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Ergonomic risk assessment is crucial for preventing work-related musculoskeletal disorders (WMSDs), which often arise from repetitive tasks, prolonged sitting, and load handling, leading to absenteeism and increased healthcare costs. Biomechanical risk assessment, such as RULA/REBA, is increasingly being enhanced by camera-based motion capture systems, either marker-based (MBSs) or markerless systems (MCBSs). This systematic review compared MBSs and MCBSs regarding accuracy, validity, and reliability for industrial ergonomic risk analysis. A comprehensive search of PubMed, WoS, ScienceDirect, IEEE Xplore, and PEDro (31 May 2025) identified 898 records; after screening with PICO-based eligibility criteria, 20 quantitative studies were included. Methodological quality was assessed with the COSMIN Risk of Bias tool, synthesized using PRISMA 2020, and graded with EBRO criteria. MBSs showed the highest precision (0.5–1.5 mm error) and reliability (ICC > 0.90) but were limited by cost and laboratory constraints. MCBSs demonstrated moderate-to-high accuracy (5–20 mm error; mean joint-angle error: 2.31° ± 4.00°) and good reliability (ICC > 0.80), with greater practicality in field settings. Several studies reported strong validity for RULA/REBA prediction (accuracy up to 89%, κ = 0.71). In conclusion, MCBSs provide a feasible, scalable alternative to traditional ergonomic assessment, combining reliability with usability and supporting integration into occupational risk prevention.

This study explores the optimization of iron electrocoagulation for treating laundry greywater, which accounts for up to 38% of domestic greywater. Characterized by high concentrations of surfactants, detergents, and suspended solids, laundry greywater presents complex challenges for treatment processes, posing significant environmental and health risks. Utilizing response surface methodology (RSM), this research developed a second-order polynomial regression model focused on key operational parameters such as the area-to-volume ratio (A/V), current density, electrolysis time, and settling time. Optimal treatment conditions were identified: an A/V ratio of 30 m 2 /m 3 , a current density of 10 mA/cm 2 , an electrolysis duration of 50 min, and a settlement period of 12 h. Under these conditions, exceptional treatment outcomes were achieved, with turbidity removal reaching 94.26% and COD removal at 99.64%. The model exhibited high effectiveness for turbidity removal, with an R 2 value of 94.16%, and moderate effectiveness for COD removal, with an R 2 value of 75.90%. The interaction between the A/V ratio and electrolysis time particularly underscored their critical role in electrocoagulation system design. Moreover, these results highlight the potential for optimizing electrocoagulation parameters to adapt to daily fluctuations in greywater production and meet specific household reuse needs, such as toilet flushing. This tailored approach aims to maximize contaminant separation and coagulant efficiency, balance energy use and operational costs, and contribute to sustainable water management.

Cybersecurity, complimenting authentication, has become the backbone of the Internet of Things. In the authentication process, the word authentication is of the utmost importance, as it is the door through which both Mr. Right Guy and Mr. Wrong Guy can pass. It is the key to opening the most important and secure accounts worldwide. When authentication is complete, surely there will be passwords. Passwords are a brain-confusing option for the user to choose when making an account during the registration/sign-up process. Providing reliable, effective, and privacy-preserving authentication for individuals in mobile networks is challenging due to user mobility, many attack vectors, and resource-constrained devices. This review paper explores the transformation and modern mobile authentication schemes, categorizing them into password, graphical, behavioral, keystroke, biometric, touchscreen, color, and gaze-based methodologies. It aims to examine the strengths and limitations focused on challenges like security and usability. Standard datasets and performance evaluation measures are also discussed. Finally, research gaps and future directions in this essential and emerging area of research are discussed.

The occurrence of sand and dust storms (SDSs) is essential for the geochemical cycling of nutrients; however, it is considered a meteorological hazard common to arid regions because of the adverse impacts that SDSs brings with them. One common implication of SDSs is the transport and disposition of aerosols coated with anthropogenic contaminants. Studies have reported the presence of such contaminants in desert dust; however, similar findings related to ubiquitous emerging contaminants, such as per- and poly-fluoroalkyl substances (PFAS), have been relatively scarce in the literature. This article reviews and identifies the potential sources of dust-associated PFAS that can accumulate and spread across SDS-prone regions. Furthermore, PFAS exposure routes and their toxicity through bioaccumulation in rodents and mammals are discussed. The major challenge when dealing with emerging contaminants is their quantification and analysis from different environmental media, and these PFAS include known and unknown precursors that need to be quantified. Consequently, a review of various analytical methods capable of detecting different PFAS compounds embedded in various matrices is provided. This review will provide researchers with valuable information relevant to the presence, toxicity, and quantification of dust-associated PFAS to develop appropriate mitigation measures.

Load balancing to alleviate network congestion remains a critical challenge in Vehicular Ad Hoc Networks (VANETs). During route and response scheduling, road side units (RSUs) risk being overloaded beyond their calculated capacity. Despite recent advancements like RSU-based load transfer, NP-Hard hierarchical geography routing, RSU-based medium access control (MAC) schemes, simplified clustering, and network activity control, a significant gap persists in employing a load-balancing server for effective traffic management. We propose a server-based network congestion handling mechanism (SBNC) in VANETs to bridge this gap. Our approach clusters RSUs within specified ranges and incorporates dedicated load balancing and network scheduler RSUs to manage route selection and request–response scheduling, thereby balancing RSU loads. We introduce three key algorithms: optimal placement of dedicated RSUs, a scheduling policy for packets/data/requests/responses, and a congestion control algorithm for load balancing. Using the VanetMobiSim library of Network Simulator-2 (NS-2), we evaluate our approach based on residual energy consumption, end-to-end delay, packet delivery ratio (PDR), and control packet overhead. Results indicate substantial improvements in load balancing through our proposed server-based approach.

This study addresses the optimization of fully convolutional networks (FCNs) for deployment on resource-limited devices in real-time scenarios. While prior research has extensively applied quantization techniques to architectures like VGG-16, there is limited exploration of comprehensive layer-wise quantization specifically within the FCN-8 architecture. To fill this gap, we propose an innovative approach utilizing full-layer quantization with an error minimization algorithm, accompanied by sensitivity analysis to optimize fixed-point representation of network weights. Our results demonstrate that this method significantly enhances sparsity, achieving up to 40%, while preserving performance, yielding an impressive 89.3% pixel accuracy under extreme quantization conditions. The findings highlight the efficacy of full-layer quantization and retraining in simultaneously reducing network complexity and maintaining accuracy in both image classification and semantic segmentation tasks.

The electrochemical performance of in-house developed, spark plasma-sintered, Aluminum metal–matrix composites (MMCs) was evaluated using different electrochemical techniques. X-ray diffraction (XRD) and Raman spectra were used to characterize the nanocomposites along with FE-SEM and EDS for morphological, structural, and elemental analysis, respectively. The highest charge transfer resistance (Rct), lowest corrosion current density, lowest electrochemical potential noise (EPN), and electrochemical current noise (ECN) were observed for GO-reinforced Al-MMC. The addition of honeycomb-like structure in the Al matrix assisted in blocking the diffusion of Cl− or SO4−2. However, poor wettability in between Al matrix and Al2O3 reinforcement resulted in the formation of porous interface regions, leading to a degradation in the corrosion resistance of the composite. Post-corrosion surface analysis by optical profilometer indicated that, unlike its counterparts, the lowest surface roughness (Ra) was provided by GO-reinforced MMC.

Grammar error correction systems are pivotal in the field of natural language processing (NLP), with a primary focus on identifying and correcting the grammatical integrity of written text. This is crucial for both language learning and formal communication. Recently, neural machine translation (NMT) has emerged as a promising approach in high demand. However, this approach faces significant challenges, particularly the scarcity of training data and the complexity of grammar error correction (GEC), especially for low-resource languages such as Indonesian. To address these challenges, we propose InSpelPoS, a confusion method that combines two synthetic data generation methods: the Inverted Spellchecker and Patterns+POS. Furthermore, we introduce an adapted seq2seq framework equipped with a dynamic decoding method and state-of-the-art Transformer-based neural language models to enhance the accuracy and efficiency of GEC. The dynamic decoding method is capable of navigating the complexities of GEC and correcting a wide range of errors, including contextual and grammatical errors. The proposed model leverages the contextual information of words and sentences to generate a corrected output. To assess the effectiveness of our proposed framework, we conducted experiments using synthetic data and compared its performance with existing GEC systems. The results demonstrate a significant improvement in the accuracy of Indonesian GEC compared to existing methods.

Lane level traffic data such as average waiting time and flow data at each turn direction not only enable navigation systems to provide users with more detailed and finer-grained information; it can also pave the way for future traffic congestion prediction. Although few studies considered extracting traffic flow data from a video at the lane level, it is not clear how many vehicles required turn left in fine-grained lanes during a fixed time. Many previous works focus on applying sensor data instead to videos to extract traffic flow. However, the reversible lanes and various shooting angles obstruct the progress of constructing a traffic data collection system. A framework is proposed to get these data in the intersection directly from a video and solve the problem of vehicle occlusion based on the delayed matching model. First, the different direction lanes are detected automatically by clustering trajectory data which are generated by tracking each vehicle. Experiments are conducted on urban intersections to show that our method can generate these traffic data effectively.

In today’s business environment, emails are essential across all sectors, including finance and academia. There are two main types of emails: ham (legitimate) and spam (unsolicited). Spam wastes consumers’ time and resources and poses risks to sensitive data, with volumes doubling daily. Current spam identification methods, such as Blocklist approaches and content-based techniques, have limitations, highlighting the need for more effective solutions. These constraints call for detailed and more accurate approaches, such as machine learning (ML) and deep learning (DL), for realistic detection of new scams. Emphasis has since been placed on the possibility that ML and DL technologies are present in detecting email spam. In this work, we have succeeded in developing a hybrid deep learning model, where Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) are applied distinctly to identify spam email. Despite the fact that the other models have been applied independently (CNNs, LSTM, GRU, or ensemble machine learning classifier) in previous studies, the given research has provided a contribution to the existing body of literature since it has managed to combine the advantage of LSTM in capturing the long-term dependency and the effectiveness of GRU in terms of computational efficiency. In this hybridization, we have addressed key issues such as the vanishing gradient problem and outrageous resource consumption that are usually encountered in applying standalone deep learning. Moreover, our proposed model is superior regarding the detection accuracy (90%) and AUC (98.99%). Though Transformer-based models are significantly lighter and can be used in real-time applications, they require extensive computation resources. The proposed work presents a substantive and scalable foundation to spam detection that is technically and practically dissimilar to the familiar approaches due to the powerful preprocessing steps, including particular stop-word removal, TF-IDF vectorization, and model testing on large, real-world size dataset (Enron-Spam). Additionally, delays in the feature comparison technique within the model minimize false positives and false negatives.