Search Results (11,207)

To address the dilemma of ‘non-grain use of cultivated land’ and support China’s requisition–compensation balance policy, this study developed a multi-dimensional assessment framework integrating the production, ecological, and economic dimensions (3D evaluation model), using Shenyang City as a case study to demonstrate the framework’s operational application and policy relevance. Based on 34,704 Third National Land Survey (TNLS) parcels (27,408.39 ha), we applied the constraint factor assessment method and entropy-weighted composite index model. The results show that non-cultivated agricultural land (NCAL) is generally marginally suitable (citywide average score: 2.50/4), with highly suitable areas accounting for only 4.04% (1106.30 ha). These areas exhibit a triangular spatial pattern distributed across northeastern Faku County, central Sujiatun District, and southern Xinmin City. Sensitivity tests using equal weights and ±20% dimension-weight perturbations confirm that high-suitability area remains limited (3.37–5.63% under entropy-weight scenarios; 8.54% under equal weights). Primary limiting factors include severe organic matter deficiency (average 19 g/kg), shallow soil depth, unfavorable pH, land requiring engineering restoration (94%), and punctiform heavy metal contamination (7.53% of plots, 2065.05 ha as spatially excluded areas). Consequently, we propose a five-tier sequential restoration framework: (1) near-term priority recultivation of highly suitable areas; (2) mid-term topsoil reconstruction for moderately suitable areas; (3) medium-to-long-term topsoil stripping and thickening for low-suitability areas; (4) long-term soil amelioration and slope-to-terrace conversion for marginally suitable areas; and (5) strict prohibition of restoration in unsuitable areas. This study establishes a spatially explicit decision-making system integrating “evaluation–classification–sequencing”, and distinguishes technical suitability from economic, institutional, and policy feasibility, providing a decision-support framework for scientifically implementing the cultivated land requisition–compensation balance policy. Future empirical studies using post-restoration monitoring data are needed to test its predictive accuracy against observed restoration outcomes. Full article

A coupling coefficient optimization procedure for a boost-extender topology converter with coupled inductors is presented. The method focuses on minimizing inductor current ripple to reduce losses and improve magnetic core utilization, enabling a potential increase in converter power density. The major innovation of this study is the theoretical formulation of the coupling coefficient and leakage inductance in the UU/UI cores. The study includes a theoretical loss analysis covering two dominant loss mechanisms of the topology. A magnetic core structure enabling current ripple cancelation is presented and analyzed using a reluctance model. To address the analytically intractable field distribution at the core center tap, an empirical leakage model is introduced. The model is validated through ANSYS Maxwell simulations 2023.R1 and corroborated by experimental measurements. The results demonstrate that appropriate coupling coefficient selection significantly suppresses current ripple and enables a more efficient and compact converter design. Simulation and experimental results on experimental prototype of 100 W running at 100 kHz and attaining voltage gain of 20 V to 200 V confirm the accuracy of the models and the effectiveness of the optimization procedures. Full article

Severity factors are widely used to compress torrefaction temperature–time history into a single scalar descriptor. However, whether such scalar representations are structurally sufficient to describe realised conversion across heterogeneous biomass samples remains unclear. In this study, we evaluated the adequacy of single-scalar severity descriptors using a literature-derived dry torrefaction dataset comprising 154 observations from 7 published studies, covering multiple biomass categories and operating conditions. A severity factor, SF(α), was formulated, and its scaling parameter α was optimised through a systematic α-sweep to maximise its relationship with the experimentally determined extent of conversion (EOC). Based on the optimised formulation, EOC was decomposed into severity-implied conversion (SIC) and conversion offset (CO), separating the dominant severity-controlled trajectory from sample-specific deviations. The optimised formulation (α* = 65.1) showed a strong global correlation with EOC (R2 = 0.8593), confirming that severity captures the main average conversion trend. However, nested model comparisons showed that including CO consistently improved explanatory power for both absolute fuel properties and enhancement ratios, with the greatest gains in enhancement space. SIC and CO accounted for 85.9% and 14.1% of the total variance, respectively, indicating that a non-negligible component of conversion variability was not captured by the single severity descriptor. These results show that, although a single severity scalar is useful for describing dataset-level trends, it does not fully resolve sample-level torrefaction behaviour within the analysed dataset. The SIC–CO framework is therefore proposed not as a new severity index or a pre-measurement predictive model, but as a post hoc diagnostic framework for identifying the explanatory limits of scalar severity representations in biomass torrefaction analysis. Full article

Decades of cultivation and the often exclusive use of mineral fertilisers as a substitute for organic inputs have reduced the soil organic carbon (SOC) content of agricultural soils, meaning they now represent a potential sink for carbon sequestration to mitigate climate change and improve soil function. As well as being a legacy of management, SOC will also be dependent on local scale climate, topography, and soil properties; accounting for this local context is important when benchmarking fields and quantifying the potential for additional carbon sequestration. We developed a landscape-scale methodology, using a handheld infrared device, for baselining SOC stocks in the top 30 cm across a 45,000 ha farm cluster in the UK. The cluster is exploring opportunities for landscape-scale environmental improvement with a focus on natural flood protection and water pollution reduction through conversion of arable land to permanent grassland. We used the baseline data to estimate additional benefits of arable reversion for soil carbon sequestration. Because all the farms in the cluster share the same pedoclimatic conditions, variance in SOC at the field scale could be confidently attributed to differences in soil type and land use. Average SOC stocks in arable and permanent pasture fields were 103.9 and 140.3 Mg C ha⁻¹, respectively. Variance in %SOC was modelled using soil series, sample depth, land use, and clay content, and fields were benchmarked based on deviation from the expected value. The fields with the largest SOC stocks were identified and used as references to predict future potential sequestration. The conversion of arable land to permanent pasture resulted in a predicted average uplift in SOC of 55.0 Mg C ha⁻¹. Our landscape-scale methodology provides robust evidence on current and future carbon stocks for public subsidy schemes and natural capital markets that account for local constraints and opportunities. Full article

The aim of the present study is to propose a new mathematical model of compartment type for an epidemic problem using fractional order derivatives. This epidemic model takes into account vaccination, hospitalization, asymptomatic infection, and health awareness programs. Caputo fractional derivatives are used to model the temporal non-locality of epidemic phenomena in the proposed model. The qualitative analysis of the model includes the characterization of equilibrium points and their stability. The disease-free equilibrium (DFE) is shown to be locally asymptotically stable when the basic reproduction number $R_0<1$, and unstable otherwise. Conversely, an endemic equilibrium emerges when $R_0>1$, corresponding to the instability of the DFE. Periodic oscillation is observed for a higher rate of infection transmission. A fractional optimal control problem is formulated to minimize disease prevalence through vaccination, hospitalization, and treatment strategies, supported by sustained awareness campaigns. The results emphasize the role of vaccination, treatment and awareness campaign in controlling Mpox outbreaks, showing their success in minimizing the epidemic. In addition, a fractional optimal control model is proposed to reduce disease prevalence using preventive measures such as vaccinations and treatments coupled with awareness impacts. From these results, one can clearly understand that vaccinations and continuous public health awareness are essential in reducing Mpox cases, which help flatten epidemic trends. Full article

Background/Objectives: Evidence on spinal versus general anesthesia in unilateral total knee arthroplasty (TKA) may not extend to one-week-staged bilateral surgery, where older patients receive two anesthetics in a short interval and intra-operative spinal-to-general conversion is common but rarely reported transparently. We compared peri-operative acute kidney injury (AKI) and transfusion between strategies in this setting. Methods: We retrospectively analyzed 207 patients (414 surgeries) undergoing one-week-staged bilateral primary TKA at one center. Co-primary endpoints were creatinine-based AKI (patient level) and packed-red-blood-cell transfusion (surgery level). Because 42 general-anesthesia-classified surgeries had an attempted spinal injection, the primary analysis used the initial anesthetic plan (an intention-to-treat analogue), reclassifying these as spinal, with as-treated classification as a sensitivity analysis; AKI was modeled at the patient level (any general anesthesia versus spinal–spinal) and transfusion per surgery. Results: Median age was 75 years and 82.6% were female; AKI affected 74 of 207 patients (35.7%) and transfusion 185 of 414 surgeries (44.7%). The adjusted any-general-anesthesia versus spinal–spinal estimate was not statistically significant and opposite the spinal-protective hypothesis (adjusted odds ratio 0.49, 95% confidence interval 0.23–1.01, p = 0.054), and no pre-specified sensitivity scenario survived Benjamini–Hochberg correction. Transfusion did not differ between strategies; among secondary endpoints, length of stay, hemoglobin drop, peak C-reactive protein, and intra-operative hypotension likewise showed no significant difference after multiplicity correction. Conclusions: These hypothesis-generating findings do not support changing anesthetic practice; the choice should remain individualized. Approximately 12% of attempted spinal anesthetics converted intra-operatively to general anesthesia—a record-based observation, not a validated failure rate. Full article

This study aimed to characterize the reproductive performance of the local Algerian population (LAP) compared with the New Zealand White (NZW) rabbits, by evaluating sexual behavior, semen characteristics, and their modulation by environmental factors, namely photoperiod and temperature-humidity index (THI). Mature bucks (n = 14/breed) were monitored from January to April, with two successive ejaculates collected weekly. Sexual behavior, macroscopic and microscopic semen parameters, and testosterone concentrations were assessed. The effects of breed, ejaculate order, environmental factors, and their interactions were analyzed using Generalized Linear Mixed models. LAP and NZW bucks exhibited similar sexual behavior and blood testosterone levels (p > 0.05). Collection failures and ejaculate rejection causes were mainly clustered within specific individuals rather than being breed-dependent. However, LAP bucks showed higher sperm concentration (p = 0.01), viability (p = 0.02), and membrane integrity (p = 0.04) than NZW bucks, whereas most motility and quantitative semen traits remained comparable between breeds. Increasing photoperiod significantly improved reproductive performance (p < 0.05). Conversely, within the investigated range, THI mainly affected semen collection efficiency through increased urine contamination (p < 0.001), with limited effects on intrinsic sperm quality. Significant breed × environment interactions for sperm concentration (p = 0.03) suggested differential responsiveness between breeds, with LAP bucks showing a stronger positive response to increasing photoperiod and less pronounced variation under THI fluctuations. Overall, LAP bucks exhibited a more favorable seminal profile under the conditions of the present study, supporting the valorization of this local genetic resource for artificial insemination programs under Algerian conditions. Further studies are required to confirm these patterns under summer heat-stress conditions and evaluate their impact on fertility outcomes. Full article

Air pollution negatively impacts human health and environmental sustainability, particularly in areas with high industrial activity. This study comparatively evaluated deep learning-based models for estimating PM₁₀ and SO₂ pollutants in Dilovası and Ereğli (Turkey), industrial areas with high pollutant loads. The study utilized Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), an RNN–GRU stacked hybrid model, an attention-based hybrid model, and the proposed GRU–LSTM dual-path hybrid model. The proposed method consists of four main stages: data conversion into a time-series format, data preprocessing and feature generation, model architecture development, and model training and performance evaluation. The dataset consisted of 365 daily PM₁₀ and SO₂ observations obtained from the Air Monitoring Center for the Dilovası and Ereğli monitoring stations. Model performance was evaluated using the coefficient of determination (R²), training time, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) metrics. The findings showed that the hybrid models provided higher accuracy compared to the single-track models. Specifically, the proposed GRU–LSTM dual-path hybrid model produced the highest R² and lowest error values for both pollutant parameters in both the Dilovası and Ereğli regions. In Dilovası, this model achieved R² = 0.97 for SO₂ and R² = 0.96 for PM₁₀; in Ereğli, it reached R² = 0.92 for SO₂ and R² = 0.98 for PM₁₀. Thus, it has been shown that the GRU–LSTM dual-path hybrid model, which models short-term and long-term temporal dependencies in parallel, is an effective and reliable method for air pollutant forecasting in industrial areas. These findings demonstrate the potential of the proposed model to support air quality monitoring, early warning systems, and environmental decision-making in industrial regions. Full article

Whānau (family) is a foundational unit of Māori social organisation, and the replacement of Māori family structures with Western nuclear models is widely regarded as among the most significant tools of colonisation. As Māori move toward decolonisation and re-Indigenisation, approaches to family and identity are shifting from imported structures. However, takatāpui and irahuhua (LGBTQ+ and gender-diverse Māori) are rarely explicitly included in these movements. Contemporary framings of whānau within Māori discourse can inadvertently reiterate colonial talking points, particularly regarding binary gender roles, divisions of labour, and the boundaries of what constitutes whānau. Building on takatāpui scholarship and the findings from three separate wānanga (targeted collective conversations) across a 1.5-year period with 18 irahuhua participants, the article examines the contrasts and connections between “coming out” and “coming in” to tradition and whānau. These conversations revealed that when participants enact self-determination and “come in” to their whānau, they demonstrate pathways to strengthen and restore Māori understandings of whānau and challenge the role of historic inquiry in modern Māori politics. Grounded in one wānanga participant’s understanding that “family pressures that make you feel divided [are] just what the coloniser wanted,” this article explores how takatāpui and irahuhua strengthen their communities and demonstrate sovereignty in settler contexts. Full article

Leftover spaces, often associated with neglected urban corners, bear physical and conceptual similarities to ignored parts of designed wholes. This study proposes an analytical approach to develop resilient intervention strategies by analyzing the production of leftover spaces through users’ emotional experiences. An experimental pilot study was conducted along Söğütlüçeşme Street in Kadıköy, Istanbul, where all corner points were typologically classified based on morphological characteristics. To measure the impact of these configurations on spatial emotional characters, a survey was implemented using Plutchik’s wheel of emotions. Following a quantitative analysis of emotion frequencies and intensities, findings were visualized via radar charts and spatialized using QGIS 3.40 to generate an emotional map. The resulting emotional maps were further used to identify spatial vulnerabilities and resilience priorities across the study area. By making the gaps between point-based emotional clusters continuous through the IDW interpolation method, the emotional topography of the study area was modeled, thereby presenting an analytical framework that identifies emotional thresholds, spatial vulnerabilities, and resilience priorities. Results indicate that as the physical boundaries of corner voids expand, influenced by angling and massing decisions, public diversity increases, creating a positive emotional atmosphere. Conversely, compressed voids demonstrate a higher potential for producing leftover spaces. This study reveals that mapping user emotions as a data layer is critical for constructing more inclusive and resilient urban environments. Full article

Introduction/Objectives: Patient-centered communication is essential in oncology care, where healthcare professionals often manage emotionally demanding conversations, uncertainty, complex decisions, and patient involvement in care. However, the relationship between communication knowledge, empathic listening, and practical communication skills remains insufficiently examined. This study aimed to examine the associations between communication knowledge, empathic listening, and interpersonal communication skills among healthcare professionals involved in oncology care. Methods: A cross-sectional study was conducted in Croatia from May to November 2025 on a convenience sample of 138 healthcare professionals involved in oncology care. Communication knowledge was assessed using a study-specific questionnaire, empathic listening using an adapted Active Empathic Listening Scale, and interpersonal communication skills using an adapted Interpersonal Communication Skills Inventory. Because the instruments were adapted to the oncology care context, their dimensions were examined using exploratory factor analysis and interpreted as sample-specific exploratory constructs. Descriptive statistics, correlation analyses, and multiple linear regression analyses were performed. Results: Clear message delivery and assertiveness had the highest self-reported score, whereas emotional interaction management had the lowest. Communication knowledge was not an independent predictor of communication skills dimensions. Processing and responding positively predicted clear message delivery and assertiveness (β = 0.361; p = 0.001; R² = 13.4%), while noticing emotional and nonverbal cues negatively predicted emotional interaction management (β = −0.234; p = 0.032; R² = 7.6%). The explained variance of the models was low. Conclusions: The findings suggest limited but potentially relevant associations between selected dimensions of empathic listening and self-reported communication skills in oncology care. Communication knowledge, measured using a study-specific exploratory instrument, was not independently associated with communication skills. Because of the exploratory design, self-report measures, adapted instruments, and convenience sampling, the results should be interpreted with caution. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Stereo matching has become an increasingly important component of modern autonomous systems. Developing deep learning-based stereo-matching models that deliver high accuracy while operating in real time continues to be a major challenge in computer vision. In the domain of cost volume-based stereo matching, accurate disparity estimation depends heavily on large-scale cost volumes. However, such large volumes store substantial redundant information and also require computationally intensive aggregation units for processing and regression, making real-time performance unattainable. Conversely, small-scale cost volumes followed by lightweight aggregation units provide a promising route for real-time performance, but lack sufficient information to ensure highly accurate disparity estimation. To address this challenge, we propose the Enhanced Shuffle Mixer (ESM) to mitigate information loss associated with small-scale cost volumes. ESM restores critical details by integrating primary features into the disparity upsampling unit. It quickly extracts features from the initial disparity estimation and fuses them with image features. These features are mixed by shuffling and layer splitting, then refined through a compact feature-guided hourglass network to recover more detailed scene geometry. The ESM focuses on local contextual connectivity with a large receptive field and low computational cost, leading to improved disparity estimation accuracy while maintaining real-time performance under the evaluated settings. The compact version of ESMStereo achieves an inference speed of 116 FPS on RTX 4070S and 91 FPS on the AGX Orin. Full article

Optical performance monitoring is essential for adaptive and intelligent coherent optical communication systems. In this paper, a Transformer-based multi-task meta-learning framework is proposed for joint modulation format identification and electrical signal-to-noise ratio (ESNR) estimation from original received waveforms. A simulated coherent optical communication system is established to generate QPSK, 16QAM, and 32QAM signals under different launch-power conditions. The received I/Q waveforms are directly used as model inputs, avoiding handcrafted feature extraction or constellation-image conversion. The proposed model employs a shared one-dimensional Transformer encoder to extract temporal waveform representations. A prototypical classification branch is used for few-shot modulation format identification, while an ESNR regression branch is introduced for continuous signal-quality estimation. The two tasks are jointly optimized under an episodic support-query training mechanism. Experimental results show that the proposed method achieves 99.99% modulation identification accuracy on the test episodes. For ESNR estimation, the model obtains an MAE of 0.1194 dB, an RMSE of 0.1738 dB, and an R² value of 99.83%. These results demonstrate that the proposed framework can simultaneously provide accurate modulation decisions and reliable ESNR estimation, showing its potential for waveform-based optical performance monitoring. Full article

It is essential to exercise control over the environmental impact of deep excavation construction in soft soil areas from the perspective of deformation in order to ensure engineering safety. A three-dimensional finite element model of the foundation pit was developed, thereby creating a comparison between the results of the numerical simulation and the actual on-site monitoring data. This process served to validate the precision of the simulations. The focal point of the study pertained to the three-dimensional effects of support structure deformation and ground settlement during excavation. A comprehensive analysis of the spatial distribution and evolutionary patterns of underground diaphragm wall deformation and ground settlement behind the wall at varying excavation depths was conducted. The results demonstrated that both support structure deformation and ground settlement behind the excavated structure exhibited substantial spatial effects. In particular, larger deformations were observed near the symmetrical plane of the excavation centre. Conversely, greatly smaller deformations were observed in the corners of the excavation. The research findings aim to provide useful references for practical engineering projects. Full article