Search Results (17,338)

Background: Directionality algorithms are fundamental to modern hearing aids, enhancing speech perception in noisy environments by improving the signal-to-noise ratio. Conventional adaptive approaches rely on heuristic optimization criteria and often improve speech understanding from specific directions, typically the front, at the expense of spatial awareness. A deep neural network (DNN)-based directionality system was developed that dynamically optimizes spatial filtering through a data-driven framework capable of representing complex acoustic scenes. The system intelligently enhances target speech while preserving awareness of environmental sounds. Methods: The proposed DNN-based directionality system was evaluated against conventional directionality algorithms by assessing word recognition performance, perceived speech clarity, and listener preference across diverse acoustic scenarios. To complement subjective measures, an artificial intelligence (AI)-based objective intelligibility metric was additionally developed using automated speech-to-text analysis, enabling scalable and consistent benchmarking across devices. Results: Results across the behavioral and objective assessments indicate improved speech intelligibility outcomes together with maintained access to environmental sounds. Mean SRT50 improved by 1.4 dB relative to legacy directionality for target speech presented at 90° and by 4.7 dB relative to an omnidirectional pattern when frontal target speech was accompanied by a rear interfering talker. Spatial adaptation further improved environmental sound audibility in three out of four conditions and significantly improved the detection threshold for speech presented at 135° by 2.42 dB. Conclusions: These results show that the system can preserve speech originating away from the front while using acoustic context and spatial filtering to attenuate competing speech that interferes with a frontal conversation. Full article

Pre-packaged fermented soybean whey-based tofu (FSW-tofu) was stored under dynamic temperature conditions (4–20 °C) that simulated typical supermarket and e-commerce cold-chain transport modes. Changes in total viable count (TVC), psychrophilic bacterial count (PBC), hardness, springiness, chewiness, and water-holding capacity were monitored over 35 d, and a hybrid prediction model integrating mechanistic kinetics with machine learning was established. Results indicated that both temperature fluctuation amplitude and frequency significantly affected microbial proliferation and textural degradation. Under the e-commerce mode, exposure to 20 °C accelerated the TVC, reaching 5 lg CFU/g at 17 d, earlier than under the supermarket mode (27 d). However, the sustained low-temperature stress in the supermarket mode caused more profound degradation of the protein gel network, leading to more severe textural deterioration at the equivalent TVC threshold. The Baranyi–Roberts–Ratkowsky non-isothermal growth model and quality response functions served as the base framework, while random forest and gradient boosting trees were used for residual correction, yielding a coupled mechanistic-data-driven model. Independent validation yielded R² > 0.89 and relatively low RMSE, confirming the model’s good generalization and predictive accuracy. This approach combines mechanistic interpretability with machine learning accuracy to provide a rapid assessment tool for the cold-chain quality management of FSW-tofu. Full article

Conceptual design is a critical phase in the development of propulsion plants. Its aim is to explore multidimensional design spaces to provide guidelines for the detailed design phase. This exploration is necessarily broad in scope and shallow in fidelity. Lately, developments in computing hardware have allowed the use of simplified computational fluid dynamics (CFD) models for conceptual design purposes, thereby increasing significantly the amount of data to be processed and generalised. In this context, the present work benchmarks two specific surrogate-model implementations for the conceptual design of a rocket-type pulse detonation engine: a tensor-based method based on high-order singular value decomposition (HOSVD) and a fully connected feed-forward neural network (NN). Two complementary comparisons were considered: HOSVD versus NN, using the same factorial databases to assess the effect of the surrogate method; and factorial versus low-discrepancy sampling, using the same NN architecture to assess the effect of the database distribution. The benchmark, which involved five input architecture parameters and five output operation parameters, was performed for both the direct analysis problem (outputs obtained from inputs) and the inverse design problem (inputs obtained from outputs). Three different situations were considered in the comparison—dimensionally balanced, overdetermined, and underdetermined—to simulate conditions that typically arise in these design phases. Three databases of different sizes were generated for the comparison. The results provide a case-specific assessment of the performance of the two implementations under the conditions considered and may provide useful guidance on the application of these data-analysis approaches in conceptual design. Full article

Reliable in-situ defect classification is essential for ensuring the mechanical performance of parts produced by material extrusion. However, machine learning and deep learning models often suffer from poor generalization because training datasets typically consist of many correlated signal segments generated from only a limited number of independent print jobs. To address this problem, we analyze how job-specific characteristics are distributed across signal components and find that they are concentrated primarily in the signal level rather than in the residual component. Motivated by this observation, we propose Level-Aware Residual Mixup(LARM), a data augmentation method that separately interpolates the level and residual components of sensor signals. LARM preserves realistic job-level characteristics by restricting level interpolation to values observed in real print jobs while allowing flexible mixing of residual components within the same defect class. We evaluate LARM against representative augmentation methods across diverse classification models under a realistic job-level leave-one-group-out cross-validation protocol. Experimental results demonstrate that LARM achieves better generalization than both training without augmentation and representative augmentation methods. Full article

Temporal interaction streams are often distributed across data owners, while downstream labels and communication budgets remain limited. Existing temporal graph prompts typically adapt at a centralized trainer, whereas federated graph methods commonly exchange larger trainable states. This paper presents FedTIP, a prompt-level federated method for client-local few-shot adaptation of temporal interaction graphs. A centralized historical stage constructs a temporal knowledge bank whose encoder is frozen during downstream federation. Clients optimize interaction-conditioned prompts on local supervised events and transmit a compact prompt-side message. Prototype anchoring and a reliability rule based on support coverage and update magnitude combine heterogeneous client updates, while prompt-side proximal regularization and stage-scoped negative sampling preserve the temporal protocol. Experiments on Wikipedia, Reddit, and MOOC cover temporal node classification and transductive and inductive link prediction. FedTIP records the highest mean AUC-ROC in the nine reported task–dataset cells, with gains of 1.83–14.69 points over the strongest federated baseline in each cell. Its measured cumulative float32 uplink is 0.40 MiB per downstream task, 64.6–99.6% below the evaluated baselines. Full article

Solar thermal energy storage systems play a vital role in bridging the gap between intermittent solar availability and continuous energy demand; however, their efficiency is severely constrained by the inherently low thermal conductivity of phase change materials. Integrating physical heat transfer enhancements, such as radial fins, offers a practical solution, but evaluating these non-linear thermal dynamics across diverse design configurations typically incurs heavy computational costs. To address this challenge, this research investigates an artificial intelligence-based predictive framework capable of accurately modeling complex phase change dynamics in fin-configured storage tanks. Utilizing high-fidelity 2D Computational Fluid Dynamics simulation data of a stainless-steel double-tube storage tank filled with RT-50 paraffin wax across 10, 20, and 29 fin configurations, a Multi-Layer Perceptron Artificial Neural Network trained with the Bayesian Regularization algorithm was developed. The model predicts liquid fraction, latent heat distribution, and buoyancy-driven natural convection (Reynolds number) based on fin count and time. The optimal architecture, featuring 30 hidden neurons, achieved exceptional predictive precision, yielding a coefficient of determination of 0.99999, along with individual Mean Squared Error values of 1.29 × 10⁻³ for liquid fraction, 5.73 × 10⁻¹ for latent heat distribution, and 2.64 × 10⁻⁵ for Reynolds number, with average prediction deviation rates consistently below 0.5%. These results demonstrate that high-precision surrogate modeling can effectively replace computationally intensive numerical simulations, offering significant practical implications for the real-time thermal monitoring, rapid design optimization, and intelligent control of advanced solar energy storage technologies. Full article

River-connected lakes are characterized by complex hydrological regimes, where hydrodynamic conditions serve as key physical drivers of aquatic ecosystem evolution and eutrophication. However, traditional water-balance methods struggle to accurately quantify water exchange under strong seasonal water-level fluctuations and the backwater effect of the Yangtze River, resulting in significant gaps in understanding lake hydrodynamic features and their seasonal eutrophication response patterns. Taking Dongting Lake as an example, this study employed a two-dimensional hydrodynamic model coupled with the advection–dispersion equation of a conservative tracer to simulate the spatiotemporal patterns of flow velocity and water turnover time during the dry season, rising-water season, wet season, and receding-water season using observed hydrological data from 2017 to 2025. Field sampling data and structural equation modeling were further used to identify the pathways through which hydrodynamic conditions affect lake trophic status. Flow velocity and water turnover time exhibited significant spatiotemporal heterogeneity: water turnover time was generally within 10 d in main flood channels but exceeded 60 d in stagnant floodplain areas and local topographic depressions. Seasonally, it was shortest in the wet season due to enhanced hydrological connectivity, yet longest in the dry season because of weakened hydraulic connection. The effects of hydrodynamics on trophic status were strongly season-dependent: during the rising-water season, hydrodynamics inhibited nutrient accumulation through dilution and flushing; during the wet season, strong runoff promoted external nutrient input; and during the dry season, hydrodynamics mainly affected trophic status by modifying physical habitat conditions for algal growth. These findings reveal the hydrological and hydrodynamic mechanisms regulating eutrophication in typical river-connected lakes, providing direct scientific support for hydrological regulation optimization, zonal eutrophication prevention and control, and water environmental carrying capacity assessment in Dongting Lake and similar systems, enabling lake managers to formulate differentiated pollution control strategies based on the hydrodynamic–trophic status response relationships across different hydrological seasons. Full article

Lithium-ion batteries (LIBs) typically operate within a temperature range of 20–50 °C, depending on the chemical composition and application of the battery. Overheating may lead to degradation and thermal runaway, while overcooling may cause lithium plating and internal short circuits. Absorbing heat during the melting of phase-change materials (PCMs) has become an attractive passive thermal management strategy. However, organic PCMs such as paraffin are flammable, and most strategies for improving their inherently low thermal conductivity, such as adding carbon fillers and engineering porous scaffolds, sacrifice latent heat, electrical safety, or cost. Flame retardants achieve flame retardancy at the cost of 15–25% of latent heat, while inorganic PCMs eliminate flammability but introduce undercooling and corrosion. This study synthesizes the PCM–battery thermal management system (BTMS) literature with this safety–performance tension as its organizing principle, covering composite PCM strategies for thermal enhancement, flame-retardant approaches and thermal runaway mitigation, hybrid PCM–active cooling systems, and emerging directions that transcend rather than manage the trade-off. It also identifies the gaps between laboratory demonstration and commercial deployment: durability data measured in hundreds rather than thousands of cycles, absent standardized testing protocols, and manufacturing routes that have not left the university laboratory. Full article

The booming data volume of modern time-domain surveys demands fast, robust early classification of sparsely sampled light curves, as newly discovered transients typically have only a handful of observations. We compare classifiers for extremely sparse light curves (3–30 observations) on a benchmark of approximately 1.72 million segments spanning seven astrophysical classes from ZTF and ATLAS. Methods include handcrafted-feature approaches (XGBoost, feature-based Transformers) and end-to-end LSTM and Transformer models. A pre-trained end-to-end Transformer achieves test accuracy of 0.946 (macro F1 0.950), exceeding 90% accuracy with only seven observations, but falls to 0.513 without pre-training. XGBoost-Reduced (38 features, excluding LS descriptors) reaches 0.922, while XGBoost-Full (56 features) reaches 0.913. Reliability diagnostics confirm LS periods and false-alarm probabilities are unreliable on 3–30-point segments; restricting training and evaluation to ≥15 points does not reverse the full-scale preference for the Reduced catalog. On CPU, XGBoost runtime is dominated by feature extraction (ratio ≈ 16:1); adding LS descriptors increases total processing time by ∼7.8% (feature extraction by ∼7.0%) without a commensurate accuracy gain. A lightweight LSTM attains 0.847 accuracy with 0.2 M parameters. These results offer practical guidance for model selection in real-time survey pipelines. Full article

Organic Rankine Cycles (ORCs) have become one of the most promising technologies for converting low- and medium-grade waste heat into useful power. However, conventional ORC analyses are typically performed using dimensional variables and application-specific operating conditions, limiting transferability across working fluids, heat sources, and system capacities. This study presents a dimensionless similarity framework for ORCs based on Buckingham Π analysis, exergy-based normalization, and reduced thermodynamic coordinates. The methodology transforms conventional ORC performance into a generalized dimensionless representation by normalizing net power output with available heat-source exergy and scaling operating conditions using fluid critical properties. The resulting multidimensional framework incorporates reduced evaporator and condenser pressures, heat-source temperature and utilization parameters, reduced critical temperature, component efficiencies, and the acentric factor. Performance surfaces and sensitivity analyses reveal a well-defined reduced-pressure ridge, with the reference optimum centered near ${\Pi }_{P,e}\approx 0.45$. Parametric assessment demonstrates that the exact location and width of this ridge are conditional on the governing similarity parameters, while the optimal region remains comparatively confined in reduced-pressure space across the investigated domain. Validation using 100 kW–1 MW waste-heat-recovery systems, R245fa and R1233zd(E), and published experimental data supports substantial performance collapse in reduced coordinates. The framework provides a generalized basis for transferable ORC performance mapping, comparison, and preliminary design. Full article

In wireless sensor networks, sensor nodes (SNs) powered by small-capacity batteries or supercapacitors are used to monitor an environment. These nodes are typically deployed in areas with harsh environmental conditions, such as volcanic craters and forests. They rapidly deplete their energy by continuously consuming power during data generation, transmission, reception, and idle states. In this study, a Mixed Integer Linear Programming (MILP) model aimed at maximizing the network lifetime of sensor-node-based systems is briefly introduced, and the effects of varying the transmission distances of nodes on routing and network lifetime are discussed. The MILP model is implemented on a 10-node scenario (1 data-generating node, 1 data-collecting node, and 8 transceiver nodes), and its impact on routing and network lifetime is analyzed. For each scenario, 100 simulations were conducted and validated on real hardware. Full article

The Hechangzhou Anabranching Reach is a typical tidal anabranching segment of the Yangtze River with intense riverbed evolution driven by altered water-sediment regimes and channel regulation. Submerged dikes stabilize river regimes and navigation yet trigger differential local clear-water scour; most prior studies only adopt short-term bathymetric data and lack long-term quantitative analysis across diverse hydrological scenarios, so the synergistic geomorphic mechanism under the control of the submerged dike group remains unclear. This study integrates the 2002–2025 flow division ratio series and multi-period bathymetric data from 2018 to 2026 to quantify spatiotemporal deep-channel adjustments, scour-deposition patterns and cross-sectional deformation of the two anabranches, and evaluate morphological effects of river training structures and relevant potential scour risks. The submerged dike group regulates diversion patterns and keeps the left anabranch’s flow division ratio below 70%. The right anabranch deep channel undergoes persistent vertical incision and reverses its historical deposition trend, while the left anabranch features spatially uneven scour: intense scour zones migrate within the SD1–SD2 reach, and persistent landward erosion occurs between SD2 and SD3. The reach’s polarized evolution of intensified scour and attenuated deposition arises from the joint control of the submerged dike group, bank protection and reduced basin sediment supply. This study reveals the spatially differentiated riverbed evolution driven by engineering-induced hydrodynamic redistribution, offering scientific support for refined waterway management and scour prevention in analogous tidal anabranching rivers. Full article

Conducting landscape ecological risk (LER) assessments and identifying their driving mechanisms is a key approach to balancing regional development with ecological conservation and enhancing ecological security. Taking the Beipanjiang River Basin (Guizhou section)—a typical karst mountainous watershed—as the study subject, this research analyzed the spatiotemporal evolution of LER in the basin using landscape pattern indices and spatial autocorrelation analysis based on five sets of land-use data from 1985 to 2024. The optimal geographical detector (OPGD) was employed to identify the driving factors. The results are as follows: (1) From 1985 to 2024, the average LER index in the study area decreased by 37.07%. The proportion of the area classified as the lowest-risk zone increased from 18.76% to 38.83%, while the proportions of the medium-risk and higher-risk zones decreased to 14.97% and 3.55%, respectively, indicating an optimization of the ecological security pattern. (2) The center of gravity of the lowest-risk zones shifted northwestward, while the medium-risk and higher-risk zones contracted northeastward. The global Moran’s I ranged from 0.477 to 0.574. High–high clustering zones contracted in the north, and regional stress capacity decreased. Low–low clusters expanded in the south, forming a continuously consolidated ecological barrier zone. (3) The interactions between landscape fragmentation and factors such as elevation, precipitation, and NDVI all exhibited a two-factor enhancement effect, with the risk pattern being primarily driven by the synergy of multiple factors. This study provides a scientific basis for adjusting the land-use structure of the watershed, mitigating ecological risks, and promoting high-quality sustainable development. Full article

Graphs are essential tools in introductory STEM courses, yet students often read them as pictures rather than quantitative representations of changing quantities. This study examined how 18 first-semester general chemistry students interpreted two structurally parallel, three-question graphs in chemistry and physics: a concentration-time graph of a chemical reaction and a position–time graph of a walking person. Using eye-tracking, written work, and accuracy data, we found high but asymmetric performance (82% in chemistry; 89% in physics), with rate calculations as the most challenging items in both domains. Students commonly conflated value with rate and sometimes averaged from the origin across discontinuities, with these difficulties more prevalent in chemistry. Trials resulting in correct answers typically followed a question → axes → curve pattern with brief returns to the prompt, whereas trials resulting in incorrect answers often showed early and repeated attention to salient non-target graph features without careful attention to axes. These findings support teaching graph comprehension as a discipline; it is a general but context-sensitive skill and has potential value in an axes-first, rise-over-run-based protocol for rate-of-change reasoning. Full article

Background/Objectives: Antimicrobial resistance threatens public health globally, and critical access hospitals (CAHs) serving rural communities face structural barriers to implementing antibiotic stewardship programs (ASPs). This study evaluated whether a regional educational intervention could improve antimicrobial stewardship knowledge and implementation among rural healthcare professionals. Methods: A quantitative, quasi-experimental repeated cross-sectional evaluation was conducted following Antibiotic Stewardship conferences in 2023 and 2024. Participants from 38 rural and critical access hospitals completed pre-conference, post-conference, 6-month follow-up, and implementation surveys. Quantitative data were analyzed using descriptive statistics and unpaired chi-square tests, with Fisher’s exact test substituted where expected cell counts were below 5, with significance set at p < 0.05. Results: Seventy healthcare professionals participated across both years. Correct identification of the use of an antibiogram to guide empiric (rather than definitive) therapy improved from 27% pre-conference to 58% post-conference (p = 0.029). Confidence in antibiogram interpretation increased from 67% to 100% post-intervention (p = 0.004, Fisher’s exact) but declined to 50% (5 of 10) at 6-month follow-up. Knowledge of the minimum isolate threshold required to construct an antibiogram improved from 23% (7 of 30) pre-conference to 64% (7 of 11) at 6-month follow-up (p = 0.026, Fisher’s exact). Despite this, prescribing intent for conditions where antibiotics are typically unnecessary showed minimal improvement: recommendations for antibiotic use in middle ear infections decreased from 50% to 36.84%, and for mpox from 20% to 15.79%. All participants reported intent to modify clinical practice; however, time constraints, technology limitations, and lack of resources were consistently cited as primary barriers for implementation across both survey years. Conclusions: A regional educational intervention improved short-term antimicrobial stewardship knowledge and confidence among rural healthcare professionals, but knowledge retention declined over time and structural barriers persisted. Education alone is insufficient; sustained reinforcement and rural-adapted ASP models incorporating protected ASP time, decision-support tools and external partnerships are needed to translate knowledge gains into consistent stewardship practice. Full article