automated feature extraction and intelligent recognition of the target metal minerals. This method achieves segmentation of the target minerals by generating binary images of the desired metal minerals. Finally, the analysis and measurement of the embedded granularity of the target metal minerals are completed by combining the maximum Feret diameter. Results Compared with traditional manual microscopic measurement methods, the application of image measurement based on deep learning in mineral particle analysis has increased the processing speed by approximately 119.8 times and the measurement accuracy 169.5 times when measuring the same mineral particles, demonstrating its significant improvement in terms of processing efficiency and measurement accuracy. Conclusion The deep learning-based method for detecting the embedded granularity of metal minerals under a microscope significantly reduces the detection time and enhances the detection accuracy for mineral embedded granularity. Moreover, it eliminates the influence of subjective factors, which is of great significance for promoting the intelligent development of process mineralogy.

Cortical stimulation is emerging as an experimental tool in basic research and a promising therapy for a range of neuropsychiatric conditions. As multielectrode arrays enter clinical practice, the possibility of using spatiotemporal patterns of electrical stimulation to induce desired physiological patterns has become theoretically possible, but in practice can only be implemented by trial-and-error because of a lack of predictive models. Experimental evidence increasingly establishes traveling waves as fundamental to cortical information-processing, but we lack an understanding of how to control wave properties despite rapidly improving technologies. This study uses a hybrid biophysical-anatomical and neural-computational model to predict and understand how a simple pattern of cortical surface stimulation could induce directional traveling waves via asymmetric activation of inhibitory interneurons. We found that pyramidal cells and basket cells are highly activated by the anodal electrode and minimally activated by the cathodal electrodes, while Martinotti cells are moderately activated by both electrodes but exhibit a slight preference for cathodal stimulation. Network model simulations found that this asymmetrical activation results in a traveling wave in superficial excitatory cells that propagates unidirectionally away from the electrode array. Our study reveals how asymmetric electrical stimulation can easily facilitate traveling waves by relying on two distinct types of inhibitory interneuron activity to shape and sustain the spatiotemporal dynamics of endogenous local circuit mechanisms.SIGNIFICANCE STATEMENT Electrical brain stimulation is becoming increasingly useful to probe the workings of brain and to treat a variety of neuropsychiatric disorders. However, stimulation is currently performed in a trial-and-error fashion as there are no methods to predict how different electrode arrangements and stimulation paradigms will affect brain functioning. In this study, we demonstrate a hybrid modeling approach, which makes experimentally testable predictions that bridge the gap between the microscale effects of multielectrode stimulation and the resultant circuit dynamics at the mesoscale. Our results show how custom stimulation paradigms can induce predictable, persistent changes in brain activity, which has the potential to restore normal brain function and become a powerful therapy for neurological and psychiatric conditions.

The article solves an urgent problem, which is to develop a new approach to finding solutions to improve technological methods to combat negative water leakage in underround spaces. We propose the use of dynamic programming methods to select the optimal technology to secure such spaces. In accordance with the algorithm proposed in this paper, the problem was broken into a number of stages. At each stage, an optimal solution was sought (organisation of transport, delivery of materials to the destination, selection of materials, etc.). Thus, we applied a decomposition approach that allowed us to take into account the variety of parameters that affect the efficiency of the process. All these stages and their corresponding technological solutions were formalised by building network models. In these network models, vertices corresponded to solutions, and the distances between vertices (edges) corresponded to the value of the optimisation parameter. Thus, the shortest route from the initial to the final vertex corresponded to the optimal technological solution to combat negative water leakage in underground spaces. Based on the systematisation of data on technologies to combat water inflow into underground spaces, basic and refined models were developed. These models allowed us to take into account the risks associated with water breakthroughs into underground spaces. To minimise the risks, additional measures to combat water inflows are envisaged. In the practical part of this study, the results of the selection of a method with which to control water inflows are presented. This method involves the use of anchoring to reduce water filtration. According to the results of field observations, no water breakthroughs into the underground space were recorded. [ABSTRACT FROM AUTHOR]

The frustum model simulates network evolution by extending cliques, which represent highly interacting groups in social networks. In each time-step, new vertices are added adjacent to existing cliques of prescribed order. The model exhibits several features of social networks, such as densification, short distances, bad spectral expansion, and high local clustering. [ABSTRACT FROM AUTHOR]

This study introduces a hierarchical pyramid structure as a novel framework for social network analysis, differing fundamentally from traditional hierarchical models by its expansive, connected topology from the apex downwards. We undertake a comprehensive examination of key social network attributes, such as degree distribution, clustering coefficients, and small-world properties, through this new lens. Our empirical investigation supports a hypothesis regarding the predominance of large pyramids in real-world social networks, corroborating findings from the existing literature. Notably, our results reveal significant variations in the size distribution of basic pyramids across different types of networks. This disparity indicates that current generative models for social networks may not adequately capture the complexities inherent in real-world interactions, thus underscoring the need for innovative modeling techniques. Moreover, this paper proposes a pyramid-based criterion for defining the small-world characteristic within individual networks, a departure from conventional metrics that typically generalize across network types. By applying this framework, we conduct detailed analyses of a broad spectrum of real-world networks, validating the efficacy and adaptability of the pyramid structure in advancing social network research. [ABSTRACT FROM AUTHOR]

Under normal conditions the principal cells of the striatum, medium spiny neurons (MSNs), show structured cell assembly activity patterns which alternate sequentially over exceedingly long timescales of many minutes. It is important to understand this activity since it is characteristically disrupted in multiple pathologies, such as Parkinson's disease and dyskinesia, and thought to be caused by alterations in the MSN to MSN lateral inhibitory connections and in the strength and distribution of cortical excitation to MSNs. To understand how these long timescales arise we extended a previous network model of MSN cells to include synapses with short-term plasticity, with parameters taken from a recent detailed striatal connectome study. We first confirmed the presence of sequentially switching cell clusters using the non-linear dimensionality reduction technique, Uniform Manifold Approximation and Projection (UMAP). We found that the network could generate non-stationary activity patterns varying extremely slowly on the order of minutes under biologically realistic conditions. Next we used Simulation Based Inference (SBI) to train a deep net to map features of the MSN network generated cell assembly activity to MSN network parameters. We used the trained SBI model to estimate MSN network parameters from ex-vivo brain slice calcium imaging data. We found that best fit network parameters were very close to their physiologically observed values. On the other hand network parameters estimated from Parkinsonian, decorticated and dyskinetic ex-vivo slice preparations were different. Our work may provide a pipeline for diagnosis of basal ganglia pathology from spiking data as well as for the design pharmacological treatments. [ABSTRACT FROM AUTHOR]

Once a dam breaks, it will cause serious loss of life and property. The dam failure process shows typical chain characteristics, and identifying the typical patterns and evolutionary characteristics of dam failure accident chains is conducive to the development of engineering disaster prevention and mitigation work. Embankment dam is one of the main dam types of reservoir dams in China. Aiming at the problems of complex disaster-causing factors, strong uncertainty and single quantitative analysis method of dam failure events, the paper takes embankment dam as the research object, and establishes a complex network model of embankment dam failure accident chains by deeply exploring the potential risk events and dam failure patterns in historical dam failure data. Based on the differences in the chronological characteristics of embankment dam failures, it quantifies the correlation degree and influence degree among the risk events in typical dam failureaccident chains using a risk event co-occurrence matrix. The method of quantifying the probability of evolution of embankment dam failure accident chains is proposed, and the evolution results are compared with the existing research results, which verifies the effectiveness and feasibility of the proposed method to a certain extent. The research results provide important technical support for strengthening the risk prevention and control of reservoir dam. [ABSTRACT FROM AUTHOR]

Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized AF treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in atrioventricular (AV) nodal refractory period and conduction delay. Methods: A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1- minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation. Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, aresp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of aresp. [ABSTRACT FROM AUTHOR]

Based on complex network theory, this paper establishes a Boolean competitive relationship network model and a weighted competitive relationship network model for building material enterprises based on big data and performs empirical analyses with construction prefabricated component (PC) enterprises in the Beijing–Tianjin–Hebei region as research samples. Results show the competitive relationship network of PC enterprises in the Beijing–Tianjin–Hebei region is characterised by the development of a small-world network, and its cumulative degree and out-degree (in-degree) intensity distributions are single-scale networks with fast-decaying tails. The network has strong network clustering and assortativity, and it can truly reflect the competitive status and dynamics of the enterprises. [ABSTRACT FROM AUTHOR]

Clearing and successfully characterizing ecosystem service flow paths has become a key bottleneck restricting in-depth research on the supply and demand relationships of ecosystem services. At present, although some explorations have been performed using water ecosystem services as a pioneer, the nature of its network and the fact that ecological base flow needs to be eliminated have been ignored. This study used InVEST and network models to consider ecological base flow, quantifying the supply, demand, and flow paths of freshwater ecosystem services in the Xiangjiang River Basin. The results showed that the overall distribution of the water supply in the Xiangjiang River Basin from 2000 to 2020 shows a pattern of higher supply in the south and lower supply in the north. The distribution of water demand shows higher levels in the north and lower levels in the south. The network density remains at its maximum level. The results of this study have provided a scientific basis for water resource management in river basins and improving ecological compensation mechanisms. [ABSTRACT FROM AUTHOR]

Gamma oscillations are widely seen in the cerebral cortex in different states of the wake-sleep cycle and are thought to play a role in sensory processing and cognition. Here, we study the emergence of gamma oscillations at two levels, in networks of spiking neurons, and a mean-field model. At the network level, we consider two different mechanisms to generate gamma oscillations and show that they are best seen if one takes into account the synaptic delay between neurons. At the mean-field level, we show that, by introducing delays, the mean-field can also produce gamma oscillations. The mean-field matches the mean activity of excitatory and inhibitory populations of the spiking network, as well as their oscillation frequencies, for both mechanisms. This mean-field model of gamma oscillations should be a useful tool to investigate large-scale interactions through gamma oscillations in the brain. [ABSTRACT FROM AUTHOR]

Introduction: The transmission dynamics of the recent mpox outbreak highlights the lack of infrastructure available to rapidly respond to novel STI outbreaks, of which Asia and Oceania remains particularly susceptible. Here, we simulate outbreaks in this setting and propose the use of pre-emptive vaccination within the men who have sex with men (MSM) community before the arrival and establishment of the virus. Materials and methods: Using data driven heterogeneous sexual contact networks, we simulated outbreaks of mpox in Singapore, Hong Kong, and Sydney. An individual based SEIR compartmental model was used to simulate epidemic trajectories and the impact of different vaccination uptakes was assessed in their ability to avert or suppress outbreaks upon the arrival of mpox within the MSM populations. Results: The highly dense sexual networks of Singapore and Sydney experience rapid outbreaks, with infection peaks occurring at day 41 and 23 respectively, compared to Hong Kong which occurs at day 77. Across the simulations with no vaccination, 68.2%–89.7% of the MSM community will become infected with mpox across the different cities, over a simulation period of 1 year. By implementing vaccination strategies, the infection rate across the cities can be reduced to as low as 3.1% of the population (range: 3.1%–82.2%) depending on the implementation and uptake of the vaccine. Vaccination is also extremely effective in slowing the start of the epidemic, delaying the epidemic peak by 36–50 days in Hong Kong, or even preventing the outbreak of mpox. Discussion: With extremely dense and well-connected sexual contact networks, where 65.2%–83.2% of the population are connected to a super-spreader in the different contact networks, pre-emptive or immediate vaccination upon identification of the first case is strongly recommended to help better manage the outbreak of mpox and prevent potential straining of healthcare systems.

Soil pore is an important part of soil structure. According to the causes of formation, soil pores can be divided into biological pores formed by animal movement and plant root development and non-biological pores formed by dry-wet and freeze-thaw alternation or artificial tillage. The soil pore structure affects the migration of water, gas, nutrients and so on in the soil, especially the macropores can also produce water or solute preferential migration. Studying soil pores is of great significance for predicting soil hydraulic properties, reducing groundwater pollution and soil nutrient loss. Based on previous studies on soil pore structure, this paper systematically summarized the role of soil pores, influencing factors and the advantages and disadvantages of various research methods. This paper not only introduces traditional methods (including direct and indirect methods), but also summarizes the new research on soil pores combined with computed tomography (CT) technology and other science and technology in recent years. Finally, the prospect and development trend of soil pore research in the future were predicted, so as to provide reference for further research on soil pore structure.

The reliable way to discern human emotions in various circumstances has been proven to be through facial expressions. Facial expression recognition (FER) has emerged as a research topic to identify various essential emotions in the present exponential rise in research for emotion detection. Happiness is one of these basic emotions everyone may experience, and facial expressions are better at detecting it than other emotionmeasuring methods. Most techniques have been designed to recognize various emotions to achieve the highest level of general precision. Maximizing the recognition accuracy for a particular emotion is challenging for researchers. Some techniques exist to identify a single happy mood recorded in unrestricted video. Still, they are all limited by the processing of extreme head posture fluctuations that they need to consider, and their accuracy still needs to be improved. This research proposes a novel hybrid facial emotion recognition using unconstraint video to improve accuracy. Here, a Deep Belief Network (DBN) with long short-term memory (LSTM) is employed to extract dynamic data from the video frames. The experiments conducted uses decision-level and feature-level fusion techniques are applied unconstrained video dataset. The outcomes show that the proposed hybrid approach may be more precise than some existing facial expression models. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people's lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided. [ABSTRACT FROM AUTHOR]

The mechanical behavior of thermoplastics is strongly rate-dependent, and oftentimes it is difficult to find constitutive models that can accurately describe their behavior in the small to moderate strain regime. In this paper, a hyperelastic network model (modified neo-Hookean) and a set of experiments are presented. The testing consists of monotonic tensile loading as well as stress relaxation and zero stress creep. Two materials were tested, polyoxymethylene (POM) and recycled polypropylene (rPP), representing one more rigid and brittle and one softer and more ductile semi-crystalline polymer. The model contains two main novelties. The first novelty is that the stiffness is allowed to vary with the elastic deformation (in contrast to a standard neo-Hookean model). The second novelty is that the exponent governing viscous relaxation is allowed to vary with the viscous deformation. The basic features of the new model are illustrated, and the model was fitted to the experimental data. The model proved to be able to describe the experimental results well. [ABSTRACT FROM AUTHOR]

Introduction: The transmission dynamics of the recent mpox outbreak highlights the lack of infrastructure available to rapidly respond to novel STI outbreaks, of which Asia and Oceania remains particularly susceptible. Here, we simulate outbreaks in this setting and propose the use of pre-emptive vaccination within the men who have sex with men (MSM) community before the arrival and establishment of the virus. Materials and methods: Using data driven heterogeneous sexual contact networks, we simulated outbreaks of mpox in Singapore, Hong Kong, and Sydney. An individual based SEIR compartmental model was used to simulate epidemic trajectories and the impact of different vaccination uptakes was assessed in their ability to avert or suppress outbreaks upon the arrival of mpox within the MSM populations. Results: The highly dense sexual networks of Singapore and Sydney experience rapid outbreaks, with infection peaks occurring at day 41 and 23 respectively, compared to Hong Kong which occurs at day 77. Across the simulations with no vaccination, 68.2%e89.7% of the MSM community will become infected with mpox across the different cities, over a simulation period of 1 year. By implementing vaccination strategies, the infection rate across the cities can be reduced to as low as 3.1% of the population (range: 3.1%e82.2%) depending on the implementation and uptake of the vaccine. Vaccination is also extremely effective in slowing the start of the epidemic, delaying the epidemic peak by 36e50 days in Hong Kong, or even preventing the outbreak of mpox. Discussion: With extremely dense and well-connected sexual contact networks, where 65.2%e83.2% of the population are connected to a super-spreader in the different contact networks, pre-emptive or immediate vaccination upon identification of the first case is strongly recommended to help better manage the outbreak of mpox and prevent potential straining of healthcare systems. [ABSTRACT FROM AUTHOR]

Users are now able to provide opinions and feedback in the form of reviews for any product, service, or business on social networking and e-commerce websites. Due to the significant user effect of reviews, spammers utilize phony reviews to elevate their organization or product and denigrate their rivals. On any given platform, it is thought that 14% of the reviews are fraudulent. To identify bogus reviews, several academics have put forth several strategies. The drawback of existing techniques is that they analyze the entire review text, which lengthens calculation times and reduces accuracy. In our suggested method, just these elements and their corresponding feelings are used for the detection of phony reviews. Aspects that have been retrieved are sent to CNN for learning. To detect false reviews, the reproduced attributes are input into long short-term memory (LSTM). As far as we are aware, despite the optimization it provides, aspects of replication and extraction are not employed to detect fake reviews, which is a big contribution from us. Performance comparisons with more modern methods are done using the Ott and Yelp Filter datasets. Analysis of the results of experiments shows that our suggested strategy beats current strategies. To demonstrate that dense hybrid network models (d-HNM) are superior to established machine learning techniques for difficult computing problems, our approach is also contrasted with others. [ABSTRACT FROM AUTHOR]