Search Results (2,144)

Compared to traditional marine productivity, marine new-quality productivity (MNQP) is composed of advanced productive forces driven by the deepening application of new technologies, is characterized by the rapid emergence of new industries, new business models, and new modes of operation, and is marked by a substantial increase in total factor productivity in the marine economy. It has, therefore, become a new engine and pathway for China’s development into a maritime power. The main research approaches and conclusions of this paper are as follows: ① Using a combined order relation analysis method–Entropy Weight Method (G1-EWM) weighting method that integrates subjective and objective factors, we measured the development level of China’s MNQP from 2006 to 2021 across two dimensions: “factor structure” and “quality and efficiency”. The findings indicate that China’s MNQP is developing robustly and still holds considerable potential for improvement. ② Utilizing Gaussian Kernel Density Estimation and Spatial Markov Chain analysis to examine the dynamic evolution of China’s MNQP, the study identifies breaking the low-end lock-in of MNQP as crucial for accelerating balanced development. Spatial imbalances in China’s MNQP may exist both at the national level and within the three major marine economic zones. ③ To further examine potential spatial imbalances, Dagum Gini decomposition was employed to assess regional disparities in China’s MNQP. The DER polarization index and EGR polarization index were used to analyze spatial polarization levels, revealing an intensifying spatial imbalance in China’s MNQP. ④ Finally, geographic detectors were employed to identify the factors influencing spatial imbalances in China’s MNQP. Results indicate that these imbalances result from the combined effects of multiple factors, with marine economic development emerging as the core determinant exerting a dominant influence. The core conclusions of this study provide theoretical support and practical evidence for advancing the enhancement of China’s MNQP, thereby contributing to the realization of the goal of building a maritime power. Full article

Dairy cow body temperature is a key physiological indicator that reflects metabolic level, immune status, and environmental stress responses, and it has been widely used for early disease recognition. Infrared thermography (IRT), as a non-contact imaging technique capable of remotely acquiring the surface radiation temperature distribution of animals, is regarded as a powerful alternative to traditional temperature measurement methods. Under practical cowshed conditions, IRT images of dairy cows are easily affected by complex background interference and generally suffer from low resolution, poor contrast, indistinct boundaries, weak structural perception, and insufficient texture information, which lead to significant degradation in target detection and temperature extraction performance. To address these issues, a lightweight detection model named YOLOv11n-CGSD is proposed for dairy cow IRT images, aiming to improve the accuracy and robustness of region of interest (ROI) detection and body temperature extraction under complex background conditions. At the architectural level, a C3Ghost lightweight module based on the Ghost concept is first constructed to reduce redundant feature extraction while lowering computational cost and enhancing the network capability for preserving fine-grained features during feature propagation. Subsequently, a space-to-depth convolution module is introduced to perform spatial rearrangement of feature maps and achieve channel compression via non-strided convolution, thereby improving the sensitivity of the model to local temperature variations and structural details. Finally, a dynamic sampling mechanism is embedded in the neck of the network, where the upsampling and scale alignment processes are adaptively driven by feature content, enhancing the model response to boundary temperature changes and weak-texture regions. Experimental results indicate that the YOLOv11n-CGSD model can effectively shift attention from irrelevant background regions to ROI contour boundaries and increase attention coverage within the ROI. Under complex IRT conditions, the model achieves P, R, and mAP50 values of 89.11%, 86.80%, and 91.94%, which represent improvements of 3.11%, 5.14%, and 4.08%, respectively, compared with the baseline model. Using T_max as the temperature extraction parameter, the maximum error (Max. Error) and mean error (MAE. Error) in the lower udder region are reduced by 33.3% and 25.7%, respectively, while in the around the anus region, the Max. Error and MAE. Error are reduced by 87.5% and 95.0%, respectively. These findings demonstrate that, under complex backgrounds and low-quality IRT imaging conditions, the proposed model achieves lightweight and high-performance detection for both lower udder (LU) and around the anus (AA) regions and provides a methodological reference and technical support for non-contact body temperature measurement of dairy cows in practical cowshed production environments. Full article

The miniaturization of dielectric sensing has driven the development of both oscillator- and receiver-based sensors. Wide-frequency-range and low-power-consumption voltage-controlled oscillators (VCOs) are required as a reference clock for receiver-based dielectric spectroscopy. In this paper, we propose a switched coupled inductor VCO offering sufficiently wide bandwidth in a power-efficient manner. The proposed switched coupled inductor offers higher coupling factor and mutual inductance compared to direct switched inductor schemes along with a higher quality factor and tuning range. The proposed switched coupled inductor improved the frequency tuning range by 21% compared to the conventional VCO. The measurement results show that the proposed VCO oscillates from 4.7 to 8.8 GHz frequency, suitable for dielectric spectroscopy sensors. With only 4.5 mW power consumption, the proposed VCO can achieve −103.3 dBc/Hz phase noise at 1 MHz offset, with a resulting tuning range figure-of-merit (FOMT) of −187.4 dBc/Hz. Full article

This study addresses the ongoing challenge of accurately classifying beers by fermentation type and product category, an issue of growing importance for quality control, authenticity assessment, and product differentiation in the brewing sector. We applied a multiblock chemometric framework that integrates phenolic profiling obtained via GC–MS, antioxidant and antiradical activity derived from in vitro assays, and complementary colorimetric and physicochemical measurements. Principal Component Analysis (PCA) revealed clear compositional structuring within the dataset, with p-coumaric, gallic, syringic, and malic acids emerging as major contributors to variance. Supervised machine-learning classification demonstrated robust performance, achieving approximately 93% accuracy in discriminating top- from bottom-fermented beers, supported by a well-balanced confusion matrix (25 classified and 2 misclassified samples per group). When applied to ale–lager categorization, the model retained strong predictive ability, reaching 90% accuracy, largely driven by the C* chroma value and the concentrations of tyrosol, acetic acid, homovanillic acid, and syringic acid. The integration of multiple analytical blocks significantly enhanced class separation and minimized ambiguity between beer categories. Overall, these findings underscore the value of multi-block chemometrics as a powerful strategy for beer characterization, supporting brewers, researchers, and regulatory bodies in developing more reliable quality-assurance frameworks. Full article

The rapid digitalization of healthcare has led to the widespread availability of mobile health (mHealth) applications, including those aimed at mental health and well-being. The present study followed the PRISMA guidelines and systematically reviewed English and/or German mental health apps available in the Google Play Store to evaluate their functional quality and behavior-change potential. It utilized the Mobile App Rating Scale (MARS) to assess app quality, including engagement, functionality, esthetics, and information quality, and the App Behavior Change Scale (ABACUS) to evaluate the potential for behavior change by inclusion of behavior change techniques (BCTs). A total of 77 apps were reviewed, with findings indicating an average functional quality and moderate behavior-change potential, as the reviewed apps only utilized a limited amount of BCTs. Notably, only a small fraction of apps had been evaluated in randomized controlled trials (RCTs). Further analysis showed that MARS and ABACUS scores had limited predictive power regarding app popularity as measured by stars awarded by users and number of user ratings in the Google Play Store. The study highlights the need for more rigorous testing of mHealth apps and suggests that factors beyond those measured by MARS and ABACUS may influence app popularity. In addition to the scientific value, this review provides insights for both users interested in mental health support via apps and developers aiming to enhance the quality and impact of mental health applications. Full article

Background: Patient-reported outcome measures (PROMs) provide important insights into recovery after anterior cruciate ligament reconstruction (ACLR). Previous research suggests that males and females recover differently after ACLR, with females reporting greater pain, slower functional gains, and lower psychological readiness at later stages of rehabilitation. However, it is unknown if patient-reported outcomes differ by sex early after ACLR. To address this gap, we conducted a cross-sectional analysis comparing patient-reported outcome measures between sexes three months after ACLR. We hypothesized that females would report worse PROMs compared to males. Methods: This cross-sectional analysis used data from a prospectively maintained ACL reconstruction cohort. Fifty-six individuals (female: 23 and male: 33) with primary, unilateral ACLR completed PROMs three months after surgery. These PROMs included the Knee Injury and Osteoarthritis Outcome Score (KOOS; Symptoms, Pain, Activities of Daily Living, Sport and Recreation, Quality of Life), International Knee Documentation Committee (IKDC) subjective score, Knee Outcome Survey–Activities of Daily Living Scale (KOS-ADLS), Anterior Cruciate Ligament–Return to Sport After Injury (ACL-RSI), and the Tampa Scale of Kinesiophobia (TSK). All outcomes were expressed on a 0 to 100 percent scale, with higher scores indicating better outcomes, except for TSK, where lower scores indicated better outcomes. Normality was assessed within sex, using the Shapiro–Wilk test. Two-tailed independent-samples t-tests with Welch correction were used for approximately normal variables; otherwise, Mann–Whitney U tests were utilized (α = 0.05). Several outcomes had limited statistical power to detect MCID-sized differences, and findings for these measures should be interpreted cautiously. Results: No significant differences between sexes were found for any of the PROMs. Males trended towards having better KOOS Sport and Recreation and IKDC, but these were not statistically significant, and the effect sizes were small-to-moderate. Conclusions: No statistically significant sex-based differences were detected in PROMs at approximately 3 months after ACLR, indicating that any sex-related divergences between these measures may not occur until later in recovery. Full article

Accurate and reliable wind speed measurement is essential for applications such as wind power generation and meteorological monitoring. Data fusion from multiple anemometers mounted on wind measurement towers is a key approach to obtaining high-precision wind speed information. In this study, a hierarchical data fusion strategy is proposed to enhance both the quality and efficiency of multi-sensor fusion on wind measurement towers. At the local fusion stage, multi-sensor wind speed data are denoised and fused using an unscented Kalman filter enhanced with fuzzy logic and a robustness factor (FLR-UKF). At the global decision fusion stage, decision-level fusion is achieved through an extreme learning machine (ELM) neural network optimized by a Q-learning-improved Aquila optimizer (QLIAO-ELM). By incorporating a spiral surrounding attack mechanism and a Q-learning-based adaptive strategy, QLIAO-ELM significantly enhances global search capability and convergence speed, enabling the ELM network to obtain superior parameters within limited computational time. Consequently, the accuracy and efficiency of decision fusion are improved. Experimental results show that, during the local fusion phase, the RMSE of FLR-UKF is reduced by 26.46% to 28.6% compared to the traditional UKF; during the global fusion phase, the RMSE of QLIAO-ELM is reduced by 27.1% and 14.0% compared to ELM and ISSA-ELM, respectively. Full article

The Czochralski method is the dominant technique for producing power-electronics-grade silicon crystals. At the beginning of the seeding stage, an excessively high (or low) temperature at the solid–liquid interface can cause the time required for the seed to reach the specified length to be too long (or too short). However, the time taken for the seed to reach a specified length is strictly controlled in semiconductor crystal growth to ensure that the initial temperature is appropriate. An inappropriate initial temperature can adversely affect crystal quality and production yield. Accurately evaluating whether the current temperature is appropriate for seeding is therefore essential. However, the temperature at the solid–liquid interface cannot be directly measured, and the current manual evaluation method mainly relies on a visual inspection of the meniscus. Previous methods for detecting this temperature classified image features, lacking a quantitative assessment of the temperature. To address this challenge, this study proposed using the duration of the seeding stage as the target variable for evaluating the temperature and developed an improved multimodal fusion regression network. Temperature signals collected from a central pyrometer and an auxiliary pyrometer were transformed into time–frequency representations via wavelet transform. Features extracted from the time–frequency diagrams, together with meniscus features, were fused through a two-level mechanism with multimodal feature fusion (MFF) and channel attention (CA), followed by masking using spatial attention (SA). The fused features were then input into a random vector functional link network (RVFLN) to predict the seeding duration, thereby establishing an indirect relationship between multi-sensor data and the seeding temperature achieving a quantification of the temperature that could not be directly measured. Transfer comparison experiments conducted on our dataset verified the effectiveness of the feature extraction strategy and demonstrated the superior detection performance of the proposed model. Full article

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with $RMSE$ = 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments. Full article

Landslide deformation monitoring plays a critical role in geohazard prevention and risk mitigation in mountainous regions, where timely and reliable deformation information is essential for early warning and disaster management. Monitoring landslide deformation in mountainous areas remains a persistent challenge, largely due to rugged topography, dense vegetation cover, and low interferometric coherence—factors that substantially limit the effectiveness of conventional InSAR methods. To address these issues, this study aims to develop a robust time-series InSAR framework for enhancing deformation detection and measurement density under low-coherence conditions in complex mountainous terrain, and accordingly introduces the Sequential Estimation and Total Power-Enhanced Expectation–Maximization Inversion (SETP-EMI) approach, which integrates dual-polarization Sentinel-1 SAR time series within a recursive estimation framework, augmented by polarimetric coherence optimization. This methodology allows for dynamic assimilation of SAR data, improves phase quality under low-coherence conditions, and enhances the extraction of distributed scatterers (DS). When applied to Zhenxiong County, Yunnan Province—a region prone to geohazards with complex terrain—the SETP-EMI method achieved a landslide detection rate of 94.1%. It also generated approximately 2.49 million measurement points, surpassing PS-InSAR and SBAS-InSAR results by factors of 22.5 and 3.2, respectively. Validation against ground-based leveling data confirmed the method’s high accuracy and robustness, yielding a standard deviation of 5.21 mm/year. This study demonstrates that the SETP-EMI method, integrated within a DS-InSAR framework, effectively overcomes coherence loss in densely vegetated plateau regions, improving landslide monitoring and early-warning capabilities in complex mountainous terrain. Full article

Extremely low-frequency magnetic fields (ELF-MFs) generated by high-voltage power lines raise concerns about their potential impact on health and well-being. Previous research suggests that chronic exposure to ELF-MFs can contribute to sleep disturbances, headaches, and mood disorders, possibly through physiological stress responses and melatonin disruption. This study examines whether self-reported happiness mediates the relationship between exposure to ELF-MFs and health symptoms among people living near a 161 kV transmission line in the city of Or Akiva in Israel. A total of 427 participants completed questionnaires on physical symptoms and life satisfaction, while fixed-site ELF-MF measurements were conducted at and around homes. The structural equation modelling (SEM) was then applied to assess the direct and indirect effects of exposure to ELF-MFs, complemented by logistic regressions for confounder analysis. The results indicate that higher exposure to ELF-MFs was associated with lower happiness and increased symptoms, including poor sleep and reduced mobility (p < 0.05). On the contrary, greater happiness was correlated with fewer headaches, better sleep quality, improved mobility, and reduced perceived need for medical care (p < 0.01). Mediation analysis also revealed that happiness partially buffers the adverse effects of ELF-MFs on headaches, mood, and sleep problems (p < 0.05). Full article

The data collection and analytical capabilities of the Instrumentation and Control (I&C) system in nuclear power plants (NPPs) continue to advance, thereby enhancing operational state awareness and enabling more precise control. However, the data acquisition, transmission, and storage devices in nuclear power plant (NPP) I&C systems typically operate in harsh environments. This exposure can lead to device failures and susceptibility to external interference, potentially resulting in data anomalies such as missing samples, signal skipping, and measurement drift. This paper presents a Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP)-based method for anomaly detection and correction in NPP I&C system data. The goal is to improve operational data quality, thereby supplying more reliable input for system analysis and automatic controllers. Firstly, the short-term prediction algorithm of operation data based on the GRU model is studied to provide a reference for operation data anomaly detection. Secondly, the MLP model is connected to the GRU model to recognize the difference between the collected value and the prediction value so as to distinguish and correct the anomalies. Finally, a series of experiments were conducted using operational data from a pressurized water reactor (PWR) to evaluate the proposed method. The experiments were designed as follows: (1) These experiments assessed the model’s prediction performance across varying time horizons. Prediction steps of 1, 3, 5, 10, and 20 were configured to verify the accuracy and robustness of the data prediction capability over short and long terms. (2) The model’s effectiveness in identifying anomalies was validated using three typical patterns: random jump, fixed-value drift, and growth drift. The growth drift category was further subdivided into linear, polynomial, and logarithmic growth to comprehensively test detection performance. (3) A comparative analysis was performed to demonstrate the superiority of the proposed GRU-MLP algorithm. It was compared against the interactive window center value method and the ARIMA algorithm. The results confirm the advantages of the proposed method for anomaly detection, and the underlying reasons are analyzed. (4) Additional experiments were carried out to discuss and verify the mobility (or transferability) of the prediction algorithm, ensuring its applicability under different operational conditions. Full article

Background: Therapeutic applications of saline solutions in oral healthcare range from mineral waters to standardized sodium chloride preparations. Despite widespread traditional use, their scientific foundation remains inadequately characterized. This scoping review aimed to systematically map the available evidence for salt-based oral health interventions, characterize study populations and outcomes, and identify research gaps to guide future investigations. Methods: Following JBI methodology and PRISMA-ScR guidelines, four electronic databases (PubMed, Scopus, Web of Science, and Cochrane Library) were systematically searched for publications from 2000 to 2025. Studies were classified along a spectrum from geological mineral waters to artificial preparations. Narrative synthesis was employed with systematic gap identification. Results: Seventeen studies met inclusion criteria, with a median sample size of 41 participants and a median follow-up of 4 weeks. Evidence distribution revealed concentration on hypersaline Dead Sea derivatives (n = 7, 41%) and European thermal waters (n = 5, 29%), with limited representation of marine-derived (n = 1, 6%) and simple saline solutions (n = 3, 18%). Reported outcomes included periodontal parameters, xerostomia symptoms, viral load, mucositis severity, and dentin hypersensitivity, with variable methodological quality across studies. Heterogeneity in interventions, comparators, and outcome measures precluded direct comparisons. Conclusions: The current evidence base for salt-based oral interventions remains limited and methodologically heterogeneous. While preliminary findings suggest potential applications across multiple clinical domains, small sample sizes, short follow-up periods, and inconsistent outcome measures preclude definitive recommendations. Standardized protocols and adequately powered trials are needed before evidence-based clinical integration. Full article

As a crucial endogenous resource of firms, history has increasingly been recognized for its role in shaping strategies. However, little is known about how historical competitive experience affects digital orientation (DO), a vital strategic foundation that enables firms to capture value from digital transformation. This study addresses this gap by investigating the impact of competitive experience on firms’ DO and the factors shaping this relationship. Using a panel dataset of 4281 Chinese A-share listed firms from 2012 to 2023, we measure DO through MD&A-based text analysis and test our hypotheses with a two-way fixed-effects model. The results reveal an inverted U-shaped relationship between competitive experience and DO. This indicates that moderate competitive experience stimulates DO, while excessive competitive experience can induce rigidity and constrain DO. Interestingly, market turbulence decreases the positive and increases the negative effect of competitive experience on DO, whereas market competition exerts the opposite moderating effect. Further analysis shows that this positive effect is enhanced within a higher-quality innovation environment. Our findings highlight the importance of history in shaping firms’ digital strategic posture in an emerging market. By treating competitive experience as a strategic resource, managers can transform their competitive legacy into a powerful engine for DO, especially under favorable environments. Full article

Psoriasis is a chronic, immune-mediated inflammatory skin disorder that significantly impacts patients’ quality of life. While TNF-α inhibitors are frequently used to treat moderate-to-severe cases, not all patients respond adequately. Brodalumab, a monoclonal antibody targeting the IL-17 receptor A, has emerged as an alternative for individuals unresponsive to prior therapies. This prospective study investigated the effects of brodalumab on serum cytokine levels—specifically IL-6, IL-17A, IFN-α, IFN-γ, and TNF-α—and their correlation with disease severity as assessed by Psoriasis Area and Severity Index (PASI). Eighteen patients with moderate-to-severe psoriasis who were unresponsive to TNF-α inhibitors received brodalumab for 12 weeks. Cytokine concentrations were measured at baseline and week 12 using an automated immunoassay (ELLA), and clinical outcomes were evaluated using PASI. The results showed a significant increase in IL-17A levels, while changes in IL-6, IFN-α, IFN-γ, and TNF-α did not reach statistical significance. No significant correlations were found between changes in cytokine levels and PASI improvement. However, the small number of available serum samples at week 12 (n = 11) limited the statistical power to detect treatment-related changes in cytokine levels. These findings suggest that while brodalumab influences specific immune markers, the clinical response may not be directly reflected by serum cytokine levels. This highlights the multifactorial nature of psoriasis pathogenesis and underscores the need for further studies to clarify the role of cytokine biomarkers in treatment response. Full article