Search Results (12,966)

The Dvorak technique has represented a fundamental tool for understanding the power of tropical cyclones based on their shape and geometric evolution. However, it should be noted that the Dvorak technique is purely morphological in nature and was developed for wind, not precipitation. The role of shape methods in precipitation prediction remains uncertain, particularly in the context of modern multi-sensor capabilities. This uncertainty forms the motivation for the present study. In an attempt to enrich Dvorak’s technique, this study proposes a novel hypothesis. This study tests the hypothesis that higher precipitation intensity is associated with more organized cloud-system morphology, as captured by simple geometric descriptors and indicative of dynamically coherent convection. A total of 3419 cloud-system objects (after size filter) were utilized to establish geometric relationships in each of them. For the case study of Hurricane Patricia over the Mexican coast in 2015, 3858 geometric shapes were processed. The cloud-system morphology was derived from geostationary imagery (GOES-13) and collocated with satellite precipitation estimates in order to isolate intense-rainfall objects (>50 mm/h). For each object, simple geometric descriptors were computed, and shape variability was summarised via Principal Component Analysis (PCA). The present study sought to evaluate the associations with rain-rate metrics (mean, mode, maximum) using rank correlations and k-means clustering. Furthermore, sensitivity analyses were conducted on the rain threshold and minimum object size. A Shape Descriptor: ratio between perimeter and diameter was identified as a promising tool to enhance early prediction models of extreme rainfall, contributing to enhanced meteorological risk management. The study indicates that cloud shape can serve as a valuable indicator in the classification and forecasting of intense cloud systems. Full article

The oscillation stability of renewable energy plants under varying grid strengths can be improved through the optimized allocation of grid-following (GFL) and grid-forming (GFM) power converter devices. However, in practical operation, the wide variations in both the output of renewable energy plants and the strength of the grid present significant challenges in simultaneously ensuring stability, economic efficiency, and robustness. To address this, this paper proposes a two-level optimization method for the allocation of GFL and GFM devices, aiming to enhance oscillation stability in renewable energy plants. The method considers the complementary dynamic behaviors of GFL and GFM strategies, whose complementary dynamic behaviors contribute to balanced and stable operation. The upper-level optimization model accounts for the wide range of variability in renewable plant outputs, with the primary objective and constraint being the assurance of oscillation stability under low short-circuit ratio (SCR) conditions at a minimal cost. Based on the GFM configuration determined by the upper-level model, the lower-level optimization model further evaluates the upper SCR limit within which oscillation stability can still be maintained. This prevents instability that may arise from GFM devices operating under high-SCR conditions. By iteratively solving the upper- and lower-level models, an optimized GFL-GFM allocation strategy is obtained, which ensures oscillation stability across a wide SCR range while balancing cost-effectiveness and practical operability. Case studies are also conducted to validate the method. It is indicated that when SCR = 1.5, configuring 15% of the wind generators in the GFM strategy can ensure stability of the wind plant across typical operating scenarios, while when SCR > 3, switching these generators to the GFL strategy can likewise avoid the oscillation issues. Full article

This paper addresses the challenge of cooperatively escorting a moving underwater target, such as a human-occupied vehicle (HOV), using a multi-AUV formation in complex ocean environments. We propose a comprehensive framework, RG-SAPF scheme, that integrates a rigidity graph (RG)-based reconfigurable formation control strategy with a safe artificial potential field (SAPF) motion planning method. The RG-based controller enables the AUVs to form and dynamically reconfigure a 3D escort formation around the target using only relative position information, ensuring the target remains within the formation’s convex hull. Meanwhile, the SAPF algorithm, enhanced with an adaptive Widrow–Hoff rule, enables real-time and collision-free path planning in obstacle-rich environments. Simulation and experimental results demonstrate that the proposed method effectively maintains formation integrity, supports flexible obstacle avoidance, and provides continuous target escort under dynamic conditions, validating its potential for practical underwater escort applications. Full article

Auxetic structures, characterized by a negative Poisson’s ratio and unique form-fitting deformation, are adopted for designing a bra pad that would facilitate bras with a flexible and adaptive fit. This study compares the pressure distribution between auxetic and traditional molded bra pads, highlighting the advantages of auxetic materials in applying uniform pressure and addressing health concerns. Seven athletic female participants with a bra size of 75B comprise the study sample. Anthropometric data of naked breasts are collected by using three-dimensional (3D) scanning to obtain the underbust and full bust dimensions in the standing and leaning forward positions, while the pressure distribution is measured with the Novel Pliance^® pressure measurement system in three poses: standing, static cycling, and dynamic cycling. The results show that the auxetic designs of bra pads consistently apply a more uniform pressure distribution compared to traditional foam pads, with mean pressures of 2.92 kPa for auxetic pads compared to 4.81 kPa for traditional foam pads during static cycling. Moreover, auxetic pads reduced maximum pressure by 25% compared to molded cups, and spatial variability was halved (SD 0.85 kPa vs. 1.70 kPa). Notably, at the bra neckline, auxetic pads exhibit increased pressure as the body leans forward, demonstrating their ability to adapt to changing breast shapes while maintaining adequate bra-breast contact. In contrast, in the lower breast area, the auxetic pads show a decrease in pressure, which indicates their capacity to accommodate variations in breast girth or volume without exerting excessive force. These findings highlight the superior adaptability and wear comfort provided by an auxetic structure, which shows its potential to address the dynamic support needs of active women. Overall, the auxetic designs of a bra pad in this study represent a significant advancement in sports bra technology and offer a promising alternative to traditional molded cups in activewear design. Full article

The diversity of forest tree life strategies is fundamental to species coexistence in mixed stands. Growth rate is one of the most important elements of a species’ life strategy. This aspect has been relatively well recognised in even-aged stands. However, the situation is different in uneven-aged stands, particularly in multi-species stands comprising species with different light demands. In this study, we aimed to compare stem basal area increment (BAI) in regard to five species forming multi-species, uneven-aged deciduous forests in Central Europe as an important element of their growth strategy. Particular attention was paid to the relationship between this feature and tree height and competitive status. These relationships were analysed using a linear mixed model. The BAI was positively correlated with tree height, while a negative correlation was observed between BAI and increasing competitive level. However, the observed variations in the trends of these relationships were not associated with the light demands of the compared species. In general, the majority of the studied species demonstrated similar growth dynamics. This may suggest that the role of this trait in shaping species coexistence is modulated by other life-history strategy components and by specific growth conditions. An exception to this is the most light-demanding species, black alder (Alnus glutinosa (L.) Gaertner), which, contrary to expectations, exhibits a lower basal area increment under uneven-aged conditions. Full article

Non-suicidal self-injury (NSSI) has become an increasingly salient mental health concern among adolescents, and it commonly co-occurs with depression, anxiety, borderline personality disorder, substance use, and childhood maltreatment, forming a complex psychological risk structure. Despite a growing body of literature, a systematic understanding of the structural links between NSSI and psychiatric comorbidities remains limited. This study uses bibliometric and visualization methods to map the developmental trajectory and knowledge structure of the field and to identify research hotspots and frontiers. Drawing on the Web of Science Core Collection, we screened 1562 papers published between 2005 and 2024 on adolescent NSSI and comorbid psychological problems. Using CiteSpace 6.3.R1, VOSviewer 1.6.20, and R 4.3.3, we constructed knowledge graphs from keyword co-occurrence, clustering, burst-term detection, and co-citation analyses. The results show an explosive growth of research in recent years. Hotspots center on comorbidity mechanisms of mood disorders, the impact of childhood trauma, and advances in dynamic assessment. Research has evolved from describing behavioral features toward integrative mechanisms, with five current emphases: risk factor modeling, diagnostic standard optimization, cultural sensitivity, stratified intervention strategies, and psychological risks in special populations. With big data and AI applications, the field is moving toward dynamic prediction and precision intervention. Future work should strengthen cross-cultural comparisons, refine comorbidity network theory, and develop biomarker-informed differentiated interventions to advance both theory and clinical practice. Full article

Artificial intelligence (AI) is increasingly transforming biomedical research and patient care by integrating complex biological, radiological, and healthcare information. In the field of viral respiratory infections, AI-driven approaches have shown great promise in elucidating the complexity of lung immune responses and the dynamic interplay between host and pathogen. Applications include predicting cytokine storm and acute respiratory distress syndrome (ARDS), integrating imaging findings with immunological and laboratory data, and identifying molecular and cellular signatures through single-cell and multi-omics analyses. Similar methodologies have been applied to influenza and respiratory syncytial virus (RSV), providing insights into the mechanisms distinguishing protective from maladaptive pulmonary immunity. This narrative review summarizes current evidence on how AI can evolve into a form of translational intelligence, capable of bridging mechanistic immunology with clinical application. The review explores AI-based models for disease severity prediction, patient stratification, and therapeutic response assessment, as well as emerging approaches in drug repurposing and vaccine response prediction. By integrating biological complexity with clinical context, AI offers new opportunities to uncover immune signatures predictive of antiviral or immunomodulatory efficacy and to guide personalized management strategies. Full article

Individual beliefs are a critical factor in understanding human action and behavior. Certain beliefs, such as irrational beliefs and burnout, influence all forms of learning and social interaction within the school environment, primarily limiting both individual and collective development. The former are associated with the inherent human tendency to adhere to habits and behaviors not strictly dictated by rationality, often stemming from irrational thoughts held by the individual. The latter, examined within the framework of the Job Demands–Resources Theory, pertain to occupational characteristics that differentially affect employees’ well-being, job demands, and available resources. The present study aims to investigate the role of these variables in relation to teachers’ Innovative Work Behavior, a recurring, multi-stage process oriented toward the implementation of new ideas within the school context. The sample consisted of 337 preschool educators who completed self-report questionnaires. Multiple linear regression analysis indicated that both irrational beliefs (positively) and the dimension of work disengagement (negatively) significantly influenced innovative work behavior, underscoring the distinct contributions of personal belief systems and burnout dimensions. Furthermore, a hierarchical cluster analysis revealed both heterogeneity among educators and common, distinct response patterns. The identification of five different clusters suggests that the examined characteristics and the underlying beliefs represent individual traits that change dynamically, leaving open the possibility of nonlinear relationships present in the workplace. Five profiles were identified, namely Disengaged-Low Innovators, Resilient-Balanced Innovators, Adaptive Innovators, Strained but Innovative Innovators, and Belief-Driven Innovators, which highlight the complex ways in which disengagement, exhaustion, and irrational beliefs combine to shape innovative work behavior. The findings are interpretable within the framework of contemporary theories in organizational psychology and management and can be utilized by educational principals to enhance school climate and teacher performance. Full article

Cities around the world are facing growing challenges related to climate change, urban sprawl, infrastructure strain, and digital transformation. In response, smart and sustainable urban development has become a global focus, aiming to integrate technology and environmental stewardship to improve the quality of life. The smart and sustainable city concept is typically applied at the city scale; however, its impact is most tangible at the neighborhood level, where residents interact directly with infrastructure, services, and community spaces. A variety of global frameworks have been developed to assess sustainability and technological integration. However, these models often fall short in addressing localized needs, particularly in regions with distinct environmental and cultural contexts. In Saudi Arabia, Vision 2030 emphasizes livability, sustainability, and digital transformation, yet there remains a lack of tailored tools to evaluate smart and sustainable progress at the neighborhood scale. This study develops HayyScore, a localized evaluation framework and prototype digital platform developed to assess neighborhood performance across five core categories: (i) Environment and Urban Resilience, (ii) Smart Infrastructure and Governance, (iii) Mobility and Accessibility, (iv) Quality of Life and Social Inclusion, and (v) Economy and Innovation. The HayyScore platform operationalizes this framework through an interactive web-based tool that allows users to input data through structured forms, calculate scores, receive category-based and overall certification levels, and view results through visual dashboards. The methodology involved a comprehensive review of global frameworks, expert input to define localized indicators, and iterative prototyping of the platform using Python 3.13.5 and Streamlit 1.45.1. To demonstrate its practical application, the prototype was tested on two Saudi neighborhoods: King Abdullah Petroleum Studies and Research Center (KAPSARC) and King Fahd University of Petroleum and Minerals (KFUPM). Key platform features include automated scoring logic, category weighting, certification generation, dynamic performance charts, and a rankings page for comparing multiple neighborhoods. The platform is designed to be scalable, with the ability to add new indicators, support multilingual access, and integrate with real-time data systems in future iterations. Full article

Ultra-reliable low-latency communication (URLLC) presents significant challenges in simultaneously guaranteeing stringent latency bounds, ultra-high reliability, and efficient resource utilization under dynamic channel conditions. To address these joint constraints, a novel framework that integrates a reconfigurable intelligent surface (RIS) with cooperative non-orthogonal multiple access (NOMA) is proposed for short-packet communications. Two distinct phase configuration designs for the RIS are considered, i.e., a near-user priority strategy (NUPS) and a far-user priority strategy (FUPS). The NUPS configures the RIS to enhance the received signal power for the near user, while the FUPS optimizes the phase shifts to maximize the received power for the far user. Closed-form expressions that characterize the average block error rate (BLER) of the near and far users under the two proposed strategies in the presence of hardware impairments are derived. Specifically, the analysis for the far user considers both selection combining (SC) and maximum ratio combining (MRC) reception schemes. Based on the average BLER, we then derive a closed-form expression for the effective throughput. Simulation findings reveal the following: (1) The far user in the proposed cooperative NOMA achieves a lower average BLER than in the non-cooperative NOMA. (2) When the RIS is deployed in close proximity to the base station (BS), the NUPS can effectively leverage the RIS to enhance the far user’s signal quality through cooperation, without sacrificing the near user’s priority; and (3) SC serves as a low-complexity alternative that achieves near-optimal performance when inter-user channel conditions are favorable. Full article

Conservation Tillage Technology (CTT) is vital for mitigating soil degradation, yet its adoption rates remain far below targets. This study develops an evolutionary game model that integrates heterogeneous time preferences and the lemon market effect to explore the dynamic adoption mechanisms among boundedly rational farmers. Results show that farmers with high discount rates (indicating strong time preference) undervalue long-term benefits, creating a significant barrier to CTT adoption. The lemon market effect, where P represents the benefit from information asymmetry for non-adopters and Q is the corresponding loss for adopters, critically shapes the system equilibria: (1) when $P>Q$, a stable coexistence of adoption strategies emerges; (2) when $P<Q$, the system exhibits unpredictable heteroclinic cycles; (3) when $P=Q$, it forms a conservative Hamiltonian system characterized by stable periodic oscillations. These findings provide a dynamic analytical framework for understanding green technology diffusion and offer a theoretical basis for crafting sustainable agricultural policies in developing countries. Full article

This article describes a model for optimizing traffic flow control and generating traffic signal phases based on the stochastic dynamics of traffic and the percolation properties of transport networks. As input data (in SUMO), we use lane-level vehicle flow rates, treating them as random processes with unknown distributions. It is shown that the percolation threshold of the transport network can serve as a reliability criterion in a stochastic model of lane blockage and can be used to determine the control interval. To calculate the durations of permissive control signals and their sequence for different directions, vehicle queues are considered and the time required for them to reach the network’s percolation threshold is estimated. Subsequently, the lane with the largest queue (i.e., the shortest time to reach blockage) is selected, and a phase is formed for its signal control, as well as for other lanes that can be opened simultaneously. Simulation results show that when dynamic traffic signal control is used and a percolation-dynamic model for balancing road traffic is applied, lane occupancy indicators such as “congestion” decrease by 19–51% compared to a model with statically specified traffic signal phase cycles. The characteristics of flow dynamics obtained in the simulation make it possible to construct an overall control quality function and to assess, from the standpoint of traffic network management organization, an acceptable density of traffic signals and unsignalized intersections. Full article

Tidal dynamics substantially govern nearshore circulation patterns. The discharge of sewage at different tidal stages may have a significant impact on the dilution of pollutants. However, discussions on tidal phase sewage discharge strategy are still rare. This study focuses on the narrow tidal passage in the Ningbo-Zhoushan sea area, which receives a large amount of coastal wastewater, but the role of the unique hydrodynamic processes in the dilution of pollutants in this region remains unclear. By using a combination of on-site measurements and the FVCOM-dye simulation method, the scenario of high-concentration sewage retention in the tidal passage was demonstrated. The coastal residual circulation formed by strong tidal currents confined over 78% of the tracers within a 3 km range near the shore, and a subsurface dye accumulation zone emerged along the 25–50 m isobaths. Monsoon transitions regulated pollution plumes, inducing 5–8% seasonal variability in pollution footprints controlled by wind-tide-stratification interplay. The tidal phase discharge strategy was revealed to be highly effective in this study; both submerged discharge in deep-water zones and intermittent discharge strategies implemented in shallow-water zones significantly reduce the spatial coverage of high-concentration sewage plumes. Our findings highlight the importance of formulating discharge strategies based on tidal phases in typical narrow and deep tidal passages. Full article

Background: Sugars, also known as saccharides or carbohydrates, are essential organic compounds that ensure the human body functions properly. They are used as sources of energy, as structural elements and reserve materials. Excessive sugar consumption is prevalent in many countries and has negative health consequences. Methods: A total of 283 people living in Poland took part in the study. An interview and olfactory tests (dynamic olfactometry method) were conducted together with assessments of food preferences from 25 types of food products. Aim: To assess the impact of olfactory efficiency and individual characteristics, such as sex, age, body weight and tobacco addiction, on preferences for various sweet products. Another important objective of this study was to examine the patterns in preferences for different sweet foods. Result and Conclusions: Of all the types of sweet products analysed in the study, desserts and fruit were the most popular. Preferences varied depending on the type of sweet food or drink as well as on other factors. In terms of declared enjoyment, desserts, chocolate and sweets, including jellies and bread, formed a common group (Factor Three: ‘sweet products’); while sweet, carbonated drinks formed the group—Factor One (‘junk food’); and fruit, together with vegetables, salads, cheese and spicy dishes, formed the group—Factor Four. The most important factors influencing the preference for sweet foods and drinks were the severity of tobacco addiction, age and sex. There was no significant influence from olfactory efficiency or body mass index on the preference for any of the sweet products in the study. Full article

This conceptual article introduces Perceived Cognitive Assistance (PCA)—a novel psychological construct capturing how interactive support from Large Language Models (LLMs) alters investors’ perception of their cognitive capacity to execute complex trading strategies. PCA formalizes a behavioral shift: LLM-empowered retail investors may transition from intuitive heuristics to institutional-grade strategies—sometimes without adequate comprehension. This empowerment–distortion duality forms the theoretical contribution’s core. To empirically validate this model, this article outlines a five-step research agenda including psychological diagnostics, trading behavior analysis, market efficiency tests, and a Behavioral Shift Index (BSI). One agenda component—a dual-agent simulation framework—enables causal benchmarking in post-LLM environments. This simulation includes two contributions: (1) the Virtual Trader, a cognitively degraded benchmark approximating bounded human reasoning, and (2) the Digital Persona, a psychologically emulated agent grounded in behaviorally plausible logic. These components offer methods for isolating LLM assistance’s cognitive uplift and evaluating behavioral implications under controlled conditions. This article contributes by specifying a testable link from established decision frameworks (Theory of Planned Behavior, Technology Acceptance Model, and Risk-as-Feelings) to two estimators: a moderated regression for individual decisions (Equation (1)) and a composite Behavioral Shift Index derived from trading logs (Equation (2)). We state directional, falsifiable predictions for the regression coefficients and for index dynamics, and we outline an identification and robustness plan—versioned, time-locked, and auditable—to be executed in the subsequent empirical phase. The result is a clear operational pathway from theory to measurement and testing, prior to empirical implementation. No empirical results are reported here; the contribution is the operational, falsifiable architecture and its implementation plan, to be executed in a separate preregistered study. Full article