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.

Unmanned aerial vehicles (UAVs) play a key role in the process of contemporary environmental monitoring, enabling more frequent and detailed observations of various environmental parameters. With the rapid growth of scientific publications on this topic, it is important to identify the key trends and directions. This study uses the Top2Vec algorithm for topic modeling algorithm aimed at analyzing abstracts of more than 556 thousand scientific articles published on the arXiv platform from 2010 to 2023. The analysis was conducted in five key domains: air, water, and surface pollution monitoring; causes of pollution; and challenges in the use of UAVs. The research method included data collection and pre-processing, topic modeling, and quantitative analysis of publication activity using indicators of the rate (D1) and acceleration (D2) of change in the number of publications. The study allows concluding that the main challenge for the researchers is the task of processing data obtained in the course of monitoring. The second most important factor is the reduction in restrictions on the UAV flight duration. Among the causes of pollution, agricultural activities will be considered as a priority. Research in monitoring greenhouse gas emissions will be the most topical in air quality monitoring, while erosion and sedimentation—in the area of land surface control. Thermal pollution, microplastics, and chemical pollution are most relevant in the field of water quality control. On the other hand, the interest of the scientific community in topics related to soil pollution, particulate matter, sensor calibration, and volatile organic compounds is decreasing. © 2025 by the authors.

Packaging demand currently exceeds 144 Mt per year, of which >90% is conventional plastic, generating over 100 Mt of waste and 1.8 Gt CO2-eq emissions annually. In this review, we systematically survey three classes of lignocellulosic feedstocks, agricultural residues, fruit and vegetable by-products, and forestry wastes, with respect to their physicochemical composition (cellulose crystallinity, hemicellulose ratio, and lignin content) and key processing pathways. We then examine fabrication routes (solvent casting, extrusion, and compression molding) and quantify how compositional variables translate into film performance: tensile strength, elongation at break (4–10%), water vapor transmission rate, thermal stability, and biodegradation kinetics. Highlighted case studies include the reinforcement of poly(vinyl alcohol) (PVA) with 7 wt% oxidized nanocellulose, yielding a >90% increase in tensile strength and a 50% reduction in water vapor transmission rate (WVTR), as well as pilot-scale extrusion of rice straw/polylactic acid (PLA) blends. We also assess techno-economic metrics and life-cycle impacts. Finally, we identify four priority research directions: harmonizing pretreatment protocols to reduce batch variability, scaling up nanocellulose extraction and film casting, improving marine-environment biodegradation, and integrating circular economy supply chains through regional collaboration and policy frameworks. © 2025 by the authors.

The poor indoor air quality can be associated with the released volatile organic compounds (VOCs) from different sources. The extent of the concern may increase depending on the presence of benzene, toluene, ethylbenzene, and xylene (BTEX) and exposure to them in the indoor air. Adsorption with activated carbon, which is a very effective method, is preferred to eliminate highly volatile gaseous pollutants and reduce the extend of their negative impact. In this work, the removal efficiency of a novel activated carbons (MSRACs), prepared from stems of Corylus colurna (CCBW) by chemical processes using H2SO4, H3PO4, and HCl, was scrutinized towards BTEX pollutants. The adsorbents acquired from this lignin-based waste were investigated from porosity and surface chemistry aspects. The highest surface area of 1424 m2/g and micropore volume of 0.46 cm3/g were attained after activation of MSRAC11 adsorbent sample by H2SO4-70wt%. The performances of the fabricated adsorbent samples were evaluated and the order of MSRAC11>MSRAC24>MSRAC36 was obtained in the multiple concentrations of BTEX. This study introduces an easy method for producing efficient adsorbents from lignin-based waste for filtering indoor air and designing BTEX-capturing systems for various applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

Colorectal cancer (CRC) is the fourth most common cause of malignant tumor death. The development of novel, more effective drugs is desperately needed to treat CRC. Zingiber officinale is believed to possess anticancer properties due to its flavonoids and phenols. Using Soxhlet (SOXT) and maceration (MACR) techniques, the present study aimed to evaluate the amounts of quercetin, gallic acid, rutin, naringin, and caffeic acid in ginger capsules of Z. officinale. High-performance liquid chromatography (HPLC)/ultraviolet was used for separation and quantitation. In vitro toxicity evaluation of ginger capsules on the CRC cell line HT-29 was also conducted to assess the anticancer activity of the supplement. The cell line HT-29 (HTB-38) colorectal adenocarcinoma was utilized for the antiproliferative effect of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide. Ginger herbal supplement extract at dosages of 200 and 100 μg had strong cytotoxic effects (IC50 < 50 μg/mL) on HT-29 CRC cells via MACR. This extract is comparable to the SOXT extract, which has an IC50 of less than 50 μg/mL. The anticancer effect of ginger herbal supplement formulations against CRC lines was investigated, and the results obtained from both the MACR and SOXT extraction procedures were noteworthy. The quercetin content was the highest of all the extracts according to the HPLC data. © 2024 John Wiley & Sons Ltd.

One of the most interesting and poorly studied carriers of medicinal substances is the polymer clay composite material (PCCM). Bentonite clays are used in pharmacy for the manufacturing of various dosage forms, as well as in the adsorption of drugs to slow their release. Polymer–clay nanocomposites have demonstrated significantly improved properties compared to pure polymers. A review of recent scientific advances has shown promising results regarding the application of polymer–clay materials in medicine and bioengineering, particularly in the development of carrier sorbents with prolonged action for controlled drug release. As a result, interest in polymer–clay systems is steadily growing and gaining momentum. This paper focuses on the structure and properties of bentonite clays, including their sorption, ion exchange, binding, and rheological properties. The methods for preparing intercalated and exfoliated nanocomposites, such as radical intercalative polymerization in situ on clay surfaces, are reviewed. Furthermore, the improved efficacy and exposure times of PCCMs, combined with their enhanced bactericidal properties, are analyzed for the creation of universal and multifunctional preparations for medical use. © 2025 by the authors.