Strength Of Connections Research Articles

The Impact of Emotion Network Density on Psychological Distress in Chinese Parents of Children with Autism: A Daily Diary Study.

Parents of children with autism often experience a wide range of emotions in their daily lives. However, previous research has primarily focused on average levels of emotional challenges, neglecting the significance of daily emotion dynamics that may underlie parental psychological functioning. This study adopted a dynamic network approach to examine the strength of temporal connections within and between various emotions-referred to as emotion network density-and further explore its impacts on parental psychological distress. Participants included 76 Chinese parents (M = 36.36 years, SD = 3.95 years; 58 mothers) of children with autism. Parents reported their psychological distress at baseline and then completed measures of daily emotions over a 14-day period. The densities of overall, positive, and negative emotion networks were estimated using the Multilevel Vector Auto-Regression model. The results indicated that higher densities of the overall and negative emotion networks were associated with increased stress, anxiety, and depressive symptoms in parents. Further analysis of network components showed that the in-strength of fear and guilt (i.e., their likelihood of being affected by other emotions) and the out-strength of anger and guilt (i.e., their capacity to influence other emotions) were positively related to parental psychological distress. However, neither the overall density of the positive emotion network nor its specific components showed a significant relationship with parental psychological distress. These findings highlight the importance of considering the daily dynamics of emotions, particularly negative emotions, from a network perspective to better understand the development of psychological distress in parents of children with autism.

The cerebellar glucose metabolism in moyamoya vasculopathy and its correlation with neurocognitive performance after cerebral revascularization surgery: a [18F]FDG PET study.

The vascular cognitive impairment (VCI) is quite common in moyamoya vasculopathy (MMV). However, the abnormality of cerebellar glucose metabolism in MMV and its relationship with patients' neurocognitive performance were few reported. In this study, we aimed to investigate the relationship between neurocognitive performance and cerebellar glucose metabolism. Furthermore, the cerebellar glucose metabolism changes after combined revascularization surgery were also researched. We retrospectively analyzed the 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography ([18F]FDG PET) images and their neuropsychological scales in 93 eligible MMV patients by comparing their cerebellar standardized uptake values ratio (SUVR) and metabolic covariant network (MCN) among different neurocognitive groups. Then, forty-two MMV patients with VCI who underwent combined revascularization surgery were prospectively observed. According to their neuropsychological performance at 6-month follow-up, these patients were assigned to cognitive improved group (n = 22) and non-improved group (n = 20). The cerebellar SUVR and MCN changes were also analyzed. SUVR of right Lobule VI/Crus II/VIII decreased when cognitive impairment progression (P < 0.05, Least-Significant Difference [LSD] post hoc analysis). The cerebellar glucose metabolic pattern can be divided into two parts, in which the cerebellar posterior lobe was positively related to patients' neurocognitive performance, while the vermis and anterior lobe showed negative relationship with the neurocognitions (P < 0.001). Further MCN analysis expound that the degree of right Lobule VI/Crus II/VIII displayed decreased tendency as cognitive impairment worsened (P < 0.05, LSD post hoc analysis). After revascularization surgery, the SUVR of right cerebellar posterior lobe significantly promoted in improved group (P < 0.001). Besides, we also witnessed the SUVR improvement in left cerebral hemisphere, thalamus, and red nucleus (P < 0.001). The MCN analysis revealed that the posterior connective strength improvement among right Lobule VI and several cerebral regions significantly correlated with memory and executive screening (MES) score (P < 0.001, false discovery rate corrected). We found that the hypometabolism of cerebellar posterior lobe, especially in the right Lobule VI, was associated with MMV patients' neuropsychological performance, while the anterior lobe and vermis showed opposites tendencies. Combined revascularization surgery improved the posterior cerebellar metabolism and was associated with favorable neurocognitive outcomes, which might be related to the activation of cortico-rubral-cerebellar pathway.

Numerical Modelling of the Punching Shear Response of Shearhead Connections between a CFT Column and a Flat Reinforced Concrete Slab

In this article, the punching shear response of shearhead connections between a Reinforced Concrete (RC) flat slab and interior Concrete-Filled steel Tubular (CFT) columns under static load is investigated by using a 3D nonlinear finite element modelling approach. The accuracy of the numerical model is verified against recent experimental results by comparing the results of the load–displacement relationship, failure models and cracking patterns. Based on validated numerical models, parametric investigations are conducted to examine the effects of slab thickness, concrete compressive strength, length of shearhead arm, size of shearhead cross section and flexural reinforcement ratio on the punching shear response of the connections. Then, some recommendations are given for the design of the effective parameters of shearhead connections, particularly focusing on the effective length and cross-section size of shearhear arms. Finally, a design equation considering the effect of the effective length of shearhead arms and flexural reinforcement ratio is proposed to predict the punching shear strength of shearhead connections. The comparison of punching shear strengths predicted by the proposed equation and some design codes shows that the proposed equation can predict the punching shear strength of shearhead connections more accurately than those from ACI 318-14 and Eurocode 2.

Electroencephalographic power ratio and peak frequency difference associate with central sensitization in chronic pain.

Central sensitization, or increased responsiveness of the central nervous system to sensory input, is present in many chronic pain patients. Clinically, it is detected through subjective, patient-reported measures. There is a need for reliable, direct measurements of neural response to controlled stimuli to quantify neuronal dysfunction in pain. The goal of this work is to investigate cortical activity, recorded via electroencephalogram (EEG), during objective and calibrated painful stimulation in chronic pain patients.&#xD;Approach. Chronic pain patients (N=8) and healthy controls (N=8) participated in this study. We recorded electroencephalography (EEG) at rest (baseline) and during evoked pain tasks, including thermal and mechanical stimuli. The evoked pain was applied following the quantitative sensory testing (QST) protocol, which is a research technique that applies objective, calibrated painful stimuli. &#xD;Main results. Peak alpha frequency at rest was significantly lower in chronic pain patients compared to healthy controls (p<0.0002), while EEG alpha/theta and alpha/beta power ratios at rest were higher in patients (p<0.0002). During thermal QST, these power ratios decreased in patients and increased in controls (p<0.0002 for both). During mechanical QST, power ratios decreased or did not change. Furthermore, the peak theta-beta frequency difference at baseline was significantly lower in patients compared to controls (p<0.0002). During thermal QST, this difference increased in patients and decreased in controls; during mechanical QST, this difference increased in both patients and controls (p<0.0002). Functional connectivity analysis showed that controls had greater baseline theta connectivity strength that increased during mechanical QST (p<0.0002).&#xD;Significance. This work demonstrates differential patterns of EEG activity at rest and during acute painful stimulation in chronic pain patients compared to healthy controls. These measures may quantify an individual's tendency to experience chronic pain and central sensitization and serve as diagnostic biomarkers.

Exploring the antecedents of knowledge transfer among stakeholders in green building projects: From the configuration perspective

With the rapid development of green buildings around the world, knowledge transfer among stakeholders is of great significance in promoting the innovation and application of green building technologies. However, due to the complexity and specificity of green building projects, current studies have not fully established a comprehensive understanding of how elements from various dimensions (i.e., subject, relationship, context, and activity) collectively impact knowledge transfer among stakeholders in green building projects. Therefore, this study aims to explore the key factors that affect knowledge transfer among stakeholders in green building projects and how these factors work together to enhance the effectiveness of knowledge transfer (EKT). Firstly, the key factors influencing knowledge transfer were pinpointed: cognitive differences, connection strength, organizational climate, information technology capability, transmit willingness, receive willingness, perceived policy support, and richness of transfer channels. Then, an examination of the collected valid samples was conducted through configuration analysis utilizing the fuzzy set qualitative comparative analysis (fsQCA) method. The findings reveal five configurations, categorized as strong connection-driven, policy support-driven, and both-driven. Lastly, some combination strategies were outlined for different categories to assist green building companies in enhancing EKT. This study not only enhances the existing green building literature by developing a framework of factors that influence knowledge transfer among stakeholders, but also provides a reference for corporate executives to choose the best strategies to enhance EKT in green building projects.

Possibilities of analyzing the network connectivity of ideological and monothematic radical online communities on VKontakte

The article discusses the possibilities of applying social network analysis methods to search for network connectivity of audiences of single-issue (anti-vaxxer, eco-radical, antinuker) and ideological (radical Islamist, left-wing and right-wing) radical online communities of the VKontakte social network. To determine the network connectivity between online communities of ideological and single-issue radicals using a dictionary of linguistic markers, an automatic search and selection of communities was performed by screening the selected units. Then, a weighted graph of connections was constructed and analysed. In the graph, the nodes are communities, and the edges connect communities that have at least one common subscriber. The analysis of the following metrics is illustrative: weighted degree, degree centrality, and weighted betweenness centrality. To assess the strength of connections between single-issue communities and communities of ideological radicals, the degree of assortativity was measured by compiling a matrix that describes the assortativity of communities by their type. It is shown that, despite the communities’ different sizes and information activity, this approach makes it possible to assess their network connectivity.

Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks

Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic changes, yet it cannot collect this information from subcortical structures, such as the thalamus, which is involved in several key functional networks. To address this drawback, we propose a machine-learning-based approach to predict cortical-thalamic functional connectivity using cortical fNIRS data. We applied graph convolutional networks (GCN) to two datasets obtained from healthy adults and neonates with early brain injuries, respectively. Each dataset contained fNIRS connectivity data as input to the predictive models, while the connectivity from functional magnetic resonance imaging (fMRI) served as training targets. GCN models performed better compared to conventional methods, such as support vector machine and feedforward fully connected artificial neural networks, on both identifying the connections as binary classification tasks, and regressing the quantified strengths of connections. We also propose the addition of inter-subject connections into the GCN kernels could improve performance and that GCN models are resilient to noise in fNIRS data. Our results show it is feasible to identify subcortical activity from cortical fNIRS recordings. The findings can potentially extend the use of fNIRS in clinical settings for brain monitoring in critically ill patients.

КЛЮЧЕВЫЕ КОНЦЕПТЫ И СВЯЗИ МЕЖДУ РАЗДЕЛАМИ ШКОЛЬНОГО КУРСА АСТРОНОМИИ

The problem of identifying the key concepts of the school astronomy course and establishing intra-subject relationships between different sections of the course is analyzed. As result of the content analysis of the school astronomy textbook, 42 key concepts have been identified, the knowledge of which is of fundamental importance for the course assimilation. In order to determine the strength of intra-subject connections, the keyword lists are obtained for each section of the astrono-my course indicating the number of their uses, which are actually formalized models of the compared texts. Using special computer programs on ABCPascal, are cal-culated: 1) the cosine measure of proximity between any two sections; 2) the Dice measure that reacts to the presence of words in texts and does not take into account the number of word uses. The paradigmatic connection between different terms was taken into account, for example: Sun – star, Saturn – planet, etc. Matrices of proximity measures are obtained, and based on them are constructed: 1) the graph showing intra-subject connections between the astronomy textbook sections; 2) the graph that takes into account the «distances» between sections in a multidimensional semantic space. In order to assess the integration degree of various sections into the astronomy course, the average coefficients of semantic proximity of each section with other sections are calculated. It is found that their greatest values are in sections 3 (Motion of celestial bodies), 5 (Methods of studying celestial bodies) and 7 (Stars). They are close to each other and form the astronomy textbook core.

Cortico-thalamic tremor circuits and their associations with deep brain stimulation effects in essential tremor.

Essential tremor (ET) is one of the most common movement disorders in adults. Deep brain stimulation (DBS) of the ventralis intermediate nucleus (VIM) of the thalamus and/or the posterior subthalamic area (PSA) has been shown to provide significant tremor suppression in patients with ET, but with significant inter-patient variability and habituation to the stimulation. Several non-invasive neuromodulation techniques targeting other parts of the central nervous system, including cerebellar, motor cortex, or peripheral nerves, have also been developed for treating ET, but the clinical outcomes remain inconsistent. Existing studies suggest that pathology in ET may emerge from multiple cortical and subcortical areas, but its exact mechanisms remain unclear. By simultaneously capturing neural activities from motor cortices and thalami, and hand tremor signals recorded via accelerometers in fifteen human subjects who have undergone lead implantations for DBS, we systematically characterized the efferent and afferent cortico-thalamic tremor networks. Through the comparisons of these network characteristics and tremor amplitude between DBS OFF and ON conditions, we further investigated the associations between different tremor network characteristics and the magnitude of DBS effect. Our findings implicate the thalamus, specifically the contralateral hemisphere, as the primary generator of tremor in ET, with a significant contribution of the ipsilateral hemisphere as well. Although there is no direct correlation between the cortico-tremor connectivity and tremor power or reduced tremor by DBS, the strength of connectivity from the motor cortex to the thalamus and vice versa at tremor frequency predicts baseline tremor power and effect of DBS. Interestingly, there is no correlation between these two connectivity pathways themselves, suggesting that, independent of the subcortical pathway, the motor cortex appears to play a relatively distinct role, possibly mediated through an afferent/feedback loop in the propagation of tremor. DBS has a greater clinical effect in those with stronger cortico-thalamo-tremor connectivity involving the contralateral thalamus, which is also associated with bigger and more stable tremor measured with an accelerometer. Interestingly, stronger cross-hemisphere coupling between left and right thalami is associated with more unstable tremor. Together this study provides important insights into a better understanding of the cortico-thalamic tremor generating network and its implication for the development of patient-specific therapeutic approaches for ET.

Quantifying information stored in synaptic connections rather than in firing patterns of neural networks.

A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of connections among the constituent neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing patterns of neural networks, little is known about quantifying information encoded by its synaptic connections. Here, we develop a theoretical framework using continuous Hopfield networks as an exemplar for associative neural networks, and data that follow mixtures of broadly applicable multivariate log-normal distributions. Specifically, we analytically derive the Shannon mutual information between the data and singletons, pairs, triplets, quadruplets, and arbitrary n-tuples of synaptic connections within the network. Our framework corroborates well-established insights about storage capacity of, and distributed coding by, neural firing patterns. Strikingly, it discovers synergistic interactions among synapses, revealing that the information encoded jointly by all the synapses exceeds the 'sum of its parts'. Taken together, this study introduces an interpretable framework for quantitatively understanding information storage in neural networks, one that illustrates the duality of synaptic connectivity and neural population activity in learning and memory.

Linear-time quasi-static stability detection for modular self-reconfigurable robots

We address the problem of detecting potential instability in the planned motion of modular self-reconfigurable robots. Previous research primarily focused on determining the system’s unique physical state but overlooked the mutual compensation effects of connection constraints. We introduce a linear-time quasi-static stability detection method for modular self-reconfigurable robots. The internal connections, non-connected contacts, and environmental contacts are considered, and the problem is modeled as a second-order cone program problem, whose solving time linearly increases with the number of modules. We aim to determine the critical stable state of the system and that is achieved by finding the required minimum characteristic connection strength. By analyzing the critical stable state, we can assess the system’s stability and identify potential broken connection points. Furthermore, the internal stability margin is defined to evaluate the configuration’s stability level. The suspension and object manipulation configurations are first demonstrated in simulation to analyze the effectiveness of the proposed algorithm. Subsequently, a series of physical experiments based on FreeSN were carried out. The calculated stable motion ranges of manipulator configurations are highly consistent with the actual sampling boundaries. Moreover, the proposed algorithm successfully predicts stability and identifies broken connections in diverse configurations, encompassing quadruped and closed-chain configurations on both even and uneven terrains. The load experiment further demonstrates that the impacts from unmodeled factors and input errors under normal conditions can be on a small scale. By combining the proposed detection method and stability margin, we open the door to realizing real-time motion planning on modular self-reconfigurable robots.

Altered default-mode and frontal-parietal network pattern underlie adaptiveness of emotion regulation flexibility following task-switch training.

Emotion regulation flexibility (ERF) refers to one's ability to respond flexibly in complex environments. Adaptiveness of ERF has been associated with cognitive flexibility, which can be improved by task-switching training. However, the impact of task-switching training on ERF and its underlying neural mechanisms remains unclear. To address this issue, we examined the effects of training on individuals' adaptiveness of ERF by assessing altered brain network patterns. Two groups of participants completed behavioral experiments and resting-state fMRI before and after training. Behavioral results showed higher adaptiveness scores and network analysis observed a higher number of connectivity edges, in the training group compared to the control group. Moreover, we found decreased connectivity strength within the default mode network (DMN) and increased connectivity strength within the frontoparietal network (FPN) in the training group. Furthermore, the task-switch training also led to decreased DMN-FPN interconnectivity, which was significantly correlated to increased adaptiveness of ERF scores. These findings suggest that the adaptiveness of ERF can be supported by altered patterns with the brain network through task-switch training, especially the increased network segregation between the DMN and FPN.

Navigating the Fourth Industrial Revolution: insights from a comprehensive bibliometric study on Industry 4.0

Abstract The purpose of this study is to outline the current state of research and development in Industry 4.0 by highlighting key topics, cooperative networks, and emerging trends that characterize this ground-breaking stage of manufacturing and technology. The article answers the question what are the key keywords, countries, collaboration networks and most frequently occurring terms in Industry 4.0 research, and what conclusions can be drawn from the bibliometric analysis regarding their frequency, strength of connections and mutual relationships. With a focus on the incorporation of digital technology into manufacturing processes, the article aims to provide a detailed overview of the international initiatives driving the fourth industrial revolution. This study uses bibliometric analysis to look at 4,981 scientific papers from 2020 to 2024 that are available in the Scopus database. The text of these articles is carefully examined, with an emphasis on titles, abstracts, and keywords, in order to map out the network of co-authorships and the frequency of certain terminologies. The VOSviewer program was used to provide a network visualization, offering a pictorial depiction of the connections of coauthoring nations and the phrases that are shared across the corpus of work. The results show a strong and complex web of global partnerships, suggesting a broad dedication to pushing the boundaries of Industry 4.0. Five significant co-authorship clusters were found, demonstrating the prominent significance that certain nations have played in various Industry 4.0 research domains. Key phrases like “digital transformation,” “smart manufacturing,” “machine learning,” and “internet of things” were used a lot, highlighting the importance of digitization and smart manufacturing technology. This publication offers a comprehensive statistical and visual study of the worldwide research dynamics in Industry 4.0, making it a unique contribution to the body of knowledge. Understanding the complex nature of the fourth industrial revolution is made easier with its mapping of cooperative networks and thematic goals as well as its emphasis on the discourse’s essential place for sustainability.

LIMITATIONS IN FATIGUE STRENGTH EVALUATION OF THE WEB-FLANGE CONNECTION OF STEEL RUNWAY BEAMS - А REVIEW

This study reviews the current state of research and limitations on the fatigue strength of web-flange connections in steel runway beams for overhead cranes. It evaluates key factors influencing fatigue strength, including stress-strain behavior, notch classifications, and various web-flange configurations (welded, rolled, combined). The research stresses the need for accurate fatigue life assessments, particularly for both new and older structures built with simplified standards. Key findings show the impact of notch classifications and stress interactions due to bending, tensile, and compressive forces. The study aims to improve calculation methods, offering recommendations for refining fatigue verification techniques, and assesses connection configurations' effectiveness in achieving desired fatigue life. The practical implications point to increased steel crane runway beams' durability through better fatigue life prediction and localized stress analysis.

Comparison Between Different Source Localization and Connectivity Metrics of Spiky and Oscillatory MEG Activities

Epilepsy is considered the second neurological disease in a coma after stroke. Famous markers of epilepsy are repetitive seizures, their origin is stroma and cortical deformation. A neurologist would be assisted by identifying Epileptogenic Zones EZ when diagnosing epilepsy.. Source localization is utilized to identify regions known as EZ, which are of excessive discharges. It consists of both forward and inverse problems. The forward problem models the head through analytical and numerical methods. The inverse problem can be resolved using several techniques to locate the cerebral abnormal sources, via the electrophysiological recording biomarkers. In our study, we will investigate four distributed inverse problem methods: minimum norm estimation MNE, standardized low-resolution brain electromagnetic tomography sLORETA, maximum entropy on the mean MEM, Dynamic statistical parametric maps dSPM, to define epileptic networks connectivity of spiky and oscillatory events. We will examine the epileptic network connectivity using Phase Locking Value (PLV), Phase Transfert Entropy (PTE) for oscillatory events, cross-correlation (CC), and Granger Causality (GC) for spiky events applied on 5 pharmaco resistant subjects. We suggest rating the effectiveness of these networks in locating EZ through a phase of confrontation within iEEG transitory and oscillatory networks connectivity by exploring concordant nodes, their distance, propagation delays connection strength, and their cooperation in recognition of seizure onset zone. All studied techniques of the inverse problem, connection metrics, for both biomarkers of the 5 patients succeed in detecting at least one part of SOZ. sLORETA provides the highest concordant nodes and the closed one for spiky events using CC and GC. sLORETA also depicts the lowest propagation delay for oscillatory events using PTE. Through the 5 patients, MEM, dSPM, and MNE using CC, CG for spiky events, and PTE, PLV for oscillatory activities provide about 72 % of concordant nodes between MEG and iEEG.

Formation of the spatial structure of the bird population in High-mountainous Dagestan and the concept of preserving its avifauna

The presented work is the concluding part of a previously published article (Vilkov 2023) and based on a mathematical analysis of the empirical material collected during route bird surveys in 1998–2021 in the High­mountainous Province of Dagestan. Based on the long­term average population density data for 96 birds nesting and likely nesting, a cluster analysis was carried out, during which the identified avifaunal communities were grouped according to the similarity of their abundance. Using correlation analysis, environmental factors were identified that determine the territorial variability of bird population abundance. Based on the estimates of the strengths of connections determined from the Jaccard­Naumov coefficients similarity data matrix, a graph was constructed that reflects the spatial structure of the bird population at the level of selected key areas. When interpreting the spatial structure of the bird population using cluster and correlation analysis data, as well as faunal and ecological ranking data, similarities between the compared avifaunal communities were revealed, and 4 main trends in bird population changes in factor space were identified. Based on the results of assessing the strength and generality of the relationships between structure­forming factors and the territorial variability of the bird population (taking into account the areas of landscapes), the most significant was the impact of afforestation, the area of open spaces and the area of residential landscapes. When interpreting spatial differences taking into account natural and anthropogenic changes, it is possible to create a predictive model of the transformation of avifaunal communities within the study area. Analysis of existing threats that have a negative impact on the number and species diversity of birds allowed developing a population management concept based on 4 strategic directions. 1. Gasification of High­Mountain Dagestan to prevent deforestation and restore the species diversity of forest birds. 2. To preserve the species diversity of meadow birds, redirect cattle grazing to areas of meadows with increased overgrowth of grassy vegetation. 3. Increase the area of fields for sowing grain crops to preserve the species diversity of granivorous birds. 4. To preserve the species diversity of birds of prey, install perches in a mosaic pattern in the fields of grain crops.

Effects of structural parameters on the load distribution unevenness in CFRP hybrid bonded‐bolted joint

AbstractCarbon Fiber Reinforced Polymer (CFRP) Hybrid Bonded/Bolted (HBB) joint structures are distinguished for their superior jointing performance among current mechanical joint systems, making them a favored option for mechanical joints. These structures are characterized by many structural parameters, with the relationship between these parameters and jointing performance being notably complex. Additionally, the laminate's brittleness and the uneven distribution of bolt loads in multi‐bolt joint structures impair the overall jointing performance. To investigate the impact of structural parameters on the uneven load distribution within CFRP HBB joint structures and improve their jointing performance, a finite element analysis (FEA) model grounded in 3D Hashin failure criterion is developed. Validation of the model with experimental data confirmed the uneven load distribution among bolts in multi‐bolt joints. The study elucidated the influence of changes in structural parameters (overlap length, bolt‐hole spacing, and clearance fit) on the uneven load distribution and the connection strength of CFRP HBB joint structures. A negative correlation is found between the unevenness of load distribution and connection strength, offering insights for enhancing and researching connection strength in CFRP HBB joint structures.Highlights Developed a FEA model based on the 3D Hashin failure criterion for CFRP Bonded‐Bolted joints. Identified key factors affecting HBB joint load distribution and jointing performance. Evaluated the impact of overlap length, bolt‐hole spacing, and clearance fit on CFRP joints. Suggested design optimizations for enhancing the performance of HBB joints.

Strong long ties facilitate epidemic containment on mobility networks.

The analysis of connection strengths and distances in the mobility network is pivotal for delineating critical pathways, particularly in the context of epidemic propagation. Local connections that link proximate districts typically exhibit strong weights. However, ties that bridge distant regions with high levels of interaction intensity, termed strong long (SL) ties, warrant increased scrutiny due to their potential to foster satellite epidemic clusters and extend the duration of pandemics. In this study, SL ties are identified as outliers on the joint distribution of distance and flow in the mobility network of Shanghai constructed from 1 km × 1 km high-resolution mobility data. We propose a grid-joint isolation strategy alongside a reaction-diffusion transmission model to assess the impact of SL ties on epidemic propagation. The findings indicate that regions connected by SL ties exhibit a small spatial autocorrelation and display a temporal similarity pattern in disease transmission. Grid-joint isolation based on SL ties reduces cumulative infections by an average of 17.1% compared with other types of ties. This work highlights the necessity of identifying and targeting potentially infected remote areas for spatially focused interventions, thereby enriching our comprehension and management of epidemic dynamics.

Improving predictive accuracy for punching shear strength in fiber-reinforced polymer concrete slab-column connections via robust deep learning

Amidst the evolving landscape of structural engineering, this study addresses the pressing need for a robust, data-driven model in the context of alternative reinforcement techniques such as Fiber-Reinforced Polymer (FRP) bars and the application of Machine Learning (ML) methodologies for predicting the punching shear strength (PSS). Structural engineering heavily relies on accurate predictions of PSS for ensuring the safety and durability of various constructions. However, existing models often lack the comprehensiveness and precision required to effectively address the diverse and complex factors influencing PSS in FRP-reinforced concrete structures. This research explored the shear strength of FRP-reinforced concrete slabs, with a particular focus on the different types of FRP, namely Carbon Fiber-Reinforced Polymer (CFRP), Basalt Fiber-Reinforced Polymer (BFRP), Glass Fiber-Reinforced Polymer (GFRP), and Hybrid Fiber-Reinforced Polymer (HFRP). Leveraging an innovative deep learning approach, the study utilizes a dataset comprising 285 samples gathered from 56 distinct studies to train models that incorporate 11 dependent variables. This comprehensive approach aims to capture the intricate interplay between geometric, material, and other pertinent factors influencing PSS. The study employs a connection weight method to evaluate the significance of various features and introduces a novel Graphical User Interface (GUI) to enhance user interaction with the Deep Neural Network (DNN) model designed for PSS prediction. Through meticulous experimentation and analysis, the research identifies an optimal DNN architecture that achieves a minimum Mean Absolute Error (MAE) of 0.81, validated through k-fold cross-validation. Key conclusions highlight the critical influence of specific features, such as slab depth, column-to-slab area ratio, and modulus of elasticity, on the predictive accuracy of the model. Conversely, certain factors like slab area, reinforcement ratio, and concrete compressive strength negatively impact predictions. SHAP analysis further elucidates the significance of FRP type and column length-to-width ratio as pivotal factors affecting PSS predictions. These findings underscore the potential of the developed model in enhancing the dependability and practicality of structural engineering applications. Moreover, they point towards future research directions aimed at refining predictive accuracy and exploring broader applications in the field of building engineering.

Asymmetric neuromodulation in the respiratory network contributes to rhythm and pattern generation.

Like other brain circuits, the brainstem respiratory network is continually modulated by neurotransmitters that activate slow metabotropic receptors. In many cases, activation of these receptors only subtly modulates the respiratory motor pattern. However, activation of some receptor types evokes the arrest of the respiratory motor pattern as can occur following the activation of µ-opioid receptors. We propose that the varied effects of neuromodulation on the respiratory motor pattern depend on the pattern of neuromodulator receptor expression and their influence on the excitability of their post-synaptic targets. Because a comprehensive characterization of these cellular properties across the respiratory network remains challenging, we test our hypothesis by combining computational modelling with ensemble electrophysiologic recording in the pre-Bötzinger complex (pre-BötC) using high-density multi-electrode arrays (MEA). Our computational model encapsulates the hypothesis that neuromodulatory transmission is organized asymmetrically across the respiratory network to promote rhythm and pattern generation. To test this hypothesis, we increased the strength of neuromodulatory connections in the model and used selective agonists in situ while monitoring pre-BötC ensemble activities. The model predictions of increasing slow inhibition were consistent with experiments examining the effect of systemic administration of the 5HT1aR agonist 8-OH-DPAT. Similarly, the predicted effects of increasing slow excitation in the model were experimentally confirmed in pre-BötC ensemble activities before and after systemic administration of the µ-opioid receptor agonist fentanyl. We conclude that asymmetric neuromodulation can contribute to respiratory rhythm and pattern generation and accounts for its varied effects on breathing.