Search Results (14,990)

The efficient and timely management of human evacuation during emergency events is an important area of research where the Internet of Things (IoT) can be of great value. Significant areas of application for optimum evacuation strategies include buildings, sports arenas, cultural venues, such as museums and concert halls, and ships that carry passengers, such as cruise ships. In many cases, the evacuation process is complicated by constraints on space and movement, such as corridors, staircases, and passageways, that can cause congestion and slow the evacuation process. In such circumstances, the Internet of Things (IoT) can be used to sense the presence of evacuees in different locations, to sense hazards and congestion, to assist in making decisions based on sensing to guide the evacuees dynamically in the most effective direction to limit or eliminate congestion and maximize safety, and notify to the passengers the directions they should take or whether they should stop and wait, through signaling with active IoT devices that can include voice and visual indications and signposts. This paper uses an analytical queueing network approach to analyze an emergency evacuation system, and suggests the use of the Pull Policy, which employs the IoT to direct evacuees in a manner that reduces downstream congestion by signalling them to move forward when the preceding evacuees exit the system. The IoT-based Pull Policy is analyzed using a realistic representation of evacuation from an existing commercial cruise ship, with a queueing network model that also allows for a computationally very efficient comparison of different routing rules with wide-ranging variations in speed parameters of each of the individual evacuees.Numerical examples are used to demonstrate its value for the timely evacuation of passengers within the confined space of a cruise ship. Full article

Crowdsourced geospatial platforms constitute complex socio-technical systems in which data quality and reliability emerge from collective user behavior rather than centralized control. This study proposes a system-oriented, unsupervised machine learning framework to assess the reliability of crowdsourced building data using only intrinsic indicators. The framework is demonstrated through a large-scale analysis of OpenStreetMap building polygons in Tehran. Six intrinsic indicators—reflecting contributor activity, temporal dynamics, semantic instability, and geometric evolution—were normalized using fuzzy membership functions and objectively weighted based on their discriminative influence within a K-means clustering process. Five reliability classes were identified, ranging from very low to very high reliability. The resulting classification exhibited strong internal validity (average silhouette coefficient = 0.58) and pronounced spatial coherence (Global Moran’s I = 0.85, p < 0.001). This approach eliminates dependence on authoritative reference datasets, enabling scalable, reproducible, and feature-level reliability assessment in open geospatial systems. The framework provides a transferable methodological foundation for trust-aware analysis and decision-making in participatory and data-intensive systems. Full article

In the context of the rapid transformation of global agricultural production towards intensification and intelligence, the precise and intelligent management of horticultural crop production processes is key to enhancing resource utilization efficiency and industry profitability. Crop growth and development models, as digital representations of the interactions between environment, crops, and management, are core tools for achieving intelligent decision-making in facility production. This paper provides a comprehensive review of the advancements in intelligent management models and systems for horticultural crop growth and development. It introduces the developmental stages of horticultural crop growth models and the integration of multi-source data, systematically organizing and analyzing the modeling mechanisms of crop growth and development process models centered on developmental stages, photosynthesis and respiration, dry matter accumulation and allocation, and yield and quality formation. Furthermore, it summarizes the current status of expert decision-support system software development and application based on crop models, achieving comprehensive functionalities such as data and document management, model parameter management and optimization, growth process and environmental simulation, management plan design and effect evaluation, and result visualization and decision product dissemination. This illustrates the pathway from theoretical research to practical application of models. Addressing the current challenges related to the universality of mechanisms, multi-source data assimilation, and intelligent decision-making, the paper looks forward to future research directions, aiming to provide theoretical references and technological insights for the future development and system integration of intelligent management models for horticultural crop growth and development. Full article

Urban intersections are critical nodes for roadway safety, congestion management, and autonomous vehicle coordination. Traditional traffic control systems based on fixed-time signals and static sensors lack adaptability to real-time risks such as red-light violations, near-miss incidents, and multimodal conflicts. This study presents a grid-enabled framework integrating computer vision and machine learning to enhance real-time intersection intelligence and road safety. The system overlays a computational grid on the roadway, processes live video feeds, and extracts dynamic parameters including vehicle trajectories, deceleration patterns, and queue evolution. A novel active learning module improves detection accuracy under low visibility and occlusion, reducing false alarms in collision and violation detection. Designed for edge-computing environments, the framework interfaces with signal controllers to enable adaptive signal timing, proactive collision avoidance, and emergency vehicle prioritization. Case studies from multiple intersections typical of US cities show improved phase utilization, reduced intersection conflicts, and enhanced throughput. A grid-based heatmap visualization highlights spatial risk zones, supporting data-driven decision-making. The proposed framework bridges static infrastructure and intelligent mobility systems, advancing safer, smarter, and more connected roadway operations. Full article

Industrial packaging systems exert substantial environmental pressures, including material resource depletion, greenhouse gas emissions, and the accumulation of post-consumer waste. As global supply chains expand and sustainability regulations intensify, demand for environmentally responsible packaging solutions continues to rise. This study evaluates the environmental footprint of industrial packaging by integrating recent developments in life cycle assessment (LCA), ecological footprint (EF) methodologies, material innovations, and circular economy models. The assessment examines the sustainability performance of conventional and alternative packaging materials, plastics, aluminum, corrugated cardboard, and polylactic acid (PLA). Findings indicate that although corrugated cardboard is renewable, it still presents a measurable environmental burden, with evidence suggesting that incorporating solar energy into production can reduce its footprint by more than 12%. PLA-based trays demonstrate promising environmental performance when sourced from renewable feedstocks and directed to appropriate composting systems. Despite these advancements, several systemic challenges persist, including ecological overshoot in industrial regions where EF may exceed local biocapacity limitations in waste management infrastructure, and significant economic trade-offs. Transportation-related emissions and scalability constraints for bio-based materials further hinder large-scale adoption. Existing research suggests that integrating sustainable packaging across supply chains could meaningfully reduce environmental impacts. Achieving this transition requires coordinated cross-sector collaboration, standardized policy frameworks, and embedding advanced environmental criteria into packaging design and decision-making processes. Full article

Background and Objectives: Interest in biomarkers reflecting the inflammatory nature of Hodgkin lymphoma (HL) is increasing. This study aimed to evaluate the prognostic significance of the Systemic Inflammation Response Index (SIRI) in patients with HL. Materials and Methods: In this study, 105 patients diagnosed with classical HL at the Hematology Clinic of Recep Tayyip Erdoğan University Faculty of Medicine between January 2015 and April 2025 were retrospectively evaluated. Patients were divided into 2 groups according to the SIRI cut-off value. Results: A high SIRI (≥3.78) was significantly associated with advanced disease stage, poor performance status, higher IPS-7 and IPS-3 scores, non-response or partial response to treatment, relapse, and increased mortality. A positive correlation was found between SIRI and IPS 7 scores (p < 0.001, rho = 0.355). In the univariate analysis for progression-free survival (PFS), hemoglobin, IPS 7 score, and SIRI were identified as prognostic factors; in the multivariate analysis, high SIRI was identified as an independent prognostic factor (p = 0.033). In the univariate analysis for overall survival (OS), age, hemoglobin, albumin, lymphocyte count, IPS 7 score, and SIRI were identified as prognostic factors; and, in the multivariate analysis, age over 45 and high SIRI were identified as independent prognostic factors (p = 0.016, p = 0.012). In the survival analysis, high SIRI levels were associated with shorter PFS and OS (p = 0.001, p < 0.001). Additionally, PFS and OS durations were shorter in patients with high IPS 7 scores (p < 0.001, p < 0.001). Conclusions: A high SIRI prior to treatment was identified as an independent prognostic factor in HL patients and was associated with shorter PFS and OS. This index may help identify high-risk patients and assist clinicians in their decision-making processes by enabling individualized risk assessment. Full article

Mobile authentication is crucial for device security; however, conventional techniques such as PINs and swipe patterns are susceptible to social engineering attacks. This work explores the integration of touch stroke analysis and Explainable AI (xAI) to address these vulnerabilities. Unlike static methods that require intervention at specific intervals, continuous authentication offers dynamic security by utilizing distinct user touch dynamics. This study aggregates touch stroke data from 150 participants to create comprehensive user profiles, incorporating novel biometric features such as mid-stroke pressure and mid-stroke area. These profiles are analyzed using machine learning methods, where the Random Tree classifier achieved the highest accuracy of 97.07%. To enhance interpretability and user trust, xAI methods such as SHAP and LIME are employed to provide transparency into the models’ decision-making processes, demonstrating how integrating touch stroke dynamics with xAI produces a visible, trustworthy, and continuous authentication system. Full article

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

Packaging is a fundamental component of food supply chains, enabling product protection, handling, and distribution from production to final consumption. In this context, the selection of secondary and tertiary packaging dimensions plays a critical role in improving logistics efficiency and reducing greenhouse gas (GHG) emissions associated with material use and transportation. This study proposes a sustainable packaging logistics (SPL) framework that systematically evaluates and optimizes packaging carton dimensions to enhance pallet utilization, transport efficiency, and packaging material efficiency. The framework is applied to a real-world case study from a meat processing company, demonstrating how alternative carton dimension configurations, while maintaining a constant product weight and functional equivalence, can significantly influence pallet-loading efficiency, transported payload, and associated CO₂-equivalent emissions. Rather than constituting a full life cycle assessment (LCA), the proposed approach adopts LCA-informed indicators to quantify material and transport related emission implications of packaging design choices. By integrating packaging design, palletization constraints, and logistics performance, the SPL framework provides a structured analytical basis for identifying packaging configurations that reduce material intensity and transport-related emissions. The results highlight the importance of packaging dimension optimization as a practical and scalable strategy for emission reduction in food supply chains. The proposed framework is intended to support decision-making in packaging design and to serve as a robust preparatory tool for future, more comprehensive LCA studies. Full article

Intrinsic motivation provides a principled mechanism for driving exploration in reinforcement learning when external rewards are sparse or delayed. A central challenge, however, lies in defining meaningful novelty signals in high-dimensional and combinatorial state spaces, where observation-level density estimation and prediction-error heuristics often become unreliable. In this work, we propose an information-theoretic framework for intrinsically motivated reinforcement learning grounded in the Information Bottleneck principle. Our approach learns compact latent state representations by explicitly balancing the compression of observations and the preservation of predictive information about future state transitions. Within this bottlenecked latent space, intrinsic rewards are defined through information-theoretic quantities that characterize the novelty of state–action transitions in terms of mutual information, rather than raw observation dissimilarity. To enable scalable estimation in continuous and high-dimensional settings, we employ neural mutual information estimators that avoid explicit density modeling and contrastive objectives based on the construction of positive–negative pairs. We evaluate the proposed method on two representative combinatorial routing problems, the Travelling Salesman Problem and the Split Delivery Vehicle Routing Problem, formulated as Markov decision processes with sparse terminal rewards. These problems serve as controlled testbeds for studying exploration and representation learning under long-horizon decision making. Experimental results demonstrate that the proposed information bottleneck-driven intrinsic motivation improves exploration efficiency, training stability, and solution quality compared to standard reinforcement learning baselines. Full article

There is an increasing number of academic and regulatory investigations into the behavioral and psychological implications of using Buy Now, Pay Later (BNPL) services due to their rapid growth. There have been extensive investigations into impulse purchases using BNPL services; however, there has been relatively little focus placed upon examining post-purchase regret associated with BNPL service use. The purpose of this paper is to present a systematic review of the extant literature investigating how BNPL service use relates to both impulsive purchasing behavior and post-purchase regret. A total of ten empirical studies were identified through a comprehensive search of the Scopus database according to the PRISMA 2020 guidelines, which were all published between 2018 and 2025. The results indicated that BNPL features, including deferred payments, perceived affordability, and urgency cues, are consistent predictors of both greater impulsive purchasing and lower levels of payment salience. The results of this review, however, reveal that many existing studies have failed to directly measure post-purchase regret and instead rely on proxy indicators, including financial distress, emotional discomfort, and decreased well-being. These findings, therefore, highlight a major theoretical and methodological void in the existing literature. In addition, by providing a synthesis of the current evidence base, this review aims to provide a clearer understanding of how BNPL features influence both consumer decision-making processes and post-purchase emotional responses; additionally, this review highlights the necessity for future research to utilize valid measures of regret, longitudinal designs and ethically informed analytical frameworks when investigating the psychological impacts of adopting BNPL services. Full article

Social organizations increasingly rely on Natural Language Processing (NLP) to analyze large-scale textual data for high-stakes decisions, including university admissions, financial aid allocation, and job hiring. Current methods primarily employ topic modeling and sentiment analysis, but they fail to capture the complex ways emotions and cultural contexts shape meaning in text, potentially perpetuating bias and undermining equitable decision-making. To address this gap, we introduce the Behavioral and Emotional Theme Detection (BET) framework, a novel approach that integrates emotional, cultural, and sociological dimensions into topic detection and emotion analysis. By applying BET to English and Hebrew datasets, we showcase its multilingual adaptability and its potential to reveal rich thematic content and emotional resonance in biographical texts. Our results demonstrate that BET not only enhances the granularity and diversity of detected themes but also tracks shifts in emotional framing over time, offering deeper insights into how individuals deploy linguistic resources to position their identities, enabling more equitable assessment practices. Full article

In digital marketplaces, trust in e-commerce platforms has evolved from a protective heuristic into a powerful mechanism of behavioral conditioning. This review interrogates how trust cues such as star ratings, fulfillment badges, and platform reputation shape consumer cognition, systematically displace critical evaluation, and create asymmetries in perceived quality. Drawing on over 47 high-quality studies across experimental, survey, and modeling methodologies, we identify seven interlocking dynamics: (1) cognitive outsourcing via platform trust, (2) reputational arbitrage by low-quality sellers, (3) consumer loyalty despite disappointment, (4) heuristic conditioning through trust signals, (5) trust inflation through ratings saturation, (6) false security masking structural risks, and (7) the shift in consumer trust from brands to platforms. Anchored in dual process theory, this synthesis positions trust not merely as a transactional enabler but as a socio-technical artifact engineered by platforms to guide attention, reduce scrutiny, and manage decision-making at scale. Eventually, platform trust functions as both lubricant and leash: streamlining choice while subtly constraining agency, with profound implications for digital commerce, platform governance, and consumer autonomy. Full article

The safe utilization of underground spaces constitutes a critical challenge for densely populated cities, making geosafety risk prevention in underground development a focal point for both academic research and governmental governance. As the pivotal link and ultimate objective in geological safety management, risk prevention facilitates the transition from theoretical research to administrative practice. This study establishes a management-oriented technical framework for geological risk preventive zoning in underground space utilization, addressing the current research gap where zoning methodologies inadequately integrate with governmental decision-making processes due to insufficient consideration of multidimensional attributes from both researcher and administrator perspectives. Taking Xiong’an New Area in China as a case study, the framework employs a tri-level analytical structure, restrictive tier, limiting tier, and influencing tier, with phased weighting methodologies, CRITIC-EWM for objective weighting vs. AHP-FAHP for subjective weighting. The scientifically validated results demonstrate the framework’s feasibility and scalability. Limitations and future research directions are identified to guide subsequent studies in this field. Full article

Carbapenem-resistant Klebsiella pneumoniae (CRKP) has become one of the most serious problems confronting modern healthcare, particularly in intensive care units where patients are highly susceptible, procedures are frequent, and antibiotic exposure is often prolonged. In this review, carbapenem resistance in K. pneumoniae is presented not as a fixed feature of individual bacteria, but as a process that is constantly changing and closely interconnected. We bring together evidence showing how the spread of successful bacterial lineages, the exchange of resistance genes, and gradual genetic adjustment combine to drive both the rapid spread and the long-lasting presence of resistance. A major focus is placed on mobile genetic elements, including commonly encountered plasmid backbones, transposons, and insertion sequences that carry carbapenemase genes such as blaKPC, blaNDM, and blaOXA-48-like. These elements allow resistance genes to move easily between bacteria and across different biological environments. The human gut plays a particularly important role in this process. Its microbial community serves as a largely unseen reservoir where resistance genes can circulate and accumulate well before infection becomes clinically apparent, making prevention and control more difficult. This review also discusses the key biological factors that shape resistance levels, including carbapenemase production, changes in the bacterial cell membrane, and systems that expel antibiotics from the cell, and explains how these features work together. Advances in molecular testing have made it possible to identify resistance more quickly, supporting earlier clinical decisions and infection control measures. Even so, current tests remain limited by narrow targets and may miss low-level carriage, hidden genetic reservoirs, or newly emerging resistance patterns. Finally, we look ahead to approaches that move beyond detection alone, emphasizing the need for integrated surveillance, thoughtful antibiotic use, and coordinated system-wide strategies to lessen the impact of CRKP. Full article