Search Results (1,643)

In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article

Distributed acoustic sensing (DAS) systems have been widely employed in oil and gas resource exploration, pipeline monitoring, traffic and transportation, structural health monitoring, hydrophone usage, and perimeter security due to their ability to perform large-scale distributed acoustic measurements. Conventional DAS relies on Rayleigh backscattering (RBS) from standard single-mode fibers (SMFs), which inherently limits the signal-to-noise ratio (SNR) and sensing robustness. Ultra-weak fiber Bragg grating (UWFBG) arrays can significantly enhance backscattering intensity and thereby improve DAS performance. This review provides a comprehensive overview of recent advances in UWFBG arrays for high-performance DAS. We introduce major inscription techniques for UWFBG arrays, including the drawing tower grating method, ultraviolet (UV) exposure through UV-transparent coating fiber technologies, and femtosecond laser direct writing methods. Furthermore, we summarize the applications of UWFBG arrays in DAS systems for the enhancement of RBS intensity, suppression of fading, improvement of frequency response, and phase noise compensation. Finally, the prospects of UWFBG-enhanced DAS technologies are discussed. Full article

Background: Electronic Health Records (EHRs) are a rich source of clinical information used for patient monitoring, disease progression analysis, and treatment outcome assessment. However, their large-scale, heterogeneity, and temporal characteristics make them difficult to analyze. Topic modeling has emerged as an effective method to extract latent structures, detect disease characteristics, and trace patient trajectories in EHRs. Recent neural and transformer-based approaches such as BERTopic has significantly improved coherence, scalability, and domain adaptability compared to earlier probabilistic models. Methods: This Systematic Literature Review (SLR) examines topic modeling and its variants applied to EHR data over the past decade. We follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to identify, screen, and select relevant studies. The reviewed techniques span traditional probabilistic models, neural embedding-based methods, and temporal extensions designed for pathway and sequence modeling in clinical data. Results: The synthesis covers trends in publication patterns, dataset usage, application domains, and methodological contributions. The reviewed literature demonstrates strengths across different modeling families, while also highlighting challenges related to scalability, interpretability, temporal complexity, and privacy when analyzing large-scale EHRs. Conclusions: Topic modeling continues to play a central role in understanding temporal patterns and latent structures in EHRs. This review also outlines future possibilities for integrating topic modeling with Agentic AI and large language models to enhance clinical decision-making. Overall, this SLR provides researchers and practitioners with a consolidated foundation on temporal topic modeling in EHRs and its potential to advance data-driven healthcare. Full article

Due to increasing human activities underwater, there is a growing demand for high-speed underwater optical communication (UOWC) data links for security surveillance, environmental monitoring, pipeline inspection, and other applications. Line-of-sight communication is impossible under certain conditions due to misalignment, physical obstructions, irregular usage, and difficulty adjusting the receiver orientation, especially when used in environments with mobile users or submerged sensor networks. Therefore, non-line-of-sight (NLOS) optical communication is used in this study. Advanced modulation schemes—quadrature amplitude modulation and orthogonal frequency-division multiplexing (QAM-OFDM)—were used to transmit the signal underwater between two network nodes. QAM increases the data transfer rate, while OFDM reduces dispersion and inter-symbol interference (ISI). The proposed UOWC system is investigated using a 532 nm green laser diode (LD). Reliable high-speed data transmission of up to 15 Gbps is achieved over horizontal distances of 134 m, 43 m, 21 m, and 5 m in four different aquatic environments—pure water (PW), clear ocean (CLO), coastal ocean (COO), and harbor II (HarII), respectively. The system achieves effectively error-free performance within the simulation duration (BER < 10⁻⁹), with a received optical signal power of approximately −41.5 dBm. Clear constellation patterns and low BER values are observed, confirming the robustness of the proposed architecture. Despite the limitations imposed by non-line-of-sight (NLOS) communication and the diversity aquatic environments, our proposed architecture excels at underwater long-distance data transmission at high speeds. Full article

The proliferation of smart devices and the Internet of Things (IoT) has led to massive data generation, particularly in complex domains such as aerospace. Cloud computing provides essential scalability and advanced analytics for processing these vast datasets. However, relying solely on the cloud introduces significant challenges, including high latency, network congestion, and substantial bandwidth costs, which are critical for real-time on-orbit spacecraft services. Cloud-edge Internet of Things (cloud-edge IoT) computing emerges as a promising architecture to mitigate these issues by pushing computation closer to the data source. This paper proposes an improved YOLOV8-based model specifically designed for edge computing scenarios within a cloud-edge IoT framework. By integrating the Cross Stage Partial Spatial Pyramid Pooling Fast (CSPPF) module and the WDIOU loss function, the model achieves enhanced feature extraction and localization accuracy without significantly increasing computational cost, making it suitable for deployment on resource-constrained edge devices. Meanwhile, by processing image data locally at the edge and transmitting only the compact segmentation results to the cloud, the system effectively reduces bandwidth usage and supports efficient cloud-edge collaboration in IoT-based spacecraft monitoring systems. Experimental results show that, compared to the original YOLOV8 and other mainstream models, the proposed model demonstrates superior accuracy and instance segmentation performance at the edge, validating its practicality in cloud-edge IoT environments. Full article

Uncertainty-aware decision-making increasingly relies on multimodal sensing pipelines that must fuse correlated measurements, propagate uncertainty, and trigger reliable control actions. This study develops a unified mathematical framework for multimodal data fusion and Bayesian decision-making under uncertainty. The approach integrates adaptive Covariance Intersection (aCI) for correlation-robust sensor fusion, a Gaussian state–space backbone with Kalman filtering, heteroskedastic Bayesian regression with full posterior sampling via an affine-invariant MCMC sampler, and a Bayesian likelihood-ratio test (LRT) coupled to a risk-sensitive proportional–derivative (PD) control law. Theoretical guarantees are provided by bounding the state covariance under stability conditions, establishing convexity of the aCI weight optimization on the simplex, and deriving a Bayes-risk-optimal decision threshold for the LRT under symmetric Gaussian likelihoods. A proof-of-concept agro-environmental decision-support application is considered, where heterogeneous data streams (IoT soil sensors, meteorological stations, and drone-derived vegetation indices) are fused to generate early-warning alarms for crop stress and to adapt irrigation and fertilization inputs. The proposed pipeline reduces predictive variance and sharpens posterior credible intervals (up to 34% narrower 95% intervals and 44% lower NLL/Brier score under heteroskedastic modeling), while a Bayesian uncertainty-aware controller achieves 14.2% lower water usage and 35.5% fewer false stress alarms compared to a rule-based strategy. The framework is mathematically grounded yet domain-independent, providing a probabilistic pipeline that propagates uncertainty from raw multimodal data to operational control actions, and can be transferred beyond agriculture to robotics, signal processing, and environmental monitoring applications. Full article

Research often emphasises dysfunctional Social Networking Site (SNS) usage. In contrast, the current research examined a more positive element of human functioning, specifically how motivations to use SNSs may be associated with meaning in life, which can help give purpose and direction to people’s lives. A sample of 384 undergraduate students (aged 18 to 50; M = 20.95; SD = 4.95; 81.5% females) completed questionnaire-based measures of motivations to use SNSs, self-reported time spent on SNSs, and meaning in life (coherence, purpose, and mattering). Multiple regressions showed that models for coherence, purpose, and mattering explained 5.8–8.8% of the variance (R² = 0.058–0.088). Self-expression was positively associated with coherence (β = 0.128), purpose (β = 0.16), and mattering (β = 0.137). Following/monitoring others predicted higher coherence (β = 0.158), and using SNSs to find information predicted higher purpose (β = 0.12). Academic purposes were positively related to mattering (β = 0.12). By contrast, using SNSs for new friendships predicted lower coherence (β = −0.197) and mattering (β = −0.154), entertainment predicted lower coherence (β = −0.178), and greater time on SNSs predicted lower purpose (β = −0.186). Overall, different motivations for using SNSs are associated with different facets of meaning in life. Full article

Incremental computing is becoming increasingly important for processing large-scale datasets. In satellite imagery, spatial resolution, temporal depth, and large files pose significant computational challenges, requiring efficient architectures to manage processing time and resource usage. Accordingly, in this study, we propose a dynamic architecture, termed Multi-Cube, for optical satellite time series. The framework introduces a modular and baseline-aware approach that enables scalable subdivision, incremental growth, and consistent management of spatiotemporal data. Built on NetCDF, xarray, and Zarr, Multi-Cube automatically constructs stable multidimensional data cubes while minimizing redundant reprocessing, formalizing automated internal decisions governing cube subdivision, baseline reuse, and incremental updates to support recurrent monitoring workflows. Its performance was evaluated using more than 83,000 Sentinel-2 images (covering 2016–2024) across multiple areas of interest. The proposed approach achieved a 5.4× reduction in end-to-end runtime, decreasing execution time from 53 h to 9 h, while disk I/O requirements were reduced by more than two orders of magnitude compared with a traditional sequential reprocessing pipeline. The framework supports parallel execution and on-demand sub-cube extraction for responsive large-area monitoring while internally handling incremental updates and adaptive cube management without requiring manual intervention. The results demonstrate that the Multi-Cube architecture provides a decision-driven foundation for integrating dynamic Earth observation workflows with analytical modules. Full article

Availability of irrigation water during growing seasons in the Republic of South Africa (RSA) remains a significant concern. Persistent droughts and unpredictable rainfall patterns attributed to climate change, coupled with an increasing population, have exacerbated irrigation water scarcity. Globally, treated wastewater has been utilised as an irrigation water source; however, despite global advances in the usage of treated wastewater, its suitability for irrigation in RSA remains a contentious issue. Considering this uncertainty, this review article aims to unravel the South African scenario on the suitability of treated wastewater for irrigation purposes and highlights the potential environmental impacts and public health risks. The review synthesised literature in the last two decades (2000–present) using Web of Science, ScienceDirect, ResearchGate, and Google Scholar databases. Findings reveal that treated wastewater can serve as a viable irrigation source in the country, enhancing various soil parameters, including nutritional pool, organic carbon, and fertility status. However, elevated levels of salts, heavy metals, and microplastics in treated wastewater resulting from insufficient treatment of wastewater processes may present significant challenges. These contaminants might induce saline conditions and increase heavy metals and microplastics in soil systems and water bodies, thereby posing a threat to public health and potentially causing ecological risks. Based on the reviewed literature, irrigation with treated wastewater should be implemented on a localised and pilot basis. This review aims to influence policy-making decisions regarding wastewater treatment plant structure and management. Stricter monitoring and compliance policies, revision of irrigation water standards to include emerging contaminants such as microplastics, and intensive investment in wastewater treatment plants in the country are recommended. With improved policies, management, and treatment efficiency, treated wastewater can be a dependable, sustainable, and practical irrigation water source in the country with minimal public health risks. Full article

Large-Format Additive Manufacturing (LFAM) offers the ability to 3D print composites at multi-meter scale and high throughput by utilizing a screw-based extrusion system that is compatible with pelletized feedstock. As such, LFAM systems like the Big Area Additive Manufacturing (BAAM) system provide a pathway for incorporating AM techniques into industry-scale production. Despite significant growth in LFAM techniques and usage in recent years, typical Multi-Material (MM) techniques induce weak points at discrete material boundaries and encounter a higher frequency of delamination failures. A novel dual-hopper configuration was developed for the BAAM platform to enable in situ switching between material feedstocks that creates a graded transition region in the printed part. This research studied the influence of extrusion screw speed, component design, transition direction, and material viscosity on the transition behavior. Material transitions were monitored using compositional analysis as a function of extruded volume and modeled using a standard Weibull cumulative distribution function (CDF). Screw speed had a negligible influence on transition behavior, but averaging the Weibull CDF parameters of transitions printed using the same configurations demonstrated that designs intended to improve mixing increased the size of the blended material region. Further investigation showed that the relative difference and change in complex viscosity influenced the size of the blended region. These results indicate that tunable properties and material transitions can be achieved through selection and modification of composite feedstocks and their complex viscosities. Full article

Unmanned helicopters play a critical role in various fields including defense, disaster response, and infrastructure inspection. Military platforms such as the MQ-8C Fire Scout represent high-value assets exceeding $40 million per unit including development costs, particularly when compared to expendable multicopter drones costing approximately $500–2000 per unit. Unexpected failures of these high-value assets can lead to substantial economic losses and mission failures, making the implementation of Health and Usage Monitoring Systems (HUMS) essential. However, the scarcity of failure data in unmanned helicopters presents significant challenges for HUMS development, while the economic feasibility of investing resources comparable to manned helicopter programs remains questionable. This study presents a novel cross-scale transfer learning framework for vibration-based anomaly detection in unmanned helicopters. The framework successfully transfers knowledge from a source domain (Airbus large manned helicopter) using publicly available data to a target domain (Stanford small RC helicopter), achieving excellent anomaly detection performance without labeled target domain data. The approach consists of three key processes. First, we developed a multi-task learning transformer model achieving an F-β score of 0.963 (β = 0.3) using only Airbus vibration data. Second, we applied CORAL (Correlation Alignment) domain adaptation techniques to reduce the distribution discrepancy between source and target domains by 79.7%. Third, we developed a Control Effort Score (CES) based on control input data as a proxy labeling metric for 20 flight maneuvers in the target domain, achieving a Spearman correlation coefficient ρ of 0.903 between the CES and the Anomaly Index measured by the transfer-learned model. This represents a 95.5% improvement compared to the non-transfer learning baseline of 0.462. Full article

Rice blast, caused by Magnaporthe oryzae, is a devastating disease that jeopardizes global rice production and food security. Precision agriculture demands timely and accurate monitoring tools to enable targeted intervention. This study introduces a novel deep learning framework that fuses time-series hyperspectral imaging with an advanced Autoformer model (AutoMSD) to dynamically track rice blast progression. The proposed AutoMSD model integrates multi-scale convolution and adaptive sequence decomposition, effectively decoding complex spatio-temporal patterns associated with disease development. When deployed on a 7-day hyperspectral dataset, AutoMSD achieved 86.67% prediction accuracy using only 3 days of historical data, surpassing conventional approaches. This accuracy at an early infection stage underscores the model’s strong potential for practical field deployment. Our work provides a scalable and robust decision-support tool that paves the way for site-specific disease management, reduced pesticide usage, and enhanced sustainability in rice cultivation systems. Full article

Microservice architecture has become a foundational component of modern distributed systems due to its modularity, scalability, and deployment flexibility. However, the increasing complexity and dynamic nature of service interactions have introduced substantial challenges in accurately detecting runtime anomalies. Existing methods often rely on multiple monitoring metrics, which introduce redundancy and noise while increasing the complexity of data collection and model design. This paper proposes a novel spatiotemporal anomaly detection framework that integrates Dynamic Graph Neural Networks (D-GNN) combined with Long Short-Term Memory (LSTM) networks to model both the structural dependencies and temporal evolution of microservice behaviors. Unlike traditional approaches, our method uses only CPU utilization as the sole monitoring metric, leveraging its high observability and strong correlation with service performance. From a symmetry perspective, normal microservice behaviors exhibit approximately symmetric spatiotemporal patterns: structurally similar services tend to share similar CPU trajectories, and recurring workload cycles induce quasi-periodic temporal symmetries in utilization signals. Runtime anomalies can therefore be interpreted as symmetry-breaking events that create localized structural and temporal asymmetries in the service graph. The proposed framework is explicitly designed to exploit such symmetry properties: the D-GNN component respects permutation symmetry on the microservice graph while embedding the evolving structural context of each service, and the LSTM module captures shift-invariant temporal trends in CPU usage to highlight asymmetric deviations over time. Experiments conducted on real-world microservice datasets demonstrate that the proposed method delivers excellent performance, achieving 98 percent accuracy and 98 percent F1-score. Compared to baseline methods such as DeepTraLog, which achieves 0.93 precision, 0.978 recall, and 0.954 F1-score, our approach performs competitively, achieving 0.980 precision, 0.980 recall, and 0.980 F1-score. Our results indicate that a single-metric, symmetry-aware spatiotemporal modeling approach can achieve competitive performance without the complexity of multi-metric inputs, providing a lightweight and robust solution for real-time anomaly detection in large-scale microservice environments. Full article

Cadmium (Cd) is a typical pollutant with strong toxicity even at low concentrations. In the marine environment, Cd is a problem of magnitude and ecological significance due to its high toxicity and accumulation in living organisms. The clam Meretrix meretrix is a useful bioindicator species for evaluating heavy-metal stress. This study investigated the extent of recovery from Cd²⁺-induced oxidative and immune impairments in M. meretrix gills achieved by short-term depuration. Clams were exposed to 3 mg/L Cd²⁺ for six days or three days followed by three days of depuration, and the Cd contents, morphological structure, osmoregulation, oxidative stress, and immune responses in the gills were evaluated. The results showed that gill Cd contents increased with exposure, reaching 9.857 ± 0.074 mg·kg⁻¹ on day 3 but decreased slightly to 8.294 ± 0.056 mg·kg⁻¹ after depuration, while reaching 18.665 ± 0.040 mg·kg⁻¹ on day 6 after continuous exposure. Histological lesions, including lamellar fusion, hemolymphatic sinus dilation, and ciliary degeneration, partially recovered after depuration. Reactive oxygen species (ROS) and malondialdehyde (MDA) levels decreased significantly, while DNA-protein crosslinking rate (DPC) and protein carbonyl (PCO) showed minor reductions. Total antioxidant capacity (T-AOC) and the activities of Ca²⁺/Mg²⁺-ATPase (CMA), cytochrome c oxidase (COX), succinate dehydrogenase (SDH), and lactate dehydrogenase (LDH) increased by over 10% during depuration, though these changes were not statistically significant. Lysozyme (LZM) activity and MT transcript levels increased progressively with Cd exposure, indicating their suitability as biomarkers of Cd stress. Acid and alkaline phosphatase (ACP, AKP) activities and Hsp70 and Nrf2 mRNA transcripts exhibited inverted U-shaped response consistent with hormetic response. ACP and AKP activity levels rose by more than 20% after depuration, suggesting partial restoration of immune capacity. Overall, Cd exposure induced oxidative damage, metabolic disruption, and immune suppression in M. meretrix gills, yet short-term depuration allowed partial recovery. These findings enhance understanding of Cd toxicity and reversibility in marine bivalves and reinforce the usage of biochemical and molecular markers for monitoring Cd contamination and assessing depuration efficiency in aquaculture environments. Full article

This paper proposes an integrated, stress-based framework for fatigue diagnosis of wind turbine blades that is tailored to field deployments where detailed structural design information is unavailable. The approach combines a data-driven reduced-order model (ROM) for directional damage equivalent loads (DELs) with a physics-based Soderberg index and a one-class support vector machine (SVM) anomaly detector. The framework is implemented and evaluated using measurements from a 2 MW onshore turbine equipped with blade-root strain gauges and standard SCADA monitoring. Ten-minute operating windows are formed by synchronizing SCADA records with high-frequency strain data, converting strain to stress, and computing DELs via Rainflow counting for flapwise, edgewise, and torsional blade root directions. SCADA inputs are summarized by their 10 min statistics and augmented with yaw misalignment features; these are used to train LightGBM-based ROMs that map operating conditions to directional DELs. On an independent test set, the DEL-ROM achieves coefficients of determination of approximately 0.87, 0.99, and 0.99 for flapwise, edgewise, and torsional directions, respectively, with small absolute errors relative to the measured DELs. The Soderberg index is then used to define conservative Normal/Alert/Alarm classes based on representative material parameters, while a one-class SVM is trained on DEL- and stress-based fatigue features to learn the distribution of normal operation. A simple AND-normal/OR-abnormal rule combines the Soderberg class and SVM label into a hybrid diagnostic decision. Application to the field dataset shows that the proposed framework provides interpretable fatigue-safety margins and reliably highlights operating periods with elevated flapwise fatigue usage, demonstrating its suitability as a scalable building block for digital-twin-enabled condition monitoring and life-extension assessment of wind turbine blades. Full article