Search Results (885)

Enhancing system strength to ensure voltage security has become a critical challenge for power systems with high penetration of renewable energy (RE). As China accelerates its clean-energy transition, the conventional grid dominated by synchronous generators is evolving into a dual-high system characterized by both high shares of wind–solar generation and extensive power-electronic interfaces. This shift fundamentally alters the mechanisms of voltage support, rendering traditional short circuit ratio (SCR) index inadequate for describing grid strength. To address this gap, this study proposes a multi-renewable-station short circuit ratio (MRSCR) index that quantitatively evaluates the voltage support strength of RE-dominated systems, and further analyzes the mechanism by which multiple agents on the generation and grid sides affect MRSCR, enhancing the generality and applicability of the proposed index. The MRSCR is further formulated as a voltage security constraint and integrated into an energy storage planning model considering multi-agent collaborative optimization. The proposed model jointly optimizes the siting and capacity configuration of grid-forming energy storage under voltage security constraints. Case studies on the IEEE 14-bus system and a real provincial grid show that incorporating the MRSCR indicator effectively enhances the system’s voltage support performance and operational resilience, achieving these improvements with only a 5.45% increase in daily operating cost compared with baseline planning results. The framework provides a practical offline tool for energy storage planning, enabling both enhanced renewable integration and improved voltage security. Full article

Background: Chronic total occlusion (CTO) percutaneous coronary intervention (PCI) remains among the most complex procedures in interventional cardiology, with variable technical success and heterogeneous long-term outcomes. Conventional angiographic scores such as J-CTO and PROGRESS-CTO provide only modest predictive accuracy and neglect critical patient and operator-related factors. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools, capable of integrating multimodal data and offering enhanced diagnostic, procedural, and prognostic insights. Methods: We performed a structured narrative review of the literature between January 2010 and September 2025 using PubMed, Scopus, and Web of Science. Eligible studies were peer-reviewed original research, reviews, or meta-analyses addressing AI/ML applications in CTO PCI across imaging, procedural planning, and prognostic modeling. A total of 330 records were screened, and 33 studies met the inclusion criteria for qualitative synthesis. Results: AI applications in diagnostic imaging achieved high accuracy, with deep learning on coronary CT angiography yielding AUCs up to 0.87 for CTO detection, and IVUS/OCT segmentation demonstrating reproducibility > 95% compared with expert analysis. In procedural prediction, ML algorithms (XGBoost, LightGBM, CatBoost) outperformed traditional scores, achieving AUCs of 0.73–0.82 versus 0.62–0.70 for J-CTO/PROGRESS-CTO. Prognostic models, particularly CatBoost and neural networks, achieved AUCs of 0.83–0.84 for 5-year mortality in large registries (n ≈ 3200), surpassing regression-based methods. Importantly, comorbidities and functional status emerged as stronger predictors than procedural strategy. Future Directions: AI integration holds promise for real-time guidance in the catheterization laboratory, robotics-assisted PCI, federated learning to overcome data privacy barriers, and multimodality fusion incorporating imaging, clinical, and patient-reported outcomes. However, clinical adoption requires prospective multicenter validation, harmonization of endpoints, bias mitigation, and regulatory oversight. Conclusions: AI represents a paradigm shift in CTO PCI, providing superior accuracy over conventional risk models and enabling patient-centered risk prediction. With continued advances in federated learning, multimodality integration, and explainable AI, translation from research to routine practice appears within reach. Full article

In many traditional villages in China, substantial government investment has been directed toward reconstructing public spaces for tourism development. Yet, many of these newly built spaces remain underused, revealing a persistent mismatch between top–down planning and villagers’ everyday needs. To address this gap, this study employs a mixed-methods approach to evaluate the quality of rural public spaces. Drawing on a systematic review, a four-dimensional assessment model—encompassing environmental, social, cultural, and economic attributes—was developed and operationalized through 17 specific indicators. The model was applied to three traditional villages in Chongqing, Southwest China, using field observation, questionnaire surveys, confirmatory factor analysis, and semi-structured interviews. The findings show that while environmental and cultural qualities are generally appreciated, villagers’ overall evaluations are strongly shaped by livelihood considerations and the extent to which public spaces support everyday practices. In tourism-oriented villages, public spaces often function primarily as attractions rather than as sites of daily life, limiting their social usefulness despite significant investment. The results demonstrate that economic indicators, which are often overlooked in existing studies, are essential for assessing the quality of public space in traditional villages and for strengthening community engagement. These insights contribute to a more comprehensive understanding of rural public space and offer practical guidance for rural revitalization and community-based planning. Full article

This retrospective study evaluated the clinical utility of Virtual Reality (VR) in visualizing extracardiac CHD (eCHD) abnormalities involving great vessels, pericardium, or structures outside the heart in nine pediatric patients. Anonymized computed tomography angiography (CTA) DICOM images were processed using Elucis (Version 1.10 elucis next) software to generate interactive 3D models via segmentation. VR models were reviewed for a variety of cases: vascular rings (two with right aortic arch, aberrant left subclavian artery, and diverticulum of Kommerell; two with double aortic arch), pericardial teratomas (n = 2), right superior vena cava draining into the left atrium (n = 1), left pulmonary artery sling (n = 1), and aortopulmonary window (n = 1). VR video images were presented during weekly heart center conferences. A survey conducted among heart center staff assessed the perceived value of VR in clinical practice. A total of 62% found traditional diagnostic modalities very effective, 100% considered VR a valuable diagnostic tool, 65% responded positively to VR image resolution, 50% highlighted its educational benefit, 81% believed VR enhanced diagnostic accuracy and surgical planning, and 100% would recommend its use to colleagues. This study demonstrates the successful integration of VR-based segmentation into clinical workflows, underlining its potential as both an educational resource and a tool to support diagnostic and surgical decision-making. Full article

Tourism is a key spatial process linking human mobility, resource consumption, and environmental change. Despite growing awareness of climate risks, sustainable travel behavior often remains inconsistent with pro-environmental attitudes, reflecting the persistent attitude–behavior gap. This study examines how psychological factors—sustainability motives, ecological identity, and climate attitudes—interact with artificial intelligence (AI) transparency to shape travel decisions with spatial and environmental consequences. Using survey data from 1795 leisure travelers and a discrete-choice experiment simulating hotel booking scenarios, the study shows that ecological identity and climate attitudes reinforce sustainability motives and intentions, while transparent AI recommendations enhance perceived clarity, data visibility, and reliability. These transparency effects amplify the influence of eco-scores on revealed spatial preferences, with trust mediating the relationship between transparency and sustainable choices. Conceptually, the study integrates psychological and technological perspectives within a geographical framework of human–environment interaction and extends this lens to rural destinations, where travel decisions directly affect cultural landscapes and climate-sensitive ecosystems. Practically, the findings demonstrate that transparent AI systems can guide spatial redistribution of tourist flows, mitigate destination-level climate pressures, and support equitable resource management in sustainable tourism planning. These mechanisms are particularly relevant for rural areas and traditional cultural landscapes facing heightened vulnerability to climate stress, depopulation, and uneven visitation patterns. Transparent and trustworthy AI can thus convert environmental awareness into spatially sustainable behavior, contributing to more resilient and balanced tourism geographies. Full article

The Satisfiability Problem (SAT), a fundamental NP-complete problem, is widely applied in integrated circuit verification, artificial intelligence planning, and other fields, where the growing scale and complexity of practical problems demand higher solving efficiency. Due to redundant search paths, serialized reasoning steps, and inefficient pure literal detection, traditional serial SAT solvers require efficient parallelization of the pure literal rule. This paper adopts a parallel solving algorithm for the pure literal rule based on matrix representation. The algorithm can solve the shortcomings of poor universality, insufficient parallel collaborative mechanisms, and clause reduction. We first introduce a Clause-Numerical Incidence Matrix (CNIM) representation to provide a unified mathematical model for parallel operations. Second, we design a Column Vectors Pure Literal Parallel Topological Detection (CVPLPTD) algorithm that achieves pure literal detection with $O(mn/p)$ time complexity (p being the number of parallel threads) within the coefficient range $[1.0\times mn/p, 1.2\times mn/p]$. Finally, we adopt a dynamic matrix reduction strategy that compresses the matrix scale through row and column deletion after each pure literal assignment to reduce computational load. These innovations integrate matrix algebra and parallel computing, effectively breaking through the efficiency limitations of solving large-scale SAT problems while ensuring good universality across different computing platforms. Full article

Northern japonica rice holds a significant position in China’s food security. However, the traditional nitrogen fertilizer management model (nitrogen application rate > 225 kg/ha, base fertilizer proportion > 50%) has led to serious sustainability problems: the nitrogen utilization rate is only 25–30%, resulting in a large amount of fertilizer waste and economic losses. At the same time, it causes a decline in rice quality, manifested as a 15–20% increase in chalkiness and an 8–12% decrease in palatability value. It has also brought about environmental problems such as soil acidification and eutrophication of water bodies. As an important japonica rice production area, the Liaohe Plain has significant differences in the response of semi-upright and curved panicle varieties to nitrogen fertilizer. However, the agronomic physiological mechanism for the coordinated improvement of yield and quality of japonica rice with different panicle types is still unclear at present, which limits the sustainable development of rice production in this region. For this purpose, in this study, the typical semi-upright spike variety Shendao 47 and the curved spike variety Shendao 11 from the Liaohe Plain were used as materials, and five nitrogen fertilizer treatments were set up: N1, no nitrogen application; N2–N4, conventional nitrogen application rate of 165–225 kg/ha; and N5, and optimized nitrogen application rate of 195 kg/ha allocated in the proportion of 40% base fertilizer, 15% tillering fertilizer, 25% tillering fertilizer, 15% panicle fertilizer, and 5% grain fertilizer. The synergistic regulatory effect of nitrogen fertilizer management on yield and rice quality was systematically explored, and the key agronomic physiological mechanisms were analyzed. The research results show that: (1) The optimized nitrogen fertilizer treatment (N5) achieved a significant increase in yield while reducing the input of nitrogen fertilizer. The yields of Shendao 47 and Shendao 11 reached 10.71–11.82 t/ha and 9.50–10.62 t/ha, respectively, increasing by more than 35% compared with the treatment without nitrogen. (2) The N5 treatment simultaneously improved the processing quality (the whole polished rice rate increased by 4.11%) and the appearance quality (the chalkiness decreased by 63.8% to 77%). (3) The dry matter accumulation during the tillering stage (≥3.2 t/ha) and the net assimilation rate during the scion development stage (≥12 g/m²/d) were identified as key agronomic physiological indicators for regulating the yield-quality synergy. Optimizing nitrogen fertilizer management ensures an adequate supply of photosynthetic products through the high photosynthetic rate of flag-holding leaves and the extended lifespan of functional leaves. The phased nitrogen application strategy of “40% base fertilizer + 25% tillering fertilizer + 15% panicle fertilizer + 5% grain fertilizer” proposed in this study provides a theoretical and practical basis for the sustainable development of japonica rice production in the Liaohe Plain. This plan has achieved the coordinated realization of multiple goals including resource conservation (reducing nitrogen by 13%), environmental protection (lowering the risk of nitrogen loss), food security guarantee (stable increase in yield), and quality improvement (enhancement of rice quality), effectively promoting the development of the northern japonica rice industry towards a green, efficient and sustainable direction. Develop in the right direction. Full article

With the acceleration of global urbanisation, the pace of evolution in urban waterfront areas has intensified, consequently hastening the renewal rate of their constituent public spaces. Compared to the macro-level planning and regulation of traditional port and coastal waterfronts, balancing the historical preservation of urban heritage waterfront public spaces with contemporary demands has emerged as a critical issue in urban regeneration. This study examines the historical waterfront area of the Xiaoqinhuai River in Yangzhou, establishing a public space perception evaluation framework encompassing five dimensions: spatial structure, landscape elements, environmental perception, socio-cultural context, and facility systems. This framework comprises 33 secondary indicators. The perception assessment system was developed through a literature review, field research, and expert interviews, refined using the Delphi method, and weighted via the Analytic Hierarchy Process (AHP). Finally, cloud modelling was employed to evaluate perceptions among residents and visitors. Findings indicate that spatial structure and socio-cultural dimensions received high perception ratings, highlighting historical layout and cultural identity as strengths of the Xiaoqinhuai Riverfront public space, while significant shortcomings were noted in terms of landscape elements, environmental perception, and facilities. These deficiencies manifest primarily in limited vegetation diversity, inadequate hard paving and surface materials, insufficient landscape node design, poor thermal comfort, suboptimal air quality and olfactory perception, uncomfortable resting facilities, limited activity diversity, and inadequate slip-resistant surfaces. Further analysis reveals perceptual differences between residents and visitors: the former prioritise daily living needs, while the latter emphasise cultural experiences and recreational facilities. Based on these findings, this paper proposes targeted optimisation strategies emphasising the continuity of historical context and enhancement of spatial inclusivity. It recommends improving public space quality through multi-dimensional measures including environmental perception enhancement, landscape system restructuring, and the tiered provision of facilities. This research offers an actionable theoretical framework and practical pathway for the protective renewal, public space reconstruction, and optimisation of contemporary urban historic waterfront areas, demonstrating broad transferability and applicability. Full article

Background: nowadays, traditional delivery options are challenging to the urban last-mile logistics and sustainability goals. The purpose of this study is to investigate the practical factors that drive frequent e-shoppers to actively switch their intention from conventional delivery options to utilizing smart lockers. Methods: the hypothetical framework tested integrating constructs from the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), and supplementary constructs such as privacy and convenience. Data were collected via a structured online questionnaire from 513 respondents in major Egyptian cities, including Alexandria and Cairo. The framework was tested using Structural Equation Modeling (SEM) via SmartPLS 4.0 software to assess the relationship between constructs and switching intention. Results: the analysis confirms that switching intention to use smart lockers is positively driven by Perceived Usefulness, Perceived Ease of Use, Convenience, Privacy, and Perceived Behavioral Control. Notably, a positive attitude towards smart lockers was found to have a non-significant effect on the intention to switch in the Egyptian context. Conclusions: this research contributes to addressing the gap in the extant literature by focusing on analyzing the unique contextual determinants in the emerging last-mile logistics within a developing market context. Full article

Snake-like robots, characterized by their high flexibility and multi-joint structure, exhibit exceptional adaptability to complex terrains such as snowfields, jungles, deserts, and underwater environments. Their ability to navigate narrow spaces and circumvent obstacles makes them ideal for operations in confined or rugged environments. However, efficient motion in such conditions requires not only mechanical flexibility but also effective path planning to ensure safety, energy efficiency, and overall task performance. Most existing path planning algorithms for snake-like robots focus primarily on finding the shortest path between the start and target positions while neglecting the optimization of energy consumption during real operations. To address this limitation, this study proposes an energy-efficient path planning method based on an improved A* algorithm enhanced with deep reinforcement learning: Dueling Double-Deep Q-Network (D3QN). An Energy Consumption Estimation Model (ECEM) is first developed to evaluate the energetic cost of snake robot motion in three-dimensional space. This model is then integrated into a new heuristic function to guide the A* search toward energy-optimal trajectories. Simulation experiments were conducted in a 3D environment to assess the performance of the proposed approach. The results demonstrate that the improved A* algorithm effectively reduces the energy consumption of the snake robot compared with conventional algorithms. Specifically, the proposed method achieves an energy consumption of 68.79 J, which is 3.39%, 27.26%, and 5.91% lower than that of the traditional A* algorithm (71.20 J), the bidirectional A* algorithm (94.61 J), and the weighted improved A* algorithm (73.11 J), respectively. These findings confirm that integrating deep reinforcement learning with an adaptive heuristic function significantly enhances both the energy efficiency and practical applicability of snake robot path planning in complex 3D environments. Full article

Efficient scheduling of automated rail transportation in hilly orchards is critical for maintaining fruit freshness and ensuring timely market delivery. This study develops a dynamic scheduling method for multi-transporter orchard rail systems through mathematical modeling, reinforcement learning algorithms, and field validation. We formulated a comprehensive scheduling model and designed four distinct frameworks to address randomly arriving tasks. In the optimal framework (Framework 3, which was chosen due to its hybrid strategy combining periodic global planning and local task point adjustment), we compared six rule-based heuristic algorithms against three reinforcement learning approaches: centralized SAC, decentralized MARL-DQN, and conventional DQN. Additionally, two emergency response strategies were developed and evaluated. Simulation experiments demonstrated that Framework 3 maintained high load factors while reducing task completion times. The centralized SAC algorithm outperformed other methods, achieving 1533.71 ± 50.09 reward points compared to 863.67 ± 30.54 for rule-based heuristics, a 77.6% improvement. For emergency tasks, Strategy 2 achieved faster response times with minimal disruption to routine operations. Field trials on a 153 m physical track with four autonomous transporters validated the DQN algorithm, confirming good sim-to-real consistency. This research provides a practical solution for dynamic scheduling challenges in hilly orchards, offering measurable efficiency improvements over traditional methods. Full article

Spatial distribution similarity analysis has extensive application value in multiple domains including geographic information science, urban planning, and engineering site selection. However, traditional regional similarity analysis methods face three key challenges: high sensitivity to directional changes, limitations in feature interpretability, and insufficient adaptability to multi-type data. Addressing these issues, this paper proposes a rotation-invariant spatial distribution similarity analysis method based on ring vectors. This method comprises three stages. First, the traversal starting point of the ring vector is dynamically selected based on the maximum value point of the regional feature matrix. Next, concentric ring features are extracted according to this starting point to achieve multi-scale characterization. Finally, the bidirectional weighted comprehensive distance of ring vectors between regions is calculated to measure the similarity between regions. Three experimental sets verified the method’s effectiveness in terrain matching, engineering site selection, and urban functional area identification. These results confirm its rotational invariance, feature interpretability, and adaptability to multi-type data. This research provides a new technical approach for spatial distribution similarity analysis, with significant theoretical and practical implications for geographic information science, urban planning, and engineering site selection. Full article

Localized cutaneous squamous cell carcinoma (cSCC) has a favorable prognosis, unlike advanced disease, especially with clinical perineural invasion (PNI), which poses substantial management challenges due to aggressivity and higher recurrence, metastasis, and mortality risks. PNI, a high-risk staging feature, has worse outcomes, particularly when clinically evident rather than incidental. Clinical PNI (cPNI) is evident by clinical symptoms (such as pain, paresthesia, or motor deficits) or radiologic findings, whereas incidental PNI (iPNI) is identified only histologically without associated symptoms or radiologic evidence. PNI remains a novel area with varying practice patterns across institutions. Improving risk stratification and tailoring multidisciplinary approaches are critical for optimizing outcomes. Our review outlines clinical practice patterns at our institution, providing insights into managing cSCC with PNI, focusing on diagnosis, imaging, staging, and emerging immunotherapies. A structured search was conducted using the terms “perineural invasion,” “cutaneous squamous cell carcinoma,” and “immunotherapy.” cPNI has a poor prognosis and requires nuanced clinical decision-making. Surgery and radiation remain central to management. Adjuvant therapy offers substantial survival benefit in cSCC with PNI, with improved disease-free and overall survival compared with surgery alone, supporting its use in appropriately selected high-risk patients. Traditional systemic therapies, including cisplatin and cetuximab, remain foundational but have shown only moderate response rates and limited durability in advanced or neurotropic cSCC. In contrast, immunotherapy—now preferred for advanced or unresectable cases—has transformed management, with programmed cell death protein-1 (PD-1) inhibitors showing promising results (up to 69% response rate) and disease stabilization. Neoadjuvant immunotherapy may enable tumor downstaging, improve radiation planning, and reduce surgical morbidity. Imaging for squamous cell carcinoma (SCC) with PNI aids staging and surveillance, but symptoms remain key for detecting recurrence. Our multidisciplinary approach emphasizes personalized care. Larger trials are needed to define the optimal role and sequencing of immunotherapy in this high-risk patient population. Full article

Background: Wireless Vital Parameter Continuous Monitoring (WVPCM) allows the continuous tracking of patient physiological parameters, facilitating the earlier detection of clinical deterioration, especially in low-intensity care settings. The aim of this study is to evaluate the effectiveness of using WVPCM compared to the usual monitoring of critically ill patients hospitalized in Internal Medicine wards. An investigation of the attitude of health professionals towards the use of new technologies in daily practice to improve patient management was also carried out. Methods: The LIght Monitor Study (LIMS) is a prospective, open-label, randomized, multi-center pilot trial comparing WVPCM and conventional nurse monitoring during the first 72 h of hospitalization. A central randomization unit used computer-generated tables to allocate patients to two different types of monitoring. The main outcome was the occurrence of major complications. The study planned to enroll 296 critically ill patients with a Modified Early Warning Score (MEWS) ≥ 3 and/or National Early Warning Score (NEWS) ≥ 5 across two Internal Medicine (IM) Units in Italy. The investigation of the attitude of nurses towards the use of WVPCM was carried out by using a questionnaire and a qualitative survey. Results: Due to the COVID-19 outbreak, the study was interrupted early and only 135 patients (WVPCM = 68; standard care = 67) were randomized. One patient in the control group was excluded from analysis because of drop-out, leaving 134 patients for intention to treat analysis. No statistically significant differences between standard care and WVPCM were observed in terms of major complications (37.5%, vs. 31.2% p = 0.475), in-hospital mortality (17.5% vs. 11.1%, p = 0.309), and median hospital length of stay (9 vs. 10 days, p = 0.463). WVPCM decreased nursing workload compared to the control, as the average time spent by nurses on the detection of vital signs per patient was 0 min per patient per day compared to 24.4 min (p < 0.001) observed in the control group. Twenty-two percent of patients in the WVPCM group (15/68) experienced discomfort with the device, resulting in its removal. The investigation of nurses involved 16 out of 18 people participating in the study. Opinions on the wireless device for patient monitoring were particularly favorable; most of them considered remote monitoring clearly superior to traditional in-person visits and easy to use after a brief practice period. All participants recognized the safety benefits of the system. Conclusions: The reduced sample size of this pilot study does not allow us to draw any conclusions on the superiority of WVPCM compared to standard care in terms of clinical outcomes. However, we observed a positive trend in the reduction of major complications. Full article

This study focuses on a multi-objective heterogeneous parallel machine planning problem for industrial silicon smelting. Specifically, under the conflicting objectives of minimizing carbon emissions, rollover penalty costs, and load imbalance, the total production demand of industrial silicon is allocated monthly across multiple machines. We first establish the mathematical model of the problem accounting for real-life management requirements. To solve the model, a Gaussian learning-based Pareto evolutionary algorithm (GLPEA) is proposed. The algorithm is developed based on a nondominated sorting framework and incorporates two key innovations: (1) a generation-wise dynamic Gaussian mixture component selection strategy that adaptively fits the multimodal distribution of elite solutions, and (2) a hybrid offspring generation mechanism that integrates traditional evolutionary operators with a Gaussian sampling strategy trained on perturbed solution sets, thereby enhancing exploration capability while maintaining convergence. The effectiveness of GLPEA is validated on 40 problem instances of varying scales. Compared with NSGA-II and MOEA/D, GLPEA achieves average improvements of 5.78% and 89.23% in IGD, and 1.03% and 264.43% in HV, respectively. We make the source codes of GLPEA publicly available to facilitate future research on practical applications. Full article