Search Results (18,935)

Hand representation maps of the primate primary motor (M1) and somatosensory (SI) cortices exhibit plasticity, with their spatial extent modifiable through training. While activation and map enlargement during tapping tasks are well documented, the directionality of information flow between these regions remains unclear. We applied Information Imbalance Gain Causality (IIG) to examine the propagation and temporal dynamic of BOLD activity among Area 4 (precentral gyrus), Area 3a (fundus of the central sulcus), and SI areas (postcentral gyrus). Data were collected from both hemispheres of nine participants performing alternating right–left hand finger tapping inside a 1.5T fMRI scan. The results revealed strong information flow from both the precentral and postcentral gyri toward the sulcus during tapping task, with weaker bidirectional exchange between the gyri. When not engaged in tapping, both gyri communicated with each other and the sulcus. During active tapping, flow bypassed the sulcus, favoring a more direct postcentral to precentral way. Overtime, postcentral to sulcus influence strengthened during non task periods, but diminished during tapping. These findings suggest that M1, Area 3a, and SI areas form a dynamic network that supports rapid learning processing, where Area 3a of the sulcus may contribute to maintaining representational plasticity during complex tapping tasks. Full article

This article is aimed at assessing energy–economy models with a focus on their ability to capture the dynamic structural changes of economic systems and the related energy supply chains. A narrative literature review approach was employed, synthesizing relevant peer-reviewed research. The search yielded 229 publications spanning from 2015 to 2024. After applying screening criteria based on methodological transparency, quantitative modelling, and explicit energy–economy integration, 120 articles were retained, from which 23 representative modelling frameworks were selected. The review identifies five key dimensions shaping the realism and applicability of integrated models: geographical and temporal scope, technological detail, modelling approach, and the degree of micro- and macroeconomic realism. Results show a growing adoption of multi-scale modelling and a gradual shift toward hybrid structures combining technological and macroeconomic components. However, significant gaps remain: only 26% of the models move beyond equilibrium assumptions; 17% incorporate behavioural or heterogeneous agents; and almost half rely on exogenous technological change. Moreover, the representation of policy instruments—particularly performance standards, sectoral benchmarks, and public investment mechanisms—remains incomplete across most frameworks. Overall, this analysis highlights the need for more transparent coupling strategies, enhanced behavioural realism, and improved representation of financial and transition risks. These findings inform the methodological development of next-generation models and indicate priority areas for future research aimed at improving the robustness of policy-relevant transition assessments. Full article

Small and medium-sized enterprises (SMEs) play a central role in employment and regional economic development, yet they are highly vulnerable to shocks such as pandemics, energy price spikes, and supply chain disruptions. Scenario modelling, stress testing, and digital twins are used to assess resilience, yet most applications focus on large firms in single-country settings. This article develops a model to stress test the resilience of Slovenian SMEs, calibrated with parameters and mechanisms derived from South African SME resilience studies. A system dynamics model with stocks for cash, inventory, and productive capacity is specified and subjected to demand, supply, financial, and compound shock scenarios, with and without resilience measures such as liquidity buffers, customer and supplier diversification, and basic digital planning capabilities. Results indicate non-linear tipping points where small reductions in liquidity sharply increase the likelihood of distress, and show that combinations of liquidity, diversification, and collaborative supply chain practices reduce the depth and duration of output losses. The study demonstrates how evidence from an African context can inform resilience strategies in a small European economy and provides a transparent, portable modelling architecture that can be adapted to other settings. Implications are discussed for SME managers and for policies supporting sustainable, resilient enterprise ecosystems. Full article

This study investigates how emotionally structured online interaction networks shape consumer perception of new energy vehicle (NEV) technology. Drawing on discussion forum data from two leading NEV brands, Brand_T and Brand_B, we focus on how users respond to brand technological narratives and how these responses translate into distinct patterns of peer-to-peer interaction. Using a behavioral network analysis framework, we integrate sentiment analysis, topic modeling, and Exponential Random Graph Modeling (ERGM) to uncover the psychological and structural mechanisms underlying consumer engagement. Three main findings emerge. First, users display brand-specific emotional-cognitive profiles: Brand_T communities show broader technological engagement but more heterogeneous emotional responses, whereas Brand_B communities exhibit more emotionally aligned discussions. Second, emotional homophily is a robust driver of interaction ties, particularly in Brand_B forums, where positive sentiment clusters into dense and supportive discussion subnetworks. Third, perceived technological benefits, rather than risk sensitivity, are consistently associated with higher interaction intensity, underscoring the motivational salience of anticipated gains over cautionary concerns in shaping engagement behavior. The study contributes to behavioral science and transportation behavior research by linking consumer sentiment, cognition, and social interaction dynamics in digital environments, offering an integrated theoretical account that bridges the Elaboration Likelihood Model, social identity processes, and behavioral network formation. This advances the understanding of technology perception from static individual evaluations to dynamic, group-structured outcomes. It highlights how emotionally patterned interaction networks can reinforce or recalibrate technology-related perceptions, offering practical implications for NEV manufacturers and policymakers seeking to design psychologically informed communication strategies that support sustainable technology adoption. Full article

This study analyzes the socio-cultural and behavioral determinants of FinTech adoption and access to credit among Ecuadorian SMEs. A probabilistic sample of 600 firms, operating in the services, commerce, information and communication technologies (ICT), and industry sectors, was surveyed to ensure representation of the country’s productive structure. The model integrates financial literacy, institutional trust, and perceived accessibility as key independent variables, with FinTech adoption as a digital behavioral factor and access to credit and credit conditions as the primary dependent outcomes. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by multi-group invariance tests and cluster analysis, the study evaluates seven hypotheses linking cognitive, perceptual, and digital mechanisms to financing behavior and firm performance. Results show that financial literacy and institutional trust significantly improve access to formal credit, with perceived accessibility acting as a partial mediator. FinTech adoption enhances credit conditions but remains limited among micro and small firms. Based on these findings, the study recommends strengthening financial education programs, simplifying credit procedures to reduce perceived barriers, and developing trust-building regulatory frameworks for digital finance. The results highlight the importance of socio-cultural and behavioral factors in shaping SME financing decisions and contribute to the understanding of financial inclusion dynamics in emerging economies. Full article

This study introduces a novel approach for classifying flight trajectory curvature, focusing on early-stage flight characteristics to detect anomalies and deviations. The method intentionally avoids direct coordinate data and instead leverages a combination of trajectory-derived and meteorological features. This research analysed 9849 departure flight trajectories originating from 14 different airports. Two distinct trajectory classes were established through manual visual inspection, differentiated by curvature patterns. This categorisation formed the ground truth for evaluating trained machine learning (ML) classifiers from different families. The comparative analysis demonstrates that the Random Forest (RF) algorithm provides the most effective classification model. RF excels at summarising complex trajectory information and identifying non-linear relationships within the early-flight data. A key contribution of this work is the validation of specific predictors. The theoretical definitions of direction change (using vector values to capture dynamic movement) and diffusion distance (using scalar values to represent static displacement) proved highly effective. Their selection as primary predictors is supported by their ability to represent the essential static and dynamic properties of the trajectory, enabling the model to accurately classify flight paths and potential deviations before the flight is complete. This approach offers significant potential for enhancing real-time air traffic monitoring and safety systems. Full article

High-precision prediction of the crystal diameter during the growth of electronic-grade silicon single crystals is a critical step for the fabrication of high-quality single crystals. However, the process features high-temperature operation, strong nonlinearities, significant time-delay dynamics, and external disturbances, which limit the accuracy of conventional mechanism-based models. In this study, mechanism-based models denote physics-informed heat-transfer and geometric models that relate heater power and pulling rate to diameter evolution. To address this challenge, this paper proposes a hybrid deep learning model combining a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and self-attention to improve diameter prediction during the shoulder-formation and constant-diameter stages. The proposed model leverages the CNN to extract localized spatial features from multi-source sensor data, employs the BiLSTM to capture temporal dependencies inherent to the crystal growth process, and utilizes the self-attention mechanism to dynamically highlight critical feature information, thereby substantially enhancing the model’s capacity to represent complex industrial operating conditions. Experiments on operational production data collected from an industrial Czochralski (Cz) furnace, model TDR-180, demonstrate improved prediction accuracy and robustness over mechanism-based and single data-driven baselines, supporting practical process control and production optimization. Full article

Efficient clustering and cluster head (CH) selection are the critical parameters of wireless sensor networks (WSNs) for their prolonged network lifetime. However, the performances of the traditional clustering algorithms like LEACH and HEED are not satisfactory when they are implemented on a dense WSN due to their unbalanced load distribution and high contention nature. In the traditional methods, the cluster heads are selected with respect to the residual energy criteria, and often create a circular cluster shape boundary with a uniform node distribution. This causes the cluster heads to become overloaded in the high-density regions and the unutilized cluster heads gather in the sparse regions. Therefore, frequent cluster head changes occur, which is not suitable for a real-time dynamic environment. In order to avoid these issues, this proposed work develops a density-aware adaptive clustering (DAAC) protocol for optimizing the CH selection and cluster formation in a dense wireless sensor network. The residual energy information, together with the local node density and link quality, is utilized as a single cluster head detection metric in this work. The local node density information assists the proposed work to estimate the sparse and dense area in the network that results in frequent cluster head congestion. DAAC is also included with a minimum inter-CH distance constraint for CH crowding, and a multi-factor cost function is used for making the clusters by inviting the nodes by their distance and an expected transmission energy. DAAC triggers re-clustering in a dynamic manner when it finds a response in the CH energy depletion or a significant change in the load density. Unlike the traditional circular cluster boundaries, DAAC utilizes dynamic Voronoi cells (VCs) for making an interference-aware coverage in the network. This makes dense WSNs operate efficiently, by providing a hierarchical extension, on making secondary CHs in an extremely dense scenario. The proposed model is implemented in MATLAB simulation, to determine and compare its efficiency over the traditional algorithms such as LEACH and HEED, which shows a satisfactory network lifetime improvement of 20.53% and 32.51%, an average increase in packet delivery ratio by 8.14% and 25.68%, and an enhancement in total throughput packet by 140.15% and 883.51%, respectively. Full article

Autoantibodies have long been regarded as passive reflections of immune dysregulation in connective tissue diseases (CTDs). Recent advances in systems immunology and molecular pathology have fundamentally redefined them as active molecular fingerprints that delineate distinct disease endophenotypes with predictive power for clinical trajectories and therapeutic responses. Rather than mere epiphenomena, autoantibodies encode precise information about dominant immune pathways, organ tropism, and pathogenic mechanisms. This review synthesizes emerging evidence that autoantibody repertoires—defined by specificity, structural properties, and functional characteristics—stratify patients beyond traditional clinical taxonomy into discrete pathobiological subsets. Specific signatures such as anti-MDA5 in rapidly progressive interstitial lung disease, anti-RNA polymerase III in scleroderma renal crisis, and anti-Ro52/TRIM21 in systemic overlap syndromes illustrate how serological profiles predict outcomes with remarkable precision. Mechanistically, autoantibody pathogenicity is modulated by immunoglobulin isotype distribution, Fc glycosylation patterns, and tissue-specific receptor expression—variables that determine whether an antibody functions as a biomarker or pathogenic effector. The structural heterogeneity of autoantibodies, shaped by cytokine microenvironments and B-cell subset imprinting, creates a dynamic continuum between pro-inflammatory and regulatory states. The integration of serological, transcriptomic, and imaging data establishes a precision medicine framework: autoantibodies function simultaneously as disease classifiers and therapeutic guides. This endophenotype-driven approach is already influencing trial design and patient stratification in systemic lupus erythematosus, systemic sclerosis, and inflammatory myopathies, and is reshaping both clinical practice and scientific taxonomy in CTDs. Recognizing autoantibodies as endophenotypic determinants aligns disease classification with pathogenic mechanism and supports the transition towards immunologically informed therapeutic strategies. Full article

With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. Currently, some Job Shop Scheduling Problems with Transportation (JSP-T) only consider job scheduling and vehicle task allocation, and does not focus on the problem of collision free paths between vehicles. This article proposes a novel solution framework that integrates workshop scheduling, material handling robot task allocation, and conflict free path planning between robots. With the goal of minimizing the maximum completion time (Makespan) that includes handling, this paper first establishes an extended JSP-T problem model that integrates handling time and robot paths, and provides the corresponding workshop layout map. Secondly, in the scheduling layer, an improved Deep Q-Network (DQN) method is used for dynamic scheduling to generate a feasible and optimal machining scheduling scheme. Subsequently, considering the robot’s position information, the task sequence is assigned to the robot path execution layer. Finally, at the path execution layer, the Priority Based Search (PBS) algorithm is applied to solve conflict free paths for the handling robot. The optimized solution for obtaining the maximum completion time of all jobs under the condition of conflict free path handling. The experimental results show that compared with algorithms such as PPO, the scheduling algorithm proposed in this paper has improved performance by 9.7% in Makespan, and the PBS algorithm can obtain optimized paths for multiple handling robots under conflict free conditions. The framework can handle scheduling, task allocation, and conflict-free path planning in a unified optimization process, which can adapt well to job changes and then flexible manufacturing. Full article

This paper presents a modulation signal classification and recognition algorithm based on data augmentation and time–frequency joint attention (DA-TFJA) for underwater acoustic (UWA) communication systems. UWA communication, as an important means of marine information transmission, plays a key role in fields such as marine engineering, military reconnaissance, and marine science research. Accurate recognition of modulated signals is a core technology for ensuring the reliability of UWA communication systems. Traditional classification and recognition methods, mostly based on pure neural network algorithms, suffer from insufficient feature representation and limited generalization performance in complex and changing UWA channel environments. They also struggle to address complex factors such as multipath, Doppler shift, and noise interference, often resulting in scarce effective training samples and inadequate classification accuracy. To overcome these limitations, the proposed DA-TFJA algorithm simulates the characteristics of real UWA channels through two novel data augmentation strategies: the adaptive time–frequency transform enhancement algorithm (ATFT) and dynamic path superposition enhancement algorithm (DPSE). An end-to-end recognition network is developed that integrates a multiscale time–frequency feature extractor (MTFE), two-layer long short-term memory (LSTM) temporal modeling, and a time–frequency joint attention mechanism (TFAM). This comprehensive architecture achieves high-precision recognition of six modulation types, including 2FSK, 4FSK, BPSK, QPSK, DSSS, and OFDM. Experimental results demonstrate that compared with existing advanced methods, DA-TFJA achieves a classification accuracy of 98.36% on the measured reservoir dataset, representing an improvement of 3.09 percentage points, which fully verifies the effectiveness and practical value of the proposed approach. Full article

Traditional routing algorithms optimizing for distance or travel time are inadequate for electric vehicles (EVs), which require energy-aware planning considering battery constraints and charging infrastructure. This work presents an energy-optimal routing system for EVs that integrates personalized consumption modeling with real-time environmental data. The system employs a Long Short-Term Memory (LSTM) neural network to predict State-of-Charge (SoC) consumption from real-world driving data, learning directly from spatiotemporal features including velocity, temperature, road inclination, and traveled distance. Unlike physics-based models requiring difficult-to-obtain parameters, this approach captures nonlinear dependencies and temporal patterns in energy consumption. The routing framework integrates static map data, dynamic traffic conditions, weather information, and charging station locations into a weighted graph representation. Edge costs reflect predicted SoC drops, while node penalties account for traffic congestion and charging opportunities. An enhanced A* algorithm finds optimal routes minimizing energy consumption. Experimental validation on a Nissan Leaf shows that the proposed end-to-end SoC estimator significantly outperforms traditional approaches. The model achieves an RMSE of 36.83 and an $R^2$ of 0.9374, corresponding to a 59.91% reduction in error compared to physics-based formulas. Real-world testing on various routes further confirms its accuracy, with a Mean Absolute Error in the total route SoC estimation of 2%, improving upon the 3.5% observed for commercial solutions. Full article

Understanding the mechanisms underlying seasonal community dynamics is important for predicting biodiversity responses to environmental fluctuations, enhancing ecological forecasting, and informing conservation strategies. In this study, we use standard transect and mist netting methods investigated seasonal altitudinal migration patterns of montane bird species in the Gyirong Valley, Central Himalayas. Our results showed four distinct altitudinal migration patterns among montane bird species: no shift, downslope shift, upslope shift, and contraction to mid-elevation zones. Species with smaller body weight and higher ratios of wing length, tail length, and tarsus length to body weight tended to migrate to lower elevations. Insectivorous birds exhibited a collective downslope shift, while omnivorous birds showed a wider range of migratory responses to seasonal variation. Migratory behavior was found to dynamically modulate the association between phenotypic traits and habitat preferences. During the breeding season, species (70.44%) and functional turnover (80.02%) dominated, while in the non-breeding season, nestedness significantly contributed to species (49.37%) and functional diversity (38.09%). In addition, migration can disrupt the direct influence of environmental variables on biodiversity patterns, providing important insights for montane biodiversity conservation under climate change. Our results highlight the critical need to safeguard low-elevation winter habitats and create dynamic protected areas to aid bird conservation amidst climate change. Full article

Serotonergic signaling is traditionally conceived as a transient, vesicle-mediated process restricted to the synaptic cleft. Here, we propose an expanded model in which serotonin can also be inserted into the plasma membrane of neurons and glial cells, forming a stable, membrane-associated reservoir that prolongs its availability beyond classical synaptic timescales. In this framework, the synapse emerges not as a simple neurotransmitter–receptor interface but as a dynamic, multiscale medium where membrane order, hydration, and quantum-level processes jointly govern information flow. Two temporal “tunnels” appear to regulate serotonin bioavailability: its aggregation in synaptic vesicles during exocytosis, and its cholesterol-dependent insertion into neuronal and glial membranes at the tripartite synapse. Lipid raft microdomains enriched in cholesterol and gangliosides thus act as active regulators of a continuum between transient and constitutive serotonin signaling. This extended serotonergic persistence prompts a reconsideration of current pharmacological models and the action of antidepressants such as fluoxetine, which not only inhibits the serotonin transporter (SERT) but also accumulates in lipid rafts, perturbs raft organization, and alters serotonin–cholesterol equilibria, contributing to SERT-independent effects. Grounded in the recently established fundamental parameters of biological systems, this model invites a broader, quantum-informed rethinking of synaptic transmission. Full article

High-rise building areas (HRBs), a key urban land-cover type defined by distinct morphological and functional characteristics, play a critical role in urban development. Their spatial distribution and temporal dynamics serve as essential indicators for quantifying urbanization and analyzing the evolution of urban spatial structure. This study addresses the dynamic monitoring needs of HRBs by developing a temporal change detection model, TCA-Unet (Temporal Change-Aware U-Net), based on a temporal change-aware attention module. The model adopts a dual-path design, combining a temporal attention encoder and a change-aware encoder. By explicitly modeling temporal difference features, it captures change information in temporal remote sensing images. It incorporates a multi-level weight generation mechanism that dynamically balances temporal features and change-aware features through an adaptive fusion strategy. This mechanism effectively integrates temporal context and enhances the model’s ability to capture long-term temporal dependencies. Using the Xiong’an New Area and its surrounding regions as the study area, experiments were conducted using Sentinel-2 time-series imagery from 2017 to 2024. The results demonstrate that the proposed model outperforms existing approaches, achieving an overall accuracy (OA) of 90.98%, an F1 score of 82.63%, and a mean intersection over union (mIoU) of 72.22%. Overall, this study provides an effective tool for extracting HRBs for dynamic monitoring and offers valuable guidance for urban development and regulation. Full article