Search Results (3,750)

After the completion of temporary repair of naval ship equipment, cost settlement has long relied on an ex post auditing model, which results in long cycles and a lack of immediate pricing references for the military. To address this issue, a comprehensive unit price estimation method based on Proximal Policy Optimization (PPO) is proposed, which rapidly generates reasonable unit prices for each process after the repair is completed, thereby providing a quantitative benchmark for negotiation. The unit price estimation problem is formulated as a Markov decision process, and a multi-objective reward function combining range reward, compliance penalty, and final accuracy reward is designed. To alleviate the sparse reward problem, potential-based reward shaping using the Critic network is introduced, which decomposes the final accuracy signal into each pricing step. The clipping mechanism of PPO is adopted to limit the policy update amplitude, thereby improving training stability. Experimental results on 12,000 desensitized real repair records show that the proposed method achieves a mean absolute percentage error (MAPE) of 11.3%, a coefficient of determination (R²) of 0.913, and an abnormal estimation rate (AER) of 3.5%. Compared with standard PPO, the AER is reduced by 59%. The proposed method can sequentially output reasonable unit prices after repair completion, exploring a technical pathway for transforming temporary repair funding from ex post auditing to immediate verification. Full article

Unmanned ground agricultural machinery is required to operate efficiently in complex and dynamic field environments, which presupposes accurate and reliable environmental perception capabilities. This requires the machinery to perceive and respond to various typical elements in both driving and operational environments, such as obstacles, crop rows, and field boundaries. This paper focuses on typical environmental elements and analyzes the environmental perception technologies used in unmanned ground agricultural machinery during field navigation and operation. First, the working principles, advantages, limitations, and application scenarios of commonly used sensors, including vision and radar sensors, are comprehensively reviewed. In addition, the critical role of multi-sensor fusion in enhancing perception robustness and adaptability is highlighted. Subsequently, this paper centers on the specific environmental elements encountered by unmanned ground agricultural machinery. From this perspective, existing perception methods are systematically categorized and reviewed across three domains: image data, point cloud data, and multimodal data fusion. The performance differences and applicable scenarios of these methods in practical applications are also analyzed. Finally, the current challenges facing environmental perception technologies for unmanned agricultural machinery are analyzed, including multi-sensor fusion complexity, the computational–real-time trade-off, and the scarcity of specialized datasets. Future development trends and potential research directions are also discussed. This review aims to provide a reference and foundation for advancing environmental perception technologies in unmanned ground agricultural machinery. Full article

The efficiency of airport resource allocation is improved through the establishment of a scientific multi-granularity configuration scheme for flight slot coordination parameters. In this study, a collaborative configuration method for hourly and 15 min coordination parameters is proposed, with Beijing Capital International Airport serving as a case study. Short-term traffic clusters are frequently omitted by traditional hourly parameters, thereby leading to sudden delay surges. First, local delays were extracted from March 2024 Automatic Dependent Surveillance-Broadcast (ADS-B) trajectory data. Subsequently, a delay prediction model was constructed through the integration of a non-stationary queuing model and a gradient boosting regression tree. Second, simulated timetables were generated via a Monte Carlo method under various parameter combinations. With a constant daily flight volume utilized as the experimental baseline, a mapping relationship was established between parameter combinations and expected local delays. Finally, feasible delay regions were delineated and interpretable configuration rules were extracted via a decision tree to maximize schedule flexibility. It was indicated by the results that at an hourly parameter of 70 flights, the target delay is maintained below 8 min by tightening the 15 min parameter to 19 flights. The findings suggest that average load is controlled by hourly parameters, while traffic clustering in high-load scenarios is effectively suppressed by 15 min parameters. A quantitative reference is provided by this method for the configuration of multi-granularity time parameters at hub airports. Full article

Alfalfa Verticillium wilt, caused by Verticillium alfalfae, is a globally significant disease with increasing incidence and expanding epidemic areas. This study surveyed six major alfalfa-producing regions in Inner Mongolia, China—Chifeng, Tongliao, Ulanqab, Ordos, Bayannur, and Hohhot—and successfully isolated V. alfalfae exclusively from samples collected in Hohhot and Bayannur. Based on morphological characterization, multi-locus phylogenetic analysis (act, tef1-α, gapdh, and ts genes), and pathogenicity tests fulfilling Koch’s postulates, all 33 isolates were consistently identified as V. alfalfae, with disease severity levels ranging from 3.04 to 4.79 on the susceptible cultivar Zhongmu No. 1. As a preliminary assessment, the in vitro sensitivity of a representative strain, Va8, to eight commercial fungicides was evaluated using the mycelial growth inhibition method. Among the tested fungicides, 30% difenoconazole–propiconazole exhibited the strongest inhibitory effect (EC₅₀ = 0.14 μg/mL), followed by 10% trifloxystrobin & 20% tebuconazole (EC₅₀ = 0.20 μg/mL). However, given the substantial virulence variation observed among isolates, these sensitivity data should be interpreted with caution, as population-level differences may exist. These findings represent the first confirmed report of V. alfalfae in Inner Mongolia and provide a preliminary yet critical reference for prioritizing candidate fungicides for future multi-isolate and field evaluations. Full article

Objectives: This study evaluates the protocol-level image quality and quantitative diffusion metrics of a clinically implemented deep-learning–reconstructed readout-segmented echo-planar imaging protocol with water-excitation spectral fat saturation (DL-rs-EPI with WEXfs) compared with conventional rs-EPI using spectral attenuated inversion recovery (SPAIR) at 3 T. Methods: Overall, 80 patients underwent breast magnetic resonance imaging (MRI) with both conventional rs-EPI with SPAIR and DL-rs-EPI with WEXfs protocols (b-values: 0, 800, and 1200 s/mm²). ROI-based relative image-quality metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and lesion contrast, were assessed at b = 800 and b = 1200 s/mm²; apparent diffusion coefficient (ADC) values were calculated using multi-b-value data. Fat suppression, background diffusion signal, lesion conspicuity, and artifact severity were qualitatively evaluated. A temperature-controlled diffusion phantom (CaliberMRI) was scanned; ADC values were compared with reference values at 24 °C. Results: DL-rs-EPI with WEXfs demonstrated higher ROI-based relative SNR estimates (b800: 5.79 vs. 5.28; b1200: 5.41 vs. 4.94; p < 0.001) and CNR estimates (b800: 3.35 vs. 3.12, p = 0.024; b1200: 3.67 vs. 3.37, p = 0.001), with unchanged lesion contrast. Tumor ADC values were comparable between protocols, whereas normal fibroglandular tissue ADC values were slightly higher, and ADC contrast increased with DL-rs-EPI with WEXfs. Phantom ADC values from both protocols closely matched reference values at 24 °C, without significant differences. DL-rs-EPI with WEXfs demonstrated more homogeneous fat suppression and reduced background diffusion signal, with comparable lesion conspicuity and artifact severity. Conclusions: The combined DL-rs-EPI with WEXfs protocol demonstrated improved qualitative and relative quantitative image quality while preserving tumor ADC measurements. As a protocol-level evaluation, these composite improvements support its clinical feasibility for high-quality breast DWI without implying the isolated effect of DL reconstruction alone. Full article

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article

Reliable estimates of future precipitation are essential for adaptive water management in large river basins. This study presents a machine-learning approach that combines six CMIP6 models to examine precipitation changes in the Yangtze River Basin. ERA5 monthly precipitation for 1979–2025 served as the reanalysis reference. The random forest model incorporated individual model outputs, ensemble statistics, geographic variables, and monthly cyclic terms. It was trained with data from 1979–2009, evaluated for 2010–2014, and then applied to the period 2015–2099 under SSP1-2.6, SSP2-4.5, and SSP5-8.5. Compared with the simple multi-model mean, the proposed method showed better agreement with ERA5 and generally smaller reconstruction errors during the validation period. Annual precipitation is projected to increase under all three pathways, with the largest increase under SSP5-8.5. Precipitation remains concentrated from May to August, while spring totals and intra-annual variability increase more clearly under high-emission conditions. Mean precipitation remains highest in the humid middle and lower reaches, while the magnitude and significance of future trends vary across the basin. Inter-model spread remains greater than the differences among emission pathways and reaches 85.92 mm under SSP5-8.5 during 2071–2099. These results represent uncertainty-aware climate estimates rather than verified forecasts. They can support flood-risk assessment, reservoir planning, and adaptive water management in the Yangtze River Basin. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

Employee attrition is a significant organizational challenge associated with substantial financial costs and the erosion of institutional knowledge. This study presents an AI-based Management Information System (MIS) that integrates machine learning (ML) models to forecast employee turnover and support technical interpretability for HR decision-making. Using the IBM HR Analytics Dataset comprising 1480 employee records with 38 features, we implemented a rigorous preprocessing pipeline—including Synthetic Minority Over-sampling Technique (SMOTE) applied exclusively within training folds to prevent data leakage, one-hot encoding, Z-score normalization, and mean-value imputation. Four ML classifiers—Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost—were evaluated under a stratified 80/20 split with 5-fold cross-validation. XGBoost achieved the highest performance, attaining an accuracy of 87.83%, a ROC-AUC of 0.94, a PR-AUC of 0.96, and an F1-score of 93.04%, attributed to its sequential boosting mechanism and built-in L1/L2 regularization. Beyond predictive performance, the system incorporates SHapley Additive exPlanations (SHAP) to deliver feature-level transparency, enabling HR professionals to engage in proactive, informed retention interventions while retaining full decision-making authority. Within-dataset comparisons confirm that the proposed framework outperforms prior methods evaluated on the same benchmark; cross-study accuracy comparisons are reported as contextual reference only, given differences in datasets and experimental protocols. The system facilitates human oversight by positioning AI as a decision-support collaborator rather than an autonomous replacement in workforce management. Future work will address real-time deployment, controlled user studies with HR practitioners, and validation with actual organizational HR data. Full article

Fusing spatial observations from sensors with heterogeneous error characteristics is a persistent challenge in geostatistics. Classical kriging assumes a Gaussian likelihood for all observations, an assumption that fails when sensors exhibit one-sided or asymmetric noise. We present a Variable Rank Kriging (VRK) formulation that supports per-observation heterogeneous likelihoods where each observation may define its own likelihood function, thus enabling principled fusion of sensors whose noise structures are significantly different in terms of distribution family and magnitude. Within this framework, we use the exponential (one-sided) likelihood as a case study to demonstrate the method and compare it with sampling-based numerical alternatives for general likelihoods without closed forms. A non-collocated RTK calibration workflow uses kriging predictions from a sparse high-accuracy reference to characterise sensor-specific likelihood parameters without requiring co-located paired observations. Synthetic 1-D and 2-D experiments show that correct per-point likelihood specification reduces RMSE by up to 92% (1-D) and 57% (2-D) relative to a misspecified Gaussian model while also eliminating systematic positive bias. A demonstration using NEON Airborne Observation Platform lidar data at Harvard Forest confirms these findings in a practical, real-world scenario. Across multiple subsamples of the lidar dataset, the exponential likelihood reduces vegetated-zone RMSE by $20.6\%$ (open zone: $18.6\%$) and mean absolute bias by $26.5\%$ relative to a heteroscedastic Gaussian baseline. The open-source vrk Python ($>=$3.10) package provides a reproducible implementation that can be applied to any spatial domain that requires multi-sensor spatial fusion with heterogeneous error structures. Full article

Emerging sustainability demands and calls for lowering materials’ environmental impact have directed authors to examine a class of polymers characterised as covalent adaptable networks and referred to as vitrimers. In this study, composite plates were made using vitrimer resin as the matrix material and continuous unidirectional flax fibre fabrics as the reinforcement. A specific early-stage composite part production method is proposed to make the multi-ply flax/vitrimer composite plate. The development of natural fibre–reinforced vitrimer composites is of clear research interest as a promising approach towards sustainable and recyclable novel material systems. Specimens prepared with all the plies oriented 0° exhibited a 129.4 MPa tensile strength and a 12.4 GPa tensile modulus, indicating a 334% increase in tensile strength when compared to the average value of 29.8 MPa obtained for neat vitrimer specimens and a 1140% improvement in the tensile modulus compared to the 1.0 GPa reached for neat vitrimer. The specimens whose plies were oriented 90° are found to deliver a tensile strength of 12.2 MPa and a 1.3 GPa tensile modulus. Applying the classical composite material micromechanics equation to calculate the 0°-direction tensile modulus demonstrated a good agreement with the experimentally obtained value—a 9.6% difference was discovered. Proper fibre/matrix interfacial adhesion was detected when the flax/vitrimer specimens’ surfaces after fracture were examined under scanning electron microscope. The research findings on tensile mechanical properties reveal that the observed flax/vitrimer composites may be potential candidates for replacing typical synthetic fibre–reinforced materials rated for automotive applications and intended for in-plane loaded parts, particularly some inner-body vehicle elements. Full article

This paper investigates the problem of multi-target search by an Autonomous Underwater Vehicle (AUV) swarm in unknown three-dimensional (3D) underwater environments with obstacles under limited communication conditions. To address this problem, a distributed cooperative search framework is proposed. Within this framework, an adaptive dual-state search mechanism driven by a target response function is designed. This mechanism enables the swarm to transition between independent large-scale roaming search and precise cooperative search. On this basis, a multi-target search method is developed by integrating a virtual force model, motion-constrained 3D Particle Swarm Optimization (PSO), and a sectional 3D tangent-plane obstacle-avoidance method. Simulation results demonstrate the effectiveness and engineering feasibility of the proposed framework. Under the conditions of unknown terrains and communication limits, the AUV swarm can adaptively execute state transitions, safely avoid 3D obstacles, and complete multi-target search tasks. Specifically, as the swarm size increases from 30 to 60 AUVs, the mean number of iterations drops from 432.97 to 269.73, while the total energy consumption expectedly rises from 11.79 × 10⁴ to 15.51 × 10⁴, reflecting a well-balanced trade-off between efficiency and cost. This study provides a practical distributed control reference for AUV swarms in complex communication-constrained underwater scenarios. Full article

Plot combine harvesters are specialized machines used in breeding trials, germplasm evaluation, and small-batch seed harvesting. Compared with conventional field combine harvesters, they have higher requirements for sample independence, grain integrity, seed purity, low residual grain, rapid plot switching, and plot-level data reliability. However, existing studies remain relatively fragmented, and many studies mainly focus on individual components, whereas analyses of whole-machine coordination, residual-grain control, crop adaptability, and data integration remain insufficient. This paper presents a structured review of the research progress in plot combine harvesters from an agricultural-engineering perspective, covering representative international and domestic models, headers, threshing and separation systems, cleaning systems, residual-seed removal devices, simulation methods, intelligent monitoring, and seed-quality sensing. Existing evidence indicates that plot combine harvesters are developing toward whole-machine low-residue design, coordinated threshing–cleaning–conveying optimization, standardized evaluation methods, sample identification, data traceability, and long-term field validation under continuous multi-plot harvesting conditions. Key challenges include coordinating small-batch intermittent material flow, controlling residual grain during frequent plot switching, balancing threshing completeness with seed protection, improving adaptability to different crops and breeding materials, and validating intelligent sensing technologies under field conditions. This paper provides an engineering reference for improving the mechanization, precision, and intelligence of breeding-trial harvesting equipment. Full article

With the advancement of systematic ecological protection and restoration, ecosystem coordination assessment and multi-scale differentiation analysis have become increasingly important for regional ecological governance. In this context, this study develops a multi-scale coupling coordination evaluation framework for the mountain–water–forest–farmland–lake (MWFFL) system in Dangtu County, Anhui Province. The framework integrates 14 indicators across five subsystems, uses a combined weighting method based on the Entropy Weight Method and Analytic Hierarchy Process, and applies the coupling coordination degree (CCD) model and trend analysis to characterize inter-system coordination and its spatiotemporal patterns at the regional and ecosystem scales. The results indicate that land use is dominated by arable land, with water bodies forming the structural backbone and construction land distributed in clusters. From 2020 to 2024, the mean CCD remained stable around 0.675, indicating that the overall coupling coordination level was relatively stable. Spatially, the CCD pattern remained higher in the southwest and lower in the northwest, with a new high-value clustering zone emerging in the south. At the ecosystem scale, the four ecological restoration units showed distinct spatiotemporal patterns of coupling coordination. This multi-scale MWFFL evaluation framework supports regional ecological monitoring and provides a reference for restoration effectiveness assessment in similar regions under the life community concept. Full article

This study addresses the stratified and turbulent tropospheric delays that impede interferometric synthetic aperture radar (InSAR) deformation monitoring in alpine canyon regions. We introduce a tropospheric delay model that incorporates time-varying Gaussian coefficients and coupled zenith wet delay (ZWD) by combining diverse multi-source data. This model was incorporated into StaMPS for InSAR processing. Evaluation results demonstrated that (1) the model accurately captured seasonal and diurnal tropospheric variations, achieving a root mean squared error (RMSE) of 2.01 cm relative to the GNSS reference data; (2) the model corrected stratified and turbulent delays and reduced interferometric phase standard deviation (STD) by 9.28% compared to the Generic Atmospheric Correction Online Service (GACOS); and (3) the deformation accuracy improved by 19.07% over GACOS. Discussion results indicate that accounting for time-varying Gaussian coefficients is essential and that coupling ZWD to rectify turbulent delays outperformed the filtering method. The observed negative interferogram corrections result from the random intensity of turbulent delays. These findings confirm the effectiveness of the proposed model for high-precision InSAR deformation monitoring in complex alpine terrains. The proposed model aims to enhance studies of tropospheric delay variations in alpine canyon regions and to mitigate such delays in InSAR-based geological hazard monitoring. Full article