Search Results (8,052)

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

Background: Hereditary transthyretin amyloidosis (ATTRv) is a rare, autosomal dominant multisystem disease caused by pathogenic variants in the transthyretin (TTR) gene. Although ATTRv is classically categorized into “cardiac” and “neurologic” phenotypes, recent evidence suggests a more complex and overlapping disease spectrum. Objectives: This study investigates the relationship between neurological staging and cardiac involvement through an integrated assessment of patients with confirmed TTR mutations. Methods and Results: Fifty-eight patients with genetically confirmed ATTRv (41% female, mean age 60 ± 15 years) were evaluated. Genotypes included Phe64Leu, Val30Met, Val122Ile, and others. Patients were stratified by neurological stage: G0 (asymptomatic carriers), G1 (symptomatic but ambulatory), and G2 (requiring walking support). Cardiac assessment included clinical evaluation, echocardiography with tissue Doppler, global longitudinal strain (GLS), and NT-proBNP levels. Cardiac markers worsened with neurological stage. NT-proBNP, left ventricular mass index, maximal wall thickness, and E/E′ ratio increased progressively, while GLS declined (G0: –19%, G1: –14%, G2: –13%; p < 0.001), indicating a progressive structural and functional myocardial disease. Ejection fraction remained preserved. Neurological stage independently predicted cardiac dysfunction after age adjustment. Conclusions: This is the first study to assess cardiac abnormalities across neurological stages in a well-characterized cohort of ATTRv patients. Cardiac involvement in ATTRv begins early, even in asymptomatic carriers, and progresses with neurological deterioration. GLS and diastolic parameters are sensitive indicators of early myocardial dysfunction, highlighting the need for integrated neurologic and cardiac monitoring in all patients with ATTRv, regardless of initial phenotype. Full article

Achieving carbon reduction is essential in advancing China’s dual carbon goals and promoting a green transformation in the transportation sector. Changes in energy structure and intensity constitute key drivers for sustainable and low-carbon development in this field. To explore the spatial spillover effects of the energy structure and intensity on the green transition of transportation, this study constructs a panel dataset of 30 Chinese provinces from 2007 to 2020. It employs a super-efficiency SBM model, non-parametric kernel density estimation, and a spatial Markov chain to verify and quantify the spatial spillover effects of green total factor productivity (GTFP) in the transportation sector. A dynamic spatial Durbin model is then used for empirical estimation. The main findings are as follows: (1) GTFP in China’s transportation sector exhibits a distinct spatial pattern of “high in the east, low in the west”, with an evident path dependence and structural divergence in its evolution; (2) GTFP displays spatial clustering characteristics, with “high–high” and “low–low” agglomeration patterns, and the spatial Markov chain confirms that the GTFP levels of neighboring regions significantly influence local transitions; (3) the optimization of the energy structure significantly promotes both local and neighboring GTFP in the short term, although the effect weakens over the long term; (4) a reduction in energy intensity also exerts a significant positive effect on GTFP, but with clear regional heterogeneity: the effects are more pronounced in the eastern and central regions, whereas the western and northeastern regions face risks of negative spillovers. Drawing on the empirical findings, several policy recommendations are proposed, including implementing regionally differentiated strategies for energy structure adjustment, enhancing transportation’s energy efficiency, strengthening cross-regional policy coordination, and establishing green development incentive mechanisms, with the aim of supporting the green and low-carbon transformation of the transportation sector both theoretically and practically. Full article

Phase-shifting transformers (PST) are widely used to control power flows. However, conventional designs can vary only the phase angle, leaving the voltage magnitude unaffected or requiring structurally complex devices. This study proposes a compact PST topology that realizes simultaneous, decoupled control of both voltage magnitude and phase angle through two coordinated sets of windings. Closed-form equations are derived to link the phase-shifting and voltage regulation windings turn ratios to any target magnitude ratio and phase-shift angle, providing a unified design framework that guarantees the full practical operating range. Steady-state tests verify that the device can change the phase or adjust the magnitude independently without cross-coupling. Dynamic tests demonstrate that, when a tap command is issued, the line currents and active power converge to new set-points within a few fundamental periods and with minimal oscillation. Furthermore, the PST’s application to loop closing operations in 220 kV networks is investigated, where simulation results show it can suppress loop closing currents by over 90% under adverse voltage mismatch conditions. These results confirm that the proposed PST offers a fast, economical alternative to Flexible AC Transmission Systems (FACTS) equipment for real-time power flow balancing, renewable integration and inter-area exchange in modern transmission networks. Full article

Background/Objectives: This study assessed how to best prepare pediatric and adolescent cancer patients for amputation and support them afterward. Methods: This pilot qualitative study explored pre- and post-amputation experiences from the perspectives of nine pediatric and adolescent survivors who underwent amputation. Hour-long audio-recorded semi-structured interviews were transcribed and analyzed using the Sort and Sift, Think and Shift qualitative approach. Results: Participants described the informational supports they received before surgery, including guidance on what to expect, contact with amputation-related organizations, and exposure to tangible tools, such as a physical model of a knee joint. Emotional support from fellow amputees and healthcare providers, particularly surgeons, was also found to be meaningful. Individuals also identified unmet needs and gaps in emotional care. These included clearer guidance on post-surgical adaptations (e.g., basic self-care and navigating physical limitations) and the need for information tailored to their learning styles. Many emphasized the importance of improved pain management resources, expanded access to mental health services for both them and their families, and support in adjusting to changes in body image and social relationships. Participants also shared advice for future patients, recommending strategies such as personalizing hospital rooms, connecting with other amputees through social media, and using art to process their experience and say goodbye to the lost limb. Conclusions: Interviews with nine cancer survivors provide guidance for improving holistic, patient-centered care throughout the amputation process. Informational and emotional support should be tailored to an individual’s learning style and specific needs, in addition to their age at the time of surgery. Full article

This paper proposes a structured design framework and system architecture for a non-intrusive spatial system aimed at supporting child behavior in everyday domestic environments. Rooted in ethical considerations, our approach defines four core behavior-guided design strategies: routine recovery, emotion-responsive adjustment, behavioral transition induction, and external linkage. Each strategy is meticulously translated into a detailed system logic that outlines input conditions, trigger thresholds, and feedback outputs, designed for implementability with ambient sensing technologies. Through a comparative conceptual analysis of three sensing configurations—low-resolution LiDARs, mmWave radars, and environmental sensors—we evaluate their suitability based on technical feasibility, spatial integration, operationalized privacy metrics, and ethical alignment. Supported by preliminary technical observations from lab-based sensor tests, low-resolution LiDAR emerges as the most balanced option for its ability to offer sufficient behavioral insight while enabling edge-based local processing, robustly protecting privacy, and maintaining compatibility with compact residential settings. Based on this, we present a working three-layered system architecture emphasizing edge processing and minimal-intrusion feedback mechanisms. While this paper primarily focuses on the framework and design aspects, we also outline a concrete pilot implementation plan tailored for small-scale home environments, detailing future empirical validation steps for system effectiveness and user acceptance. This structured design logic and pilot framework lays a crucial foundation for future applications in diverse residential and care contexts, facilitating longitudinal observation of behavioral patterns and iterative refinement through lived feedback. Ultimately, this work contributes to the broader discourse on how technology can ethically and developmentally support children’s autonomy and well-being, moving beyond surveillance to enable subtle, ambient, and socially responsible spatial interactions attuned to children’s everyday lives. Full article

This study presents an acoustic membrane design utilizing a thin foil sound resonance mechanism to enhance sound absorption and insulation performance. The membranes incorporate single-layer and double-layer structures featuring parallel foil square wedge-shaped coffers and a flat bottom panel, separated by air cavities. The enclosed air cavity significantly improves the sound insulation capability of the acoustic membrane. Parametric studies were conducted to investigate key factors affecting the sound transmission loss (STL) of the proposed acoustic membrane. The analysis examined the influence of foil thickness, substrate thickness, and back cavity depth on acoustic performance. Results demonstrate that the membrane structure enriches vibration modes in the 500–6000 Hz frequency range, exhibiting multiple acoustic attenuation peaks and broader noise reduction bandwidth (average STL of 40–55 dB across the researched frequency range) compared to conventional resonant cavities and membrane-type acoustic metamaterials. The STL characteristics can be tuned across different frequency bands by adjusting the back cavity depth, foil thickness, and substrate thickness. Experimental validation was performed through noise reduction tests on an air compressor pump. Comparative acoustic measurements confirmed the superior noise attenuation performance and practical applicability of the proposed membrane over conventional acoustic treatments. Compared to uniform foil resonators, the combination of plastic and steel materials with single-layer and double-layer membranes reduced the overall sound level (OA) by an additional 2–3 dB, thereby offering exceptional STL performance in the low- to medium-frequency range. These lightweight, easy-to-manufacture membranes exhibit considerable potential for noise control applications in household appliances and industrial settings. Full article

Cultivated land concentration and contiguity, as a core element of agricultural modernization development, holds strategic significance for enhancing agricultural production efficiency and ensuring national food security. This study employs vector patches as research units and classifies spatial connections between patches into direct and indirect connections. We quantify six types of spatial relationships between patches using binary encoding, enabling precise delineation of concentrated contiguous cultivated land. A Patch Connectivity Index is proposed. Combined with the Patch Area Index and Patch Shape Index, an evaluation system for cultivated land concentration and contiguity is established. Using Suixi County as a case study, we investigate the spatiotemporal evolution of its cultivated land concentration and contiguity from 2019 to 2023. Overall, patch connectivity exhibits a “single-element dominant, multi-element complementary” structural pattern, while the evaluation grading of cultivated land concentration and contiguity follows a normal distribution. Between 2019 and 2023, the average patch area decreased while the average number of connections between patches increased, indicating significant improvement in cultivated land concentration and contiguity levels. By adjusting spatial relationships between patches, the effective integration and utilization of cultivated land resources can provide theoretical foundations and practical references for agricultural modernization development. Full article

Global climate change has intensified the frequency, intensity, and spatial heterogeneity of drought events, posing severe threats to the stability of terrestrial ecosystems. Plant drought resilience, which encompasses a plant’s capacity for drought resistance, post-stress recovery, and long-term adaptation and transformation to sustain ecosystem functionality, has emerged as a central focus in botanical and ecological research. This review synthesizes the conceptual evolution of plant drought resilience, from early emphasis on resistance and recovery to the current multi-dimensional framework integrating adaptation and transformation, and synthesizes advances in understanding multi-scale drought resilience in terrestrial plants—spanning molecular, physiological, individual, community, and ecosystem levels. Key mechanisms include molecular/physiological adaptations (osmotic adjustment, antioxidant defense, hydraulic regulation, carbon–water reallocation via gene networks and aquaporins), morpho-anatomical traits (root architectural plasticity, leaf structural modifications, and hydraulic vulnerability segmentation), community/ecosystem drivers (biodiversity effects, microbial symbioses, and soil–plant–feedback dynamics). We critically evaluate quantitative metrics and expose critical gaps, including neglect of stress legacy effects, oversimplified spatiotemporal heterogeneity, and limited integration of concurrent stressors. Future research should prioritize multi-scale and multi-dimensional integrated analysis, long-term multi-scenario simulations with field validation, and harnessing plant–microbe interactions to enhance drought resilience, providing a theoretical basis for ecosystem sustainability and agricultural production under climate change. Full article

Diaphragm springs, as critical components in commercial vehicle clutch assemblies, directly determine the clutch’s working performance. The design of diaphragm springs, which possess a distinct symmetrical structure that underpins their mechanical behavior, centers on obtaining the large-end nonlinear load–displacement curve—a typical large deformation-induced nonlinear problem. Traditional design relies on the A-L formula, but studies show finite element analysis (FEA) yields results closer to actual measurements. This study established an FEA model of the diaphragm spring’s disc spring (excluding separation fingers) and validated its correctness by comparing it with the A-L formula. Then, using FEA on models with separation fingers, it analyzed factors influencing the large-end load–displacement characteristics. Leveraging the inherent symmetry of the diaphragm spring structure, particularly the symmetrical distribution of separation fingers, the analysis process efficiently captures uniform mechanical responses during deformation, while this symmetric arrangement also ensures balanced load distribution during clutch operation, a critical factor for stabilizing the load–displacement curve. Results indicate the separation finger root is a key factor, with larger root holes, square holes (compared to circular ones), and more separation fingers reducing stiffness to effectively adjust the curve; in contrast, the tip and length of separation fingers have little impact, making the latter unsuitable for design adjustments. Full article

Recently, Transformers have made significant progress in the joint classification task of HSI and LiDAR due to their efficient modeling of long-range dependencies and adaptive feature learning mechanisms. However, existing methods face two key challenges: first, the feature extraction stage does not explicitly model category ambiguity; second, the feature fusion stage lacks a dynamic perception mechanism for inter-modal differences and uncertainties. To this end, this paper proposes a Deep Fuzzy Fusion Network (DFNet) for the joint classification of hyperspectral and LiDAR data. DFNet adopts a dual-branch architecture, integrating CNN and Transformer structures, respectively, to extract multi-scale spatial–spectral features from hyperspectral and LiDAR data. To enhance the model’s discriminative robustness in ambiguous regions, both branches incorporate fuzzy learning modules that model class uncertainty through learnable Gaussian membership functions. In the modality fusion stage, a Fuzzy-Enhanced Cross-Modal Fusion (FECF) module is designed, which combines membership-aware attention mechanisms with fuzzy inference operators to achieve dynamic adjustment of modality feature weights and efficient integration of complementary information. DFNet, through a hierarchical design, realizes uncertainty representation within and fusion control between modalities. The proposed DFNet is evaluated on three public datasets, and the extensive experimental results indicate that the proposed DFNet considerably outperforms other state-of-the-art methods. Full article

Food security among college students is an increasing concern, with potential implications for their health, academic performance, and future well-being. This study investigated food security within a college community in Mexico, focusing on food availability, access (both economic and physical), and consumption patterns. A mixed-methods approach was employed at Ciudad Universitaria, BUAP, Mexico, between 2023 and 2024. Stratified random sampling was used, resulting in a final sample of 606 students. Data were collected through structured questionnaires covering sociodemographic characteristics and eating habits, the ELCSA, structured cafeteria observations, semi-structured interviews with key informants, and three focus groups. Statistical analysis was performed using chi-square tests (p < 0.05). Post hoc analysis with Bonferroni adjustment confirmed that origin (p = 0.0017), mode of transportation (p = 2.31 × 10⁻⁵) and private vehicles (p = 1.77 × 10⁻⁵) were the key determinants. Although the environment offered a variety of options, processed and ultra-processed products dominated the food choices. A total of 95.9% of students purchased food on campus, yet only 21.8% reported engaging in healthy eating habits. Focus groups revealed that students’ food choices were influenced by availability, access, and perceptions of affordability and convenience. These findings highlight the urgent need for targeted interventions to improve food security and promote healthier dietary practices within the college setting. Full article

The application of lattices as protective materials/structures is rapidly increasing. This requires improving impact absorption capabilities to protect goods in packaging and prevent human injuries in protective devices. This study aims to improve the accuracy of impact and energy absorption measurements for lattices, addressing the limitations of current methods such as energy-impact diagrams and instrumented drop-impact testers. A novel test setup is introduced by utilizing a modified Charpy test machine equipped with appropriate instrumentation to directly measure both energy and acceleration. Other modifications include adjustments to the machine components and the introduction of a new sandwich configuration for the test specimen, ensuring compatibility with the machine’s geometry and the test objectives. The attractiveness of the proposed test setup lies in its simplicity and efficiency. Unlike drop-impact test machines—which require complex, time-consuming, and error-prone data integration and derivation—the proposed method eliminates the need for postprocessing, as both energy and impact are recorded directly and instantaneously by the machine. The advantage over existing setups becomes particularly evident when considering that, in the presence of noise and high-frequency fluctuations—characteristic of sensor data from impact events—errors in numerical operations can range from 30% to over 100%. The functionality of the proposed test setup is evaluated through a series of experiments, and the results are compared with those obtained from existing methods. Our findings demonstrate the effectiveness of the new setup in providing accurate and direct measures of absorption parameters, offering a significant improvement over the traditional approaches. Full article

Background: Right ventricular (RV) strain using two-dimensional speckle tracking is a reliable and clinically significant tool for detecting RV systolic dysfunction, but it varies by age, vendor, and software. Objectives: To establish pediatric age-specific normal values and Z-score equations for biventricular strain using GE Healthcare equipment and software. Methods: Children 0–18 years with structurally and functionally normal hearts, who visited the Wilhelmina Children’s Hospital Utrecht between October 2020 and December 2023, were retrospectively included and divided into age groups: 0 years, 1–4 years, 5–9 years, 10–13 years, and 14–18 years. Left ventricular (LV) and RV global longitudinal strain (GLS) and RV free wall longitudinal strain (FWGLS) were analyzed. Results: We included 129 subjects (57% male) (0 years: n = 17; 1–4 years: n = 22; 5–9 years: n = 34; 10–13 years: n = 35; 14–18 years: n = 20). Low R² values were strain-adjusted for age, height, and body surface area (all < 0.3), and the sample size limited Z-score equation reliability. Therefore, data are presented as mean ± SD or median [IQR] stratified by age. LV GLS, RV GLS, and RV FWGLS showed a nonlinear relationship with age, peaking at the 1–4 years age group and decreasing with age. Conclusions: LV GLS, RV GLS, and RV FWGLS showed age-related differences in children using GE equipment and software, which highlights the importance of age-specific normal strain values, including Z-score equations as a function of age. Full article

Brain natriuretic peptide (BNP) is an important biomarker for the diagnosis and prediction of chronic heart failure (CHF). Aiming at the problems of the low sensitivity and poor portability of traditional BNP detection methods, this study proposes a Surface-enhanced Raman-scattering (SERS) fiber-optic sensor based on a multimode fiber (MMF)–no core fiber (NCF) structure. The sensor achieves BNP detection by significantly amplifying the Raman signal of the toluidine blue (TB) marker through the synergistic effect of NCF’s unique optical transmission modes and localized surface plasmon resonance (LSPR). To optimize the sensor performance, we first investigated the effect of the NCF length on the Raman signal, using Rhodamine 6G (R6G), and determined the optimal structural parameters. Combined with the microfluidic chip integration technology, the antibody–BNP–antibody sandwich structure was adopted, and TB was used as the Raman label to realize the quantitative detection of BNP. Experimental results demonstrate that the detection limit of the sensor is lower than the clinical diagnostic threshold and exhibits stability. The sensor sensitivity can be adjusted by regulating the laser power. With its stability and high portability, this sensor provides a new solution for the early diagnosis of heart failure and demonstrates broad application prospects in biomarker detection. Full article