Search Results (6,417)

We survey recent advances in second-language (L2) Mandarin lexical tones research and show how an interpretable computational approach can deliver parameter-aligned feedback across perception–production (P ↔ P). We synthesize four strands: (A) conventional evaluations and tasks (identification, same–different, imitation/read-aloud) that reveal robust tone-pair asymmetries and early P ↔ P decoupling; (B) physiological and behavioral instrumentation (e.g., EEG, eye-tracking) that clarifies cue weighting and time course; (C) audio-only speech analysis, from classic F0 tracking and MFCC–prosody fusion to CNN/RNN/CTC and self-supervised pipelines; and (D) interpretable learning, including attention and relational models (e.g., graph neural networks, GNNs) opened with explainable AI (XAI). Across strands, evidence converges on tones as time-evolving F0 trajectories, so movement, turning-point timing, and local F0 range are more diagnostic than height alone, and the contrast between Tone 2 (rising) and Tone 3 (dipping/low) remains the persistent difficulty; learners with tonal vs. non-tonal language backgrounds weight these cues differently. Guided by this synthesis, we outline a tool-oriented framework that pairs perception and production on the same items, jointly predicts tone labels and parameter targets, and uses XAI to generate local attributions and counterfactual edits, making feedback classroom-ready. Full article

Escalating energy prices have positioned households as pivotal agents in advancing demand-side energy efficiency. This study examines three complementary energy-saving strategies among Polish households: (1) habitual, low-cost actions such as switching off unnecessary lighting; (2) capital-intensive investments, including LED lighting and energy-efficient appliances; and (3) time-based and prosumptive strategies linked to dynamic tariffs and photovoltaic systems. The empirical analysis is based on a nationwide survey conducted using the Computer-Assisted Web Interviewing method, involving 401 respondents. The study’s contribution lies in integrating these strategies within a single analytical model and providing the first empirical evidence on their socio-demographic determinants in Central and Eastern Europe, with Poland as a representative case. The results show that older individuals more often adopt everyday habitual practices, whereas higher income and education levels are associated with investment-oriented behaviours. Urban households tend to favour technological solutions, while rural households more frequently adopt time-of-use tariffs and PV systems. Two complementary pathways are identified: a behavioural–habitual path and an investment–technological path. The findings offer guidance for public policy by showing that energy savings increase when financial incentives are combined with clear communication and low-effort decision tools that help households optimise energy use regardless of demographic profile. Full article

The optimization of volatile fatty acid (VFA) production from complex wastes under anaerobic conditions remains constrained in terms of productivity by the common use of long hydraulic retention times (HRTs, 20–30 days). Extended HRTs can limit process productivity by reducing substrate turnover and reactor throughput, while promoting further conversion of VFAs into methane and other end products. Despite its importance, the combined influence of pH and HRT on VFA yields and process optimization has not been comprehensively evaluated. This study investigates the effects of pH and short HRT on VFA production, microbial community structure, and hydrolysis and acidification efficiency in continuous stirred-tank reactors (CSTRs) fed with carbohydrate-rich feedstock (carrot residue pulp). Operating at an HRT of 11 days and an organic loading rate (OLR) of 4.4 g COD·L⁻¹·d⁻¹ at 25 °C under pH 5.1 resulted in a VFA bioconversion efficiency of ~45% and an acidification efficiency of 84%, without compromising VFA profile or productivity compared to reactors operated at 14 days HRT and 3.3 g COD·L⁻¹·d⁻¹. The shorter HRT and higher OLR enhanced hydrolysis efficiency (60%) and promoted greater microbial diversity, supporting robust hydrolytic activity and stable production dominated by acetic and butyric acids. These findings challenge the conventional assumption that longer retention times inherently improve process stability and demonstrate that operational conditions might improve reactor space–time yield in VFA-oriented fermentations. Full article

To address the limitations of existing power system low-carbon dispatching studies—such as over-reliance on generation-side carbon mitigation, price-oriented demand response (DR) failing to guide carbon reduction, and the low solution efficiency of traditional carbon emission flow (CEF)-based two-stage models—this paper proposes a data-driven CEF framework integrated with a bi-level economic and low-carbon dispatching model. First, a data-driven CEF calculation method is developed: It eliminates the need for complex power flow post-processing while maintaining calculation accuracy through multiple linear regression. On this basis, a bi-level optimization model is constructed: The upper level focuses on optimizing the economic and low-carbon objectives of power grid operation, while the lower level regulates industrial, commercial, and residential load aggregators (LAs) via carbon-intensity-oriented DR strategies and economic compensation mechanisms. Finally, a sample-based optimization algorithm combined with convex relaxation is proposed to solve the model, avoid the static setting of power flow and carbon intensity, and improve solution efficiency. Case studies demonstrate the following: the proposed method reduces the calculation time of node carbon intensity from 5 min to less than 100 ms, with the coefficient of determination (R²) ranging from 0.969 to 0.998; compared with the two-stage method, it achieves a 4.26% reduction in total scheduling cost, a 3.80% decrease in total carbon emissions, a 53.27% drop in carbon trading cost, and a 21.6% shortening in iteration time. These results verify that the proposed method can effectively enhance the source−load interaction and improve the accuracy and efficiency of low-carbon scheduling. This study provides a feasible technical path for the low-carbon transition of new-type power systems. Full article

This paper presents a method for determining the equivalent circuit parameters of magnetically coupled air-core coils used in wireless power transfer (WPT) systems. The proposed approach enables fast and accurate modeling of inductively coupled energy transfer structures, which is essential for the design and optimization of high-efficiency wireless energy systems. The equivalent circuit of the analyzed system was developed using Cauer circuits, while a two-dimensional (2D) axisymmetric electromagnetic field model was employed to derive the equations. The model was implemented in proprietary software based on the edge-element finite element method (FEM) using the A–V formulation. The A–V formulation combines the magnetic vector potential A and the electric scalar potential V, enabling simultaneous representation of magnetic field distribution and current flow in conducting regions. The eddy currents in the conductors were considered in the electromagnetic field analysis. Simulations were carried out for two operating states: short-circuit and idle. The results were used to determine the parameters of the horizontal and magnetizing branches of the equivalent circuit of considered system and to analyze the frequency dependence of the resistances and inductances of the coupled coil system. The proposed modeling approach provides an effective and energy-oriented tool for the design of wireless power transfer systems with improved efficiency and reduced computational cost. The proposed method reproduces impedance characteristics with an accuracy of 0.2 × 10⁻³% in the idle state and 1.4 × 10⁻³% in the short-circuit state compared to the full FEM model, while significantly reducing the computation time. Full article

In naturally ventilated dairy barns, many questions regarding airflow, indoor air quality, and emissions are still unanswered, often resulting in inaccurate environmental control of the housing. Particularly, limited understanding of the implications of the constantly changing outdoor weather conditions in interaction with the building design and the role of the characteristics of the animals’ movement inside the building enhances uncertainties in the estimation of airflows within and across the barns. Computational fluid dynamics (CFD) have been used in the past to better understand the dynamics of barn climate, but the models are typically too slow to be used for real-time prediction and control. We investigated the effect of animal characteristics (i.e., animal location, orientation, body posture, and dimensions) on the pressure drop in the animal occupied zone considering inlet wind speed from 0.1 m s⁻¹ to 5 m s⁻¹ and wind direction of 0° and 90° in a CFD model. The cow position in general had little impact on the pressure drop at low wind speeds, but became relevant at higher wind speeds. Cows distributed in a more organized alignment showed less airflow resistance, and, therefore, a lower pressure drop and higher air velocities. Moreover, the cow breed affected the pressure drop, with higher withers resulting in a higher pressure drop and air resistance. In contrast, the effects of cow lying–standing ratio on the pressure drop and airflow resistance coefficients were negligible for both investigated wind directions. Our study aims to provide guidance for optimizing parametrizations of the animal occupied zone in order to enhance the speed of simulations without significant loss in model accuracy. In addition, the conclusions drawn from our study may support the adaption of building design and herd management to improve the effectiveness of ventilation concepts of naturally ventilated dairy barns. Full article

This study presents a frequency-domain analysis of a finite-element (FEM)-based rotor–nacelle model for wind turbines, validated against the open-source time-domain tool OpenFAST. The analysis was carried out using METHOD, an in-house computational framework implemented in Python. While time-domain models remain standard for nonlinear aeroelastic simulations, frequency-domain approaches offer advantages in early-stage design, control development, and system identification due to their efficiency, transparency, and suitability for parametric studies. The FEM model includes flexible blades, hub, and nacelle dynamics and includes tower and fixed or floating platform components with rotor–tower frequency interactions. In this work, a fixed tower is considered to isolate rotor behaviour. Beam-element formulation enables the computation of natural frequencies, mode shapes, and frequency response functions, and an equivalent rotor model is implemented in OpenFAST for consistent benchmarking. Validation results show close correspondence between the two modelling approaches. Key operational parameters agree within 3%, while structural responses, including flap-wise deflection, bending moments, and resultant quantities, typically fall within an overall accuracy range of 5–15%, consistent with expected differences arising from reference-frame conventions and modelling assumptions. Discrepancies are discussed in terms of numerical damping, model assumptions (differences in the axis system), and the influence of structural simplifications. Overall, the FEM model captures the dominant dynamic behaviour with satisfactory accuracy and a consistent orientation of global response. Computational efficiency results further highlight the advantages of the METHOD framework. Wind-field generation is completed roughly an order of magnitude faster, and long-duration aeroelastic simulations achieve substantial speed-ups, reaching more than one order of magnitude for multi-hour cases, demonstrating strong scalability relative to OpenFAST. Overall, the results confirm that a well-constructed yet still simplified frequency-domain FEM rotor model can provide a robust and computationally efficient alternative to conventional time-domain solvers. Moreover, the computational performance presented here represents a lower bound, as further improvements are readily achievable through parallelisation and solver-level optimisation. Future papers will present the full-system aero-hydro-elastic coupling for fixed and floating offshore wind turbine applications. Full article

Magnetic localization technology plays a significant role in medical device navigation and human–computer interaction. However, existing localization methods based on local optimization suffer from poor initial solutions and slow convergence. To address the aforementioned challenges, this paper presents a hybrid localization approach, referred to as the Improved Artificial Lemming Algorithm (IALA) Integrated with Levenberg–Marquardt (LM) Optimization. Building upon the Artificial Lemming Algorithm (ALA), the proposed method incorporates an adaptive Gaussian–Lévy hybrid mutation strategy designed to enhance search performance through improved exploration–exploitation dynamics, as quantitatively demonstrated by the diversity-based analysis where IALA maintains higher exploration percentages on multimodal functions while achieving superior optimization results on high-dimensional problems. By introducing a competitive foraging mechanism inspired by the aggressive behavior of the Tasmanian Devil Optimization (TDO) algorithm, it enhances population diversity and search initiative. Furthermore, a time-varying tracking and escape strategy is adopted to improve dynamic optimization performance in complex solution spaces. The proposed method leverages IALA to generate high-quality initial solutions, significantly accelerating the convergence speed and stability of the LM algorithm, thereby improving the overall performance of the permanent magnet localization system. The experimental results show that, using a horizontal test platform of 60 mm × 60 mm with 41 uniformly distributed test points, and acquiring data at vertical heights ranging from 15 mm to 65 mm in 5 mm increments for two distinct orientations of the permanent magnet, the IALA-LM algorithm achieves an average localization success rate of 96.9% over 902 trials, with a mean position error of 1.1 mm and a mean orientation error of 0.17°. Compared with the standard LM algorithm, the proposed IALA-LM algorithm reduces the position error by approximately 66.7% (from 3.3 mm to 1.1 mm) and the orientation error by approximately 94.3% (from 3.0° to 0.17°). Consequently, the proposed method enables high-precision, high-stability, and high-efficiency localization of permanent magnets. It can provide reliable spatial pose estimation support for demanding applications such as miniature implantable or ingestible medical devices (e.g., capsule endoscopy, intramedullary nail fixation, and tumor localization), human–computer interaction, and industrial inspection. Full article

Urethral stricture is a disease of fibrotic narrowing that compromises the urethral mucosa and spongiosum. Oral mucosal graft urethroplasty delivers excellent outcomes in complex cases, yet its procedural demands restrict availability beyond specialized centers. Endoscopic transplantation of oral mucosa has been proposed; while feasibility is shown, clinical efficacy remains suboptimal. We asked whether extracellular vesicles from stem cells of human exfoliated deciduous teeth (SHED-EVs) promote oral mucosa fibroblast (OMF) growth under urethra-mimetic paracrine conditions and whether culture growth phase tunes EV function. SHED-EVs were collected during logarithmic (SHED-EV-L) or stationary (SHED-EV-S) phases under xeno-free conditions, isolated by a standardized workflow, and characterized by nanoparticle tracking analysis. miRNA cargo was profiled with a human miRNA microarray platform and normalized for comparative analyses. OMF proliferation was quantified in a horizontal indirect co-culture with urethral epithelial cells using incubator-based time-lapse imaging. SHED-EV-L produced a sustained pro-proliferative effect across 24–96 h, whereas SHED-EV-S showed a weaker early effect with a late catch-up; both exceeded vehicle at 96 h. Fibrosis-related miRNA heat maps showed culture growth phase-dependent patterns: SHED-EV-L displayed relatively higher signals for miR-31-3p, miR-146b-3p, several let-7 members, and selected miR-181 isoforms, whereas SHED-EV-S showed a marked relative increase of miR-486-3p; miR-21, miR-99/100, and miR-205 were broadly comparable between phases. These findings indicate that culture growth phase is a practical design lever that orients SHED-EV cargo and function, supporting phase-matched formulations for adjunctive transurethral applications and motivating in vivo validation and manufacturing-oriented quality controls. Full article

Background: Anatomic pathology laboratories operate under conditions requiring high precision, strict documentation, biosafety protocols, and tight turnaround times. Evidence from Greece is limited, and joint assessment of burnout and leadership in this setting is rare. Objective: The aim of this study was to estimate burnout levels among anatomic pathology personnel in Attica and examine their association with perceived leadership style. Methods: A cross-sectional survey of public and private laboratories was carried out. The questionnaire included demographics and work characteristics, the Copenhagen Burnout Inventory (CBI), and the Multifactor Leadership Questionnaire Form 5X (MLQ-5X). Results: Burnout levels were moderate to low overall, with personal burnout highest, work-related intermediate, and colleague-related lowest. Women and employment type were associated with personal burnout (p < 0.05). Passive/avoidant leadership (including management by exception–passive and laissez-faire) showed positive associations with burnout, whereas transformational leadership and favorable leadership outcomes—particularly, perceived effectiveness and satisfaction with the leader—were inversely associated; transactional leadership followed the same direction but less robustly (p < 0.05 where supported). Conclusions: Burnout among anatomic pathology personnel in Attica is non-trivial and varies across domains. Leadership dimensions display differential links with burnout, indicating potentially modifiable organizational targets for intervention. Significance: To our knowledge, this is the first study in Greece and among the first in Europe to jointly apply CBI and MLQ-5X in anatomic pathology laboratories, offering practical evidence to inform leadership-oriented interventions. Full article

Fusarium head blight (FHB) severely impacts wheat yield and grain quality, threatening global food security. In a field experiment, key photosynthetic, water relations, and leaf angular (morphological) traits were measured in the flag leaves of FHB-resistant and FHB-susceptible wheat genotypes under Fusarium-inoculated conditions. Measurements were conducted at 10 and 18 days post-inoculation (dpi) to evaluate the genotype- and time-dependent physiological and structural responses of resistant vs. susceptible genotypes to FHB infection over time. Fusarium infection induced distinct time- and genotype-specific changes across multiple physiological traits. At 10 dpi, when no visible symptoms were observed in either genotype, the resistant variety exhibited increased stomatal and total conductance, enhanced transpiration, earlier reductions in vapor pressure and H₂O mole fractions, improved photosynthetic efficiency, and dynamic leaf pitch adjustments, while the susceptible variety decreased them. By 18 dpi, the resistant genotype had recovered water vapor dynamics and reversed leaf pitch changes, whereas the susceptible variety continued to exhibit physiological disruption. These results are consistent with the possibility that the coordinated regulation of water vapor conductance, leaf water status, photosynthetic performance, and leaf orientation contributes to FHB resistance. Understanding the interplay between physiological and morphological traits at early infection could guide targeted breeding strategies and early phenotypic selection tools. Full article

Broad-scale assessments often track forest productivity, yet they rarely quantify how soil conditions determine whether these gains persist as long-lived carbon and generate measurable economic value. This study focused on Northeast China, where forests include boreal coniferous stands dominated by Dahurian larch, temperate conifer–broadleaf mixed forests with Korean pine, and temperate deciduous broadleaf forests dominated by Mongolian oak. We combined GLASS net primary productivity and ESA CCI Land Cover to delineate forest pixels, used 2000 to 2005 as the baseline, and converted productivity anomalies into pixel level carbon economic value using a consistent pricing rule. Forest NPP increased significantly during 2000 to 2018 (slope = 1.57, p = 0.019), and carbon economic value also increased over time during 2006 to 2018 (slope = 2.24, p = 0.002), with the highest values in core mountain forests and lower values in the western forest–grassland transition zone. Correlation analysis, explainable random forests, and variance partitioning characterized spatial and temporal dynamics from 2000 to 2018 and identified environmental controls. Carbon value increased over time and showed marked spatial heterogeneity that mirrored productivity patterns in core mountain forests. Climate was the dominant predictor of value, while higher soil pH and clay content were negatively associated with value. The random forest model explained about 70% of the variance in carbon value (R² = 0.695), and variance partitioning indicated substantial unique and joint contributions from climate and soil alongside secondary topographic effects. The automatable framework enables periodic updates with new satellite composites, supports ecological compensation zoning, and informs soil-oriented interventions that enhance the monetized value of forest carbon sinks in data-limited regions. Full article

End-to-end (E2E) autonomous driving offers a promising alternative to traditional modular pipelines by mapping raw sensor data directly to vehicle controls, thereby mitigating error propagation. However, prevalent approaches largely rely on dense Bird’s-Eye-View (BEV) feature maps, which incur high computational overhead and necessitate complex post-processing for trajectory generation. To address these limitations, we propose HiPro-AD, a proposal-centric sparse E2E planning framework that fundamentally diverges from dense BEV paradigms. HiPro-AD integrates an efficiency-oriented IM-ResNet-34 encoder with a novel STFormer. This transformer dynamically fuses multi-view spatial features and historical temporal context via a proposal-anchored mechanism, focusing computation strictly on regions relevant to sparse trajectory proposals. Furthermore, trajectory selection is refined by a Pairwise Ranking Scorer, which identifies the optimal plan from diverse candidates based on relative quality. On the NAVSIM benchmark, HiPro-AD achieves a PDMS of 92.6 using only camera input, surpassing prior dense BEV and multimodal methods. On the closed-loop Bench2Drive benchmark, it attains a 37.31% success rate and a driving score of 65.48 with a latency of 67 ms, demonstrating real-time capability. These results validate the efficiency and robustness of our sparse paradigm in complex driving scenarios. Full article

The construction sector undeniably has an impact on sustainability in its three dimensions: economic, social, and environmental. In this context, 3D concrete printing (3DCP) has emerged over the last decade as an attractive technology for transforming this sector. It enables the manufacture of construction elements while saving time, reducing waste, and eliminating the need for molds. However, assessments of the environmental performance of implementing this technology are limited, particularly under representative production conditions. This study evaluates the footprint family indicators, carbon footprint (CF), ecological footprint (EF), and water footprint (WF), of different mixtures of 1 m3 of 3D-printed concrete, with 1m of a high printed wall. These mixtures were made with a proportion of fresh solid aggregates; brick and concrete rubble (as demolition waste (CDW) materials) were used as partial replacements for cement. In addition, the environmental impact of using two printing technologies, gantry and robotic arm systems, is analyzed. The results show that materials are the main source of environmental impacts; the replacement of some of the cement reduces CF and EF by up to 20% and 19%, respectively, while preserving printability and buildability, as demonstrated by the stable fabrication of 1 m-high printed wall elements. However, moderate increases in WF were observed, which were associated with the electricity consumption of waste processing. These results confirm the potential for valorizing CDW in 3D printing mixtures. This environmental assessment under full-scale printing conditions supports sustainability-oriented decision-making in the construction industry. Full article

Collaborative multi-UAV swarms are central to many missions. This review covers the most recent two years. It organizes the literature with a scenario-aligned taxonomy. The taxonomy has 12 cells (Path/Distribution/Coverage × offline/online × static/dynamic). Nine cells are well populated and analyzed. For each, representative techniques, reported limitations, and scenario-appropriate use are summarized. Cross-scenario trade-offs are made explicit. Key examples include scalability vs. energy efficiency and centralized vs. decentralized (hybrid) architectures. The review also links offline pre-planning to online execution through architecture choices, digital-twin validation, and safety-aware collision avoidance in cluttered airspace. Unlike prior algorithm-centric or bibliometric surveys, this work applies a scenario-conditioned taxonomy, ties best-suited method families to each populated cell, and surfaces reported limitations alongside trade-offs. The result is deployment-oriented guidance that maps methods to mission context. Finally, five near-term priorities are highlighted: (i) compute-aware real-time adaptivity on resource-constrained platforms; (ii) scalable multi-objective scheduling with coupled motion and cooperative control; (iii) bandwidth-aware, conflict-resilient intra-swarm communication with reliability guarantees; (iv) certifiable planning for dense urban low-altitude corridors; and (v) energy-aware, hierarchical planners that couple offline pre-planning with online replanning. Full article