Search Results (1,701)

The widespread adoption of electric vehicles intensifies the spatiotemporal mismatch between charging demand and station capacity, leading to operational inefficiencies. This paper proposes a cooperative charging scheduling strategy based on a novel queuing model that integrates virtual charging piles and state variables to accurately estimate queuing time, overcoming the limitations of conventional methods. A bi-level optimization model is established to coordinate grid load balancing and station-level queue management. An adaptive large-neighborhood search algorithm combining heuristic rules with mathematical solving is developed for efficient solution. Numerical experiments demonstrate that the proposed strategy outperforms existing approaches by significantly increasing fulfilled charging demand and reducing queuing times with only minimal travel distance increase. Analysis further reveals a computational performance trade-off related to scheduling frequency, providing critical insights for practical implementation. Full article

Fluid Catalytic Cracking (FCC) constitutes a critical process in petroleum refining, facing increasing pressure to align with sustainable development goals by improving energy efficiency and reducing environmental impact. This study tackles a multi-objective optimization challenge in FCC operations, seeking to simultaneously maximize the gasoline production and minimize the coke yield—the latter being directly linked to $\mathrm{C}{\mathrm{O}}_2$ emissions in FCC. A data-driven optimization model leveraging a dual Long Short-Term Memory architecture is developed to capture complex relationships between operating variables and product yields. To efficiently solve the model, an Improved Multi-Objective Whale Optimization Algorithm (IMOWOA) is proposed, integrating problem-specific adaptive multi-neighborhood search and dynamic restart mechanisms. Extensive experimental evaluations demonstrate that IMOWOA achieves superior convergence characteristics and comprehensive performance compared to established multi-objective algorithms. Relative to the yields before optimization, the proposed methodology increases the gasoline yield by 0.32% on average, coupled with an average reduction of 0.11% in the coke yield. For the studied FCC unit with an annual processing capacity of 2.6 million tons, the coke reduction corresponds to an annual $\mathrm{C}{\mathrm{O}}_2$ emission reduction of approximately 10,277 tons, delivering benefits to sustainable FCC operations. Full article

Background: This study introduces the Adaptive Graph Fusion Network, an interpretable graph-based learning framework developed for large-scale prediction of intensive care outcomes. The proposed model dynamically constructs patient similarity networks through a density-aware kernel that adjusts neighborhood size based on local data distribution, thereby representing both frequent and rare clinical patterns. Methods: To characterize physiological evolution over time, the framework integrates a short-horizon convolutional encoder that captures acute variations in vital signs and laboratory results with a long-horizon recurrent memory unit that models gradual temporal trends. The approach was trained and internally validated on the publicly available eICU Collaborative Research Database, which includes more than 200,000 admissions from 208 hospitals across the United States. Results: The model achieved a mean area under the receiver operating characteristic curve of 0.91 across six critical outcomes, with in-hospital mortality reaching 0.96, outperforming logistic regression, temporal long short-term memory networks, and calibrated Transformer-based architectures. Feature attribution analysis using SHAP and temporal contribution mapping identified lactate trajectories, creatinine fluctuations, and vasopressor administration as dominant determinants of risk, consistent with established clinical understanding while revealing additional temporal dependencies overlooked by existing scoring systems. Conclusions: These findings demonstrate that adaptive graph construction combined with multi-horizon temporal reasoning improves predictive reliability and interpretability in heterogeneous intensive care populations, offering a transparent and reproducible foundation for future research in clinical machine learning. Full article

Polycyclic aromatic hydrocarbons (PAHs) play a central role in materials science due to their extended π-conjugated systems, with their stability and reactivity depending critically on their aromatic character. In this work, we systematically investigated the aromaticity and stability of a broad range of linear (acenes, phenacenes, biphenylenes, and cyclobuta-acenes) and belt-like (cyclacenes, cyclophenacenes, and cyclobiphenylenes) PAHs containing five to twelve benzene rings. A diverse set of aromaticity descriptors was employed, including geometric (HOMA), electronic (MCI, FLU) and magnetic (NICS) descriptors, plus the recently developed ${\mathit{Q}}_2$ indices, based on the components of the distributed multipole analysis (DMA) electric quadrupole tensor. These data were complemented by stability analyses using singlet–triplet energy splitting (ΔE_S–T) and fractional occupation number-weighted densities (N_FOD) values. Our results indicate that acenes and phenacenes follow a comparable aromatic trend, with inner rings possessing lower aromaticity and the edge rings showing a more pronounced aromatic character. A subtle difference is observed in the position of the most aromatic ring, which lies slightly closer to the interior in acenes. Phenacenes, however, exhibit greater overall stability, attributed to their armchair edges. For biphenylenes and cyclobuta-acenes, the antiaromatic cyclobutadiene moiety perturbs the aromaticity only in its direct neighborhood and preserves the aromaticity in the remaining chains. In belt-like systems, cyclacenes exhibit strong radical character and low stability, consistent with longstanding synthetic challenges, whereas cyclophenacenes display enhanced aromaticity and stability with extending size. Cyclobiphenylenes combine localized antiaromatic centers with preserved benzene-like aromaticity in rings distant from the cyclobutadiene unit. 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

Existing research has made significant strides in UAV formation control, particularly in active localization and certain passive methods. However, these approaches face substantial limitations in electromagnetically silent environments, often relying on strong assumptions such as fully known and stationary emitter positions. To overcome these challenges, this paper proposes a comprehensive framework for bearing-only passive localization and adjustment of UAV formations under strict electromagnetic silence constraints. We systematically develop three core models: (1) a geometric triangulation model for scenarios with three known emitters, enabling unique target positioning; (2) a hierarchical identification mechanism leveraging an angle database to resolve label ambiguity when some emitters are unknown; and (3) a cyclic cooperative strategy, Perceive-Explore-Judge-Execute (PEJE), optimized via an improved genetic algorithm with adaptive discrete neighborhood search (GA-IADNS), for dynamic formation adjustment. Extensive simulations demonstrate that our proposed methods exhibit strong robustness, rapid convergence, and high adjustment accuracy across varying initial deviations. Specifically, after adjustment, the maximum radial deviation of all UAVs from the desired position is less than 0.0001 m, and the maximum angular deviation is within 0.00013°; even for the $30\%R$ initial deviation scenario, the final positional error remains negligible. Furthermore, comparative experiments with a standard Genetic Algorithm (GA) confirm that GA-IADNS achieves superior performance: it reaches stable peak average fitness at the 6th generation (vs. no obvious convergence of GA even after 20 generations), reduces the convergence time by over $70\%$, and improves the final adjustment accuracy by more than $95\%$ relative to GA. These results significantly enhance the autonomous collaborative control capability of UAV formations in challenging electromagnetic conditions. Full article

Robotic mobile fulfillment systems have become an integral part of e-commerce warehouses. The pod repositioning problem, due to its interdependence with robot task allocation strategies, poses a significant challenge that constrains system performance. In this paper, we aim to jointly optimize the two interrelated problems of pod repositioning and task allocation. A multi-objective mixed-integer planning model is developed to minimize the maximum completion time of robots and the deviation between the pod position and the expected position. To tackle the challenges of decision coupling and a vast solution space, an adaptive genetic-neighborhood search algorithm guided by pod heat maps is designed. Additionally, to promptly correct expected layout deviations and avoid layout instability, a progressive storage mechanism is designed to update the expected layout. The numerical experiments show that compared to the staged optimization strategy, the joint optimization strategy proposed in this paper can reduce the maximum completion time by approximately 48%, and that the strategy reduces the maximum completion time by 9% to 16% compared to the nearest allocation strategy, which is commonly used and performs best in practice. Full article

This study presents a comprehensive Lyapunov-based framework for analyzing partial practical stability in nonlinear tempered fractional-order systems (TFOS). We develop novel stability concepts including ${\beta }^{*}$-practical uniform generalized Mittag–Leffler stability (${\beta }^{*}$-PUGMLS) and ${\beta }^{*}$-practical uniform exponential stability (${\beta }^{*}$-PUES) with respect to system substates. Through carefully constructed Lyapunov functions, we establish sufficient conditions under which the system’s states converge to a predefined neighborhood of the origin. The theoretical framework provides Mittag–Leffler and exponential stability criteria for tempered fractional-order systems, extending classical stability theory to this important class of systems. Furthermore, we apply these stability results to design stabilizing feedback controllers for a specific class of triangular TFOS, demonstrating the practical utility of our theoretical developments. The efficacy of the proposed stability criteria and control strategy is validated through several illustrative examples, showing that system states converge appropriately under the derived conditions. This work contributes significantly to the stability theory of fractional-order systems and provides practical tools for controlling complex nonlinear systems in the tempered fractional calculus framework. Full article

On large-scale product assembly lines, such as those used in aircraft manufacturing, multiple assembly positions and devices often coexist within a single workstation, leading to complex task interactions. As a result, the problem of parallel task execution within workstations must be effectively addressed. This study focuses on positional and equipment conflicts within workstations. To manage positional and equipment conflicts, a multi-objective optimization model is developed that integrates assembly sequence planning with the first type of assembly line balancing problem. This model aims to minimize the number of workstations, balance task loads, and reduce equipment procurement costs. An improved NSGA-II algorithm is proposed by incorporating artificial immune algorithm concepts and neighborhood search. A selection strategy based on dominance rate and concentration is introduced, and crossover and mutation operators are refined to enhance search efficiency under restrictive parallel constraints. Case studies reveal that a chromosome concentration weight of about 0.6 yields superior search performance. Compared with the traditional NSGA-II algorithm, the improved version achieves the same optimal number of workstations but provides a 5% better workload balance, 2% lower cost, a 76% larger hyper-volume, and a 133% increase in Pareto front solutions. The results demonstrate that the proposed algorithm effectively handles assembly line balancing with complex parallel constraints, improving Pareto front quality and maintaining diversity. It offers an efficient, practical optimization strategy for scheduling and resource allocation in large-scale assembly systems. Full article

Suburban green areas provide significant health, economic, social, and ecological benefits. They are a key element in advancing sustainability at local and regional scales. However, they become threatened in the presence of other competing land uses, neighborhood-change processes, and/or weak built-environment governance. Consequently, suburban green area loss and/or degradation is problematic. In this study, we tested whether socioeconomic decline is significantly correlated with loss or degradation of suburban green areas at a neighborhood scale. This phenomenon has been previously studied with a limited sample and methodology and needs further empirical documentation and more nuanced modeling and testing. We employed Social-Ecological System theory in scoping and framing this multidisciplinary study and informing multilevel panel-data regressions. This approach allowed us to identify key factors and lagged effects behind green area degradation in outer-ring suburbs of Los Angeles. In addition to internal socioeconomic factors, random components associated with ecological zonal distribution and county-level clustering registered significant variability in their influence on greater likelihood of green coverage loss and degradation in declining outer-ring suburbs. Findings from this study can inform intelligent spatial planning, management, and monitoring of suburban areas, and showcase the value of a social-ecological system lens in suburban green infrastructure research, as well as contribute to SES theoretical development and research methodology at the neighborhood scale. Full article

Post-disaster support is critical for responders’ well-being who navigate the physical, mental, and emotional challenges of crisis intervention. The January 2025 Eaton Fire destroyed structures and disproportionately impacted Black and Latino neighborhoods. AltaMed deployed approximately 230 staff members to serve as frontline responders, providing medical and psychosocial support to evacuees at the Pasadena Convention Center. AltaMed frontline responders were invited to participate in a four-phase support initiative that included on-site peer-led debriefings, a needs and experiences survey, resilience-building workshops, and formal recognition of their contributions. A mixed-methods analysis was conducted on quantitative and qualitative survey data to assess well-being. Survey participants (n = 113) were highly motivated by community service, with 93% reporting a desire to contribute during crisis response. In addition, 53% identified the emotional impact as challenging, and 56% expressed interest in additional training. Peer support and reflection activities were cited as protective factors. Responders requested additional trauma-informed resources and infection prevention training. AltaMed’s support initiative successfully addressed the stressors of disaster response. Structured recognition, resilience-building, and professional development can promote long-term workforce well-being. These findings offer scalable strategies for Federally Qualified Health Centers and health systems supporting frontline workers during emergencies exacerbated by climate change and systemic disparities. Full article

Against the backdrop of Shenzhen’s high-density urban environment, the multifunctional design of water purification plants offers dual benefits: providing residents with urban green spaces while simultaneously mitigating NIMBY sentiments due to their inherent characteristics. Unlike traditional urban development, Shenzhen’s water purification plants integrate into residents’ daily lives. Therefore, optimizing the built environment and road network structure to enhance residents’ perceptions of proximity benefits while reducing NIMBY (Not In My Backyard effect) sentiments holds significant implications for the city’s sustainable development. To address this question, this study adopted the following three-step mixed-methods approach: (1) It examined the relationships among residents’ YIMBY (Neighboring Benefits Effect) and NIMBY perceptions, perceptions of park spaces atop water purification plants, and perceptions of accessibility through questionnaire surveys and structural equation modeling (SEM), establishing a scoring framework for comprehensive YIMBY and NIMBY perceptions. (2) Random forest models and Shapley Additive Explanations (SHAP) analysis revealed nonlinear relationships between the built environment and composite YIMBY and NIMBY perceptions. (3) Spatial syntax analysis categorized the upgraded road network around the water purification plant into grid-type, radial-type, and fragmented-type structures. Scatter plot fitting methods uncovered relationships between these road network types and resident perceptions. Finally, negative perceptions were mitigated by optimizing path enclosure and reducing visual obstructions around the water purification plant. Enhancing neighborhood benefits—through improved path safety and comfort, increased green spaces and resting areas, optimized path networks, and diversified travel options—optimized the built environment. This approach proposes design strategies to minimize NIMBY perceptions and maximize YIMBY perceptions. Full article

Location effects play a crucial role in the real estate market, encompassing aspects of accessibility and neighborhood quality. While traditional measures exist for accessibility, evaluating neighborhood quality can be a complex task. Understanding these elements is essential for accurately estimating property values, whether for commercial or tax purposes. Recently developed methods based on web scraping and automatic detection using artificial intelligence have proven effective but require substantial human and financial resources, often unavailable in small cities. As a solution, this study proposes and evaluates a simpler mechanism for assessing neighborhood quality using Google Street View images and a scoring system in a human-centered approach. Based on image interpretation, a set of weights is assigned to each point, resulting in a micro-neighborhood quality assessment. This study was conducted in three Latin American cities, and the resulting variable was integrated into hedonic price models. The findings demonstrate the feasibility and effectiveness of the proposed approach. The novelty of this study lies in applying a method based on quasi-objective criteria and adapted to cities with limited technological resources. Full article

This study introduces a new integral operator $I_{p,\mu }^{\delta }$ that extends the traditional Rafid operator to meromorphic p-valent functions. Using this operator, we define and investigate two new subclasses: ${\Sigma }_p^{+}\left(\delta ,\mu ,\alpha \right)$, consisting of functions with nonnegative coefficients, and ${\Sigma }_p^{+}\left(\delta ,\mu ,\alpha ,c\right)$, which further fixes the second positive coefficient. For these classes, we establish a necessary and sufficient coefficient condition, which serves as the foundation for deriving a set of sharp results. These include accurate coefficient bounds, distortion theorems for functions and derivatives, and radii of starlikeness and convexity of a specific order. Furthermore, we demonstrate the closure property of the class ${\Sigma }_p^{+}\left(\delta ,\mu ,\alpha ,c\right)$, identify its extreme points, and then construct a neighborhood theorem. All the findings presented in this paper are sharp. To demonstrate the practical utility of our symmetric operator paradigm, we apply it to a canonical fractional electrodynamics problem. We demonstrate how sharp distortion theorems establish rigorous, time-invariant upper bounds for a solitary electrostatic potential and its accompanying electric field, resulting in a mathematically guaranteed safety buffer against dielectric breakdown. This study develops a symmetric and consistent approach to investigating the geometric characteristics of meromorphic multivalent functions and their applications in physical models. Full article

The study presents a scalable decision-support framework to assess energy-sharing strategies within mixed-use urban districts, with a focus on planning, sustainability, and policy relevance. Two renewable energy-sharing mechanisms—energy sharing (ES) and net metering (NM)—are compared through a techno-economic analysis applied to a real neighborhood in Montréal, Canada. The workflow integrates irradiance-aware PV simulation, archetype-based urban building modeling, and financial sensitivity analysis adaptable to local regulatory conditions. Key performance indicators (KPIs)—including Self-Consumption Ratio (SCR), Self-Sufficiency Ratio (SSR), and peak load reduction—are used to evaluate technical performance. Results show that ES outperforms NM, achieving higher SCR (77% vs. 66%) and SSR (40% vs. 35%), and seasonal analysis reveals that peak shaving reaches 30.3% during summer afternoons, while PV impact is limited to 15.6% in winter mornings and negligible during winter evenings. Although both mechanisms are currently unprofitable under existing Québec tariffs, scenario analysis reveals that a 50% CAPEX subsidy or a 0.12 CAD/kWh feed-in tariff could make the system viable. The novelty of this study lies in the development of a replicable, archetype-driven, and policy-oriented simulation framework that enables the evaluation of renewable energy communities in mixed-use and data-scarce urban environments, contributing new insights into the Canadian energy transition context. Full article