Search Results (671)

This paper proposes the expressions for the motor airgap torque harmonics induced by a cascaded H-bridge inverter operating with failed cells. These variable frequency drive systems (VFDs), are widely used in oil and gas applications, where a torsional vibration evaluation is a critical challenge for field engineers. This paper proposes mathematical expressions that are crucial for an accurate torsional analysis during the design stage of VFDs, as required by international standards such as API 617, API 672, etc. By accurately reconstructing the electromagnetic torque from the stator voltages and currents in the (αβ0) reference frame, the obtained expressions enable the precise prediction of the exact locations of torque harmonics induced by the inverter under various real-world operating conditions, without the need for installed torque sensors. The neutral-shifted and peak-reduction fault-tolerant control techniques are commonly adopted under faulty operation of these VFDs. However, their effects on the pulsating torques harmonics in machine air-gap remain uncovered. This paper fulfils this gap by conducting a detailed evaluation of spectral characteristics of these fault-tolerant methods. The theoretical analyses are supported by MATLAB/Simulink 2024 based offline simulation and Typhoon based virtual real-time simulation results performed on a (4.16 kV and 7 MW) vector-controlled induction motor fed by a 7-level cascaded H-bridge inverter. According to the theoretical analyses- and simulation results, the Neutral-shifted and Peak-reduction approaches rebalance the motor input line-to-line voltages in the event of an inverter’s failed cells but, in contrast to the normal mode the carrier, all the triplen harmonics are no longer suppressed in the differential voltage and current spectra due to inequal magnitudes in the phase voltages. These additional current harmonics induce extra airgap torque components that can excite the lowly damped eigenmodes of the mechanical shaft found in the oil and gas applications and shut down the power conversion system due torsional vibrations. Full article

This study investigates the thermal performance of induction motors powered by multilevel H-bridge inverters using a novel pulse-width phase shift triangle modulation (PSTM-PWM) technique. Conventional PWM methods introduce significant harmonic distortion, increasing copper and iron losses and causing overheating and reduced motor lifespan. Through experimental testing and comparison with standard PWM techniques (LS-PWM and PS-PWM), the proposed PSTM-PWM reduces harmonic distortion by up to 64% compared to the worst one and internal motor losses by up to 5.5%. A first-order thermal model is used to predict motor temperature, validated with direct thermocouple measurements and infrared thermography. The results also indicate that the PSTM-PWM technique improves thermal performance, particularly at a triangular waveform peak value of 3.5 V, reducing temperature by around 6% and offering a practical and simple solution for industrial motor drive applications. The modulation order was set to M = 7 to reduce both the losses in the power inverter and to prevent the generation of very high voltage pulses (high dV/dt), which can deteriorate the insulation of the induction motor windings over time. Full article

On-road vision-based systems rely on object detection to ensure vehicle safety and efficiency, making it an essential component of autonomous driving. Deep learning methods show high performance; however, they often require special hardware due to their large sizes and computational complexity, which makes real-time deployment on edge devices expensive. This study proposes lightweight object detection models based on the YOLOv5s architecture, known for its speed and accuracy. The models integrate advanced channel attention strategies, specifically the ECA module and SE attention blocks, to enhance feature selection while minimizing computational overhead. Four models were developed and trained on the KITTI dataset. The models were analyzed using key evaluation metrics to assess their effectiveness in real-time autonomous driving scenarios, including precision, recall, and mean average precision (mAP). BaseECAx2 emerged as the most efficient model for edge devices, achieving the lowest GFLOPs (13) and smallest model size (9.1 MB) without sacrificing performance. The BaseSE-ECA model demonstrated outstanding accuracy in vehicle detection, reaching a precision of 96.69% and an mAP of 98.4%, making it ideal for high-precision autonomous driving scenarios. We also assessed the models’ robustness in more challenging environments by training and testing them on the BDD-100K dataset. While the models exhibited reduced performance in complex scenarios involving low-light conditions and motion blur, this evaluation highlights potential areas for improvement in challenging real-world driving conditions. This study bridges the gap between affordability and performance, presenting lightweight, cost-effective solutions for integration into real-time autonomous vehicle systems. Full article

Nowadays, the circular economy is driving the construction industry towards greater sustainability for both environmental and financial purposes. One prominent area of research with significant contributions to circular economy is the reuse of steel from decommissioned structures in new construction projects. This approach is deemed far more efficient than ordinary steel recycling, due to the fact that it contributes towards reducing both the cost of the new project and the associated carbon emissions. Along these lines, the feasibility of utilizing steel wind turbine towers (WTTs) as part of a new structure is investigated herein, considering that wind turbines are decommissioned after a nominal life of approximately 25 years due to fatigue limitations. General principles of structural steel reuse are first presented in a systematic manner, followed by two case studies. Realistic data about the geometry and cross-sections of previous generation models of WTTs were obtained from the Greek Center for Renewable Energy Sources and Savings (CRES), including drawings and photographic material from their demonstrative wind farm in the area of Keratea. A specific wind turbine was selected that is about to exceed its life expectancy and will soon be decommissioned. Two alternative applications for the reuse of the tower were proposed and analyzed, with emphasis on the structural aspects. One deals with the use of parts of the tower as a small-span pedestrian bridge, while the second addresses the transformation of a tower section into a water storage tank. Several decision factors have contributed to the selection of these two reuse scenarios, including, amongst others, the geometric compatibility of the decommissioned wind turbine tower with the proposed applications, engineering intuition about the tower having adequate strength for its new role, the potential to minimize fatigue loads in the reused state, the minimization of cutting and joining processes as much as possible to restrain further CO₂ emissions, reduction in waste material, the societal contribution of the potential reuse applications, etc. The two examples are briefly presented, aiming to demonstrate the concept and feasibility at the preliminary design level, highlighting the potential of decommissioned WTTs to find proper use for their future life. Full article

Leishmaniasis, a vector-borne neglected tropical disease, affects over 6.2 million people globally. Case acquisition is increasingly recognized in the United States, and in Texas, most reported cases are locally acquired and speciated to Leishmania mexicana. We conducted a scoping literature review to systematically assess contemporary research on Leishmania in humans, animals, reservoir hosts, or vectors in Texas after 2000. Out of 22 eligible studies, the most prevalent themes were case reports, followed by studies on domestic animals, reservoirs, and vectors, with several studies bridging multiple disciplines. Climate change, urbanization, and habitat encroachment appear to be driving the northward expansion of L. mexicana, which is primarily attributed to shifts in the habitats of key vectors (Lutzomyia anthophora) and reservoirs (Neotoma spp.). Leishmania appears to be expanding into new areas, with potential for further spread. As ecological conditions evolve, strengthening surveillance and clinician awareness is crucial to understanding disease risk and improving early detection and treatment in affected communities. Full article

Chemotherapy remains a cornerstone in the treatment of esophageal cancer (EC), yet chemoresistance remains a critical challenge, leading to poor outcomes and limited therapeutic success. Mitochondrial DNA (mtDNA) has emerged as a pivotal player in mediating these responses, influencing cellular metabolism, oxidative stress regulation, and apoptotic pathways. This review provides a comprehensive overview of the mechanisms by which mtDNA alterations, including mutations and copy number variations, drive chemoresistance in EC. Specific focus is given to the role of mtDNA in metabolic reprogramming, including its contribution to the Warburg effect and lipid metabolism, as well as its impact on epithelial–mesenchymal transition (EMT) and mitochondrial bioenergetics. Recent advances in targeting mitochondrial pathways through novel therapeutic agents, such as metformin and mitoquinone, and innovative approaches like CRISPR/Cas9 gene editing, are also discussed. These interventions highlight the potential for overcoming chemoresistance and improving patient outcomes. By integrating mitochondrial diagnostics with personalized treatment strategies, we propose a roadmap for future research that bridges basic mitochondrial biology with translational applications in oncology. The insights offered in this review emphasize the critical need for continued exploration of mtDNA-targeted therapies to address the unmet needs in EC management and other diseases associated with mitochondria. Full article

E-commerce growth has reshaped consumer behavior and retail services, driving parcel demand and challenging last-mile logistics. Existing research predominantly relies on survey data and global regression models that overlook intra-urban spatial heterogeneity in shopping behaviors. This study bridges this gap by analyzing e-commerce demand’s spatial distribution from a retail service perspective, identifying key drivers, and evaluating implications for omnichannel strategies and logistics. Utilizing waybill big data, spatial analysis, and multiscale geographically weighted regression, we reveal: (1) High-density e-commerce demand areas are predominantly located in central districts, whereas peripheral regions exhibit statistically lower volumes. The spatial distribution pattern of e-commerce demand aligns with the urban development spatial structure. (2) Factors such as population density and education levels significantly influence e-commerce demand. (3) Convenience stores play a dual role as retail service providers and parcel collection points, reinforcing their importance in shaping consumer accessibility and service efficiency, particularly in underserved urban areas. (4) Supermarkets exert a substitution effect on online shopping by offering immediate product availability, highlighting their role in shaping consumer purchasing preferences and retail service strategies. These findings contribute to retail and consumer services research by demonstrating how spatial e-commerce demand patterns reflect consumer shopping preferences, the role of omnichannel retail strategies, and the competitive dynamics between e-commerce and physical retail formats. Full article

The architecture, engineering, and construction (AEC) industry faces challenges related to inefficiencies and fragmentation that highlight the need for advanced construction technologies and drive interest in innovative solutions such as the platform approach to design. This study assessed platform-based building design through (1) interviews with practitioners from China, Jordan, and the UK, which helped to define the platform approach in the AEC industry and the challenges involved, and (2) a residential building design simulation conducted to evaluate the potential of the platform approach. The simulated design’s materials costs, energy efficiency, and construction time were compared with those of the traditional building design. The results of the comparison corroborate the interview findings concerning practitioners’ perspectives on platform definition, benefits, challenges, and implementation. The findings also demonstrate the potential of the platform approach to enhance productivity and scalability through modularization, kit-of-parts configuration, and standardization. This research bridges the gap between theory and practice by supporting shareholder perspectives on building design and construction with the results of a simulated platform approach to a real-world design project. This research addresses the urgent need to better understand and test the platform approach to achieve material, energy, and construction time savings through collaborative and practice-informed design. Full article

The Threshold Pressure Gradient (TPG) phenomenon exerts a profound influence on fluid flow dynamics in heavy oil reservoirs. However, the discrepancies between the True Threshold Pressure Gradient (TTPG) and Pseudo-Threshold Pressure Gradient (PTPG) significantly impede accurate residual oil evaluation and rational field development planning. This study proposes a dual-exponential conversion model that effectively bridges the discrepancy between TTPG and PTPG, achieving an average deviation of 12.77–17.89% between calculated and measured TTPG values. Nonlinear seepage simulations demonstrate that TTPG induces distinct flow barrier effects, driving residual oil accumulation within low-permeability interlayers and the formation of well-defined “dead oil zones.” In contrast, the linear approximation inherent in PTPG overestimates flow initiation resistance, resulting in a 47% reduction in recovery efficiency and widespread residual oil enrichment. By developing a TTPG–PTPG conversion model and incorporating genuine nonlinear seepage characteristics into simulations, this study effectively mitigates the systematic errors arising from the linear PTPG assumption, thereby providing a scientific basis for accurately predicting residual oil distribution and enhancing oil recovery efficiency. Full article

Antimicrobial resistance is an escalating global threat that undermines the efficacy of modern antibiotics and places a substantial economic burden on healthcare systems—costing Europe alone over EUR 11.7 billion each year due to rising medical expenses and productivity losses. While genomics and transcriptomics have significantly advanced our understanding of the genetic foundations of resistance, they often fail to capture the dynamic, real-time adaptations that enable bacterial survival. Proteomics, particularly mass spectrometry-based strategies, bridges this gap by uncovering the functional protein-level changes that drive resistance, persistence, and tolerance under antibiotic pressure. In this review, we examine how proteomic approaches provide new insights into resistance mechanisms across various antibiotic classes, with a particular focus on β-lactams, aminoglycosides, and fluoroquinolones, highlighting clinically relevant pathogens, especially members of the ESKAPE group. Finally, we examine future directions, including the integration of proteomics with other omic technologies and the growing role of artificial intelligence in resistance prediction, paving the way for more predictive, personalized, and effective solutions to combat antimicrobial resistance. Full article

Background/Objectives: Oncogenic KRAS drives ~30% of solid tumours, yet the only approved G12C-specific drugs benefit ≈ 13% of KRAS-mutant patients, leaving a major clinical gap. We sought mutation-agnostic natural ligands from Ziziphus lotus, whose stereochemically rich phenolics may overcome this limitation by occupying the SI/II (Switch I/Switch II) groove and locking KRAS in its inactive state. Methods: Phytochemical mining yielded five recurrent phenolics, such as (+)-catechin, hyperin, astragalin, eriodictyol, and the prenylated benzoate amorfrutin A, benchmarked against the covalent inhibitor sotorasib. An in silico cascade combined SI/II docking, multi-parameter ADME/T (Absorption, Distribution, Metabolism, Excretion, and Toxicity) filtering, and 100 ns explicit solvent molecular dynamics simulations. Pharmacokinetic modelling predicted oral absorption, Lipinski compliance, mutagenicity, and acute-toxicity class. Results: Hyperin and astragalin showed the strongest non-covalent affinities (−8.6 kcal mol⁻¹) by forging quadridentate hydrogen-bond networks that bridge the P-loop (Asp30/Glu31) to the α3-loop cleft (Asp119/Ala146). Catechin (−8.5 kcal mol⁻¹) balanced polar anchoring with entropic economy. ADME ranked amorfrutin A the highest for predicted oral absorption (93%) but highlighted lipophilic solubility limits; glycosylated flavonols breached Lipinski rules yet remained non-mutagenic with class-5 acute-toxicity liability. Molecular dynamics trajectories confirmed that hyperin clamps the SI/II groove, suppressing loop RMSF below 0.20 nm and maintaining backbone RMSD stability, whereas astragalin retains pocket residence with transient re-orientation. Conclusions: Hyperin emerges as a low-toxicity, mutation-agnostic scaffold that rigidifies inactive KRAS. Deglycosylation, nano-encapsulation, or soft fluorination could reconcile permeability with durable target engagement, advancing Z. lotus phenolics toward broad-spectrum KRAS therapeutics. Full article

Recent advances in microbiome studies have deepened our understanding of endosymbionts and gut-associated microbiota in host biology. Of those, lepidopteran systems in particular harbor a complex and diverse microbiome with various microbial taxa that are stable and transmitted between larval and adult stages, and others that are transient and context-dependent. We highlight key microorganisms—including Bacillus, Lactobacillus, Escherichia coli, Pseudomonas, Rhizobium, Fusarium, Aspergillus, Saccharomyces, Bifidobacterium, and Wolbachia—that play critical roles in microbial ecology, biotechnology, and microbiome studies. The fitness implications of these microbial communities can be variable; some microbes improve host performance, while others neither positively nor negatively impact host fitness, or their impact is undetectable. This review examines the central position played by the gut microbiota in interactions of insects with plants, highlighting the functions of the microbiota in the manipulation of the behavior of herbivorous pests, modulating plant physiology, and regulating higher trophic levels in natural food webs. It also bridges microbiome ecology and applied pest management, emphasizing S. frugiperda as a model for symbiont-based intervention. As gut microbiota are central to the life history of herbivorous pests, we consider how these interactions can be exploited to drive the development of new, environmentally sound biocontrol strategies. Novel biotechnological strategies, including symbiont-based RNA interference (RNAi) and paratransgenesis, represent promising but still immature technologies with major obstacles to overcome in their practical application. However, microbiota-mediated pest control is an attractive strategy to move towards sustainable agriculture. Significantly, the gut microbiota of S. frugiperda is essential for S. frugiperda to adapt to a wide spectrum of host plants and different ecological niches. Studies have revealed that the microbiome of S. frugiperda has a close positive relationship with the fitness and susceptibility to entomopathogenic fungi; therefore, targeting the S. frugiperda microbiome may have good potential for innovative biocontrol strategies in the future. Full article

3D object detection is a fundamental task in autonomous driving. In recent years, voxel-based methods have demonstrated significant advantages in reducing computational complexity and memory consumption when processing large-scale point cloud data. A representative method, Voxel-RCNN, introduces Region of Interest (RoI) pooling on voxel features, successfully bridging the gap between voxel and point cloud representations for enhanced 3D object detection. However, its robustness deteriorates when detecting distant objects or in the presence of noisy points (e.g., traffic signs and trees). To address this limitation, we propose an enhanced approach named Self-Attention Voxel-RCNN (SA-VoxelRCNN). Our method integrates two complementary attention mechanisms into the feature extraction phase. First, a full self-attention (FSA) module improves global context modeling across all voxel features. Second, a deformable self-attention (DSA) module enables adaptive sampling of representative feature subsets at strategically selected positions. After extracting contextual features through attention mechanisms, these features are fused with spatial features from the base algorithm to form enhanced feature representations, which are subsequently input into the region proposal network (RPN) to generate high-quality 3D bounding boxes. Experimental results on the KITTI test set demonstrate that SA-VoxelRCNN achieves consistent improvements in challenging scenarios, with gains of 2.49 and 1.87 percentage points at Moderate and Hard difficulty levels, respectively, while maintaining real-time performance at 22.3 FPS. This approach effectively balances local geometric details with global contextual information, providing a robust detection solution for autonomous driving applications. Full article

This work systematically altered the molar ratio of CaO and MgO (R = [CaO]/[(CaO + MgO)], mol%) to elucidate the underlying mechanisms driving the observed changes in macroscopic properties. The results indicated that as CaO increasingly replaced MgO, the rise in the content of non-bridging oxygen led to the depolymerization of the glass structure. A quantitative analysis of Qⁿ units in the [SiO₄] tetrahedron using ²⁹Si MAS NMR revealed that a non-monotonic variation appeared when the Q⁴ unit reached a minimum at R = 0.7. Meanwhile, the chemical environment of aluminum also varies with the R, and the presence of high-coordinated aluminum species is observed when Ca²⁺ and Mg²⁺ ions coexist. In terms of overall performance, both density and molar volume exhibited a linear trend. However, thermal stability, viscosity, characteristic temperatures (including melting temperature, Littleton softening temperature, working point temperature, and glass transition temperature), and mechanical properties showed deviations from linearity. Additionally, four non-isothermal thermodynamics was employed to quantitatively assess the thermal stability of samples C-0.7 and C-1. The insights gained from this study will aid in the development of advanced glass materials with tailored properties for industrial applications. Full article

The rapid advancement of artificial intelligence (AI) and machine learning has revolutionised how systems process information, make decisions, and adapt to dynamic environments. AI-driven approaches have significantly enhanced efficiency and problem-solving capabilities across various domains, from automated decision-making to knowledge representation and predictive modelling. These developments have led to the emergence of increasingly sophisticated models capable of learning patterns, reasoning over complex data structures, and generalising across tasks. As AI systems become more deeply integrated into networked infrastructures and the Internet of Things (IoT), their ability to process and interpret data in real-time is essential for optimising intelligent communication networks, distributed decision making, and autonomous IoT systems. However, despite these achievements, the internal mechanisms that drive LLMs’ reasoning and generalisation capabilities remain largely unexplored. This lack of transparency, compounded by challenges such as hallucinations, adversarial perturbations, and misaligned human expectations, raises concerns about their safe and beneficial deployment. Understanding the underlying principles governing AI models is crucial for their integration into intelligent network systems, automated decision-making processes, and secure digital infrastructures. This paper provides a comprehensive analysis of explainability approaches aimed at uncovering the fundamental mechanisms of LLMs. We investigate the strategic components contributing to their generalisation abilities, focusing on methods to quantify acquired knowledge and assess its representation within model parameters. Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how knowledge is structured, encoded, and retrieved in AI systems. Furthermore, by adopting a mechanistic perspective, we analyse emergent phenomena within training dynamics, particularly memorisation and generalisation, which also play a crucial role in broader AI-driven systems, including adaptive network intelligence, edge computing, and real-time decision-making architectures. Understanding these principles is crucial for bridging the gap between black-box AI models and practical, explainable AI applications, thereby ensuring trust, robustness, and efficiency in language-based and general AI systems. Full article