Microplastics (MP) have emerged as significant global pollutants, garnering considerable attention from both the scientific community and the public. The significance of microplastic research lies in its capacity to inform effective pollution control strategies and mitigate associated environmental and health risks. The review illuminated the complexities and challenges inherent in analyzing microplastics and nanoplastics within anthropogenically affected water systems. It underscored the necessity of standardizing methodologies to enhance the accuracy and comparability of research findings. The adoption of advanced analytical techniques represents a significant advancement in the field, paving the way for more effective pollution mitigation strategies. By adhering to this structured approach, the review aspires to contribute a comprehensive overview of current methodologies in microplastic research and to enrich the ongoing discourse surrounding environmental pollution and its far-reaching impacts. © Engineered Science Publisher LLC 2024.

This research paper explores the potential of deep learning techniques in the early detection of cardiovascular diseases using data collected from wearable devices. With the advent of advanced wearable technology, continuous monitoring of physiological parameters, such as heart rate, electrocardiogram (ECG) signals, and physical activity patterns, has become feasible. Leveraging the power of deep neural networks, we investigate the development of predictive models capable of identifying individuals at risk of cardiovascular diseases. The study employs rigorous data preprocessing techniques, encompassing feature extraction and data segmentation, in conjunction with Machine Learning algorithms to enhance model accuracy. Additionally, regularization methods, notably dropout, are harnessed to address challenges posed by diverse datasets. The research envisions the incorporation of data derived from everyday activities to comprehensively assess their impact on heart health, ultimately aiming to proactively alert individuals to deteriorating cardiovascular conditions. The findings presented herein encourage further exploration and innovation in the realm of cardiovascular disease detection, fostering advancements that can enhance public health and well-being. © 2024 IEEE.

With a power conversion efficiency (PCE) of more than 25%, perovskite solar cells (PSCs) have shown an immense potential application for solar energy conversion. Owing to lower manufacturing costs and facile processibility via printing techniques, PSCs can easily be scaled up to an industrial scale. The device performance of printed PSCs has been improving steadily with the development and optimization of the printing process for the device functional layers. Various kinds of SnO2 nanoparticle (NP) dispersion solutions including commercial ones are used to print the electron transport layer (ETL) of printed PSCs, and high processing temperatures are often required to obtain ETLs with optimum quality. This, however, limits the application of SnO2 ETLs in printed and flexible PSCs. In this work, the use of an alternative SnO2 dispersion solution based on SnO2 quantum dots (QDs) to fabricate ETLs of printed PSCs on flexible substrates is reported. A comparative analysis of the performance and properties of the obtained devices with the devices fabricated employing ETLs made with a commercial SnO2 NP dispersion solution is carried out. The ETLs made with SnO2 QDs are shown to improve the performance of devices by ∼11% on average compared to the ETLs made with SnO2 NPs. It is found that employing SnO2 QDs can reduce trap states in the perovskite layer and improve charge extraction in devices. © 2023 The Authors. Published by American Chemical Society.

Optical sensors offer a painless method of monitoring blood glucose levels using various light technologies to analyze blood characteristics without penetrating the skin. The literature review part reflects the progress in optical sensor technology evaluates its potential in blood glucose monitoring by overcoming the limitations of conventional methods and recognizes the challenges and future prospects in this rapidly developing area of research. The results of empirical studies are then presented. The methodology is presented as a non-invasive method of blood glucose monitoring based on near-infrared spectroscopy. To precisely evaluate blood glucose concentrations, spectroscopy techniques involving absorption and reflection are employed at wavelengths 450, 900, 1350, and 1800 nm. After absorption and reflection of glucose molecules, light is generated. An experimental study of different samples revealed a linear relationship between the final output voltage and sugar concentration. The results demonstrate a correlation between blood glucose level and signal intensity after transmission. © 2024 Institute of Advanced Engineering and Science. All rights reserved.

In this article, the authors analyse the features of the development of digital literacy of students with hearing impairments. The inclusion and successful use of digital resources in teaching and learning contributes to the intellectual development of students with disabilities and helps in the formation of capabilities, such as cognitive, creative, etc, positively impacting the students’ overall engagement in activities. The primary purpose of this study was to create an additional modified training course in computer science for students with hearing impairments in special needs schools in Kazakhstan. The primary aim was to develop and improve the level of digital literacy of those students. In the course compiled by the authors, students work with computer graphics and 3D software, develop mobile applications and are engaged in research activities, thereby improving their engineering and technical skills and developing creative abilities. © 2022. WIETE

This work presents the results of a research study focused on the development and evaluation of an algorithmic optimal control framework for energy-efficient operation of screw compressors in smart power systems. The proposed approach is based on the Pontryagin maximum principle (PMP), which enables the synthesis of a mathematically grounded regulator that minimizes the total energy consumption of a nonlinear electromechanical system composed of a screw compressor and a variable-frequency induction motor. Unlike conventional PID controllers, the developed algorithm explicitly incorporates system constraints, nonlinear dynamics, and performance trade-offs into the control law, allowing for improved adaptability and energy-aware operation. Simulation results obtained using MATLAB/Simulink confirm that the PMP-based regulator outperforms classical PID solutions in both transient and steady-state regimes. Experimental tests conducted in accordance with standard energy consumption evaluation methods showed that the proposed PMP-based controller provides a reduction in specific energy consumption of up to 18% under dynamic load conditions compared to a well-tuned basic PID controller, while maintaining high control accuracy, faster settling, and complete suppression of overshoot under external disturbances. The control system demonstrates robustness to parametric uncertainty and load variability, maintaining a statistical pressure error below 0.2%. The regulator’s structure is compatible with real-time execution on industrial programmable logic controllers (PLCs), supporting integration into intelligent automation systems and smart grid infrastructures. The discrete-time PLC implementation of the regulator requires only 103 arithmetic operations per cycle and less than 102 kB of RAM for state, buffers, and logging, making it suitable for mid-range industrial controllers under 2–10 ms task cycles. Fault-tolerance is ensured via range and rate-of-change checks, residual-based plausibility tests, and safe fallbacks (baseline PID or torque-limited speed hold) in case of sensor faults. Furthermore, the proposed approach lays the groundwork for hybrid extensions combining model-based control with AI-driven optimization and learning mechanisms, including reinforcement learning, surrogate modeling, and digital twins. These enhancements open pathways toward predictive, self-adaptive compressor control with embedded energy optimization. The research outcomes contribute to the broader field of algorithmic control in power electronics, offering a scalable and analytically justified alternative to heuristic and empirical tuning approaches commonly used in industry. The results highlight the potential of advanced control algorithms to enhance the efficiency, stability, and intelligence of energy-intensive components within the context of Industry 4.0 and sustainable energy systems. © 2025 by the authors.

The study of surface ruptures is key to understanding the earthquake occurrence of faults especially in the absence of historical events. We present a detailed analysis of geomorphic displacements along the Dzhungarian Fault, which straddles the border of China and Kazakhstan. We use digital elevation models derived from structure-from-motion analysis of Pléiades satellite imagery and drone imagery from specific field sites to measure surface offsets. We provide direct age constraints from alluvial terraces displaced by faulting and indirect dating from morphological analysis of the scarps. We find that the southern 250 km of the fault likely ruptured in a single event in the last 4,000 years, with displacements of 10–15 m, and potentially up to 20 m at one site. We infer that this Dzhungarian rupture is likely linked with a previously identified paleo-earthquake rupture on the Lepsy Fault through a system of splays in the intervening highlands. Though there are remaining uncertainties regarding consistency in age constraints between the two fault ruptures, most of the sites along the two faults are consistent with a most recent event 2,000–4,000 years ago. Rupture on the Dzhungarian Fault alone is likely to have exceeded Mw 8, and the combined Lepsy-Dzhungarian rupture scenario may have been up to Mw 8.4. Despite being at the upper end of known or inferred continental earthquake magnitudes, our proposed scenario combining the 375 km of the Dzhungarian and Lepsy ruptures yields a slip-to-length ratio consistent with global averages and so do other historical intracontinental earthquakes in Central Asia. © Wiley Periodicals LLC. The Authors.

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities, and less favorable client conditions. To overcome this issue, a comprehensive credit assessment scoring system should be implemented to increase importers’ confidence. The article proposes a predictive-based reinforcement learning (PRL) model to help manufacturers and importers acquire more accurate and dependable credit scores while avoiding default risk. Furthermore, the proposed PRL model enhances decision-making, system efficiency, and risk-tolerant financial conditions. To attain these cutting-edge objectives, the proposed PRL model combines three algorithms. Algorithm 1 collects and aggregates data to indicate areas for improvement if credit scoring is poor. Algorithm 2 uses reinforcement learning to validate and enhance bank scores. Algorithm 3 focuses on predictive modeling for bank scoring, ensuring that the credit decision-making system is operational and constantly improving. Furthermore, reinforcement learning leverages the features from local interpretable model-agnostic explanations (LIME) and shapely additive explanations (SHAP) to generate locally reliable explanations and attribute the contribution of each feature for determining the output of the model. The Python platform tests the proposed PRL to achieve the objectives. Based on the results, The PRL model markedly enhances credit assessment precision, achieving an accuracy of over 99.5%, which outstrips current methodologies such OCLA (96.12%), PSML (84.12%), and EMPCC (91.67%). Furthermore, the PRL model augments leverage ratios, rising from 2.75% in 2015 to 3.36% in 2024.5, and increases accounts receivable turnover from 4.38% in 2015 to 7.4% in 2024.5, surpassing alternative credit evaluation methodologies. This research highlights the novelty of combining predictive analytics and reinforcement learning to revolutionize credit assessment, providing a scalable and reliable solution for manufacturers and importers. The findings establish the PRL model as a transformative approach for creating risk-tolerant and efficient financial environments. © 2025 The Authors

The design of future fusion reactors involves the production of tritium inside the breeder blanket. The most promising material for solid breeder blankets is a two-phase lithium ceramic containing orthosilicate Li4SiO4 (LOS) and metatitanate Li2TiO3 (LMT) of lithium in various proportions. Tritium is formed in lithium under neutron irradiation by the reaction 6Li(n,α)T. Further, this tritium is extracted from the blanket with a purge gas and returned to the fusion zone, realizing the concept of a closed fusion cycle. Irradiation under fission reactor conditions is still one of the few available methods for estimating the parameters of tritium generation and release from lithium-containing materials in the "in-situ" mode. This paper presents the results of experiments on neutron irradiation of two-phase lithium ceramics of various ratios (LOS + 35 mol. % LMT (pebble size 250–1250 μm), LOS + 35 mol. % LMT (pebble size 500–710 μm) and LOS + 25 mol. % LMT (pebble size 500–710 μm) at the WWR-K research reactor. Irradiation of each batch of samples lasted from 5 to 22 days. The experiments were carried out by the vacuum extraction method. This paper describes the main methodological aspects of the studies, namely the technical features of four irradiation campaigns, the sequence and scope of the studies. A comparison is also made of the initial sections of reactor experiments for all campaigns, where the reactor was sequentially brought to power, according to which the parameters of the Arrhenius dependence of the effective tritium diffusion coefficients were estimated. © 2023 Elsevier B.V.

The emergence of 6G networks increases both the suitability and the speed of Internet-of-Things (IoT) devices within vehicular communication systems (VCSs). Wireless capabilities can be improved using a 6G network to successfully manage IoT devices in VCSs. 6G networks can be employed to reduce cyberattacks due to their unclaimed and unused operating frequencies. However, 6G networks could be vulnerable to cyberattacks due to their flexibility. The issues of 6G networks can adequately be addressed using blockchain technology. If a smart vehicular system uses IoT, then faster response times, substantial power reductions, and security for possible accident avoidance and safety provision can be achieved. Therefore, an energy-efficient consortium-based blockchain-enabled heterogeneous (EBH) 6G network for IoT devices can provide the best platform for secure management vehicular systems. In this article, we introduce a perspective architecture for the IoT-enabled secure vehicular system using a blockchain-enabled heterogeneous 6G network over cloud edge computing. The heterogeneous support of a 6G network is discussed, which is highly effective for smart vehicular management systems. The primary goal of the article is to motivate the community and researchers to use multidisciplinary and cross-cutting technologies integratively. © 2024 Elsevier B.V.