Search Results (2,632)

This paper investigates weak fault feature extraction in AUV thrusters under multi-input signal conditions. Conventional methods often rely on insufficient input signals, leading to a non-monotonic mapping between fault features and fault severity. This, in turn, makes accurate fault severity identification infeasible. To overcome this limitation, this paper increases the number of input signals by utilizing all available measurable signals. To address the problems arising from the expanded signal set, a signal denoising method that combines Feature Mode Decomposition and wavelet denoising is proposed. Furthermore, a signal enhancement technique that integrates energy operators and the Modified Bayes method. Additionally, distinct technical approaches for noise reduction and enhancement are specifically designed for different input signals. Unlike conventional methods that extract features directly from raw input signals, for fault feature extraction and fusion, this study transforms the signals into the time, frequency, and time–frequency domains, extracting diverse fault features across these domains. A sensitivity factor selection method is then employed to identify the sensitive features. These selected features are subsequently fused using Dempster–Shafer evidence theory to construct the final fault feature. Finally, fault severity identification is carried out using the classical grey relational analysis. Pool experiments using the “Beaver II” AUV prototype validate the effectiveness of the proposed method. Full article

Zirconia is used widely for high-precision custom abutments; however, stress concentration can compromise osseointegration. Although glass–ceramic spray deposition (GCSD) can enhance the surface properties of zirconia, its biological effects remain unclear. In this study, the biological responses of human osteoblast-like (MG-63) cells to GCSD-modified zirconia surfaces were evaluated to assess the potential application in zirconia abutments. Disk-shaped zirconia and titanium alloy samples were prepared; titanium served as the control (Ti). Zirconia was subjected to polishing (NT), airborne-particle abrasion (AB), or GCSD with (GE) or without (GC) hydrofluoric acid (HF) etching. Surface characteristics, including wettability, surface energy (SE), and surface potential (SP), were analyzed. Cytotoxicity and MG-63 cell adhesion were assessed using the PrestoBlue assay, scanning electron microscopy (SEM), viability staining, and confocal laser scanning microscopy (CLSM). Statistical analysis was performed with a significance level of 0.05. GCSD produced a dense glass–ceramic coating on the zirconia surface, which significantly enhanced hydrophilicity as indicated by reduced water contact angles and increased SE in the GC and GE groups (p < 0.05). HF etching increased SP (p < 0.05). No cytotoxicity was observed in any group. SEM, viability staining, and CLSM revealed enhanced MG-63 cell attachment on Ti and GE surfaces and the highest viability ratio in the GE group. The NT group exhibited the lowest cell attachment and viability at all time points. GCSD effectively improved zirconia abutment surface properties by enhancing hydrophilicity and promoting MG-63 cell adhesion, with biocompatibility comparable to or better than that of titanium. Full article

The highway and bridge industry is facing persistent challenges related to the high turnover of technical personnel, which poses risks to the continuity and sustainability of infrastructure development. Although Geographic Information System (GIS) training has increasingly been advocated as a strategy to stabilize the workforce, its practical application remains relatively limited across China. Drawing on the Conservation of Resources (COR) theory, this study examines whether GIS training is associated with lower turnover intention among technical staff, potentially through enhanced perceptions of career growth and reduced work-related anxiety. Based on 412 valid responses—primarily from technical personnel employed by major infrastructure enterprises such as regional subsidiaries of the China Communications Construction Group (CCCG) and China State Construction Engineering Corporation (CSCEC)—the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the proposed relationships. The findings indicate that GIS training is negatively associated with turnover intention, with career growth partially mediating this association. Additionally, work anxiety moderates the relationship, such that the link between GIS training and turnover intention appears weaker under higher levels of anxiety. This research contributes to bridging the gap between training practices and theoretical understanding, offering insights to inform workforce retention strategies in technology-intensive industries. Full article

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being. Full article

Accelerated industrialization and surging energy demands have led to continuously rising atmospheric CO₂ concentrations. Developing sustainable methods to reduce atmospheric CO₂ levels is crucial for achieving carbon neutrality. Concurrently, the rapid development of new energy vehicles has driven a significant increase in demand for lithium-ion batteries (LIBs), which are now approaching an end-of-life peak. Efficient recycling of valuable metals from spent LIBs represents a critical challenge. This study employs conventional hydrometallurgical processing to recover valuable metals from spent LIBs. Subsequently, Ni-doped Co₃O₄ (Ni:Co₃O₄) supported on the natural mineral attapulgite (ATP) was synthesized via a sol–gel method. The incorporation of a small amount of Ni into the Co₃O₄ lattice generates oxygen vacancies, inducing a localized surface plasmon resonance (LSPR) effect, which significantly enhances charge carrier transport and separation efficiency. During the photocatalytic reduction of CO₂, the primary product CO generated by the Ni:Co₃O₄/ATP composite achieved a high production rate of 30.1 μmol·g⁻¹·h⁻¹. Furthermore, the composite maintains robust catalytic activity even after five consecutive reaction cycles. Full article

Pardosa pseudoannulata plays an important role in the biological control of insect pests. The inclusion of yeast in the culture medium is very important for the growth, development, and reproduction of Drosophila melanogaster, but there have been few studies on the influence of nutrients in the culture medium on spider development. In order to explore the effects of different yeast treatments on the growth and development of D. melanogaster and as a predator, P.  pseudoannulata, three treatments (no yeast, active yeast added, and inactivated yeast added) were adopted to modify the conventional D. melanogaster culture medium. The addition of yeast to the medium shortened the development time from larva to pupation in D. melanogaster. The emergence and larval developmental times of D. melanogaster reared with activated yeast were shorter than those of the group without yeast addition, which promoted D. melanogaster emergence and increased body weight. The addition of yeast to the medium increased the fat, protein, and glucose content in D. melanogaster. The addition of activated yeast shortened the developmental time of P.  pseudoannulata at the second instar stage but had no effect on other instars. Different yeast treat-ments in the medium had no effect on the body length or body weight of P.  pseudoannulata. Adding yeast to D. melanogaster culture medium can increase the total fat content in P.  pseudoannulata, but it has no effect on glucose and total protein in P.  pseudoannulata. Our study shows the importance of yeast to the growth and development of fruit flies. Full article

Cinnamic acid, an organic acid naturally occurring in plants of the Cinnamomum genus, has been highly valued for its medicinal properties in numerous ancient Chinese texts. This article reviews the chemical composition, pharmacological effects, and various applications of cinnamic acid and its derivatives reported in publications from 2016 to 2025, and anticipates their potential in medical and industrial fields. This review evaluates studies in major scientific databases, including Web of Science, PubMed, and ScienceDirect, to ensure a comprehensive analysis of the therapeutic potential of cinnamic acid. Through systematic integration of existing knowledge, it has been revealed that cinnamic acid has a wide range of pharmacological activities, including anti-tumor, antibacterial, anti-inflammatory, antidepressant and hypoglycemic effects. Additionally, it has been shown to be effective against a variety of pathogens such as Staphylococcus aureus, Pseudomonas aeruginosa, and foodborne Pseudomonas. Cinnamic acid acts by disrupting cell membranes, inhibiting ATPase activity, and preventing biofilm formation, thereby demonstrating its ability to act as a natural antimicrobial agent. Its anti-inflammatory properties are demonstrated by improving oxidative stress and reducing inflammatory cell infiltration. Furthermore, cinnamic acid enhances metabolic health by improving glucose uptake and insulin sensitivity, showing promising results in improving metabolic health in patients with diabetes and its complications. This systematic approach highlights the need for further investigation of the mechanisms and safety of cinnamic acid to substantiate its use as a basis for new drug development. Particularly in the context of increasing antibiotic resistance and the search for sustainable, effective medical treatments, the study of cinnamic acid is notably significant and innovative. Full article

Mineral imbalances in livestock can critically impair growth, immunity, and productivity. Yaks inhabiting the Qinghai–Tibetan Plateau face unique environmental challenges, including high-altitude-induced nutrient variability. This study investigated the status of mineral elements and their correlations with altitude in healthy yak calves across five regions in Ganzi Prefecture, located at elevations ranging from 3100 to 4100 m. Hair and serum samples from 35 calves were analyzed for 11 essential elements (Na, K, Ca, Mg, S, Cu, Fe, Mn, Zn, Co, and Se). The results revealed widespread deficiencies. Key deficiencies were identified: hair Na and Co were significantly below references value (p < 0.05), and Se was consistently deficient across all regions, with deficiency rates ranging from 35.73% to 56.57%. Serum Mg and Cu were generally deficient (Mg deficiency > 26% above 3800 m). S, Mn (low detection), and Co were also suboptimal. Serum selenium deficiency was notably severe in lower-altitude areas (≤59.07%). Significant correlations with altitude were observed: hair sodium levels decreased with increasing altitude (r = −0.72), while hair manganese (r = 0.88) and cobalt (r = 0.65) levels increased. Serum magnesium deficiency became more pronounced at higher elevations (r = 0.58), whereas selenium deficiency in serum was more severe at lower altitudes (r = −0.61). These findings indicate prevalent multi-element deficiencies in yak calves that are closely linked to altitude and are potentially influenced by soil mineral composition and feeding practices, as suggested by previous studies. The study underscores the urgent need for region-specific nutritional standards and altitude-adapted mineral supplementation strategies to support optimal yak health and development. Full article

Understanding the differential impacts of emission sources of volatile organic compounds (VOCs) on formaldehyde (HCHO) levels is pivotal to effectively mitigating key photochemical radical precursors, thereby enhancing the regulation of atmospheric oxidation capacity (AOC) and ozone formation. This investigation systematically selected and analyzed year-long VOC measurements across three urban zones in Shenzhen, China. Photochemical age correction methods were implemented to develop the initial concentrations of VOCs before source apportionment; then Positive Matrix Factorization (PMF) modeling resolved six primary sources: solvent usage (28.6–47.9%), vehicle exhaust (24.2–31.2%), biogenic emission (13.8–18.1%), natural gas (8.5–16.3%), gasoline evaporation (3.2–8.9%), and biomass burning (0.3–2.4%). A machine learning (ML) framework incorporating Shapley Additive Explanations (SHAP) was subsequently applied to evaluate the influence of six emission sources on HCHO concentrations while accounting for reaction time adjustments. This machine learning-driven nonlinear analysis demonstrated that vehicle exhaust nearly always emerged as the primary anthropogenic contributor in diverse functional zones and different seasons, with gasoline evaporation as another key contributor, while the traditional reactivity metric method, ozone formation potential (OFP), tended to underestimate the role of the two sources. This study highlights the primacy of strengthening emission reduction of transportation sectors to mitigate HCHO pollution in megacities. Full article

The fall armyworm (FAW), Spodoptera frugiperda, is a highly polyphagous pest posing a major threat to Poaceae crops, particularly corn (Zea mays), in China. However, its ability to adapt to Job’s tears (Coix lacryma-jobi), an edible and medicinal Poaceae species, under continuous rearing remains insufficiently understood. In this study, FAW survival, development, and reproduction were assessed over eight generations on two cultivars of Job’s tears, ‘Cuiyi 1’ and ‘Puyi 6’. Feeding and oviposition preferences were also examined in FAW populations reared on ‘Puyi 6’ by offering corn and ‘Puyi 6’ as hosts. Sustained rearing for five to eight generations on both cultivars significantly increased population fitness, with greater improvements observed in the ‘Puyi 6’ group. FAW reared on corn or ‘Puyi 6’ for two generations exhibited strong preferences for corn, whereas those reared on ‘Puyi 6’ for five to eight generations showed no significant host preference. These findings suggest that transgenerational adaptation markedly improved FAW performance and acceptance of Job’s tears, underscoring the need for intensified monitoring of FAW dynamics during the cultivation of Job’s tears. Full article

Perfluorooctanoic acid (PFOA) and its replacement, GenX, are per- and polyfluoroalkyl substances (PFASs) widely used in industrial and consumer applications. Pregnant women are a vulnerable population to environmental pollutants. The maternal effects of GenX and PFOA exposure during pregnancy have not been fully elucidated. In this study, pregnant mice received daily oral doses of GenX (2 mg/kg/day), PFOA (1 mg/kg/day), or Milli-Q water (control) throughout gestation. Histopathological analyses revealed significant liver abnormalities in both exposure groups, including hepatocyte swelling, cellular disarray, eosinophilic degeneration, karyopyknosis, lipid vacuolation, and increased inflammatory responses. Through transcriptomics analyses, it was found that multiple metabolic and inflammatory pathways were enriched in both exposure groups. In the GenX group, overexpression of CYP4A, c-Myc, and Oatp2 proteins and decreased expression of EGFR and β-catenin in the liver suggested disruption of lipid and bile acid metabolism. In the PFOA group, significantly upregulated protein levels of NLRP3, GSDMD, caspase-1, IL-18, and IL-1β indicated hepatic pyroptosis. Despite these distinct pathways, both compounds triggered inflammatory cytokine release in the liver, consistent with the results of the transcriptomics analysis, suggesting shared mechanisms of inflammatory liver injury. Taken together, our findings provided novel insights into the hepatotoxicity mechanisms of GenX and PFOA exposure during pregnancy, underscoring the potential health risks associated with PFAS exposure. Full article

Previous research has demonstrated that the Train Wave Signature (TWS) method enables rapid calculation of pressure waves in straight tunnels. However, its application to subway tunnels with complex structural features remains insufficiently explored. This study proposes a generalized mathematical method integrating TWS with graph theory for the simulation of pressure wave generation, propagation, and reflection in complex tunnel systems. A computational program is implemented using this method for efficient simulation. The proposed method achieves high-accuracy prediction of pressure waves in tunnels with complex geometries compared with field measurements conducted in a high-speed subway tunnel with two shafts. We discuss the impact of iteration time intervals on the results and clarify the minimum time interval required for the calculation. Moreover, the sin-type definition of TWSs enhances the precision of pressure gradient prediction, and omitting low-amplitude pressure and reflected waves from the train can improve computational efficiency without compromising accuracy. This study advances the application of TWSs in tunnels with complex structures and provides a practical solution for aerodynamic analysis in high-speed subway tunnels, balancing accuracy with computational efficiency. Full article

Accurate and reliable multi-scene positioning remains a critical challenge in autonomous driving systems, as conventional fixed-noise fusion strategies struggle to handle the dynamic error characteristics of heterogeneous sensors in complex operational environments. This paper proposes a novel noise-adaptive fusion framework integrating Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) measurements. Our key innovation lies in developing a dual noise estimation model that synergizes priori weighting with posterior variance compensation. Specifically, we establish an a priori weighting model for satellite pseudorange errors based on elevation angles and signal-to-noise ratios (SNRs), complemented by a Helmert variance component estimation for posterior refinement. For INS error modeling, we derive a bias instability noise accumulation model through Allan variance analysis. These adaptive noise estimates dynamically update both process and observation noise covariance matrices in our Error-State Kalman Filter (ESKF) implementation, enabling real-time calibration of GNSS and INS contributions. Comprehensive field experiments demonstrate two key advantages: (1) The proposed noise estimation model achieves 37.7% higher accuracy in quantifying GNSS single-point positioning uncertainties compared to conventional elevation-based weighting; (2) in unstructured environments with intermittent signal outages, the fusion system maintains an average absolute trajectory error (ATE) of less than 0.6 m, outperforming state-of-the-art fixed-weight fusion methods by 36.71% in positioning consistency. These results validate the framework’s capability to autonomously balance sensor reliability under dynamic environmental conditions, significantly enhancing positioning robustness for autonomous vehicles. Full article

Background/Objectives: For aging men experiencing lower urinary tract symptoms (LUTS), bladder diaries (BD) and uroflowmetry (UFM) are commonly used non-invasive diagnostic tools. However, bladder diaries often suffer from subjectivity and incomplete data, while traditional hospital-based uroflowmetry lacks convenience and repeatability. Therefore, there is a growing need for a user-friendly, artificial intelligence (AI)-powered at-home uroflow monitoring solution. This study aims to develop a novel, vibration-based home uroflowmetry system capable of recognizing uroflow curve patterns and measuring voiding parameters, and to compare its performance with other existing home-based uroflowmetry methods. Methods: Seventy-six male participants, all of whom provided informed consent, underwent uroflowmetry to assess voiding symptoms. An accelerometer affixed to the uroflowmeter’s urine container captured vibration signals, which were used to calculate the root mean square (RMS) values and maximum amplitude (Mmax). Simultaneously, the uroflowmeter recorded standard voiding parameters and generated uroflow curves. These vibration signals were then analyzed using a convolutional neural network (CNN) to classify six distinct uroflow curve patterns, aiding in diagnostic evaluation. Results: Seventy-six participants’ voiding volume ranged from 50 mL to 690 mL (median [Q1, Q3]: 160 [70.00, 212.50] mL). The correlation analysis revealed positive correlations between the vibration signals and voiding parameters, including the voided volume and RMS (R = 0.768, p < 0.001), Qmax and Mmax (R = 0.684, p < 0.001), voiding time and signal time (R = 0.838, p < 0.001), time to Qmax and time to Mmax (R = 0.477, p < 0.001). AI pattern recognition demonstrated high accuracy with all three indicators (precision, recall, and F1 score) surpassing 0.97. Conclusions: This AI-assisted vibration-based home uroflowmetry enables accurate voiding parameter measurement and uroflow pattern recognition, showing high precision, recall, and F1-score. It might offer a convenient solution for continuous and subjective bladder monitoring outside clinical settings. Full article

Accurate monitoring of animal behaviors enables improved management in precision livestock farming (PLF), supporting critical applications including health assessment, estrus detection, parturition monitoring, and feed intake estimation. Although both contact and non-contact sensing modalities are utilized, wearable devices with embedded sensors (e.g., accelerometers, pressure sensors) offer unique advantages through continuous data streams that enhance behavioral traceability. Focusing specifically on contact sensing techniques, this review examines sensor characteristics and data acquisition challenges, methodologies for processing behavioral data and implementing identification algorithms, industrial applications enabled by recognition outcomes, and prevailing challenges with emerging research opportunities. Current behavior classification relies predominantly on traditional machine learning or deep learning approaches with high-frequency data acquisition. The fundamental limitation restricting advancement in this field is the difficulty in maintaining high-fidelity recognition performance at reduced acquisition rates, particularly for integrated multi-behavior identification. Considering that the computational demands and limited adaptability to complex field environments remain significant constraints, Tiny Machine Learning (Tiny ML) could present opportunities to guide future research toward practical, scalable behavioral monitoring solutions. In addition, algorithm development for functional applications post behavior recognition may represent a critical future research direction. Full article