Search Results (154)

Existing heuristic algorithms are based on inspiration sources and have not yet done a good job of basing themselves on optimization principles to minimize and utilize historical information, which may lead to low efficiency, accuracy, and stability of the algorithm. To solve this problem, a personalized-template-guided intelligent evolutionary algorithm named PTG is proposed. The core idea of PTG is to generate personalized templates to guide particle optimization. We also find that high-quality templates can be generated to guide the exploration and exploitation of particles by using the information of the population particles when the optimal value remains unchanged, the knowledge of population distribution changes, and the dimensional distribution properties of particles themselves. By conducting an ablation study and comparative experiments on the challenging CEC2022 test and CEC2005 test functions, we have validated the effectiveness of our method and concluded that the stability and accuracy of the solutions obtained by PTG are superior to other algorithms. Finally, we further verified the effectiveness of PTG through four engineering problems. Full article

The Tree–Seed Algorithm (TSA) is a symmetry-driven metaheuristic algorithm that shows potential for complex optimization problems, but it suffers from local optimum entrapment and slow convergence. To address these limitations, we propose the ADTSA algorithm. First, ADTSA adopts a symmetry-driven dual-layer framework for seed generation, which promotes effective information exchange between subpopulations and accelerates convergence speed. In later iterations, ADTSA enhances the population’s exploitation ability through a population fusion mechanism, further improving the convergence speed. Moreover, we propose a historical optimal solution archiving and replacement mechanism, along with a t-distribution perturbation mechanism, to enhance the algorithm’s ability to escape local optima. ADTSA also strengthens population diversity and avoids local optima through convex lens symmetric reverse generation based on the optimal solution. With these mechanisms, ADTSA converges more effectively to the global optimum during the evolutionary process. Tests on the IEEE CEC 2014 benchmark functions showed that ADTSA outperformed several top-performing algorithms, such as LSHADE, JADE, LSHADE-RSP, and the latest TSA variants, and it also excelled in comparison with other optimization algorithms, including GWO, PSO, BOA, GA, and RSA, underscoring its robust performance across diverse testing scenarios. The proposed ADTSA’s applicability in solving complex constrained problems was also validated, with the results showing that ADTSA achieved the best solutions for these complex problems. Full article

The rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical generation and consumption patterns with models of EV population growth and initial charging-time (ICT). We introduce a novel supply–demand balance score to quantify weekly and annual deviations between projected supply and demand curves, then use this metric to guide the machine-learning model in optimizing annual growth rate (AGR) and preventing supply demand imbalance. Relative to a business-as-usual baseline, our approach improves balance scores by 64% and projects up to a 59% reduction in charging load by 2060. These results demonstrate the promise of data-driven demand-management strategies for maintaining grid reliability during large-scale EV integration. Full article

Nature-Based Solutions (NBSs) offer promising pathways to enhance ecological resilience and address urban water challenges, particularly in heritage cities where conventional gray infrastructure often fails to balance environmental needs with cultural preservation. This study proposes a strategic framework for the integration of NBSs into historic urban landscapes by employing Internal–External (IE) matrix modeling and an impact–uncertainty assessment, grounded in a structured evaluation of key internal strengths and weaknesses, as well as external opportunities and threats. The Internal Factor Evaluation (IFE) score of 2.900 indicates a favorable internal environment, characterized by the multifunctionality of NBS and their ability to reconnect urban populations with nature. Meanwhile, the External Factor Evaluation (EFE) score of 2.797 highlights moderate support from policy and public awareness but identifies barriers such as funding shortages and interdisciplinary coordination. Based on these findings, two strategies are developed: an SO (Strength–Opportunity) strategy, promoting community-centered and policy-driven NBS design, and a WO (Weakness–Opportunity) strategy, targeting resource optimization through legal support and cross-sectoral collaboration. This study breaks new ground by transforming theoretical NBS concepts into actionable, culturally sensitive planning tools that enable decision-makers to navigate the unique challenges of implementing adaptive stormwater and environmental management in historically constrained urban environments. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Lower gastrointestinal bleeding (LGIB) is a frequent and potentially life-threatening clinical condition. Over the past two decades, several prognostic scoring systems have been developed to stratify risk and guide the management of patients with LGIB. This comprehensive review aims to summarize and compare the current evidence on the utility, accuracy, and limitations of key LGIB scoring systems, including the Glasgow-Blatchford Score (GBS), AIMS65, ABC score, Oakland score, SALGIB, CHAMPS, and Rockall score. We conducted a structured literature review of studies evaluating these scores in adult patients with LGIB. For each scoring system, we analyzed its origin, components, intended use, and predictive performance regarding clinical outcomes such as severe bleeding, transfusion requirement, in-hospital mortality, rebleeding, and safe discharge. Comparative analyses of diagnostic accuracy were extracted where available. Our findings indicate that while no single score offers comprehensive predictive accuracy across all outcomes, certain tools are particularly effective for specific endpoints. The Oakland and GBS scores are useful for identifying patients at low risk who may be managed safely as outpatients. The ABC and CHAMPS scores demonstrate superior performance in predicting mortality, especially in elderly or comorbid populations. SALGIB, a newer score developed in Vietnam, shows promising performance for early triage but requires further validation. The Rockall score, although historically valuable in upper GI bleeding, offers limited applicability in LGIB due to its reliance on post-endoscopic findings. In conclusion, multiple prognostic tools are now available to support early decision-making in LGIB. Their optimal use requires understanding their strengths, limitations, and appropriate clinical contexts. Integrating these scores into routine practice, along with clinical judgment, can enhance patient outcomes and resource allocation. Full article

Promoting coordinated urban–rural development represents a key policy initiative by the Chinese government to advance rural revitalization and promote common prosperity. As a central component of China’s land management system, the Urban–Rural Construction Land Linkage Policy aims at dismantling the historical urban–rural division while fostering balanced regional growth. This research analyzes panel data spanning 2010–2022 across 294 prefecture-level cities, utilizing a multi-phase difference-in-differences (DID) approach to evaluate the policy’s effectiveness in reducing urban–rural income disparities. Empirical findings reveal that the policy implementation has substantially narrowed the income gap between urban and rural populations. Heterogeneity analysis indicates that the policy’s impact is more pronounced in China’s eastern regions. Mechanism analysis reveals that the policy narrows the income gap through two primary pathways: first, by promoting urbanization through facilitating rural-to-urban population transfer and optimizing urban spatial layout. Second, by driving industrial structure optimization through intensive land use that advances agricultural scale and modernization, while improved land resource allocation boosts secondary and tertiary industries. These findings offer empirical support and policy insights for refining urban–rural land management strategies and advancing integrated development. Full article

Artificial intelligence (AI) has found applications across diverse sectors in recent years, significantly enhancing operational efficiencies and user experiences. Educational data mining (EDM) has emerged as a pivotal AI application to transform educational environments by optimizing learning processes and identifying at-risk students. This study leverages EDM within a Moroccan university (Hassan First, University Settat, Morocco) context to augment educational quality and improve learning. We introduce a novel “Hybrid approach” that synthesizes students’ historical academic records and their in-class behavioral data, provided by instructors, to predict student performance in initial coding courses. Utilizing a range of machine learning (ML) algorithms, our research applies multi-classification, data augmentation, and binary classification techniques to evaluate student outcomes effectively. The key performance metrics, accuracy, precision, recall, and F1-score, are calculated to assess the efficacy of classification. Our results highlight the long short-term memory (LSTM) algorithm’s robustness achieving the highest accuracy of 94% and an F1-score of 0.87 along with a support vector machine (SVM), indicating high efficacy in predicting student success at the onset of learning coding. Furthermore, the study proposes a comprehensive framework that can be integrated into learning management systems (LMSs) to accommodate generational shifts in student populations, evolving university pedagogies, and varied teaching methodologies. This framework aims to support educational institutions in adapting to changing educational dynamics while ensuring high-quality, tailored learning experiences for students. Full article

The ecological consequences of the construction and operation of the Three Gorges Reservoir, particularly its unique operation strategy of storing clear water and releasing turbid water, exerts a profound influence on the composition and dynamics of local fish communities. To date, detailed and comprehensive research on seasonal changes in the fish community across the entire reservoir remains scarce. This study aims to fill this research gap by systematically investigating fish diversity through a comprehensive assessment of six main river reaches and eight major tributaries. The investigation employs environmental DNA (eDNA) technology across three critical life-cycle stages: breeding, feeding, and overwintering periods. A total of 124 fish species were recorded, comprising 10 orders, 20 families, and 80 genera. The comparative analyses of historical data suggest a significant decline in lotic and endemic fish populations, accompanied by a concurrent increase in lentic, eurytopic, and non-native fish species. Notably, the composition of fish communities exhibited similarities between breeding and overwintering periods. This study highlights the occurrence of significant seasonal fluctuations in the fish communities, showing a preference for reservoir tails and tributaries as optimal habitats. Water temperature has a predominant influence on structuring fish communities within aquatic ecosystems. This study investigates variations in the biodiversity of fish communities using historical data, with a focus on changes linked to reservoir operations and water impoundment activities. By integrating historical data, this research examines changes in fish diversity that are associated with water storage processes. It provides foundational data on the current composition and diversity of fish communities within the watershed, elucidating the spatiotemporal variations in fish diversity and the mechanisms by which environmental factors influence these communities. Furthermore, the current study serves as a valuable reference for understanding the changes in fish communities within other large reservoirs. Full article

Heartworm disease (Dirofilaria immitis) is an important zoonotic infection of major clinical importance in dogs widespread, and transmitted by culicid vectors. Although D. immitis mostly affects dogs with an overall low incidence, some islands of the Atlantic archipelagos such as the island of Gran Canaria (Canary Islands, Spain) had one of the highest historical prevalence/seroprevalence values in dogs, cats and humans. Molecular tools allow us to perform species identification diagnosis, phylogeographic and population genetics analysis that can provide key information about the factors making the disease still a threat (uncover untreated range of hosts, putative origin, etc.). In this study, we have optimized primers to amplify mitochondrial (COI, 12S) and nuclear (ITS) molecular markers from adult D. immitis worms. The genetic diversity and structure of D. immitis at the global level is limited, especially when compared with results obtained for other species of the same genus, such as D. repens. New minor haplotypes in the mitochondrial COI marker have been identified from adult D. immitis worms from infected dogs from the hyperendemic island of Gran Canaria, suggesting that the disease may have originated locally or may have been introduced from the mainland in historical times and evolved in isolation. To obtain a more complete understanding of its evolutionary history, structure and genomic diversity, comparative studies using next-generation sequencing data from endemic areas are needed, which will help in the long term to implement monitoring and control measures in a given area and to better understand its global phylogeographic history. Full article

With the advancement of industrial digitization, utilizing large datasets for model training to boost performance is a pivotal technical approach for industry progress. However, raw training datasets often contain abundant redundant features, which increase model training’s computational cost and impair generalization ability. To tackle this, this study proposes the bionic ABCCOA algorithm, an enhanced version of the bionic Coati Optimization Algorithm (COA), to improve redundant feature elimination in datasets. To address the bionic COA’s inadequate global search performance in feature selection (FS) problems, leading to lower classification accuracy, an adaptive search strategy is introduced. This strategy combines individual learning capability and the learnability of disparities, enhancing global exploration. For the imbalance between the exploration and exploitation phases in the bionic COA algorithm when solving FS problems, which often traps it in suboptimal feature subsets, a balancing factor is proposed. By integrating phase control and dynamic adjustability, a good balance between the two phases is achieved, reducing the likelihood of getting stuck in suboptimal subsets. Additionally, to counter the bionic COA’s insufficient local exploitation performance in FS problems, increasing classification error rates, a centroid guidance strategy is presented. By combining population centroid guidance and fractional-order historical memory, the algorithm lowers the classification error rate of feature subsets and speeds up convergence. The bionic ABCCOA algorithm was tested on the CEC2020 test functions and engineering problem, achieving an over 90% optimization success rate and faster convergence, confirming its efficiency. Applied to 27 FS problems, it outperformed comparative algorithms in best, average, and worst fitness function values, classification accuracy, feature subset size, and running time, proving it an efficient and robust FS algorithm. Full article

Against the backdrop of rapid rail transit development, this study takes Urumqi Metro Line 1 as a case, using geographic information system (GIS) spatial analysis and space syntax Pearson correlation coefficient methods. Focusing on an 800 m radius around station areas, the research investigates the distribution characteristics of commercial facilities and the impact of metro development on commercial patterns through the quantitative analysis and distribution trends of points of interest (POI) data across different historical periods. The study reveals that following the opening of Urumqi Metro Line 1, commercial facilities have predominantly clustered around stations including Erdaoqiao, Nanmen, Beimen, Nanhu Square, Nanhu Beilu, Daxigou, and Sports Center, with kernel density values surging by 28–39%, indicating significantly enhanced commercial agglomeration. Metro construction has promoted commercial POI quantity growth and commercial sector enrichment. Surrounding commercial areas have developed rapidly after metro construction, with the most significant impacts observed in the catering, shopping, and residential-oriented living commercial sectors. After the construction of the subway, the distribution pattern of commercial facilities presents two kinds of aggregation patterns: one is the original centripetal aggregation layout before construction and further strengthened after construction; the other is the centripetal aggregation layout before construction and further weakened after construction, tending to the site level of face-like aggregation. The clustering characteristics of different business types vary. Factors such as subway accessibility, population density, and living infrastructure all impact the distribution of businesses around the subway. The impact of subway accessibility on commercial facilities varies by station infrastructure and urban area. The findings demonstrate how transit infrastructure development can catalyze sustainable urban form evolution by optimizing spatial resource allocation and fostering transportation–commerce synergy. It provides empirical support for applying the theory of transit-oriented development (TOD) in the urban planning of western developing regions. The research not only fills a research gap concerning the commercial space differentiation law of metro systems in megacities in arid areas but also provides a scientific decision-making basis for optimizing the spatial resource allocation of stations and realizing the synergistic development of transportation and commerce in the node cities along the “Belt and Road”. Full article

Melanoma treatment comprised a few treatment choices with insufficient efficacy before the emergence of molecularly targeted medication and immune checkpoint inhibitors, which dramatically improved patient outcomes. B-Rapidly Accelerated Fibrosarcoma (BRAF) and Mitogen-Activated Protein Kinase (MAPK) Kinase (MEK) inhibitors significantly improved survival in BRAF-mutant melanoma and immune checkpoint inhibitors, such as anti-programmed cell death 1 (PD-1) and Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) agents, established new standards of care. Challenges remain, however, including the existence of resistance mechanisms and the reduced efficacy of immune-based therapies in Asian populations, particularly for acral and mucosal subtypes. This review highlights historical and current therapeutic advancements, discusses regional considerations, and explores emerging strategies aiming at globally optimizing melanoma management. Full article

As climate change intensifies, cities are experiencing more severe rainfall and frequent waterlogging. When rainfall exceeds the carrying capacity of urban drainage networks, it poses a significant risk to urban facilities and public safety, seriously affecting sustainable urban development. Compared with general urban built-up areas, they demonstrate greater vulnerability to rainfall-induced waterlogging due to their obsolete infrastructure and high heritage value, making it imperative to comprehensively enhance their waterlogging resilience. In this study, Qingdao’s historic urban area is selected as a sample case to analyze the interaction between rainfall intensity, the built environment, and population and business characteristics and the mechanism of waterlogging disaster in the historic urban area by combining with the concept of resilience; then construct a resilience assessment system for waterlogging in the historic urban area in terms of dangerousness, vulnerability, and adaptability; and carry out a measurement study. Specifically, the CA model is used as the basic model for simulating the possibility of waterlogging, and the waterlogging resilience index is quantified by combining the traditional research data and the emerging open-source geographic data. Furthermore, the waterlogging resilience and obstacle factors of the 293 evaluation units were quantitatively evaluated by varying the rainfall characteristics. The study shows that the low flooding resilience in the historic city is found in the densely built-up areas within the historic districts, which are difficult to penetrate, because of the high vulnerability of the buildings themselves, their adaptive capacity to meet the high intensity of tourism and commercial activities, and the relatively weak resilience of the built environment to disasters. Based on the measurement results, targeted spatial optimization strategies and planning adjustments are proposed. Full article

This study evaluates Spain’s biomethane production potential for 2030 and 2050, focusing on agricultural residues, livestock manure, municipal solid waste (MSW), and wastewater treatment plant (WWTP) sludge. The research aims to provide a regional analysis based on historical data on livestock populations, cultivated land, waste availability, and demographic projections. Using utilization coefficients and technological assumptions derived from existing biogas infrastructure, the study estimates that Spain could generate 9.71 TWh of biomethane by 2030, slightly below the national target of 10.41 TWh. By 2050, agricultural and livestock residues are expected to contribute 30.04 TWh, accounting for nearly 80% of total biomethane production, while the relative share of MSW and WWTP sludge will decrease. Andalusia, Castilla-La Mancha, and Castilla y León emerge as key contributors due to their extensive agricultural and livestock sectors. Catalonia and Madrid maintain significant roles driven by urban waste generation. The findings underscore the need for infrastructure expansion, particularly enhancing biomethane injection facilities into the natural gas grid, alongside financial incentives to support industry growth. This study highlights the role of biomethane in Spain’s renewable energy sector, emphasizing its potential to reduce greenhouse gas emissions, optimize organic waste utilization, and contribute to a sustainable energy transition. Full article