Search Results (19,614)

The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative solution lies in integrating these storage devices with mechanical energy harvesters, particularly triboelectric nanogenerators (TENGs), to create autonomous self-charging power systems (SCPSs). TENGs exhibit high output, versatile operational modes, material flexibility, and efficient energy harvesting from body movements. This review provides an overview of the recent advances in flexible energy storage technologies, encompassing carbon-based materials, MXenes, polymers, metal oxides, metal–organic frameworks (MOFs), and their hybrid architectures. It discusses the synergistic integration of these storage devices with TENGs to realize multifunctional SCPSs. It also highlights the fundamental design principles of flexible devices, the critical interplay of materials and architecture, and the journey towards monolithic system integration. The review also underscores the importance of managing harvesters’ pulsed output for efficient storage. Finally, a critical analysis of the challenges, including the energy density–flexibility compromise, environmental stability, and safety, is presented, alongside a forward-looking perspective on commercialization pathways for these technologies to power the next generation of autonomous wearable and sustainable electronic systems. Full article

Background/Objectives: Health systems globally are transforming toward population-based, person-centred care, yet measurement systems frequently remain anchored in provider-centric or disease-specific frameworks. This paper presents the Systemic Health System Measurement Framework (SHSMF), a population health systems measurement architecture that completes a conceptual systemic health systems design and transformation trilogy with the Unified Care Model (UCM) and Systemic Health System Population Segmentation Model, addressing how health systems can measure whether systems integration is succeeding. Methods: This study employs a conceptual framework development and implementation case study design approach, with the Systemic Health System Measurement Framework (SHSMF) developed using the Health System Transformation Playbook (HSTP) methodology. The framework organises measurement around needs-based population segments, integrates Lifelong Care and Episodic Care measurement within a unified architecture, and cascades indicators across macrosystem, mesosystem and microsystem levels. Implementation was demonstrated through the development of performance and governance dashboards development at Yishun Health, a regional population health system serving approximately 320,000 residents in Singapore (2022–2024). Results: Descriptive analytics from the Lifelong Care Dashboard (207,980 residents across seven segments) and the Episodic Care Dashboard (230,365 inpatient cases across six segments) revealed systemic patterns not readily apparent through conventional approaches. Psychosocial complexity was consistently associated with disproportionate cost trends across both dashboards despite lower medical acuity. Quality indicator performance across psychosocially complex segments was not proportionally worse, yet these segments bore disproportionate costs, a pattern consistent with the view that segment-specific care redesign addressing psychosocial needs may be associated with both an improvement in outcomes and cost efficiencies. Conclusions: The Systemic Health System Measurement Framework (SHSMF) demonstrates that a measurement architecture explicitly designed around systemic needs-based population segments improves systemic health systems accountability and provides governance opportunities that conventional approaches may not achieve. The framework and its dashboard implementation offer a transferable methodology for health systems globally seeking to implement a whole-systems measurement architecture for value-based population health management. Full article

Accurate estimation of curvature radius from interference fringes is critical in optical metrology and precision manufacturing. Conventional interferogram analytical approaches often require manual intervention and are sensitive to fringe variations related to noise and environmental vibrations. To address these limitations, we combine an improved Twyman–Green interferometer with different artificial intelligence (AI) deep learning models and utilize a self-developed MATLAB analysis program to propose a non-destructive and rapid measurement system for optical coating substrates. The proposed AI-assisted Twyman–Green interferometric system differs fundamentally from conventional wavefront sensing techniques in both principle and implementation. This paper utilizes the Twyman–Green interferometer to generate interference fringe datasets on B270 glass and sapphire substrates, and employs convolutional neural network (CNN), ResNet-18, and VGG-16 models for training and evaluation. The proposed method integrates image enhancement, fringe pattern clustering, and analysis and validation based on fast Fourier transform (FFT). Experimental results show that ResNet-18 outperforms other models, with a mean absolute percentage error of 5.44% on sapphire substrates and 3.40% on B270 glass substrates. These findings highlight the effectiveness and robustness of deep learning models, especially residual networks, in automatic ROC prediction for optical measurement applications. Full article

Mesenchymal stromal/stem cells (MSCs) are increasingly explored for immune-mediated diseases, yet standardized analytical readouts that capture coordinated immunomodulatory output across complementary secretory pathways remain limited. Here, we report the feasibility of an HPLC-based multi-analyte secretome characterization panel that quantifies two small-molecule outputs—adenosine and kynurenine—alongside two immunomodulatory proteins—interleukin-10 (IL-10) and transforming growth factor-beta (TGF-β)—in conditioned media from canine adipose-derived MSCs (cAD-MSCs). Canine immune-mediated hemolytic anemia (IMHA) was used as a disease context to motivate the selection of these analytes, given the pro-inflammatory cytokine environment characteristic of this condition. Three independent cAD-MSC lines were evaluated under baseline conditions and following cytokine stimulation with recombinant interferon-gamma (IFN-γ; 100 ng/mL) and tumor necrosis factor-alpha (TNF-α; 50 ng/mL), referred to herein as inflammatory priming or licensing. Conditioned media were collected at 72 h for metabolite analysis and 48 h for protein analysis, and quantified by HPLC using external calibration and peak integration. Across all three lines, licensing produced directionally consistent increases: mean adenosine increased 2.3-fold, mean kynurenine increased 3.1-fold, mean IL-10 increased 1.6-fold, and mean TGF-β increased 1.7-fold compared with unlicensed controls. Metabolite measurements for adenosine and kynurenine are reported with full chromatographic selectivity data; IL-10 and TGF-β measurements by reversed-phase HPLC with UV detection are presented as exploratory/semi-quantitative outputs and will require orthogonal confirmation (e.g., immunoassay) in future work. These findings are preliminary, derived from three independent donor lines with no comparator group, and are intended to support feasibility of the analytical framework rather than establish definitive performance specifications. Collectively, the data support the potential of a multi-analyte HPLC-based characterization panel to capture licensing-responsive secretory shifts across mechanistically complementary pathways, providing a foundation for expanded development and validation. Full article

Traffic flow prediction is important for traffic management and safety control in mountainous areas. In these environments, traffic flow is affected by complex terrain, changing weather, and mixed vehicle types, so the resulting time series often show strong fluctuation and poor stability. Many existing prediction models were developed for urban roads or flat highways, and their performance is therefore limited in mountainous scenarios. To address this problem, this paper proposes a hybrid model called DK-VCA Net. The model combines adaptive signal decomposition with a terrain-aware deep learning structure to separate useful traffic variation from complex noise. It also integrates traffic flow, speed, slope, and weather information to better describe mountain traffic conditions. The proposed method is evaluated using real traffic data collected at 5 min intervals from detection stations on the Guibi Expressway in Guizhou Province, China, during September 2020. Experimental results show that DK-VCA Net achieves better prediction accuracy than several representative baseline models, including 1D-CNN, LSTM, Transformer, STWave, and Mamba. Across the 15 min, 30 min, and 60 min forecasting tasks, the proposed model reduces the average RMSE by 14.8% compared with the conventional 1D-CNN model and by 8.9% compared with the baseline Transformer model. The ablation study further proves the effectiveness of the decomposition strategy, terrain-related features, and the attention mechanism. The results show that the proposed method is effective for traffic flow prediction in the studied mountainous highway scenario. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Anthropogenic polymers have become an increasingly important class of emerging contaminants in terrestrial ecosystems. While extensive research has focused on microplastics in aquatic environments, their interactions with soil systems and particularly with soil organic matter (SOM) remain insufficiently understood. Soil represents a major environmental sink for polymer residues originating from agricultural practices, urban activities, and atmospheric deposition. Accordingly, associations between polymers and SOM, including humic substances, may significantly influence the retention, mobility, and transformation of carbon in soil systems. This review synthesizes current knowledge on the influence of synthetic polymers on soil organic matter dynamics. A bibliometric and qualitative literature analysis based on publications indexed in Web of Science and Scopus from 1979 to 2025 was conducted to identify major research trends and knowledge gaps. The results indicate that polymer particles can alter soil structure, microbial activity, and sorption processes, thereby affecting the stability and cycling of soil organic carbon. Interactions between polymer surfaces and humic substances may modify aggregation processes and influence the persistence and mobility of both polymers and organic carbon compounds. Despite the rapid growth of research on microplastics, studies addressing polymer–SOM interactions remain limited and methodologically heterogeneous. Greater integration between polymer research, soil science, and land use studies is necessary to better understand the implications of polymer contamination for soil quality and carbon cycling. The findings highlight the need for standardized analytical approaches and interdisciplinary research frameworks to assess the long-term effects of polymers in soil ecosystems. Full article

The rapid growth of hiking tourism presents a critical challenge for balancing visitor safety with the sustainable management of ecologically fragile mountain environments. Traditional models developed in urban settings struggle to capture the highly non-linear, heterogeneous, and zero-inflated characteristics of wilderness trekking behavior. In order to quantify the nonlinear and threshold-based effects of environmental variables on hikers’ spatial decisions in unstructured wilderness and to identify distinct behavioral regimes for segmented management, this study introduces an explainable machine learning framework to reconstruct hikers’ spatial decision-making in a complex mountainous system in Inner Mongolia, China. Random Forest (RF), XGBoost, and LightGBM were compared in predicting trail density and the Euclidean distance to the nearest trail. Results show that transforming behavioral traces into continuous proximity surfaces dramatically improves model performance, with XGBoost achieving the highest predictive accuracy for Trail_Dist. By integrating the SHapley Additive exPlanations framework, this study moves beyond black-box prediction to reveal the nonlinear mechanisms driving hiker behavior. Key findings include: (1) Nighttime light range exhibits a U-shaped threshold effect as the primary anthropogenic attractor. (2) Elevation shows an exponential inhibitory trend above 1238 m. (3) Strong spatial coupling exists between elevation and slope, alongside a landscape compensation effect where high Normalized Difference Vegetation Index (NDVI) areas attract off-trail movements. This research provides a robust methodological pathway for predicting behavior in unstructured outdoor environments. It offers a scientific foundation for smart scenic area management, including optimized route planning, precise ecological protection zoning, and targeted emergency rescue preparedness. Full article

Alternative proteins and novel processing technologies are crucial to transforming contemporary food systems into ones with lower environmental impact while meeting the rising global demand for protein. Alternative protein sources from plants, microbes, insects, and cultivated cells offer diverse nutritional and techno-functional attributes that can partially or fully replace conventional animal proteins in meat analogs and related products. This review synthesizes the current knowledge on major categories of alternative protein sources, including plant-based ingredients, microbial- and fermentation-derived proteins, insect and other emerging sources, and cultivated (cell-based) meat, with a specific focus on their suitability for structured meat analog applications. Modern structuring and processing technologies are discussed, including the traditional wet and dry extrusion to modern technologies like high-moisture extrusion, high-pressure processing, shear-cell technology, 3D printing, fermentation-based structuring, and enzymatic protein modification. Furthermore, this review critically evaluates product design and quality attributes of meat analogs, including physicochemical properties, sensory performance, nutritional aspects, and safety considerations. This review highlights technological and scale-up challenges, as well as the necessity of multi-criteria optimization in sensory quality, nutrition, sustainability, and affordability, and presents research priorities focused on combining multiple protein sources and advanced processing pathways for next-generation meat analog. This review provides an integrated framework linking protein sources, processing technologies, antioxidant functionality, and sustainability considerations to support the development of next-generation meat analogs. In addition, this review highlights the intrinsic antioxidant potential of alternative proteins, emphasizing the role of bioactive peptides, polyphenols, and structure–function relationships in enhancing oxidative stability and product quality. Full article

The minimum fuel transfer problem in low-thrust trajectory optimization remains a major challenge and is typically addressed using bang-bang control. A novel methodology integrating Adaptive Multiresolution Collocation (AMRC) and Sequential Convex Programming (SCP) to solve the minimum-fuel low-thrust trajectory optimization problem is proposed. First, the approach employs the cubic spline wavelet-like transform for mesh refinement, where wavelet coefficients serve as error indicators to dynamically concentrate nodes in regions of rapid state variation. Then, the nonlinear programming problem is convexified via control variable relaxation and small-perturbation linearization, reformulated as a second-order cone programming (SOCP) problem, and efficiently solved using convex optimization tools. Subsequently, progressive selection of the location points ensures rapid and accurate convergence to the optimal trajectory. Finally, numerical simulations of Earth–Mars and Earth–Venus transfer validate the effectiveness and accuracy of the AMRC-based method. Compared with conventional approaches, the proposed method achieves comparable optimality while markedly improving computational efficiency, precisely localizing switching times, and improving numerical precision, requiring only 29.7% of the nodes and 14.7% of the computation time of uniform-grid convex optimization, achieving fuel-optimal deviations within 0.07% of the indirect method and demonstrating accuracy improvements of 2–3 orders of magnitude over GPOPS. Full article

Previous evidence has identified the ventromedial prefrontal cortex (vmPFC) as crucial for implementing high-level semantic memory structures (schemas) during event construction. If this is the case, one would expect reduced semantic coherence in events mentally constructed by vmPFC patients compared to healthy and brain-damaged controls. We tested this prediction by having participants mentally construct events using objects as cues and reanalyzing a published dataset using sentences as cues. In both cases, we measured the semantic coherence of patients’ mental constructions and their semantic coherence with the cue, using transformer-based sentence embeddings (S-BERT), and further corroborated the findings with E5 Multilingual and E5 Italian embedding models. Our results reveal that the hypothesized impairment in semantic coherence following vmPFC damage is, in fact, task-dependent. With minimal (object) cues, vmPFC patients’ reports exhibited reduced local coherence, increased connectedness to the cues, and reduced lexical diversity. In contrast, with extended (sentence) cues, they showed preserved- or even enhanced-local and global coherence. We suggest that vmPFC integrity is necessary to trigger schema activation under minimal cue conditions. Although extended cues may facilitate schema activation, schemas are degraded and essentialized following vmPFC damage, thereby constraining patients’ mental constructions within a narrower—hence overly coherent—semantic space. Full article

This study presents a systematic investigation of nonlinear wave interactions in a (2+1)-dimensional nonlinear Schrödinger equation with a space–time-symmetric potential. We focus on the interaction dynamics of high-order line-soliton solutions and on the anomalous scattering phenomena exhibited by high-order lump solutions, which correspond to fully localized spatiotemporal optical wave packets. Using the generalized Darboux transformation, we obtain, for the first time, explicit high-order line-soliton solutions for this model. A rigorous asymptotic analysis framework is developed to characterize the behavior of these solutions on both long and short time scales. Furthermore, high-order lump solutions are constructed, and their decomposition and anomalous scattering properties are elucidated. This work provides new insights into complex wave dynamics in higher-dimensional integrable systems and their implications for multidimensional beam propagation in nonlinear optical media. Full article

Planetary gear systems offer compact design and high-power density, but they are strongly influenced by transmission error (TE), which originates from geometric deviations and elastic deflections. This study presents a dynamic model that integrates elastic compliance, mesh stiffness, damping, and error excitation to evaluate coupled gear responses. Numerical results show that planet–ring contacts undergo larger forces and deflections than sun–planet meshes. Time–frequency analysis with continuous wavelet transform (CWT) reveals nonstationary vibration patterns, while gear tooth flank inspection confirms torque bias and micro-pitting. The findings connect modeling predictions with observed wear, offering insights for planetary gear diagnostics and design. Full article

Carotid artery disease, including atherosclerotic stenosis and non-atherosclerotic abnormalities, substantially increases ischemic stroke risk and motivates accessible tools for early screening. Current diagnostic pathways rely on clinic-based imaging and skilled operators, creating barriers to frequent monitoring and scalable deployment. We present a non-invasive diagnostic approach using a wearable MEMS accelerometer patch to capture mechano-acoustic vibrations generated by carotid blood flow at the neck. The miniature device integrates a hermetically sealed wideband accelerometer with out-of-plane sensitivity and micro-g resolution to detect subtle flow-induced vibrations. We validated the approach in a carotid flow phantom and a clinical study of 74 patients. Time–frequency representations were computed using the continuous wavelet transform (CWT), from which interpretable spectral and scalogram-derived candidate biomarkers were extracted. Six non-redundant features were then selected for multivariate classification, distinguishing pathology, defined as 50% or greater stenosis or a non-atherosclerotic abnormality, from non-pathology, defined as less than 50% stenosis. Finally, model interpretability was assessed using SHapley Additive exPlanations (SHAP) to quantify the contribution of each biomarker to predicted disease probability. These findings resulted in an AUROC of 0.97 and AUPR of 0.947, with 81.7% sensitivity and 93.6% specificity at the prespecified threshold (precision 85.4%, F1 83.5%, accuracy 89.8%), highlighting the potential of wearable seismic sensing combined with interpretable machine learning for fast screening and longitudinal monitoring of the right and left carotid arteries. Full article

With the rapid development of public digital cultural resources, the lack of cross-lingual information retrieval (CLIR) services catering to multilingual users in practical applications has created significant language barriers. This hinders the promotion of public digital culture and results in the underutilization of relevant resources. To address this need, this paper constructs M-APE, a shared semantic model that operates without reliance on parallel corpora. Through a three-step process comprising the generation, fine-tuning, and optimization of a shared semantic space, M-APE establishes a common semantic framework for diverse languages. The model utilizes a Chinese semantic space, transferred and trained on authentic public cultural corpora, as its input. Evaluation based on bilingual dictionary induction quality demonstrates that M-APE significantly enhances semantic sharing performance between Chinese and Indo-European languages, represented here by English and French, achieving an average cross-family transformation accuracy of 56.6%. Furthermore, focusing on the CLIR needs of multilingual users within China’s public cultural engineering projects, this study develops a Chinese-English-French cross-lingual information retrieval framework by integrating M-APE into public cultural domain tasks. Experimental results indicate that the proposed method achieves superior cross-lingual retrieval performance in terms of average metrics. Full article