Search Results (80,205)

This paper presents the optimal design for a 30 kW, 3-phase squirrel-cage induction motor (IM). In this paper, three optimization algorithms are used for design optimization, namely, Particle Swarm Optimization Algorithm (PSO), genetic algorithm (GA), and Sequential Quadratic Programming (SQP). The optimal goals are maximum starting torque, efficiency, and minimum material cost. The result of the IM design optimization using three optimal methods is announced and compared. Additionally, computation time and the number of iterations of each algorithm are compared to find out the most suitable algorithm for the optimal design of an induction motor. In addition, this paper proposes a solution that permits us to find only one solution satisfying all the optimal criteria. Instead of using the conventional multi-objective optimization method that normally leads to a Pareto set with many optimal points at the same optimal level, we propose a hierarchical optimization method that experiences some mono-objective optimization and then builds a function representing the multi-objective optimization. Using this method, having a global optimal point can be obtained. Comparison of the optimal algorithms and multi-objective optimization methods has given broadened insight into optimal techniques for IMs. We have found that PSO is the best method for optimization design of IMs in terms of computation time and finding the global optimal point. Full article

Background/Objectives: The emergence of immune-evasive SARS-CoV-2 variants highlights the need for adaptable vaccine strategies. Trimeric receptor-binding domain (tRBD) antigens offer structural and immunological advantages over monomeric RBDs, but DNA vaccine efficacy has been limited by inefficient antigen expression, particularly in non-dividing antigen-presenting cells. Although cytoplasmic transcription–based DNA platforms have been developed to overcome nuclear entry barriers, their utility for antigen structure–function optimization remains underexplored. This study evaluated whether integrating a rationally designed trimeric RBD with a T7-driven cytoplasmic transcription system could enhance immunogenic performance. Methods: A DNA vaccine encoding a tandem trimeric SARS-CoV-2 RBD was delivered using a T7 RNA polymerase-driven cytoplasmic transcription system. In vitro antigen expression was assessed following Lipofectamine 3000-mediated transfection. In vivo, mice were immunized with the SM-102-based Rpol/tRBD/LNP formulation, and immunogenicity was assessed by antigen-specific antibody titers, serum neutralizing activity, and T-cell response profiling, together with basic safety/tolerability evaluations. Results: The T7-driven cytoplasmic transcription system markedly increased antigen mRNA and protein expression compared with conventional plasmid delivery. Rpol/tRBD vaccination induced higher anti-RBD IgG titers, enhanced neutralizing antibody activity, and robust CD8⁺ T cell responses relative to monomeric RBD and plasmid-based trimeric RBD vaccines. Immune responses were Th1-skewed and accompanied by germinal center activation without excessive inflammatory cytokine induction, body-weight loss, or hepatic and renal toxicity. Conclusions: This study demonstrates that integrating rational trimeric antigen engineering with direct cytoplasmic transcription enables balanced and well-tolerated immune activation in a DNA vaccine context. The T7 autogene-based platform provides a flexible framework for antigen structure–function optimization and supports the development of next-generation DNA vaccines targeting rapidly evolving viral pathogens. Full article

Urban air pollution remains a critical public health issue, especially in densely populated cities where pedestrians experience direct exposure to traffic-related and environmental emissions. This study develops and tests a pollution-aware pedestrian routing framework for Thessaloniki, Greece, designed to minimize environmental exposure while maintaining route efficiency. The framework combines high-resolution air-quality data and computational techniques to represent pollution patterns at pedestrian scale. Air-quality is expressed as a continuous European Air Quality Index (EAQI) and is embedded in a network-based routing engine (OSRM) that balances exposure and distance through a weighted optimization function. Using 3000 randomly sampled origin-destination pairs, exposure-aware routes are compared with conventional shortest-distance paths across short, medium, and long walking trips. Results show that exposure-aware routes reduce cumulative AQI exposure by an average of 4% with only 3% distance increase, while maintaining stable scaling across all route classes. Exposure benefits exceeding 5% are observed for approximately 8% of medium-length routes and 24% of long routes, while short routes present minimal or no detours, but lower exposure benefits. These findings confirm that integrating high-resolution environmental data into pedestrian navigation systems is both feasible and operationally effective, providing a practical foundation for future real-time, pollution-aware mobility services in smart cities. Full article

Millimeter-wave (mmWave)/terahertz (THz) devices relying on conventional semiconductor technologies face significant performance bottlenecks, constraining their use in next-generation electronic systems. To address these challenges, this work demonstrates high-performance THz Schottky barrier diodes (SBDs) based on aligned carbon nanotube (ACNT) arrays, and the realization of a D-band second-harmonic frequency multiplier. The ACNT-SBDs exhibit superior electrical and radio-frequency (RF) characteristics, achieving a forward current density of 0.14 mA·μm⁻¹ at −1.3 V and an intrinsic cutoff frequency (f_C) of 506 GHz. The developed small-signal model of diodes shows close agreement with measurements, with S-parameter relative errors below 0.7% from 100 MHz to 67 GHz. The implemented 154 GHz D-band multiplier achieved a maximum output power of −18.97 dBm and a minimum conversion loss of 27.92 dB, outperforming previously reported frequency multipliers based on carbon nanotubes or two-dimensional (2D) materials. This study not only establishes the outstanding high-frequency response, nonlinear efficiency, and integration potential of ACNT-based devices but also provides a promising technical pathway for future THz communication and sensing applications. Full article

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

This study investigates the workability of an emerging cement based on calcined clay, considered one of the sustainable binders for reducing the carbon footprint of construction materials. Despite existing experimental data, no comprehensive analysis has been conducted. In the present paper, a literature-derived dataset was analyzed using CPM-based packing density computation and interpretable statistical analyses (distribution statistics and Pearson correlation-based projections). The novelty of this study lies in integrating the domain-knowledge-informed hierarchical analysis to identify packing density as a primary, sustainable lever to enhance LC3 fluidity while limiting reliance on superplasticizers. PCE superplasticizers (0–2.5 wt.% in the dataset) improve fluidity across packing densities; noticeable gains are observed even for low dosages (≈0.5–1 wt.%) at packing 0.36–0.38. A paradigm shift is proposed through optimizing packing density by adjusting clay and limestone content in the mix. Prioritizing packing density, alongside conventional parameters, opens new avenues for sustainability by reducing reliance on organic fluidizers in low-carbon cements. Full article

In this paper, two improvements in precipitation classification have been performed. Supervised machine learning has demonstrated considerable performances in classification tasks. However, supervised machine learning can only be applied to labeled data. In some cases, large amounts of unlabeled data contain valuable information for better classification. In the classification of precipitation intensities from satellite images, unlabeled data constitute the majority and remain largely unexplored. To exploit both labeled and unlabeled data, a Semi-Supervised Support Vector Machine (S3VM) is implemented as the first improvement for classification results. The labeling of the limited available data is derived from radar measurements covering a small portion of the Meteosat Second Generation Satellite observations. The results show that the S3VM model outperforms the standard SVM model, with up to a 15% improvement in classification accuracy compared to the standard SVM. To achieve the second improvement, the S3VM was combined with the Firefly Algorithm (FFA) to optimize its hyperparameters. This hybridization (S3VM-FFA) enabled an even more robust performance. A comparative study showed that the S3VM-FFA approach yielded highly satisfactory results, achieving a 17% improvement in classification compared to the SVM results. Based on these classifications, precipitation quantities at different scales are estimated. Similarly to the classification results, statistical evaluation parameters indicate that the S3VM-FFA outperforms both the standard SVM and the conventional S3VM. Full article

Achieving sustainable land restoration in southern Chinese ionic rare earth mining areas remains a significant challenge due to the extended duration and low efficiency of conventional remediation approaches. Although the hyperaccumulator Dicranopteris pedata possesses a remarkable capacity for rare earth element (REE) enrichment, a significant knowledge gap exists regarding how to effectively combine exogenous organic acids with agronomic practices like clipping to enhance its remediation efficiency in an environmentally sustainable manner. Crucially, the potential environmental risks associated with such synergistic strategies have not been systematically evaluated, hindering their practical application. To address this, our study focused on Dicranopteris pedata and employed integrated pot and soil column leaching experiments to systematically analyze the effects of different concentrations of citric acid and tartaric acid on REE migration and transformation within the soil–plant system. The results demonstrated that exogenous organic acids significantly reduced soil pH and promoted the conversion of REEs from the residual to the exchangeable fraction. Specifically, the 20 mmol·kg⁻¹ citric acid treatment increased the proportion of exchangeable REEs by 43.46%. Furthermore, organic acid treatments significantly altered the REE uptake patterns in Dicranopteris pedata, inhibiting the translocation and accumulation of REEs in the aboveground tissues. Soil column leaching experiments revealed that citric acid drove the migration of REEs to deeper soil layers, with the concentration peaking at 288.33 mg·kg⁻¹ at a depth of 6–8 cm; concomitantly, the REE content in the leachate reached its maximum on the 5th day. This study demonstrates that the combined application of 20 mmol·kg⁻¹ citric acid and 100% clipping management increased the annual REE accumulation in Dicranopteris pedata to 4.85 g·m⁻², thereby significantly shortening the theoretical remediation period from 25.0 years in the control to 12.1 years. Soil column leaching experiments indicated no significant secondary pollution risk associated with this strategy. These findings provide a feasible, low-risk, and sustainable technical strategy for the synergistically enhanced remediation of REE-contaminated soils, offering a promising path for ecological restoration and sustainable land management in degraded mining ecosystems. Full article

In this study, deep tight sandstone was selected as an example to propose a complete method for predicting reservoir pore structure by capillary pressure curves and conventional well log data. This method pioneers the integration of grey relational analysis, principal component analysis, ensemble clustering, and deep neural networks to establish a systematic predictive framework for transitioning from conventional logging data to pore structure types. A total of 186 core data from three wells were used in this study. First, sensitive pore structure parameters from mercury injection capillary pressure data were extracted using grey correlation analysis and principal component analysis. Then, unsupervised clustering analysis was applied to classify the reservoir pore structures in the study area, dividing it into three categories. These category labels were combined with conventional well logs to create learning samples for a deep neural network (DNN) model developed to predict reservoir pore structure categories. The accuracy of the training set of the model reached 88.2%, while the accuracy of the testing set was 80.43%. Finally, the method was applied to field well log data. The results showed significant differences in pore structure classifications among gas layers, water–gas layers, and dry layers. This method is versatile, with its core components transferable to other deep sandstone reservoir studies, and can accurately predict the pore structure of tight sandstone reservoirs, which is critical for advancing the characterization of deep and complex oil and gas reservoirs. Full article

The Aquila Optimizer (AO), inspired by the hunting behavior of Aquila birds, is a recent nature-inspired metaheuristic algorithm recognized for its simplicity and low computational cost. However, the conventional AO often suffers from premature convergence and an imbalance between exploration and exploitation when applied to complex engineering optimization problems. To overcome these limitations, this study proposes a modified Aquila Optimizer (m-AO) incorporating three enhancement strategies: an adaptive chaotic reverse learning mechanism to improve population diversity, an elite alternative pooling strategy to balance global exploration and local exploitation, and a shifted distribution estimation strategy to accelerate convergence toward promising regions of the search space. The performance of the proposed m-AO is evaluated using 23 classical benchmark functions, IEEE CEC 2022 benchmark problems, and a practical plate–fin heat exchanger (PFHE) design optimization problem. Numerical simulations demonstrate that m-AO achieves faster convergence, higher solution accuracy, and improved robustness compared with the original AO and several state-of-the-art metaheuristic algorithms. In the PFHE application, the proposed method yields a significant improvement in thermal performance, accompanied by a reduction in entropy generation and pressure drop under prescribed design constraints. Statistical analyses further confirm the superiority and stability of the proposed approach. These results indicate that the modified Aquila Optimizer is an effective and reliable tool for solving complex thermal system design optimization problems. Full article

The safe pretreatment of phosphogypsum involves a multivariable control process with strong coupling and nonlinear behavior, which limits the effectiveness of conventional control methods. To address this issue, an intelligent control strategy combining fuzzy control with a deep deterministic policy gradient (DDPG) algorithm is proposed. A multi-input multi-output control model is established using pH, moisture content, and flow rate as key variables, and a DDPG agent is employed to adaptively adjust the gain of the fuzzy controller. Simulation results demonstrate that the proposed method achieves faster response and improved stability, yielding a pH settling time of approximately 2.5 s and a steady-state moisture-content error on the order of 0.02 under representative operating conditions. Full article

Renewable-driven microgrids require transparent and adaptive coordination mechanisms to manage variability in distributed generation and flexible demand. Conventional pricing schemes and centralized demand-side programs are often insufficient to regulate real-time imbalances, leading to inefficient renewable utilization and limited prosumer participation. This work proposes a blockchain-integrated Stackelberg pricing model that combines real-time price regulation, optimal demand-side management, and peer-to-peer energy exchange within a unified operational framework. The Microgrid Energy Management System (MEMS) acts as the Stackelberg leader, setting hourly prices and demand response incentives, while prosumers and consumers respond through optimal export and load-shifting decisions derived from quadratic cost models. A distributed supply–demand balancing algorithm iteratively updates prices to reach the Stackelberg equilibrium, ensuring system-level feasibility. To enable trust and tamper-proof execution, smart-contract architecture is deployed on the Polygon Proof-of-Stake network, supporting participant registration, day-ahead commitments, real-time measurement logging, demand-response validation, and automated settlement with negligible transaction fees. Experimental evaluation using real-world demand and PV profiles shows improved peak-load reduction, higher renewable utilization, and increased user participation. Results demonstrate that the proposed framework enhances operational reliability while enabling transparent and verifiable microgrid energy transactions. Full article

Objectives: This study evaluated the analytical and clinical performance of a novel NS1 rapid diagnostic test in a dengue-endemic setting in Thailand. Methods: The K-Dengue NS1 Ag test (K.Bio Sciences, Pathumthani, Thailand) was developed. Analytical performance included determination of LOD, reproducibility, and evaluation against potentially cross-reactive pathogens and interfering substances. Unlike conventional assays employing 40 nm colloidal gold, this test incorporates 80 nm gold nanospheres to enhance detection sensitivity. The LOD was determined by serial dilution of recombinant NS1 proteins representing all four dengue virus serotypes. Clinical performance was assessed using 185 archived plasma samples collected between January 2024 and February 2025 from two tertiary care hospitals in Thailand, with a commercial NS1 ELISA serving as the reference standard. Results: The K-Dengue NS1 test demonstrated serotype-specific limits of detection (LODs) for recombinant NS1 antigen, 2.9 ng/mL (DENV-1), 0.5 ng/mL (DENV-2), 25.2 ng/mL 27 (DENV-3), and 4.5 ng/mL (DENV-4). Cross-reactivity testing revealed no false positives against closely related arboviruses or common co-infections, and no interference was observed from frequently encountered pathogens or biochemical substances. In clinical evaluation, the assay achieved a sensitivity of 98.08% (51/52), a specificity of 100% (133/133), and an overall accuracy of 99.37%. Importantly, sensitivity was significantly higher in primary infections (100.00%) than in secondary infections (93.3%, p = 0.288). Conclusions: In this clinically oriented evaluation, the K-Dengue NS1 rapid test showed high specificity and good sensitivity, particularly in primary dengue infections. While the assay may be useful as part of early diagnostic workflows in comparable healthcare settings, reduced sensitivity in secondary infections indicates that negative NS1 results should be interpreted with caution and, where appropriate, supplemented with additional diagnostic methods. Full article

The recent development in polymer science has gone beyond the traditional linear and randomly functionalizable macromolecules to the architected polymer systems, which integrate modular synthesis and dynamic responsiveness. Although the literature related to polymer synthesis and stimuli-responsive materials and applications is widely discussed, it is common to review the aspects independently, restricting a complete picture of how architectural modularity controls adaptive performance. This gap is filled in this review with an integrated framework of relating modular polymer synthesis, stimuli-responsive design, and application-oriented functionality in a single coherent design philosophy. The scientific novelty of this review is that the focus on modular polymers is not only on synthetic constructs, but is a programmable functional scaffold where the structural precision is the direct determinant of responsiveness, multifunctionality, and performance. Controlled polymerization and post-polymerization modification regimes are mentioned to be tools that allow precise positioning of functional modules, and this allows polymers to respond in predictable ways to environmental stimuli like pH, temperature, light, redox conditions, etc. In addition, the review identifies the role of a synergistic combination of various responsive modules in the emergence of behaviours that would not be reached in conventional polymer systems. This review offers a coherent viewpoint on the future of functional polymers of the next generation by bringing together synthetic approaches to nano-responsive behaviour and real-world technologies, such as drug delivery, self-healing surfaces, adaptive surfaces, and biosensing surfaces. The framework in the present paper provides a logical route towards the development of environmentally friendly, multifunctional, and adjustable polymer structures. Full article

The anti-Markovnikov addition of thiyl radicals, generated via one-electron oxidation of thiols, to C=C double bonds is a useful method for synthesizing unsymmetrical sulfides and has been widely applied in the preparation of pharmaceuticals and functional materials. However, conventional radical thiol–ene reactions require metal-based photoinitiators or organic photosensitizers, raising concerns about product isolation and environmental impact. Herein, we demonstrate an initiator-free thiol–ene coupling via electrochemical oxidation of thiols. Using a microfluidic electrochemical reactor, the electrochemically generated thiyl radicals undergo rapid and selective addition to alkenes, affording thioethers in reasonable yields. Substrate scope studies involving 13 alkenes and 13 thiols indicate that thiol acidity (pK_a), alkene electronic properties, and steric effects play key roles in determining reaction efficiency. Although further optimization is required to improve yields and broaden substrate scope, this electrochemical approach highlights the potential of thiol–ene coupling as a sustainable tool in green synthetic chemistry. Full article