Search Results (145,115)

As a Chinese national key protected medicinal fern naturally occurring in forest understories, Cibotium barometz faces severe threats of wild population degradation, while standardized large-scale artificial breeding technology for conservation purposes remains immature. To establish an efficient spore-based conservation propagation system for this endangered forest fern, this study quantified the independent and interactive effects of spore storage temperature, storage duration and sowing density on spore germination, gametophyte growth and sporophyte seedling establishment. Spores were preserved under four gradient temperature treatments with sequential sampling at multiple storage durations, followed by sowing trials with a series of density gradients; germination rate, seedling establishment rate and gametophyte–sporophyte conversion rate were dynamically recorded and statistically analyzed. The results demonstrated that appropriately extended storage significantly shortened the germination phase and simultaneously elevated both spore germination and sporophyte seedling formation rates. Among all temperature treatments, storage at −4 °C achieved the maximum germination and seedling establishment capacity, whereas ultra-low-temperature cryopreservation at −196 °C greatly promoted gametophyte–sporophyte conversion rate. The optimal sowing density balancing growth space and survival rate was determined to be 30 spores per cm². The complete dynamic developmental traits covering the full spore propagation life cycle of C. barometz were systematically summarized in this work. Our findings supply reliable technical parameters to standardize spore breeding protocols, and offer critical support for ex situ conservation, wild forest population restoration and sustainable resource utilization of C. barometz. Full article

Aging is a systemic degenerative process that can lead to functional decline in multiple organs, such as skeletal muscles and the heart, and accelerates the overall aging process through organ-to-organ interactions mediated by metabolites such as short-chain fatty acids (SCFAs). SCFAs serve as a crucial link connecting intestinal health and anti-aging, and their levels and functions undergo significant changes with aging. However, current research lacks understanding of the downstream molecular mechanisms of SCFAs, and intervention methods are mostly limited to simple regulation. This article clarifies the intrinsic relationship between SCFAs and aging from a systemic perspective, analyzes their regulatory mechanisms through key signaling pathways, examines their roles in tissue barrier protection, the improvement of metabolic disorders, and immune regulation, and summarizes their therapeutic potential and diversified intervention strategies in aging-related diseases. The detailed molecular mechanisms by which SCFAs regulate aging are still unclear, and there are no precise intervention plans for different aging stages and organ damage. In the future, we need to utilize techniques such as single-cell sequencing and organ models to explore the regulation of aging cell fates, providing support for the development of metabolite-mediated personalized anti-aging intervention measures. Full article

Proso millet (Panicum miliaceum L.) is a drought-tolerant cereal crop with considerable potential for dry-steppe agriculture. This study evaluated grain yield performance and stability of 104 proso millet genotypes originating from 21 countries under climatic conditions in Northern Kazakhstan during 2022–2024. Field experiments were conducted under rainfed conditions using a randomized complete block design with three replications. Grain yield data were analyzed using analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) analysis, genotype plus genotype-by-environment (GGE) biplot analysis, and the stress tolerance index (STI). The study years differed substantially in weather conditions, ranging from severe drought in 2023 (HTC = 0.495) to excessive moisture availability in 2024 (HTC = 4.245). Mean grain yield varied from 2.58 t ha⁻¹ in 2022 to 4.18 t ha⁻¹ in 2024, demonstrating the high productive potential of proso millet under Northern Kazakhstan conditions. ANOVA revealed significant effects of genotype and year on grain yield. AMMI and GGE analyses were used to visualize genotype performance patterns and identify promising germplasm. Shortandinskoe 11 and K-2754 combined relatively high grain yield with stable performance, whereas K-2804, K-2724, and K-2291 demonstrated high productivity and elevated STI values. These accessions represent valuable germplasm for breeding programs aimed at improving grain yield, stability, and drought tolerance; however, further multi-location testing is required to confirm the breeding value and stability of the identified accessions under a wider range of environmental conditions. Full article

Rural household energy-saving behavior is central to low-carbon development in ecologically fragile plateau regions. This study explores whether climate risk perception promotes household energy-saving behavior, through which livelihood capital mechanisms this effect operates, and which livelihood capital configurations support high levels of such behavior. Drawing on survey data from 315 rural households in Xining, China, a sustainable livelihood framework is integrated with the pressure–state–response model, and PLS-SEM, an ANN, and fsQCA are applied. The integrated framework regards climate risk perception as external pressure, livelihood capital as the household livelihood state, and energy-saving behavior as the behavioral response. The sustainable livelihood framework identifies the multidimensional resource conditions of rural households, whereas the pressure–state–response model specifies the causal sequence through which perceived climate pressure affects livelihood states and induces behavioral responses. The results show that climate risk perception significantly promotes energy-saving behavior. Physical, human, and social capital exert positive effects, whereas natural and financial capital exert negative effects. Moreover, natural, financial, and social capital significantly mediate the link between climate risk perception and energy-saving behavior. Multi-group analysis shows that physical capital matters more for agriculture-dominated households than non-farm households. The ANN results identify social and human capital as the strongest predictors, and the fsQCA results show that high levels of energy-saving behavior arise not from any single condition but from multiple capital configurations, in which social capital is consistently central. Energy conservation under climate risk is therefore best understood as a multidimensional, nonlinear adaptation process embedded in household livelihood structures rather than a response to any single factor. These findings extend rural energy-saving research by linking climate pressure, livelihood conditions, and configurational decision logic in a plateau socio-ecological context. Policy interventions should combine energy-efficient infrastructure, targeted financial incentives, community-based diffusion, and livelihood-sensitive support for rural households. Full article

Prefabricated mechanical, electrical, and plumbing (MEP) systems have been increasingly adopted in energy station projects; however, systematic evaluation frameworks capable of integrating construction performance, cost constraints, and uncertain multi-indicator assessments remain limited. To address this gap, this study constructs an Analytic Hierarchy Process (AHP)–Entropy–Fuzzy evaluation framework to assess the comprehensive benefits of BIM-enabled prefabricated MEP construction in energy stations. A hierarchical evaluation system was established based on five dimensions: schedule, quality, cost, safety, and environmental performance, and ten secondary indicators were defined. The Analytic Hierarchy Process was used to determine expert-based subjective weights, the entropy method was applied to capture objective data variability, and multiplicative normalization was employed to obtain combined weights. A fuzzy comprehensive evaluation model was then introduced to transform heterogeneous construction records into comparable benefit levels and scores. The prefabricated method scored 87.80 and was classified as “high”, whereas the conventional method scored 60.85 and was classified as “low”. A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based sensitivity analysis further showed that, under 10%, 20%, and 50% criterion-weight perturbations, the prefabricated group consistently achieved higher closeness coefficients than the conventional group. The smallest margin occurred when the schedule weight was reduced by 50%, but the prefabricated group retained a positive advantage. The results demonstrate that Building Information Modeling (BIM)-enabled prefabricated MEP construction can achieve superior overall project performance through the coordinated optimization of schedule, cost, safety, quality, and environmental objectives, offering a practical evaluation framework and decision-support tool for the industrialized delivery of future energy infrastructure projects. Full article

Current treatment options for hair loss remain limited. Therefore, this study compared a botanical extract derived from multiple plants with the pharmaceutical agent minoxidil for topical application. The evaluated parameters included inflammatory cytokines (IL-1β, IL-6, TNF-α), growth factors (TGF-β, VEGF, KGF), and 5α-reductase type II (SRD5A2) expression in the human keratinocyte cell line HaCaT, as measured by ELISA. Both the botanical extract and minoxidil reduced IL-6 levels by 21% and 35%, and TNF-α levels by 13% and 35%, respectively. Treatment with the botanical extract and minoxidil increased VEGF expression by 50% and 85%, and KGF by 16% and 31%, respectively, while reducing SRD5A2 expression by 21% and 28%, respectively. Overall, the results of this in vitro study suggest that the botanical extract exhibits a response pattern similar to that of minoxidil, characterized by the suppression of pro-inflammatory cytokines and SRD5A2, along with enhanced expression of growth factors VEGF and KGF in HaCaT cells. These results provide a promising basis for further in vivo studies. Full article

Software modeling involves creating multiple software models that represent different viewpoints of the system under development. These models complement one another and should remain coherent throughout the development process, as they represent a single system. Ensuring consistency across different models is crucial for building high-quality software within time and budget constraints. Existing research on consistency management primarily focuses on structural consistency and covers only a subset of UML models. In this paper, we propose a logic-based framework for detecting inconsistencies in basic UML models. We develop a comprehensive metamodel of basic UML models that addresses both structural and semantic relations among metamodel elements. Furthermore, we define new consistency rules for use within the framework to detect various types of inconsistency. The proposed framework is validated using a formal logic-based modeling language and evaluated using two case studies. Compared with existing approaches, the proposed framework provides more comprehensive coverage of inconsistencies. Experimental results demonstrate the effectiveness of the proposed framework in detecting various types of inconsistencies. Full article

Background/Objectives: Wheelchair users with multiple sclerosis (MS) often face barriers that restrict participation in physical activity and exercise training. This manuscript reports on participant feedback to guide evaluating and refining a novel exercise training program, Seated Physical activity INtervention (SPIN). SPIN was adapted from the Guidelines for Exercise in MS (GEMS) approach using a three-step community-engaged research framework based on meeting the needs of wheelchair users with MS. Methods: Semi-structured interviews were conducted with 9 participants who completed the 16-week SPIN intervention. The key SPIN intervention components were the exercise prescription, exercise equipment, and behavioral coaching grounded in Social Cognitive Theory. Formative interview domains included overall experience, enjoyable and missing components, delivery modifications, barriers, lessons learned, and additional research topics of interest. Data were analyzed and reported using a rapid qualitative analysis approach. Results: Interviews averaged 16 ± 10 min. Participants reported enjoying SPIN, noting program strengths as being flexible and appropriate for individuals with MS, receiving coaching calls by knowledgeable staff that offered support and accountability, and receiving exercise equipment and video demonstrations. Participants also identified strategies for enhancing the program such as including peer support, offering real-time feedback during exercise, and adding other wellness behavior topics (e.g., diet). Conclusions: The results offer helpful ideas to consider when developing exercise training programs for wheelchair users with MS and other disabilities that may improve health and well-being. Full article

Background/Objectives: Wearable technology is increasingly used to provide biofeedback in physical rehabilitation; however, there is no consensus on which biofeedback parameter is most appropriate for clinical use, as most studies evaluate only one arbitrarily selected parameter. This study presents a wearable multimodal biofeedback system integrating multiple parameters selected based on the prior literature and evaluates its feasibility, usability, and implementation within a rehabilitation context through a longitudinal post-stroke case study. Methods: The system integrates inertial and electromyographic sensors to monitor centre of mass (CoM-B), joint angle (ANG-B), and muscle activity (EMG-B), delivering real-time sensory cues based on the monitored parameters. Feasibility was assessed in a post-stroke participant (male, 32 years, 29 months post-stroke, left hemiparesis, Fugl-Meyer Lower Extremity Score = 27) across 15 sessions involving stand-to-sit, split-stance weight shifting, and walking tasks. Each task was practiced with all three biofeedback parameters, with five sessions per parameter. Results: The motor performance varied across biofeedback parameters and tasks. CoM-B was associated with favourable trends in motor performance during stand-to-sit, showing improvements in medio-lateral displacement (0.03/session); ANG-B during walking, showing increased ankle dorsiflexion (1 deg/session); and EMG-B during split-stance weight shifting, showing increased tibialis anterior activation (5 µV/session). Conclusions: The findings generate the hypothesis that the ability of biofeedback to elicit favourable motor performance is task-dependent, suggesting that the choice of biofeedback parameters may need to be adapted to task demands. The system demonstrated high usability and feasibility, supporting its potential for post-stroke rehabilitation. Further studies are needed to test the generated hypothesis and evaluate the system efficacy. Full article

Accurate tropical cyclone (TC) track prediction remains challenging, as numerical models suffer from high computational cost, substantial storage requirements, and physical parameterization uncertainties, while data-driven large AI models depend heavily on training data volume and high-resolution inputs, resulting in prohibitive computational overhead. To address these issues, this paper proposes TCN-GAN-DM, a three-stage deep learning framework based on the China Meteorological Administration (CMA) Tropical Cyclone Best Track Dataset. Specifically, a dual-stream temporal convolutional network (TCN) first extracts temporal features from track and meteorological sequences, respectively. A generative adversarial network (GAN) then takes these features and produces multiple physically plausible candidate tracks via noise injection. Finally, a conditional diffusion model (DM) refines the predicted positions through progressive denoising. Experimental results for TCs in 2024 show that under the fair deterministic comparison using a single fixed candidate, the model achieves a 6 h track error of 49.10 km, which is comparable to CMA-GFS (49.75 km) and HWRF (44.34 km), and substantially lower than the large AI model FuXi (120.44 km). When evaluating the oracle metric (best-of-K, K = 6) as an upper bound of coverage, the model achieves the smallest errors among all models at 6 h (24.04 km) and 12 h (55.81 km). In addition, the proposed model has advantages over CMA-GFS, HWRF, and FuXi in terms of computational resource consumption and hardware deployment cost. However, its mean track error increases more rapidly beyond 12 h, and at lead times of 18 h and 24 h the model is outperformed by HWRF, FuXi, and CMA-GFS, indicating that its current strength lies primarily in short-term prediction. Consequently, the practical utility of TCN-GAN-DM is currently demonstrated for 6–12 h TC track prediction, offering a new solution for disaster prevention and mitigation that balances accuracy and deployment cost at these specific time scales. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

To address the high deployment cost, insufficient local coverage, and limited timeliness of conventional wave-observation methods in onboard real-time applications, this study conducts a comparative investigation of centre-of-gravity-equivalent instantaneous wave-height estimation models based on three-degree-of-freedom ship motion responses under the framework of the wave buoy analogy (WBA). The heave, roll, and pitch responses of a 1:2 scaled Series 62 4667-1 planing craft model in regular head seas are used as inputs, while the synchronous instantaneous wave-height signal measured by a wave probe near the centre of gravity is used as the label. A unified protocol is established with consistent inputs, labels, window construction, data partitioning, and evaluation metrics. Six models, namely SVR, TCN, LSTM, CNN-LSTM, Transformer, and LSTM-MHA, are compared and validated using STAR-CCM+ numerical simulation data and towing-tank experimental data. The results indicate that, in the simulated case of H = 0.10 m and T = 1.5 s, LSTM-MHA achieves the highest estimation accuracy, with RMSE and R² values of 0.001231 and 0.997848, respectively, but it also has the largest model size and computational cost. In comparison, TCN achieves near-optimal accuracy with a smaller parameter count and lower inference latency, and shows stable performance across multiple conditions. The towing-tank experimental results further show that both LSTM-MHA and TCN clearly outperform the SVR baseline. Overall, accuracy in the simulation domain, robustness in the towing-tank experimental domain, and cross-domain generalisation capability are not fully consistent. Therefore, the selection of onboard instantaneous wave-height estimation models should jointly consider estimation error, model complexity, computational latency, window length, and practical deployment requirements. Full article

Online apparel size recommendation remains difficult because consumers cannot physically assess garment fit before purchase. It is a multi-dimensional fit evaluation problem, particularly for complex garments such as jackets, where multiple body areas jointly influence perceived fit. Existing methods often rely on limited anthropometric measures, heuristic rules, or behavioral data, restricting both accuracy and interpretability. To address this issue, this study proposes an interpretable multi-dimensional fit evaluation framework based on garment ease theory. The framework defines ideal ease as the target fit condition and quantifies deviations through a segment-based weighting mechanism. Section-level mappings between body and garment measurements are established, and differentiated penalties are assigned according to the semantic fit interval of each body area. Section-specific evaluations are aggregated into an overall fit score (OFS) for candidate size ranking and Top-K recommendation, while also providing detailed fit feedback. Experiments involving 270 female participants and two jacket styles show high recommendation accuracy, achieving Top-3 accuracies of 99.6% for the regular-fit jacket and 98.9% for the tight-fit jacket. Compared with traditional heuristic methods, the proposed approach demonstrates clear advantages in both performance and interpretability, offering a practical solution that balances accuracy, transparency, and deployability. Full article

Background/Objectives: During analysis of patients with genetically confirmed COL2A1-related skeletal dysplasias, we observed an unusual ossification pattern of the superior pubic ramus characterized by multiple ossification centers—a phenomenon not previously reported in this context. The study aimed to determine the consistency and frequency of multiple ossification centers of the superior pubic ramus in patients with COL2A1-related skeletal dysplasias. Methods: We retrospectively analyzed pelvic radiographs from 135 patients with genetically confirmed pathogenic COL2A1 variants. Patients were classified into four clinical subgroups: SEDC, MED, Kniest dysplasia, and Stickler syndrome. Results: Multiple ossification centers were identified in 20 (15.5%) of the included patients, all of whom had the SEDC phenotype. The sensitivity of this radiographic sign for SEDC was 36.36%, with high specificity (99.07%) and accuracy (96.5%) compared with other COL2A1-related phenotypes and historical general population data. However, these findings require cautious interpretation given the limitations of historical control data. Conclusions: We identified an uncommon atypical ossification pattern of the superior pubic ramus that may serve as a supportive radiographic feature when interpreted in conjunction with clinical and genetic findings. Full article

Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology. Full article