Search Results (745)

Induction motors piloted by voltage source inverters constitute a major source of acoustic noise in industry. The discrete tonal bands generated by induction motor stator current spectra controlled by the fixed Pulse Width Modulation (PWM) technique have damaging effects on the electronic noise source. Nowadays, the investigation of new advanced control techniques for variable speed drives has developed a potential investigation field. Finite state model predictive control has recently become a very popular research focus for power electronic converter control. The flexibility of this control shows that the switching times are generated using all the information on the drive status. Predictive Torque Control (PTC), space vector PWM and random PWM are investigated in this paper in terms of acoustic noise emitted by an induction machine fed by a three-phase two-level inverter. A comparative study based on electrical and mechanical magnitudes, as well as harmonic analysis of the stator current, is presented and discussed. An experimental test bench is also developed to examine the effect of the proposed PTC and PWM techniques on the acoustic noise of an induction motor fed by a three-phase two-level voltage source converter. Full article

Local recurrence after breast cancer surgery presents a critical challenge, demanding novel local immunotherapies capable of eliminating residual disease while avoiding systemic toxicity. In situ-forming hydrogels, functionalized with bioactive cargoes, represent a promising platform for precise spatiotemporal drug delivery directly into the post-resection tumor microenvironment. This review comprehensively examines the core design principles governing these advanced materials, highlighting their biocompatibility, stimuli-responsive behavior, tunable mechanics for conforming to surgical cavity, and capacity for multifunctional integration. A key mechanism discussed is how this controlled release profile orchestrates a temporal progression from innate immune activation to robust adaptive immunity. Despite significant promise, translational success faces substantial hurdles, including efficacy validation, scalable manufacturing, regulatory pathway definition, and the lack of predictive biomarkers. Future research priorities include optimizing drug/antigen release kinetics, establishing standardized characterization methods for complex biohybrid systems, and designing adaptive clinical trials incorporating detailed immunomonitoring. By integrating functional biomaterials with immuno-oncology, in situ-forming hydrogels offer a paradigm-shifting approach for postoperative cancer treatment. This review provides a strategic roadmap to accelerate their translation from bench to bedside. Full article

Modern research laboratories are constantly evolving to meet the growing demands for precision, quality, and flexibility in scientific work. The modernization of existing experimental test benches plays a crucial role in improving efficiency, optimizing processes, and ensuring operational safety. This requires updates to their design, experimental methods, data collection, and results recording—all of which provide the foundation for developing new research concepts. An increasing number of innovations are now guided by the principle of minimizing environmental impact. In line with this approach, an innovative modernization of a tube furnace research station was carried out, based on the concepts of sustainable development and the zero-waste philosophy. To enable thermogravimetric analyses of coffee waste, a previously incomplete tube furnace was refurbished using recycled components. The primary objective was to expand the research capabilities of the existing workstation. As part of the modernization, three indicators of reuse efficiency were calculated: the quantitative indicator W_re-use, the mass indicator $W_{re\text{-}use}^{mass}$, and the cost indicator $W_{re\text{-}use}^{value}$. A quantitative index of 78% and a mass index of approximately 76% were achieved, while the economic value of the recovered components accounted for 11% of the total value of the revitalized research station. This strategy significantly reduced waste generation, carbon dioxide emissions, and the consumption of primary raw materials. Full article

Parkinson’s disease (PD) is a disabling neurodegenerative disorder that is defined by progressive loss of dopaminergic neurons in the substantia nigra, deposition of α-synuclein aggregates, and chronic neuroinflammation. While symptomatic therapies have evolved, disease-modifying therapies remain elusive. Extracellular vesicles (EVs), particularly those derived from mesenchymal stem cells (MSC-EVs), have emerged as promising therapeutic agents because they possess a natural ability to cross the blood–brain barrier and deliver bioactive cargo. Herein, we review the dual-edged function of EVs in PD pathogenesis: facilitating the transfer of toxic α-synuclein while also conferring neuroprotective signals through MSC-EVs. We outline the mechanisms of MSC-EV-mediated neuroprotection that include the regulation of oxidative stress, neuroinflammation, and autophagy. We also emphasize new nanocarrier systems designed to bypass delivery challenges in PD therapy. While preclinical studies are extremely encouraging, significant issues regarding scalability, standardization, and clinical translation must be resolved before realizing the ultimate therapeutic potential of EV-based and nanocarrier-based approaches to PD. Full article

Excessive exhaust backpressure (EBP) in modern diesel engines disrupts gas exchange, increases residual gas fraction (RGF), and reduces combustion efficiency. Traditional experimental approaches, including simulations and bench testing, are often time-consuming and costly, which has driven growing interest in artificial neural networks (ANNs) for accurately modelling complex engine behavior. This research introduces an ANN model designed to predict the impact of EBP on the performance and emissions of a diesel engine across varying compression ratio (CR) of 12, 14, 16, and 18 and engine load (25%, 50%, 75%, and 100%) conditions. The ANN model was developed and optimised using genetic algorithms (GA) and particle swarm optimisation (PSO). It was then trained using data from an experimentally validated one-dimensional computational fluid dynamics (1D-CFD) model developed through GT-Power GT-ISE v2024, simulating engine responses under variation CR, load, and EBP conditions. The optimised ANN architecture, featuring an optimal (3-14-10) configuration, was trained using the Levenberg–Marquardt back propagation algorithm. The performance of the model was assessed using statistical criteria, including the coefficient of determination (R²), root mean square error (RMSE), and k-fold cross-validation, by comparing its predictions with both experimental and simulated data. Results indicate that the optimised ANN model outperformed the baseline ANN and other machine learning (ML) models, attaining an R² of 0.991 and an RMSE of 0.011. It reliably predicts engine performance and emissions under varying EBP conditions while offering insights for engine control, optimisation, diagnostics, and thermodynamic mechanisms. The overall prediction error ranged from 1.911% to 2.972%, confirming the model’s robustness in capturing performance and emission outcomes. Full article

This review catalogs candidate LUAD driver genes and their roles, recent discoveries, and therapeutic avenues. Beyond experimental repurposing, we evaluate modern computational methods and how they complement bench work. We conclude by appraising recent LUAD repurposing studies through a computational lens, emphasizing practical integration into translational research. Highlights: Overview of drug repurposing methods: We provide a list of six experimental and a brief taxonomy of eight computational drug repurposing method families. Recent insights into LUAD driver genes: We present a curated panel of LUAD drivers mapped to pathways, with alteration types, functions, and therapeutic implications. LUAD-focused computational repurposing studies: We provide a synthesis of recent LUAD studies presenting clear method families, highlighting exemplar pipelines, prioritized candidate drugs, and datasets. Full article

In response to the dust pollution issue during the harvesting operations of peanut pickup combines, this study involved conducting bench tests to explore the variation patterns of dust emission parameters and harvesting operation indicators under diverse working parameter conditions of the combine’s working components. A multi-factor mathematical model was established to predict both the dust emission rate of peanut pickup combines and the quality of harvesting operations. The model was utilized to identify the optimal combination of operation parameters for achieving high-quality and low-emission performance. The optimal parameter combination was determined as follows: a pod threshing roller speed of 313 r/min, a cleaning fan speed of 2535 r/min, a vine crushing roller speed of 1970 r/min, and a lifting fan speed of 1604 r/min. Under these conditions, the theoretical dust emission rate was calculated to be 10,603 mg/s, with a pod loss rate of 4.73% and a pod impurity rate of 5.21%. Compared to previous settings, the optimized operation parameters effectively reduced the combine’s dust emissions by 9.95%. Notably, the harvesting operation quality still complies with the industry standards for peanut harvesters. These research findings offer theoretical insights and robust technical support for minimizing dust pollution during the whole-feed harvesting of peanuts, contributing to more environmentally friendly and efficient peanut harvesting practices. Full article

The idea that gut microbes or a “bacillus of crime” might promote criminal behavior was popularized in the early 20th century. Today, advances in neuromicrobiology and related omics technologies are lending credibility to the idea. In recent cases of dismissal of driving while intoxicated charges, courts in the United States and Europe have acknowledged that gut microbes can manufacture significant amounts of systemically available ethanol, without a defendant’s awareness. Indeed, emergent research is raising difficult questions for criminal justice systems that depend on prescientific notions of free moral agency. Evidence demonstrates that gut microbes play a role in neurophysiology, influencing cognition and behaviors. This may lead to justice involvement via involuntary intoxication, aggression, anger, irritability, and antisocial behavior. Herein, we discuss these ‘auto-brewery syndrome’ court decisions, arguing that they portend a much larger incorporation of neuromicrobiology and multi-omics science within the criminal justice system. The legalome, which refers to the application of gut microbiome and omics sciences in the context of forensic psychiatry/psychology, will likely play an increasing role in 21st century criminal justice. The legalome concept is bolstered by epidemiology, mechanistic bench science, fecal transplant studies, multi-omics and polygenic research, Mendelian randomization work, microbiome signature research, and human intervention trials. However, a more robust body of microbiota–gut–brain axis research is needed, especially through the lens of prevention, intervention, and rehabilitation. With ethical guardrails in place, greater inclusion of at-risk or justice-involved persons in brain science and microbiome research has the potential to transform justice systems for the better. Full article

The research investigated the potential of five novel additively manufactured (AM) fiber-reinforced thermoplastic composite (FRTPC) configurations as alternatives for ablative thermal protection system (TPS) applications. The thermal stability and ablative behavior of ten samples developed via fused deposition modeling (FDM) three-dimensional (3D) printing out of fire-retardant thermoplastics were investigated using an in-house oxyacetylene torch bench. All samples featured an innovative internal thermal management architecture with three air chambers. Furthermore, the enhancement of thermal benefits was achieved through several approaches: ceramic coating, mechanical hybridization, or continuous fiber reinforcement. For each configuration, two samples were exposed to flame at 1450 ± 50 °C for 30 s and 60 s, respectively, with the front surface subjected to direct exposure at a distance of 100 mm during the ablation tests. Internal temperatures recorded at two back-side contact points remained below 50 °C, well under the 180 °C maximum allowable back-face temperature for TPS during testing. Continuous reinforced configurations 4 and 5 displayed higher thermal stability the lowest values in terms of thickness, mass loss, and recession rates. Both configurations showed half of the weight losses measured for the other tested configurations, ranging from approximately 5% (30 s) to 10–12% (60 s), confirming the trend observed in the thickness loss measurements. However, continuous glass-reinforced configuration 5 exhibited the lowest weight loss values for both exposure durations, benefiting from its non-combustible nature, low thermal conductivity, and high abrasion resistance intrinsic characteristics. In particular, the Al₂O₃ surface coated configuration 1 showed a mass loss comparable to reinforced configurations, indicating that an enhanced surface coat adhesion could provide a potential benefit. A key outcome of the study was the synergistic effect of the novel air chamber architecture, which reduces thermal conductivity by forming small internal air pockets, combined with the continuous front-wall fiber reinforcement functioning as a thermal and abrasion barrier. This remains a central focus for future research and optimization. Full article

This study presents an advanced diagnostic methodology for assessing mechanical faults in high-performance jet engines using non-contact laser vibrometry and Fourier-based vi-bration analysis. Focusing on Pratt & Whitney F100-PW-229 engines used in F-16 aircraft, thise research identifies critical measurement locations, including the gearbox, turbine, and compressor supports. High-resolution vibration signals were collected under test bench conditions and processed using fFast Fourier tTransform (FFT) techniques to extract frequency-domain features indicative of rotor imbalances, bearing wear, and structural anomalies. Comparative analysis between nominal and degraded engines confirmed strong correlations between analytical predictions and empirical spectral patterns. Thise study introduces a signal processing framework combining time–frequency analysis with Relief-F-based feature selection, laying the groundwork for future integration with ma-chine learning algorithms. This non-intrusive, efficient diagnostic method supports early fault detection, enhances engine availability, and contributes to the development of a na-tional vibration reference database, especially vital in the absence of OEM-supplied tools. Full article

Modern automotive steering systems exhibit inherent bilateral symmetry characteristics that can be mathematically described using group theory. When component failures occur, these systems experience quantifiable symmetry-breaking patterns that serve as diagnostic indicators. This research presents an approach that combines group-theoretic principles with machine learning for automotive steering system fault diagnosis. The study introduces a physics-informed neural network architecture that leverages the mathematical structure of bilateral symmetry for enhanced fault detection capabilities. Through systematic analysis of eight distinct fault categories including sensor malfunctions, actuator degradation, control system failures, and mechanical wear patterns, the proposed framework demonstrates that symmetry-breaking signatures provide reliable diagnostic features. The framework integrates symmetric convolutional operations with transformer-based attention mechanisms, optimized through physics-constrained particle swarm algorithms. Experimental validation using both simulation data (12,500 scenarios) and physical test bench measurements shows classification accuracy of 94.2% compared to traditional CNN-LSTM (86.2%), SVM (78.9%), and Random Forest (82.7%) approaches. The bilateral symmetry analysis achieves 91.8% sensitivity for fault detection in controlled laboratory environments. These results establish the practical viability of group-theoretic methods for automotive diagnostics while providing a foundation for condition-based maintenance strategies in intelligent vehicle systems. Full article

Nano-based drug delivery systems present a promising approach to improve the efficacy and safety of therapeutics by enabling targeted drug transport and controlled release. In parallel, computational approaches—particularly Molecular Dynamics (MD) simulations and Artificial Intelligence (AI)—have emerged as transformative tools to accelerate nanocarrier design and optimise their properties. MD simulations provide atomic-to-mesoscale insights into nanoparticle interactions with biological membranes, elucidating how factors such as surface charge density, ligand functionalisation and nanoparticle size affect cellular uptake and stability. Complementing MD simulations, AI-driven models accelerate the discovery of lipid-based nanoparticle formulations by analysing vast chemical datasets and predicting optimal structures for gene delivery and vaccine development. By harnessing these computational approaches, researchers can rapidly refine nanoparticle composition to improve biocompatibility, reduce toxicity and achieve more precise drug targeting. This review synthesises key advances in MD simulations and AI for two leading nanoparticle platforms (gold and lipid nanoparticles) and highlights their role in enhancing therapeutic performance. We evaluate how in silico models guide experimental validation, inform rational design strategies and ultimately streamline the transition from bench to bedside. Finally, we address key challenges such as data scarcity and complex in vivo dynamics and propose future directions for integrating computational insights into next generation nanodelivery systems. Full article

This paper presents the development, modeling, and validation of a scaled UAV-VTOL low-cost prototype equipped with a jet propulsion system with vertical take-off and landing capabilities. The prototype is designed as an experimental testbed for reusable rocket control strategies, with a particular focus on thrust vectoring and landing stabilization. The study begins with the evolution of the CAD, followed by a guide for the correct assembly of the device. The development of the electronic system included the integration of an ARM Cortex-M7 microcontroller, inertial sensors, and a LIDAR-based altitude measurement system; this was enhanced by a Kalman estimator to mitigate the sensor’s noise. A series of experimental tests were conducted to characterize the key subsystems. Actuator characterization improved the linearized nozzle control model, ensuring predictable thrust redirection. The test bench results confirmed the EDF’s thrust curve and its ability to sustain controlled flight, despite minor losses due to battery discharge variations. Furthermore, state-space modeling aided the development of controllers for altitude stabilization and attitude control, with simulations proving the feasibility of maintaining stable flight conditions. Experimental validation confirmed that the prototype provides a practical platform for future research in reusable rocket dynamics and autonomous landing algorithms. Full article

With the continuous advancement of intelligent, integrated, and sophisticated modern marine equipment, bearing fault diagnosis faces increasingly severe technical challenges. Compared with traditional industrial environments, marine propulsion systems are characterized by multi-bearing coupled vibrations and complex operating conditions. To address these characteristics, this paper proposes a fault diagnosis method that combines a least squares support vector machine (LSSVM) with multi-domain feature extraction based on an improved hippopotamus optimization algorithm (LCM-HO). This method directly extracts time, spectral, and time-frequency domain features from the raw signal, effectively avoiding complex preprocessing and enhancing its potential for field engineering applications. Experimental verification using the Paderborn bearing dataset and a self-built marine bearing test bench demonstrates that the LCM-HO-LSSVM method achieves diagnostic accuracy rates of 99.11% and 98.00%, respectively, demonstrating significant performance improvements. This research provides a reliable, efficient, and robust technical solution for bearing fault diagnosis in complex marine environments. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article