Search Results (24,312)

Ocean energy is an abundant, eco-friendly, and renewable energy resource that is useful for powering sensor networks connected to the maritime Internet of Things (MIoT). These sensor networks can be used to measure different marine environmental parameters that affect ocean infrastructure integrity and harm marine ecosystems. This ocean energy can be harnessed through hybrid nanogenerators that combine triboelectric nanogenerators, electromagnetic generators, piezoelectric nanogenerators, and pyroelectric generators. These nanogenerators have advantages such as high-power density, robust design, easy operating principle, and cost-effective fabrication. However, the performance of these nanogenerators can be affected by the wear of their main components, reduction of wave frequency and amplitude, extreme corrosion, and sea storms. To address these challenges, future research on hybrid nanogenerators must improve their mechanical strength, including materials and packages with anti-corrosion coatings. Herein, we present recent advances in the performance of different hybrid nanogenerators to harvest ocean energy, including various transduction mechanisms. Furthermore, this review reports potential applications of hybrid nanogenerators to power devices in marine infrastructure or serve as self-powered MIoT monitoring sensor networks. This review discusses key challenges that must be addressed to achieve the commercial success of these nanogenerators, regarding design strategies with advanced simulation models or digital twins. Also, these strategies must incorporate new materials that improve the performance, reliability, and integration of future nanogenerator array systems. Thus, optimized hybrid nanogenerators can represent a promising technology for ocean energy harvesting with application in the maritime industry. Full article

Background and Objective: This study presents a combined osteometric and biomechanical analysis of a Korean female cadaver exhibiting bilateral lower limb bone asymmetry with abnormal curvature and callus formation on the left femoral midshaft. Methods: To investigate bilateral bone length differences, osteometric measurements were conducted at standardized landmarks. Additionally, we developed three gait models using Meta Motivo, an open-source reinforcement learning platform, to analyze how skeletal asymmetry influences stride dynamics and directional control. Results: Detailed measurements revealed that the left lower limb bones were consistently shorter and narrower than their right counterparts. The calculated lower limb lengths showed a bilateral discrepancy ranging from 39 mm to 42 mm—specifically a 6 mm difference in the femur, 33 mm in the tibia, and 36 mm in the fibula. In the gait pattern analysis, the normal model exhibited a straight-line gait without lateral deviation. In contrast, the unbalanced, non-learned model demonstrated compensatory overuse and increased stride length of the left lower limb and a tendency to veer leftward. The unbalanced, learned model showed partial gait normalization, characterized by reduced limb dominance and improved right stride, although directional control remained compromised. Conclusions: This integrative approach highlights the biomechanical consequences of lower limb bone discrepancy and demonstrates the utility of virtual agent-based modeling in elucidating compensatory gait adaptations. Full article

The low-cost Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) is widely used in autonomous vehicles for positioning and navigation. Initial alignment is a critical stage for SINS operations, and the alignment time and accuracy directly affect the SINS navigation performance. To address the issue that low-cost SINS/GNSS cannot effectively achieve rapid and high-accuracy alignment in complex environments that contain noise and external interference, an adaptive multiple backtracking robust alignment method is proposed. The sliding window that constructs observation and reference vectors is established, which effectively avoids the accumulation of sensor errors during the full integration process. A new observation vector based on the magnitude matching is then constructed to effectively reduce the effect of outliers on the alignment process. An adaptive multiple backtracking method is designed in which the window size can be dynamically adjusted based on the innovation gradient; thus, the alignment time can be significantly shortened. Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. Simulation and experimental results demonstrate that the proposed method significantly reduces alignment time and improves alignment accuracy. Taking heading error as the critical evaluation indicator, the proposed method achieves rapid alignment within 120 s and maintains a stable error below 1.2° after 80 s, yielding an improvement of over 63% compared to the backtracking-based Kalman filter (BKF) method and over 57% compared to the fuzzy adaptive KF (FAKF) method. Full article

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

Background/Objectives: This study investigated the association between cochlear implant (CI) users’ assessed perception of musical sound quality and their subjective music perception and music-related quality of life (QoL). The aim was to provide a comprehensive evaluation by integrating a relatively objective Turkish Multiple Stimulus with Hidden Reference and Anchor (TR-MUSHRA) test and a subjective music questionnaire. Methods: Thirty CI users and thirty normal-hearing (NH) adults were assessed. Perception of sound quality was measured using the TR-MUSHRA test. Subjective assessments were conducted with the Music-Related Quality of Life Questionnaire (MuRQoL). Results: TR-MUSHRA results showed that while NH participants rated all filtered stimuli as perceptually different from the original, CI users provided similar ratings for stimuli with adjacent high-pass filter settings, indicating less differentiation in perceived sound quality. On the MuRQoL, groups differed on the Frequency subscale but not the Importance subscale. Critically, no significant correlation was found between the TR-MUSHRA scores and the MuRQoL subscale scores in either group. Conclusions: The findings demonstrate that TR-MUSHRA is an effective tool for assessing perceived sound quality relatively objectively, but there is no relationship between perceiving sound quality differences and measures of self-reported musical engagement and its importance. Subjective music experience may represent different domains beyond the perception of sound quality. Therefore, successful auditory rehabilitation requires personalized strategies that consider the multifaceted nature of music perception beyond simple perceptual judgments. Full article

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations. Full article

To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction. Full article

Inertial navigation systems (INSs) exhibit distinctive characteristics, such as long-duration operation, full autonomy, and exceptional covertness compared to other navigation systems. However, errors are accumulated over time due to operational principles and the limitations of sensors. To address this problem, this study theoretically explores a numerically simulated integrated inertial navigation system consisting of a single-axis cold atom interferometer gyroscope (CAIG) and a conventional inertial measurement unit (IMU). The system leverages the low bias and drift of the CAIG and the high sampling rate of the conventional IMU to obtain more accurate navigation information. Furthermore, an adaptive gradient ascent (AGA) method is proposed to estimate the variance of the measurement noise online for the Kalman filter. It was found that errors of latitude, longitude, and positioning are reduced by 43.9%, 32.6%, and 32.3% compared with the conventional IMU over 24 h. On this basis, errors from inertial sensor drift could be further reduced by the online Kalman filter. Full article

The displacement of bridge towers is relatively large under strong wind action. Changes in tower displacement can reflect the usage status of the bridge towers. Therefore, it is necessary to conduct performance warning research on tower displacement under strong wind action. In this paper, the triple standard deviation method, multiple linear regression method, and interpolation method are used to preprocess monitoring data with skipped points and missing anomalies. An improved multi-rate data fusion method, validated using simulated datasets, was applied to correct monitoring data at bridge tower tops. The fused data were used to feed predictive models and generate structural performance alerts. Spectral analysis confirmed that the fused displacement measurements achieve high precision by effectively merging the low-frequency GPS signal with the high-frequency accelerometer signal. Structural integrity monitoring of wind-loaded bridge towers used modeling residuals as alert triggers. The efficacy of this proactive monitoring strategy has been quantitatively validated through statistical evaluation of alarm accuracy rates. Full article

This study examines the development of the digital economy and society in the Republic of Kazakhstan by combining international benchmarking with a context-specific national framework. It highlights the limitations of existing global indices such as DESI, NRI, and EGDI in capturing the structural and institutional dimensions of digital transformation in emerging economies. To address this gap, the study introduces a novel composite metric, the Index of Digital Economy Development (IDED), which integrates five sub-indices: infrastructure, usage, human capital, economic digitization, and transformation effectiveness. The methodology involves comparative index analysis, the construction of the IDED, and statistical validation through a public opinion survey and regression modeling. Key findings indicate that cybersecurity is a critical yet under-represented component of digital development, showing strong empirical correlations with DESI scores in benchmark countries. The results also highlight Kazakhstan’s strengths in digital public services and internet access, contrasted with weaknesses in business digitization and innovation. The proposed IDED offers a more comprehensive and policy-relevant tool for assessing digital progress in transitional economies. This study contributes to the literature by proposing a replicable index structure and providing empirical evidence for the inclusion of cybersecurity in national digital economy assessments. The aim of the study is to assess Kazakhstan’s digital economy development by addressing limitations in global measurement frameworks. Methodologically, it combines comparative index analysis, the construction of a national composite index (IDED), and statistical validation using a regional survey and regression analysis. The findings reveal both strengths and gaps in Kazakhstan’s digital landscape, particularly in cybersecurity and SME digitalization. The IDED introduces an innovative, context-sensitive framework that enhances the measurement of digital transformation in transitional economies. Full article

The premature senescence of red raspberry leaves severely affects plant growth. In this study, the double-season red raspberry cultivar ‘Polka’ was used, with N₁₅₀ (0.10 g N·kg⁻¹) selected as the treatment group (T150) and N₀ (0 g N·kg⁻¹) set as the control (CK). This study systematically investigated the mechanism of premature senescence in red raspberry leaves under different nitrogen application levels by measuring physiological parameters and conducting a combined multi-omics analysis of transcriptomics and metabolomics. Results showed that T150 plants had 8.34 cm greater height and 1.45 cm greater ground diameter than CK. The chlorophyll, carotenoid, soluble protein, and sugar contents in all leaf parts of T150 were significantly higher than those in CK, whereas soluble starch contents were lower. Malondialdehyde (MDA) content and superoxide anion (O₂⁻) generation rate in the lower leaves of T150 were significantly lower than those in CK. Superoxide sismutase (SOD) and peroxidase (POD) activities in the middle and lower functional leaves of T150 were higher than in CK, while catalase (CAT) activity was lower. Transcriptomic analysis identified 4350 significantly differentially expressed genes, including 2062 upregulated and 2288 downregulated genes. Metabolomic analysis identified 135 differential metabolites, out of which 60 were upregulated and 75 were downregulated. Integrated transcriptomic and metabolomic analysis showed enrichment in the phenylpropanoid biosynthesis (ko00940) and flavonoid biosynthesis (ko00941) pathways, with the former acting as an upstream pathway of the latter. A premature senescence pathway was established, and two key metabolites were identified: chlorogenic acid content decreased, and naringenin chalcone content increased in early senescent leaves, suggesting their pivotal roles in the early senescence of red raspberry leaves. Modulating chlorogenic acid and naringenin chalcone levels could delay premature senescence. Optimizing fertilization strategies may thus reduce senescence risk and enhance the productivity, profitability, and sustainability of the red raspberry industry. Full article

The quality of water in households can be affected by plumbing design and materials, water usage patterns, and source water quality characteristics. These factors influence stagnation duration, disinfection residuals, metal release, and microbial activity. In particular, stagnation can degrade water quality and increase lead release from lead service lines. This study employs numerical modeling to assess how combined corrosion control and flushing strategies affect lead levels in household taps with lead service lines under reduced water use. To estimate potential health risks, the U.S. EPA model is used to predict the percentage of children likely to exceed safe blood lead levels. Lead exceedances are assessed based on various regulatory requirements. Results show that exceedances at the kitchen tap range from 3 to 74% of usage time for the 5 µg/L standard, and from 0 to 49% for the 10 µg/L threshold, across different scenarios. Implementing corrosion control treatment in combination with periodic flushing proves effective in lowering lead levels under the studied low-consumption scenarios. Under these conditions, the combined strategy limits lead exceedances above 5 µg/L to only 3% of usage time, with none above 10 µg/L. This demonstrates its value as a practical short-term strategy for households awaiting full pipe replacement. Targeted flushing before peak water use reduces the median time that water remains stagnant in household pipes from 8 to 3 h at the kitchen tap under low-demand conditions. Finally, the risk model indicates that the combined approach can reduce the predicted percentage of children with blood lead levels exceeding 5 μg/dL from 61 to 6% under low water demand. Full article

Artificial intelligence (AI) techniques have propelled biomedical sensors beyond measuring physiological markers to interpreting subjective states like stress, pain, or emotions. Despite these technological advances, user trust is not guaranteed and is inadequately addressed in extant research. This review proposes the Cognition–Context–Contrast (CCC) conceptual framework to explain the trust and acceptance of AI-enabled sensors. First, we map cognition, comprising the expectations and stereotypes that humans have about machines. Second, we integrate task context by situating sensor applications along an intellective-to-judgmental continuum and showing how demonstrability predicts tolerance for sensor uncertainty and/or errors. Third, we analyze contrast effects that arise when automated sensing displaces familiar human routines, heightening scrutiny and accelerating rejection if roll-out is abrupt. We then derive practical implications such as enhancing interpretability, tailoring data presentations to task demonstrability, and implementing transitional introduction phases. The framework offers researchers, engineers, and clinicians a structured conceptual framework for designing and implementing the next generation of AI biosensors. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

Outdoor access, often referred to as pop holes, is widely used to improve the production and welfare of hens. Such cage-free environments present an opportunity for precision flock management via best environmental control practices. However, outdoor access disrupts the integrity of the indoor environment, including properly planned ventilation. Moreover, complaints exist that hens do not use the holes to access the outdoor environment due to the strong incoming airflow through the outdoor access, as they behave as uncontrolled air inlets in a negative pressure ventilation system. As the egg industry transitions to cage-free systems, there is an urgent need for validated computational fluid dynamics (CFD) models to optimize ventilation strategies that balance animal welfare, environmental control, and production efficiency. We developed and validated CFD models of a cage-free hen house with outdoor access by specifying real-world conditions, including two exhaust fans, sidewall ventilation inlets, wire-meshed pens, outdoor access, and plenum inlets. The simulations of four ventilation scenarios predict the measured air flow velocity with an error of less than 50% for three of the scenarios, and the simulations predict temperature with an error of less than 6% for all scenarios. Plenum-based systems outperformed sidewall systems by up to 136.3 air changes per hour, while positive pressure ventilation effectively mitigated disruptions to outdoor access. We expect that knowledge of improved ventilation strategy will help the egg industry improve the welfare of hens cost-effectively. Full article