Search Results (6,749)

Population aging has increased the need for public open spaces that older adults can use safely, comfortably, and confidently. In many urban parks and community squares, however, resting facilities are still designed as standardized street furniture, with cold materials, insufficient hand support, limited wheelchair-inclusive space, and weak support for everyday social interaction. This study examines age-friendly public facilities as micro-scale spatial elements that shape sitting, standing, staying, communication, and willingness to remain in small urban open spaces. Drawing on field observation, behavioral analysis, semi-structured interviews, and a multi-criteria design-evaluation process, the study identifies older adults’ key facility-use needs and translates them into design indicators and alternative facility schemes. The results show that physical support and inclusive spatial use are the most important design priorities. Standing-up assistance, sitting-posture support, perceived structural stability, and age-appropriate dimensional adaptation were more influential than purely decorative or auxiliary functions. Among the three alternative schemes, the modular pergola system performed best because it combined stable hand support, independent seating, an age-friendly interactive table, shaded resting space, wheelchair-inclusive layout, and wood-based sensory comfort. The sensitivity analysis further confirmed that this scheme maintained a stable advantage under most weight-adjustment conditions. The findings suggest that age-friendly public facility design should move beyond the improvement of individual furniture products and instead integrate bodily support, spatial accessibility, social interaction, material comfort, and environmental pattern quality. This study provides a design-decision framework for improving the inclusiveness, accessibility, and health-supportive capacity of urban public open spaces for older adults. Full article

Background/Objectives: Iron deficiency remains a prevalent condition, needing specific laboratory tests for diagnosis. This study aimed to evaluate whether routine complete blood cell count (CBC) parameters can be used within a machine learning framework to predict low ferritin and low transferrin saturation, used as biochemical markers of altered iron status, potentially supporting more targeted laboratory test utilization. Methods: In this single-center retrospective outpatient study, we analyzed 32,437 records from subjects undergoing both complete blood cell count and iron metabolism testing between 2023 and 2026. Low ferritin and low transferrin saturation were defined using sex-specific thresholds. Low ferritin was present in 14,344 subjects (44.2%), whereas low transferrin saturation was present in 7791 subjects (24.0%). After cleaning data and excluding incomplete records, demographic variables and CBC indices were tested as potential predictors. The dataset was split into training and test sets with stratified sampling. Multiple supervised machine learning models, including logistic regression, decision tree, random forest, XGBoost, support vector machine, k-nearest neighbors, and Naive Bayes, were trained. Hyperparameter tuning and model selection were performed using repeated stratified 10-fold cross-validation, optimizing the area under the curve (AUC). Model performance was assessed by AUC, sensitivity, and specificity, and validated on an independent test set. Results: All models showed predictive capability for low ferritin and low transferrin saturation using CBC parameters alone. Ensemble methods, especially random forest and XGBoost, reached the best performance (AUC values of 0.80–0.87 for ferritin and 0.85–0.96 for transferrin saturation). Sensitivity and specificity were balanced, supporting clinical screening applicability. Results were maintained across validation and confirmed in the test set. Prediction of transferrin saturation showed slightly higher accuracy than ferritin. Feature importance analysis identified mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and red blood cell distribution width (RDW) as key predictors. Conclusions: CBC-based machine learning models may help identify subjects with low ferritin or low transferrin saturation, supporting subsequent targeted assessment of iron status. Full article

Radiomics has the potential to improve risk stratification in head and neck squamous cell carcinoma (HNSCC), but clinical adoption is limited by inconsistent performance across institutions. A key source of variability is how radiomic features are generated, preprocessed, and selected prior to model development. This multicenter study evaluated how radiomics parameterization and feature selection strategies affect external model performance, feature stability, and time-to-event risk stratification. We studied pre-treatment CT scans from 752 patients with primary HNSCC from three hospitals. For each scan, 1648 radiomic features were computed using 20 different preparation methods that varied in scaling, outlier removal, and gray-level bin width. We compared five feature selection methods: Graph-FS with connected components, Boruta, Lasso, RFE-RF, and mRMR. The classification models used were Random Forest, XGBoost, CatBoost, and Logistic Regression. We measured performance using external ROC-AUC, bootstrap confidence intervals, Brier score, and RobustScore. Stability of feature selection was assessed using the Kuncheva and Jaccard indices. Cox proportional hazards models confirmed time-to-event results, and consensus SHAP analysis helped explain the models. Radiomics parameterization influenced model performance, and no single configuration was optimal across all analyses. Radiomics-only models outperformed clinical-only models, while clinical–radiomics models achieved the highest overall performance. mRMR and Lasso produced the highest average external AUCs, while Graph-FS showed the greatest stability. The best classification model achieved an external AUC of 0.817. In Cox validation, the best clinical–radiomics configuration achieved an external C-index of 0.662 and separated high- and low-risk patients in the external cohort. Full article

The mean stand diameter at breast height (DBH) is a key indicator of stand structure and productivity and is widely used in forest resource inventory and management planning. When using regional inventory data, nonlinear interactions between plot-level conditions and predictor variables can undermine the stability of traditional empirical equations across varying site qualities and stand densities. To improve the accuracy and robustness of inventory-scale predictions of mean stand DBH, this study utilized data from 854 forest plots and employed stand age, site class index (SCI), and stand density index (SDI) as independent variables. The predictive performance of traditional growth equations, machine learning models (Random Forest, XGBoost, LightGBM, and support vector machine), and deep learning models (MLP and CNN, ResNet, RNN) was systematically compared, and ensemble learning strategies were further applied to optimize model performance. The results indicated that the Weibull model based solely on stand age achieved the best fit (R² = 0.669). Incorporating SCI and SDI greatly improved model explanatory capability with R² rising to 0.838. XGBoost and CNN further improved predictive performance (R² = 0.852 and 0.861, respectively), while the ensemble model exhibited the highest goodness-of-fit (R² = 0.893), outperforming all individual models. Compared with linear regression, machine learning models demonstrated superior predictive capability. A feature importance analysis indicated that stand age, site quality and stand density together drive mean stand DBH prediction, among which stand age and stand structural characteristics are the dominant influencing factors, whereas SCI and SDI have comparatively weaker effects. Overall, the ensemble model substantially enhanced the prediction accuracy of mean DBH in Pinus yunnanensis stands, thereby providing for precision forest management and ecological function assessment. Full article

Stock index price series are composed of superimposed multi-frequency components, including long-term trends, cyclical fluctuations, and stochastic noise. Effectively decoupling these heterogeneous components and modeling them separately is key to improving forecasting accuracy. Existing methods under the “decomposition–prediction” paradigm mostly employ fixed-scale decomposition, and the forecasting models are not specifically adapted to the non-stationary and high-noise characteristics of financial data, resulting in limitations in adaptivity and local dynamic capture. This paper proposes a frequency-aware adaptive multi-scale decomposition Transformer hybrid model (FAMS-Transformer). At the decomposition level, the fast Fourier transform is used to dynamically identify dominant cycles, thereby adaptively decoupling trends and fluctuations, overcoming the limitations of fixed-scale decomposition. At the forecasting level, a lightweight depthwise separable convolution is embedded between the self-attention and feedforward network of the Transformer encoder, enhancing the model’s ability to capture local temporal dynamics and achieving collaborative modeling of global dependencies and local information. Comparative experiments with 15 baseline models including LSTM, Transformer, TimesNet, and FreTS on three representative Chinese market indices—Shanghai Composite Index, Shenzhen Component Index, and Small and Medium Enterprises 100 Index—across four prediction horizons from one step to 15 steps demonstrate that FAMS-Transformer achieves the best forecasting accuracy in all scenarios. The coefficient of determination for 15-step prediction remains stably between 0.730 and 0.928. Moreover, the model still performs well on the S & P 500 dataset. Ablation studies and significance tests further validate the effectiveness of each core module and the statistical significance of the performance improvements. Full article

Frozen sand molds are the key material in digital frozen sand mold green casting technology, and their mechanical properties directly affect casting quality. Currently, these molds are primarily prepared by mechanical stirring, mixing, and compaction, which tend to cause imbalanced moisture adsorption and localized wet–dry differences, ultimately impairing the performance and quality of the castings. In this study, an ultrasonic vibration-assisted platform was established to prepare chromite sand frozen sand molds. By introducing ultrasonic vibration into the preparation process, a superior “sand grain–ice crystal” microstructure was constructed, leading to significantly enhanced mechanical properties. The tensile and compressive strengths were increased by approximately 10%, and the optimal process window for achieving the best mechanical performance of chromite sand was obtained. Full article

Linearly homomorphic signatures (LHSs) are widely used in scenarios such as network coding and the Internet of Things, but their security faces the serious threat of key leakage. To address this issue, this paper introduces a forward secure mechanism into LHSs, aiming to construct a linearly homomorphic signature (LHS) scheme that can resist the risk of key leakage. By combining the binary tree minimal cover set mechanism with lattice-based extension algorithms, we construct an LHS scheme that supports time-period key updates. We prove its forward secure unforgeability under the standard model (SM) by reducing it to the Short Integer Solution (SIS) problem. To the best of our knowledge, this scheme is the first provably secure lattice-based forward secure linearly homomorphic signature (FSLHS) scheme in the SM, filling a theoretical gap in existing research. Furthermore, we apply this scheme to a smart grid data acquisition system and verify its practicality through concrete performance analysis. Full article

The imminent threat of large-scale quantum computers motivates the deployment of post-quantum cryptography (PQC). CRYSTALS-Kyber, a leading lattice-based Key Encapsulation Mechanism, relies heavily on Number Theoretic Transform (NTT) operations, which remain a major performance and resource bottleneck. This paper presents a cross-platform NTT evaluation framework for CRYSTALS-Kyber, centered on an adaptive FPGA-based mixed-radix accelerator supporting radix-2, radix-4, and radix-8 configurations, together with comparative classical implementations and exploratory quantum-circuit prototypes. Classical evaluations show that an iterative Cooley–Tukey implementation outperforms a matrix-based baseline (≈3.6× faster for the forward NTT, ≈6.3× faster for the inverse NTT). Quantum prototypes implemented in Qiskit demonstrate proof-of-concept QFT-based NTT constructions under classical simulation environments, highlighting circuit-depth growth and noise sensitivity rather than practical hardware acceleration. The proposed FPGA design, based on a Xilinx Virtex UltraScale+ platform, employs an adaptive radix controller, LUT-based twiddle management, and Montgomery/Barrett modular arithmetic. Montgomery reduction provides superior timing and area trade-offs, with an estimated $F_{\max }$ of up to 231.48 MHz and only 5 DSPs for radix-2. At the same time, radix-2 offers the best resource/performance balance with a latency of approximately 32,804 cycles. The hybrid approach strikes a balance between near-term FPGA practicality and long-term quantum potential while preserving Kyber’s MLWE-based security. Experimental results and comparative analysis indicate that the adaptive design substantially reduces resource usage and timing overhead compared to recent HLS-based NTT accelerators. Full article

Background/Objectives: Sarcopenic obesity (SO) represents a severe clinical phenotype characterized by the coexistence of reduced skeletal muscle mass and excess adiposity, and is associated with insulin resistance, dyslipidemia, and systemic inflammation. However, easily accessible biomarkers that capture early molecular changes underlying SO are lacking. The aim of this pilot study was to compare circulating microRNA (miRNA) profiles in patients with severe obesity and a sarcopenic obesity phenotype with those of healthy controls and to identify candidate miRNAs suitable for further validation. To the best of our knowledge, this represents one of the first broad screening studies of circulating miRNAs specifically conducted in patients with severe obesity and DXA-confirmed sarcopenic obesity. Methods: In this single-center pilot study conducted in the Czech Republic, fasting plasma samples from 12 adult participants (6 with severe obesity and sarcopenic obesity phenotype, body mass index > 45 kg/m²; 6 healthy controls; age 18–65 years) were analyzed using an RT-qPCR panel comprising 384 assays, including technical controls and 352 target circulating miRNAs. Following predefined quality control and filtering criteria, 224 miRNAs were retained for the final statistical analysis. Six patients with severe obesity were classified according to the ESPEN/EASO 2022 consensus criteria for sarcopenic obesity, while EWGSOP2-based assessment was used for functional evaluation of sarcopenia. Differential expression was evaluated using fold change and exploratory statistical testing. Results: We identified a set of miRNAs with significantly altered expression in SO, including increased muscle-enriched miR-486-5p and hepatocyte-enriched miR-122-5p, and decreased vascular miR-145-5p, as well as several additional miRNAs related to myogenesis, lipid metabolism and inflammatory signaling. miR-451a, a recognized marker of hemolysis, was also increased but was interpreted with caution. Conclusions: Despite the limited sample size, the results of this study suggest that specific circulating miRNAs may reflect key pathophysiological pathways in SO and could serve as promising biomarkers to support risk stratification and monitoring in larger, hypothesis-driven studies. Full article

To enhance the thermal management of lithium-ion batteries in new-energy vehicles, various bio-inspired liquid-cooled plate channel designs were investigated to improve hotspot dissipation within the laminar flow regime. A series of three-dimensional numerical simulations were conducted to compare leaf vein-, tree branch-, honeycomb-, and spider web-inspired channels, followed by further optimization to improve thermohydraulic performance. The selected optimized bio-inspired channels were subsequently evaluated against conventional structures. Simulation results indicate that the honeycomb-inspired liquid-cooled plate channel achieved the best performance, followed by the tree branch- and spider web-inspired channels, which exhibited comparable thermohydraulic performance. The leaf vein-inspired channel demonstrated the lowest performance. The key design element for enhanced heat dissipation is the inclusion of longitudinal branch channels, which minimize flow zones with near-zero velocity and effectively mitigate local hotspots. Furthermore, the combination of longitudinal and inclined branch channels can redirect flow direction and enhance fluid mixing. Compared with the conventional channel widely adopted in existing studies, within the Reynolds number range of 260 to 920, the optimized honeycomb-inspired liquid-cooled plate channel achieves a 44.0–49.3% increase in Nusselt number and an 81% enhancement in comprehensive performance metric. Concurrently, thermal resistance is diminished by 2.6–9.2%, and pumping power is reduced by 50.0–56.8%. Full article

Accurate short-term photovoltaic (PV) power forecasting is important for grid dispatch and PV integration. However, PV power under complex weather conditions has strong fluctuation, non-stationarity, and multi-frequency coupling. These features make accurate forecasting difficult. This paper proposes a short-term PV power forecasting model named WOA-MS-TFformer-BiTCN. The model first constructs similar-day samples through daily feature extraction, Gaussian mixture clustering, and physical consistency correction. Then, the whale optimization algorithm (WOA) optimizes the key parameters of variational mode decomposition (VMD) and the forecasting network. VMD decomposes the original power sequence into modes with different frequency features. The multi-scale frequency-domain perception (MS) module extracts multi-scale frequency-domain features from these modes. TFformer captures global temporal relationships, while BiTCN models local dynamic changes. Experiments are conducted using PV data from Gansu, China. The Alice Springs PV dataset is used for cross-regional validation. The results show that the proposed model achieves the lowest MAE, RMSE and the highest R² in all 16 season-weather cases, corresponding to four seasons and four weather types, for the 15 min-ahead task. Its average MAE, RMSE and the highest R² are 0.5439, 0.7910, and 0.99898, respectively. The model also performs best on rainy samples from the Alice Springs dataset. In addition, prediction intervals based on validation-set residual quantiles provide uncertainty information for point forecasts. The results show that the proposed method improves the accuracy and stability of short-term PV power forecasting under complex weather conditions. Full article

Alpine shrub meadows on the Qinghai–Tibet Plateau are key warm-season pastures that support pastoral production and ecosystem stability in fragile high-elevation regions. Due to low temperatures, short growing seasons, and slow vegetation recovery, these pastures are highly sensitive to inappropriate grazing management. However, the effects of different grazing–rest time configurations on plant community composition and leaf functional traits in alpine shrub meadows remain insufficiently understood. In this study, we evaluated five grazing treatments in an alpine shrub meadow in Sunan County, central–eastern Qilian Mountains: 10 days grazing–20 days rest (T1), 15 days grazing–15 days rest (T2), 20 days grazing–10 days rest (T3), continuous grazing (CG), and grazing exclusion (CK). In the third year of treatment implementation, we measured the community diversity, species importance values, and leaf functional traits of four dominant species: Elymus nutans, Carex tibetikobresia, Oxytropis kansuensis, and Bistorta vivipara. T1 and T2 significantly increased species richness, Shannon–Wiener diversity, and Simpson diversity compared with CG and CK. NMDS and PERMANOVA further showed significant differences in overall community composition among grazing treatments. Grazing generally reduced the leaf length, leaf width, and leaf area, whereas T2 showed relatively stronger leaf recovery among grazing treatments. Specific leaf area, specific leaf weight, and leaf length–width ratio showed higher variability and calculated plasticity than leaf thickness and leaf dry matter content, suggesting that resource-acquisition and morphological traits were more responsive to grazing than conservative structural traits. The coefficient of variation of leaf traits was positively associated with the plasticity index, although this association should be interpreted cautiously because both indices were calculated from the same underlying trait dataset. Overall, under the conditions of this three-year, single-site experiment and a target moderate grazing intensity, the 15-day grazing–15-day rest regime performed best among the tested treatments. This regime may provide a practical reference for rotational grazing management in similar warm-season alpine shrub meadows, but its broader applicability requires further validation across different grassland types, grazing intensities, climatic conditions, and longer monitoring periods. Full article

Wastewater from street food activities is a major pollution source. In this study, sewage sludge (SS) from a treatment plant in Thailand was converted into a porous adsorbent via NaOH activation and calcination (SS-B-C600), while SS-C600 was used as a control. Characterization revealed that both samples were composed of SiO₂ with minor kaolinite. FTIR confirmed Si–O–Si vibrations in both samples, while SS-B-C600 showed enhanced –OH (Si–OH) groups, indicating improved surface hydroxylation. Activation significantly enhanced the adsorption performance for methylene blue (MB) in laboratory-scale experiments. The equilibrium data were best fitted by the Langmuir isotherm model, indicating monolayer adsorption, with maximum capacities of 3.11 mg/g (SS-C600) and 7.56 mg/g (SS-B-C600). The kinetic results were well described by the pseudo-second-order model, suggesting that the adsorption mechanism is governed by a combination of porosity and surface interactions through physisorption. DFT calculations revealed that intermolecular hydrogen bonds between MB and aluminosilicate play a key role in the formation of the complex, while the calculated interaction energy (ΔE = −304.27 kJ/mol) further confirmed the presence of strong intermolecular interactions. Moreover, SS-B-C600 showed stable performance over three reuse cycles, highlighting its potential as a cost-effective and sustainable adsorbent. Full article

Improving residential energy efficiency is essential to meeting UK net-zero targets, yet retrofit uptake in the private rented sector (PRS) remains limited. While many studies examine retrofit measures or Energy Performance Certificates (EPCs), few integrate comparative technology performance, cost–benefit outcomes, and landlord–tenant perspectives within a single housing context. This paper addresses that gap through a mixed-methods case study of a professionally managed private rented housing portfolio in South London, assessing four retrofit technologies: photovoltaic (PV) panels, air source heat pumps (ASHPs), double glazing (DG), and insulation. Quantitative analysis showed that ASHPs delivered the greatest EPC improvement, with 54.5% of properties achieving a two-band uplift, while PV panels offered the strongest financial return, with an average payback period of 11.7 years. Houses achieved the strongest overall results, with combined PV + ASHP retrofits delivering the best technical and financial performance; however, this pairing was only feasible in houses because of the physical requirements for both roof space and external unit installation, whereas flats and maisonettes were more constrained by space and installation feasibility. Stakeholder analysis findings revealed knowledge and incentive gaps: many tenants overestimated the effectiveness of double glazing, while landlords identified high upfront costs and delivery challenges as key barriers. Wider PRS decarbonisation will therefore require stronger policy support, streamlined retrofit delivery, and improved tenant awareness. Full article

Background: Reverse transcription is a key step in emerging RNA diagnostics, but reverse transcriptase (RT) enzymes often fail in the presence of inhibitors carried over from clinical samples or introduced during RNA extraction. Here, we dissect the molecular basis of inhibitor resistance in five engineered variants (V1 to V5) of Moloney Murine Leukemia Virus RT, originally optimized for thermostability and catalytic activity. Methods: Using a systematic framework that integrates structural analysis, thermal profiling, and diagnostic benchmarking, we evaluated cDNA synthesis from 40 to 70 °C under a panel of 11 clinically relevant inhibitors. Results: Across 30 mutations assessed, a recurrent set of substitutions (E69K, E302K/R, W313F, and N454K), present in RT V1 and V4, was associated with enhanced robustness, consistent with strengthened enzyme–nucleic acid engagement, while L435G likely contributes by modulating conformational flexibility. Notably, inhibitor tolerance was maximal at moderate reaction temperatures (≈40 °C), where productive enzyme–substrate interactions best offset inhibitory stress, while the wild-type enzyme was effectively inactivated by several inhibitors under the conditions tested. Although the engineered RTs remained catalytically competent at higher temperatures, increased thermal stress may destabilize productive enzyme–nucleic acid complexes, reducing resistance under inhibitory conditions. Conclusions: Together, these findings support substrate engagement as an important determinant of RT robustness and provide practical guidance for engineering inhibitor-resistant RTs for high-sensitivity RT-qPCR. Full article