Search Results (5,914)

Rotating machinery is essential to energy, aerospace, manufacturing, and other safety-critical industries. Although deep-learning-based fault diagnosis has achieved high accuracy, its black-box nature limits trust, verification, and industrial deployment. This review examines explainable artificial intelligence for rotating machinery fault diagnosis and classifies existing methods into ante hoc and post hoc approaches according to their integration with model architectures. Their physical interpretability, applicable fault scenarios, explanation quality, computational overhead, robustness, and edge-deployment potential are critically compared. Quantitative criteria, including fidelity, stability, robustness, localization, and physical consistency are discussed to support objective evaluation of explanations. The review further highlights the gap between laboratory validation and industrial operation, particularly under sensor degradation, electromagnetic interference, variable working conditions, limited computing resources, and scarce fault data. It also discusses how model-relative explanations can be mapped to calibrated vibration quantities, fault-characteristic frequencies, industrial diagnostic standards, and actionable maintenance decisions. The distinction between correlation-based attribution and causal root-cause analysis is clarified, together with the role of digital twins and human-in-the-loop decision support. Finally, future research priorities are identified in standardized benchmarking, robust lightweight models, causal reasoning, and human-centered industrial deployment. Full article

Background: In Northeast China’s cold rice (Mollisol) regions, low temperatures slow straw decomposition, causing early microbial nitrogen (N) immobilization that competes with crop demand. Delayed N release from slow-release fertilizer (SRF) exacerbates this deficit, hindering high yield and grain quality. Methods: A two-year (2024–2025) pool planting experiment was conducted on rice ‘Jinongda 667’ with a total N application rate of 150 kg ha⁻¹. Six treatments were established: a 7:3 blend of slow-release fertilizer and urea, alongside controls of conventional urea and slow-release fertilizer alone, under both straw removal and incorporation conditions. Results: This blend increased grain yield by 6.64–7.75% over conventional urea, achieving the highest yield among all treatments, whereas SRF alone under straw incorporation reduced yield by 7.57% relative to N + S. The 30% urea (45 kg N ha⁻¹) alleviated immobilization deficit (15–30 kg N ha⁻¹) during the tillering-to-jointing stage, promoting root growth and panicle formation, while 70% SRF sustained N supply during mid-to-late stages, delaying senescence and enhancing photosynthesis. At peak grain filling, SSS and SBE activities increased by 94.01% and 10.53–11.08%, respectively. Under straw incorporation, it reduced chalky grain rate by 4.56–35.55% and chalkiness by 1.77–41.86%, increased protein content by 0.47–6.95% and gel consistency by 43.59–52.99%, decreased amylose by 2.2–5.77%, and optimized RVA profiles. Conclusions: The 7:3 blend synchronizes N supply with crop demand through temporal complementarity with straw nutrient dynamics, offering a one-time fertilization strategy for achieving high yield and quality under straw incorporation. However, given inter-annual variation (2024–2025) in tillering and N accumulation, its performance may be sensitive to climatic fluctuations, warranting further validation under diverse conditions. Full article

Positioning jumps are a primary cause of trajectory discontinuities in urban canyon environments, severely hindering the widespread adoption of autonomous vehicles. This paper proposes a CNN-GRU fusion-based method for correcting such positioning jumps. A complete 15-dimensional error-state extended Kalman filter (EKF) framework is established to analyze the jump generation mechanisms from three perspectives—pseudorange distortion, inertial drift, and filter gain divergence—thereby justifying the use of inertial measurement unit (IMU) time-series data for anomaly prediction. An end-to-end mapping model is further constructed, in which a one-dimensional convolutional neural network (CNN) extracts cross-channel spatial features from multi-axis inertial data, while a gated recurrent unit (GRU) captures long-term temporal error evolution. A hysteresis navigation quality factor and a piecewise Huber loss function are incorporated to enable hierarchical adaptive optimization. Experimental results demonstrate that the proposed method reduces the positioning root mean square error (RMSE) from 1.24 m to 0.70 m, achieves a jump suppression rate of 43.5%, and maintains a single-frame inference latency of 11.8 ms, meeting the real-time requirements for future autonomous driving localization. Full article

Chicory root has been used in traditional medicine, as a vegetable, and as a coffee substitute, and over the years it has become popular for its inulin content, becoming one of the major commercial sources of inulin for food applications. This review is intended to update knowledge in the field of chicory root inulin application and commercial inulin, as its use has been on the rise over the past decade. The techno-functional behavior of inulin in foods is primarily related to its degree of polymerization, and it can be used in various products such as dairy, meat, bakery products, and confectionery. In addition to providing basic information about the structure and properties of inulin, this review explores chicory root and commercial inulin’s application in the aforementioned categories of food products by suggesting the appropriate amount of added inulin to achieve the desired properties, the form in which it is used, its functional role, key processing conditions, and its limitations. Inulin can be used to improve the nutritional value of foods, modify texture, replace fat and sugar, and enhance probiotic viability; however, its impact on sensory characteristics cannot be neglected. Full article

Background/Objectives: Multiple chronic conditions (MCCs) are highly prevalent among older adults and require effective collaboration between the patient and caregiver. Mutuality, reflecting the quality of the dyadic relationship, is associated with better self-care and health outcomes. However, the Mutuality Scale (MS) has not been validated on patient–caregiver dyads managing MCCs living in a low–middle-income country (LMIC). Aim: This study seeks to evaluate the structural and convergent validity and reliability of the MS among patient–caregiver dyads managing MCCs living in a LMIC. Methods: A cross-sectional study was conducted on MCC patients and their caregiver recruited from community and outpatient settings. The MS, Self-care of Chronic Illness Inventory (SC-CII) and Caregiver Contribution to self-care Inventory (CC-SCCII) were used for measuring mutuality, patient self-care, and Caregiver Contribution (CC) to patient self-care, respectively. Confirmatory factor analysis (CFA) was performed separately for patients and caregivers to evaluate the original four-factor structure of the MS. Convergent validity was examined through correlations with self-care and CC to patient self-care. Reliability was evaluated using composite reliability and the Global Reliability Index for multidimensional scale. Results: A sample of 406 patient–caregiver dyads was examined. Patients had a mean age of 73.9 (±6.2) years. Caregivers had a mean age of 47.8 (±15.5) years. The four-factor structure was supported in both samples, with acceptable model fit (patients: Comparative Fit Index (CFI) = 0.953 and Root Mean Square Error of Approximation (RMSEA) = 0.078; caregiver CFI = 0.945 and RMSEA = 0.085. The second-order CFA supported a hierarchical structure. Patient and caregiver mutuality scores were strongly correlated (r = 0.778, p < 0.01). Higher mutuality was associated with better patient self-care (r = 0.276–0.479) and CC to self-care (r = 0.174–0.556). Reliability indices ranged from 0.70 to 0.91 for patients and 0.66 to 0.89 for caregivers. Conclusions: The findings support the validity and reliability of the MS for assessing mutuality in patients and their caregivers managing MCCs in an LMIC characterized by limited healthcare resources and formal support. Its use provides empirical support for assessing relationship quality and facilitating dyadic care within this vulnerable population. Future longitudinal studies should evaluate its predictive validity, responsiveness, and measurement invariance across different groups and between patient and caregiver. Full article

Loneliness is a growing public health concern among older adults, underscoring the need to better understand the complex interplay of factors associated with it. Although technology can enhance social connectedness, limited attention has been given to discrepancies between desired and actual technology use in daily life. This study examined loneliness, focusing on perceived technology-use gaps alongside health-related factors, including depressive symptoms, sleep quality, functional cognition, and sensorimotor abilities. Participants included 211 adults aged 65 to 87, who completed the de Jong-Gierveld Loneliness Scale, Geriatric Depression Scale, Pittsburgh Sleep Quality Index, Daily Living Questionnaire, selected items from the Washington Group Short Set on Functioning-Enhanced, and the Daily Technology-Use Gap questionnaire. Spearman correlations and structural equation modeling (SEM) were conducted. Loneliness was significantly associated with all variables (r = −0.64 to −0.32, p < 0.01–0.001). The SEM demonstrated good fit (comparative fit index = 0.986, root mean square error of approximation = 0.068). Depressive symptoms were significantly associated with loneliness, whereas technology-use gaps were identified as significant mediators in the proposed SEM, linking health-related factors and loneliness. These findings highlight a shift from focusing on technology access to understanding the quality and satisfaction of technology use in daily life. Full article

Flammulina velutipes roots, abundant edible mushroom by-products, have potential for value-added utilization. This study evaluated the effects of Lactiplantibacillus plantarum (Lpb. plantarum) and Lactobacillus delbrueckii subsp. bulgaricus (Lab. bulgaricus) fermentation on the physicochemical properties, antioxidant activity, flavor characteristics, sensory characteristics, and metabolite profiles of F. velutipes roots. Fermentation significantly increased soluble dietary fiber (SDF) from 1.537 to 1.722 g/100 g and DPPH radical scavenging activity from 27.88% to 59.92% in Lpb. plantarum-treated samples. GC-IMS analysis showed that fermentation increased several flavor-active aldehydes, esters, alcohols, organic acids, and ketones, thereby improving aroma complexity. LC-MS-based untargeted metabolomics revealed that amino acid metabolism, fatty acid metabolism, organic acid metabolism, and phenylpropanoid-related pathways were closely associated with flavor formation and antioxidant enhancement. Both Lpb. Plantarum and Lab. bulgaricus fermentation enhanced the accumulation of organic acids, sugar alcohols, and aromatic precursors, supporting improved antioxidant activity. Notably, Lpb. plantarum elicited broader metabolic shifts involving hydroxy fatty acids, esters, while Lab. bulgaricus more strongly promoted organic acid and aromatic metabolite accumulation, contributing to a more acidic and malty flavor profile. Sensory evaluation and electronic tongue analysis further confirmed that fermentation enhanced aroma and palatability, while increasing sourness and reducing bitterness and astringency. These results suggest that starter-culture-dependent lactic acid bacteria fermentation is an effective strategy for improving the functional and sensory characteristics of F. velutipes root by-products. Full article

Cultivated grasslands provide an important strategy for reducing pressure on degraded natural grasslands and increasing high-quality forage supply. However, how stand-age-related variation affects belowground–aboveground coordination and forage production in cultivated Leymus chinensis remains unclear. Here, we compared a natural L. chinensis stand (NL) with 3-, 4-, and 5-year-old cultivated ‘Zhongke No. 1’ stands by integrating plant trait measurements, biomass assessment, nutrient analysis, forage quality evaluation, correlation analysis, and structural equation modeling. (1) Compared with NL, cultivated stands generally showed greater vegetative growth and aboveground biomass, lower fiber concentrations, and higher soluble sugar concentration and relative feed value. (2) Root and aboveground traits exhibited distinct stand-age-related patterns, with the 3Y stand showing greater root exploration traits, the 4Y stand achieving the highest aboveground biomass (4.61 t/hm²), and the 5Y stand exhibiting greater root thickening and ramet density. (3) Carbon, nitrogen, and phosphorus concentrations varied among stand ages, indicating shifts in nutrient status during stand development. (4) Correlation analysis and PLS-SEM revealed that root architecture, nutrient status, and aboveground morphology were closely associated with forage yield and quality variation. These findings highlight the importance of stand-age-dependent belowground–aboveground coordination for optimizing the management and utilization of cultivated L. chinensis grasslands. Full article

This study presents a sensor-fused field investigation and simulation-based analysis of four horizontal and vertical traffic-calming devices—two raised speed tables, a speed hump, and a raised crosswalk—installed along a 5250-ft two-lane residential collector in Nashville, TN, USA. A dual-sensor architecture combining a Miovision Scout video-based vehicle counter and WAAS/EGNOS-augmented GPS probe-vehicle logging (5 m 3-D RMS horizontal accuracy, 1 Hz sampling) was used to reconstruct 30 quality-controlled free-flow vehicle trajectories and 12-h per-lane volume counts. A spatial kinematic transform (a = v·dv/dx) was applied to extract device-specific approach-deceleration and post-device recovery-acceleration rates, and a three-parameter log-logistic cumulative-distribution function was fitted to the field-observed desired-speed percentiles (root-mean-square error below 0.043 for both speed-table devices). The camera- and GPS-derived observations were used to calibrate and statistically validate a PTV VISSIM microsimulation replica of the corridor, achieving a mean-speed calibration error of 0.71% or better at every device, a GEH statistic below 1.5 at all four analysis turning movements, and independent travel-time validation errors of 5.7–12.1%, within the accepted 15% threshold. The validated model was then used to reconstruct device- and spacing-specific May–Keller macroscopic speed–density–flow relationships, calibrated against simulated capacities of 650–775 vehicles per hour per lane at 350-, 700-, and 1050-ft device spacing. Results show capacity reductions of 20–33% relative to free-flow conditions and yield kinematically derived maximum recommended spacings of 265–630 ft to maintain crossing speeds at or below 15 mph, depending on device geometry. The findings demonstrate a reproducible, low-cost sensor-fusion workflow for quantifying the safety–capacity trade-off of traffic-calming corridors and for informing the design of sensor-in-the-loop adaptive-calming infrastructure. Full article

Reliable spatial evidence can support quality assurance and quality control for land use, land-use change and forestry (LULUCF), but land-cover and species-distribution layers do not by themselves identify IPCC Forest Land or species-pure stands. We estimated 2010 and 2020 above- and below-ground living biomass carbon within tree-covered European beech (Fagus sylvatica L.) distribution and occurrence footprints in Greece. Our operational hypothesis was that increasingly restrictive species masks would materially alter the mapped extent and carbon estimates. ESA Climate Change Initiative Biomass v6, ESA WorldCover 2021, European Forest Genetic Resources Programme (EUFORGEN) polygons, and Forest Information System for Europe (FISE) relative probability of presence layers were processed in Google Earth Engine. Biomass was converted with IPCC default carbon fractions and root:shoot ratios, and the results were summarized nationally and for GAUL Level-2 units. The broad EUFORGEN footprint covered 22,133 km², whereas the Combined overlap of EUFORGEN, FISE relative probability of presence ≥ 0.50, and tree cover covered 2742 km². Within the Combined footprint, the pixel mean living biomass carbon density was 60.33 Mg C ha⁻¹ in 2010 and 62.50 Mg C ha⁻¹ in 2020, and the area-integrated change was +0.58 Tg C; the area-normalized regional change was positive in 13 of 17 units and negative in 4. Across masks, the mean decadal change ranged from −0.50 to +3.37 Mg C ha⁻¹ and the approximate area-integrated totals from −1.10 to +0.58 Tg C. These scenario-conditioned estimates are neither official national greenhouse gas inventory estimates nor tests of statistical significance; instead, they provide reproducible spatial screening while making mask sensitivity and unpropagated uncertainty explicit. Full article

Cancer cachexia is a multifactorial systemic syndrome characterized by progressive muscle loss, with or without adipose tissue depletion, that cannot be reversed by conventional nutritional support. It affects cancer patients and is associated with reduced treatment tolerance, impaired physical function, poor quality of life, and increased mortality. The understanding of cachexia has evolved recently, from the perception of a simple nutritional disorder to a complex immune–metabolic syndrome, based on tumor–host interactions, systemic inflammation, metabolic dysregulation, and multi-organ dysfunction. This review summarizes the progression of cachexia research, highlighting key findings involving inflammatory cytokines, proteolytic pathways, mitochondrial dysfunction, and immune dysregulation. The development of therapeutic strategies is examined, from early nutritional and appetite-stimulating interventions to contemporary targeted therapies, including ghrelin receptor agonists, cytokine inhibitors, and anabolic agents. Despite advances in mechanistic understanding, numerous trials targeting single pathways have failed to produce meaningful functional or survival benefits, underscoring the limitations of reductionist approaches. Emerging evidence supports a paradigm shift toward multimodal, biomarker-guided, and patient-centered interventions that address the interconnected biological mechanisms underlying cachexia. Particular emphasis is given to novel immunomodulatory strategies, including agents such as R-ketorolac, which may restore immune homeostasis and target the root causes of cachexia. It is hypothesized that future therapeutic success will likely depend on integrated approaches combining immunological, metabolic, nutritional, and rehabilitative interventions. Full article

Small-scale and urban indoor farms typically rely on manual observation, which delays stress detection and yields inconsistent crop quality. While large language models (LLMs) and retrieval-augmented generation (RAG) have been explored for agricultural advisory systems, their integration into a single cloud-free indoor-farming platform that couples multimodal symptom interpretation with autonomous environmental control remains largely unexamined. This study presents an integrated platform built around an agentic AI advisory pipeline that runs entirely on-device on commodity hardware. The pipeline couples a RAG-grounded Mistral 7B language model with a LLaVA 7B vision-language model through condition-based routing, intent classification, multi-step reasoning, and an LLM validation gate, delivering context-aware text and image-based symptom-level guidance from a conversational interface. The advisory layer operates alongside vision-based plant monitoring and a deliberately isolated threshold-based control layer, in which an ESP32 microcontroller autonomously actuates irrigation and lighting against predefined thresholds while a Raspberry Pi 5 performs continuous plant detection and browning monitoring. On Cos lettuce, the advisory pipeline achieved 82.00% weighted accuracy across 50 queries spanning health, symptom, watering, pest, root-health, and growth-stage categories, scored against established plant pathology and postharvest references, with no incorrect responses recorded. The study contributes the design of an agentic advisory pipeline and its integration into a working, cloud-free indoor-farming platform, providing an on-device foundation for intelligent small-scale farming. Full article

The 4 km/15 min downward surface shortwave radiation (DSSR) product from Fengyun-4B (FY-4B)/AGRI shows great potential for solar energy assessment in China, but its applicability requires further validation. This study conducts a comprehensive evaluation of the FY-4B DSSR product over Guangxi for 2025, using ground-observed irradiance from a dense network of 101 photovoltaic (PV) power stations. The overall comparison shows a correlation coefficient (R) of 0.84, a root-mean-square error (RMSE) of 161.78 W/m², a relative prediction error (RPE) of 51.02%, and a mean bias error (MBE) of 56.23 W/m², indicating systematic overestimation. Seasonally, the largest discrepancies occur in spring (MBE = 85.27 W/m², RPE = 53.12%) and summer (R = 0.82, RMSE = 184.10 W/m²). Diurnally, retrievals are most reliable around 09:00–13:00 local time, deteriorating notably in the early morning and, especially, the afternoon and evening. Spatially, errors are larger in the hilly, elevated terrain of northwestern Guangxi (e.g., Hechi) than in flatter southern and coastal cities, with RPE rising from roughly 40–60% at lower elevations to around 80% above 600–700 m. Sky-condition classification confirms that data quality follows clear > cloudy > overcast sky, while AOD-binned analysis shows aerosol loading playing a secondary but non-negligible role, especially under high-AOD pollution events. Solar zenith angle (SZA) also strongly affects accuracy: R peaks around 0.75–0.8 in the 30–50° SZA range and drops below 0.4 beyond about 75°. This study offers the most spatially and dimensionally comprehensive validation of FY-4B DSSR over Guangxi to date, characterizing accuracy across seasonal, diurnal, spatial, cloud, aerosol, solar-geometry, and elevation dimensions using a denser ground-truth network than previously available, with direct implications for photovoltaic resource assessment and power forecasting in subtropical hilly regions. Full article

Multi-site water quality sequences are jointly affected by seasonal periodicity, meteorological disturbances, and inter-site differences in the Jianghuai Watershed region. Conventional quality prediction models struggle to simultaneously achieve multi-scale feature extraction, spatial heterogeneity characterization, and prediction uncertainty expression. This study used daily-scale monitoring data on dissolved oxygen (DO), pH, and ammonia nitrogen (NH3N) from 32 monitoring stations within the region in 2025 and proposed the FT-TransONet (Fourier-enhanced Temporal Transformer Operator Network) multi-site probabilistic water quality prediction model. Within a Transformer framework, the model employed a FourierTime learnable frequency-domain filtering module, a GeoBias (Geographic Bias) attention bias mechanism, and a multi-residual ensemble strategy composed of a multilayer perceptron (MLP), a gated recurrent unit (GRU), and a temporal convolutional network (TCN) combined with a mass conservation constraint, thereby achieving both point and interval prediction of key water quality indicators. The results showed that FT-TransONet achieved the lowest Macro_RMSE among all compared methods on the multi-site water quality prediction task. At a prediction horizon of three days, its Macro_RMSE reached 0.2255, which was 21.89% lower than that of the long short-term memory network and 5.57% lower than that of the strongest baseline MC-Dropout. For the three individual indicators, the model attained coefficients of determination of 0.9135, 0.9338, and 0.8660 for dissolved oxygen, pH, and ammonia nitrogen, with corresponding root-mean-square errors of 0.5145, 0.1098, and 0.0523, confirming its potential to characterize the temporal variation in the main water quality indicators. Under multi-step prediction, the error grew gently, with the Macro_RMSE rising only from 0.2255 to 0.2384 as the horizon extended from three to seven days, and the ablation experiments, together with the probabilistic prediction results, further supported the effectiveness of the proposed structural design. Validated on 32 water quality monitoring stations in the Jianghuai Watershed, the method improved multi-site prediction accuracy while accounting for stability and uncertainty quantification, providing a preliminary reference for regional water quality early warning and management. Full article

The core predicament of energy transition lies not in the availability of clean technologies, but in whether an economy possesses the institutional capacity and social foundation to systematically regulate its carbon-energy metabolic processes. Drawing upon co-evolutionary theory from evolutionary economics, this paper constructs a composite indicator of Low-Carbon Green Energy Eco-Co-evolution (LCEE) encompassing three functional dimensions: efficiency advancement, kinetic energy replacement, and boundary adherence. Concurrently, by integrating innovation ecosystem theory with inclusive development theory, we propose the concept of “Inclusive Potential of Regional Innovation Ecosystems” (IEP), characterizing the systemic potential for transforming innovation outcomes into social welfare across four dimensions: Knowledge Matrix Abundance (KMF), Cultural Capillary Permeation (CCP), Technological Community Succession (TCS), and Social Root Nourishment (SRN). Taking China’s 2016 intellectual property (IP) powerhouse construction pilot as the institutional prototype of Industrial Intellectual Property Upgrading Reform (IPR), we incorporate IPR, IEP, and LCEE into a unified causal analytical framework, proposing a testable transmission logic of ‘institutional supply → ecological development → co-evolutionary synergy. Using panel data from 30 Chinese provincial-level administrative regions over 2010–2022, we employ a Spatial Durbin Difference-in-Differences (SDM-DID) model to identify the direct and spatial spillover effects of IPR on LCEE, and embed a Double Machine Learning (DML) framework to test the mediating mechanism of IEP while controlling for high-dimensional nonlinear interference. The findings reveal that IPR exerts a significant and robust direct promoting effect on LCEE, generating positive spatial spillovers to neighboring regions through the public disclosure of patent information. IEP significantly promotes local LCEE, yet its spatial spillover lacks statistical support due to structural conflicts in inter-dimensional transmission attributes. IEP plays a significant partial mediating role between IPR and LCEE, with the indirect effect accounting for over one-third of the total effect, a finding robust to alternative machine learning algorithms, sample split adjustments, and exclusion of contemporaneous competing policies. Sub-path tests reveal that KMF bears the strongest mediating efficacy, serving as the primary transmission channel, while CCP exhibits full mediation—the institutional effect on LCEE in the cultural dimension depends almost entirely on the mediating transformation through the public cultural service system. Heterogeneity analysis further demonstrates full mediation in the Low-Carbon Green Energy Eco-Kinetic Replacement (KER) dimension, indicating that the institutional catalytic effect on clean energy substitution must be realized through IEP transformation. This paper provides empirical evidence for the proposed causal pathway through which institutional public goods indirectly enhance the synergistic quality of carbon-energy transition via the inclusive potential of innovation ecosystems, providing theoretical foundations and policy implications that, while grounded in China’s institutional context, may offer valuable reference points for emerging market economies facing similar dual pressures of technological constraints and green transition. Full article