Search Results (14,436)

This paper presents an innovative primary control strategy for a modular multilevel converter aimed at enhancing reliability and dynamic performance for power electronics applications. The proposed method utilises interactive modelling tools, including MATLAB Simulink (2022b) for algorithm design and Typhoon HIL (2023.2) for real-time validation. The circuit design and component analysis were carried out using Proteus Design Suite (v8.17) and LTSpice (v17) to optimise the hardware implementation. A power hardware-in-the-loop experimental test setup was built to demonstrate the robustness and adaptability of the control algorithm under fixed load conditions. The simulation results were compared and verified against the experimental data. Additionally, the proposed control strategy was successfully validated through experiments, demonstrating its effectiveness in simplifying control development through efficient co-simulation. Full article

The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions. Full article

Bone drilling is a widely encountered process in orthopedic surgeries and keyhole neuro surgeries. We are developing a sensor-integrated smart end-effector for drilling for robotic surgical applications. In manual surgeries, surgeons assess tool wear based on experience and force perception. In this work, we propose a machine-learning (ML)-based tool condition monitoring system based on multi-sensor data to preempt excessive tool wear during drilling in robotic surgery. Real-time data is acquired from the six-component force sensor of a collaborative arm along with the data from the temperature and multi-axis vibration sensor mounted on the bone specimen being drilled upon. Raw data from the sensors may have noises and outliers. Signal processing in the time- and frequency-domain are used for denoising as well as to obtain additional features to be derived from the raw sensory data. This paper addresses the challenging problem of identification of the most suitable ML algorithm and the most suitable features to be used as inputs to the algorithm. While dozens of features and innumerable machine learning and deep learning models are available, this paper addresses the problem of selecting the most relevant features, the most relevant AI models, and the optimal hyperparameters to be used in the AI model to provide accurate prediction on the tool condition. A unique framework is proposed for classifying tool wear that combines machine learning-based modeling with multi-sensor data. From the raw sensory data that contains only a handful of features, a number of additional features are derived using frequency-domain techniques and statistical measures. Using feature engineering, we arrived at a total of 60 features from time-domain, frequency-domain, and interaction-based metrics. Such additional features help in improving its predictive capabilities but make the training and prediction complicated and time-consuming. Using a sequence of techniques such as variance thresholding, correlation filtering, ANOVA F-test, and SHAP analysis, the number of features was reduced from 60 to the 4 features that will be most effective in real-time tool condition prediction. In contrast to previous studies that only examine a small number of machine learning models, our approach systematically evaluates a wide range of machine learning and deep learning architectures. The performances of 47 classical ML models and 6 deep learning (DL) architectures were analyzed using the set of the four features identified as most suitable. The Extra Trees Classifier (an ML model) and the one-dimensional Convolutional Neural Network (1D CNN) exhibited the best prediction accuracy among the models studied. Using real-time data, these models monitored the drilling tool condition in real-time to classify the tool wear into three categories of slight, moderate, and severe. Full article

A rational approach is proposed for evaluating the fire resistance of fiber-reinforced polymers (FRP)-strengthened prestressed concrete (PC) beams. This approach expands on conventional fire design principles for PC beams, while incorporating the effects of FRP reinforcement and fire insulation into strength calculations under fire exposure. Simplified equations are utilized to evaluate the cross-sectional temperature distribution in fire-exposed FRP-strengthened PC beams, considering both insulated and uninsulated scenarios. These cross-sectional temperature profiles are then utilized to evaluate the reductions in the strengths of concrete, steel, and FRP based on their temperature-dependent mechanical properties. The moment capacity of the FRP-strengthened PC beams is determined at various fire exposure durations by applying force equilibrium and strain compatibility principles, assuming a full bond with no relative slip between the FRP and the concrete interface under fire exposure. The critical strength limit state is applied at each time interval to determine the failure state of the FRP-strengthened PC beam, with the final time to failure considered to be the fire resistance of the beam. The proposed approach is validated by comparing its results with available test data from FRP-strengthened reinforced concrete (RC) beams. The validated model is applied to evaluate critical parameters governing the fire resistance of FRP-strengthened PC beam. The results show that, without fire insulation, FRP-strengthened PC beams undergo a significant reduction in moment capacity early into fire exposure and fail within 75 min due to the rapid strength degradation of both the CFRP and the prestressing steel. In contrast, the application of 25 mm thick fire insulation allows these beams to retain a substantial portion of their load-bearing capacity for up to 3 h of fire exposure. Full article

Virtual Machine Introspection (VMI) is a powerful technology used to detect and analyze malicious software inside Virtual Machines (VMs) from the outside. Asynchronous access to the VM’s memory can be insufficient for efficient monitoring of what is happening inside of a VM. Active VMI introduces breakpoints to intercept VM execution at relevant points. Especially for frequently visited breakpoints, and even more so for production systems, it is crucial to keep performance overhead as low as possible. In this paper, we present an empirical study that compares the performance of four VMI breakpoint-implementation variants—EPT switching (SLAT view switching) with and without fast single-stepping acceleration, instruction repair, and instruction emulation—from two VMI applications (DRAKVUF, SmartVMI) with the XEN hypervisor on 20 Intel Core i processors ranging from the fourth to the thirteenth generation. Instruction emulation was the fastest method across all 20 tested platforms. Modern processors such as the Intel Core i7 12700H and Intel Core i9 13900HX achieved median breakpoint-processing times as low as 15 µs for the emulation mechanism. The slowest method was instruction repair, followed by EPT switching and EPT switching with FSS. The order was the same for all measurements, indicating that this is a strong and generalizable result. Full article

Objective. The aim of this in vitro study was to compare the physical and mechanical properties of two resins used for provisional prostheses: a direct self-curing dimethacrylate resin and a 3D-printed resin, in order to assess their potential for different clinical applications. Methods. Flexural strength, microhardness, wear resistance, and water absorption were evaluated in accordance with ISO 4049 and ISO 10477. Samples were analyzed using scanning electron microscopy, X-ray spectroscopy, and mechanical testing, including flexural, wear, and scratch assays. Results. The 3D-printed resin demonstrated superior flexural strength (128 ± 2 MPa vs. 127 ± 16 MPa), microhardness (19.45 HV vs. 8.10 HV, p < 0.05), and wear resistance (mean wear area: 0.030 mm² vs. 0.047 mm²) compared to the self-curing dimethacrylate composite. However, it exhibited significantly higher water absorption (55.98 µg/mm³ vs. 15.0 µg/mm³), which may compromise its long-term durability in humid environments. Conclusions. Overall, the 3D-printed resin shows promising mechanical performance, but its high-water absorption remains a limitation for extended use. Further studies are required to evaluate its degradation and behavior under intraoral conditions. Clinical relevance. For the time being, self-curing resins remain the preferred choice for long-term provisional prostheses. Full article

The topic proposal concerns the axes of climate operation and modification, the consequences and/or benefits of the flow of the economy, as well as the risks to social security, amidst the evolution of human interventions, which the Anthropocene highlights. Atmospheric data demonstrates the interaction of gaseous pollutants and aerosols, with the contribution of different emission and pollution sources to its chemical composition. At the same time, satellite remote sensing of precipitation and the water cycle reveal an imbalance in components and effects, in an environment of rapid rates of commercial production and human mobility in the developed world. How does mobility prevent the full observation and modeling of the elements involved (in atmospheric and hydrological data)? What is the role of multi-sensor technologies for detecting gases and what are their applications in decontamination? With sources from bibliographic reviews, data were collected from the detection of point sources of gases and dynamic analyses of the extent of the water surface, in order to highlight the descriptive characteristics of the meteorological phenomena and their activity. The scientific approach to analyzing the individual data is based on the techno-scientific Actor-Network Theory, in order to test their connection and contribution to the overall problematic result. The aim of this study is to build an interdisciplinary analysis with documentation of vulnerabilities in the expression of weather phenomena, of the present geological time. The ambition of the study is to propose principles of regulation and precaution, related to the sustainable development of geo-resources and ways to reduce vulnerability. Full article

Artificial intelligence (AI), as an emerging interdisciplinary field dedicated to simulating and extending human intelligence, is increasingly integrating into the domain of infectious disease medicine with unprecedented depth and breadth. This narrative review is based on a systematic literature search in databases such as PubMed and Web of Science for relevant studies published between 2018 and 2025, with the aim of synthesizing the current landscape. It demonstrates transformative potential, particularly in the realm of diagnostic assistance. Confronting global challenges such as pandemic control, emerging infectious diseases, and antimicrobial resistance, AI technologies offer innovative solutions to these pressing issues. Leveraging its robust capabilities in data mining, pattern recognition, and predictive analytics, AI enhances diagnostic efficiency and accuracy, enables real-time monitoring, and facilitates the early detection and intervention of outbreaks. This narrative review systematically examines the application scenarios of AI within infectious disease diagnostics, based on an analysis of recent literature. It highlights significant technological advances and demonstrated practical outcomes related to high-throughput sequencing (HTS) for pathogen surveillance, AI-driven analysis of digital and radiological images, and AI-enhanced point-of-care testing (POCT). Simultaneously, the review critically analyzes the key challenges and limitations hindering the clinical translation of current AI-based diagnostic technologies. These obstacles include data scarcity and quality constraints, limitations in model generalizability, economic and administrative burdens, as well as regulatory and integration barriers. By synthesizing existing research findings and cataloging essential data resources, this review aims to establish a valuable reference framework to guide future in-depth research, from model development and data sourcing to clinical validation and standardization of AI-assisted infectious disease diagnostics. Full article

In low-speed flow (Ma < 0.3), pressure-sensitive paint (PSP) technology encounters a significant bottleneck in micro-pressure measurements due to the coupled interference of light source instability, camera noise, and paint photodegradation. This study introduces a hardware–algorithm collaborative decoupling framework to address the light noise–degradation coupling issue. The framework integrates real-time light source fluctuation monitoring using a photomultiplier tube (PMT), a combined histogram–wavelet denoising algorithm, and a dynamic photodegradation compensation model. A high-precision static calibration system with a pressure control error of 3.4 Pa was constructed to validate the proposed framework. The experimental results indicate that light source fluctuations contribute an error of 42.61 Pa, accounting for 33% of the total error. After collaborative optimization, the PSP measurement error was reduced to below 50 Pa, representing a 50% improvement compared to previous results (100 Pa). This study provides reliable technical support for micro-pressure measurement applications, such as low-speed wind tunnel testing of aerospace vehicles and microfluidic diagnostics. Full article

Productive and healthy soils are essential in agriculture and other economic uses of land which depend on plant growth, and are under increasing pressure globally. The physical properties of soil, its porosity and pore structure, also have a significant impact on a wide range of environmental factors, such as surface water runoff and greenhouse gas exchange. Methods exist for evaluating soil porosity that are applied in a laboratory environment or by inserting sensors into soil in the field. However, such methods do not readily sample adequately in space or time and are labour-intensive. The purpose of the current study is to investigate the potential for estimation of soil porosity and pore size using the strength of reflection of audio pulses from natural soil surfaces. Estimation of porous material properties using acoustic reflections is well established. But because of the complex, viscous interactions between sound waves and pore structures, these methods are generally restricted to transmissions at low audio frequencies or at ultrasonic frequencies. In contrast, this study presents a novel design for an integrated broad band sensing system, which is compact, inexpensive, and which is capable of rapid, non-contact, and in situ sampling of a soil structure from a small, moving, farm vehicle. The new system is shown to have the capability of obtaining soil parameter estimates at sampling distances of less than 1 m and with accuracies of around 1%. In describing this novel design, special care is taken to consider the challenges presented by real agriculture soils. These challenges include the pasture, through which the sound must penetrate without significant losses, and soil roughness, which can potentially scatter sound away from the specular reflection path. The key to this new integrated acoustic design is an extension of an existing theory for acoustic interactions with porous materials and rigorous testing of assumptions via simulations. A configuration is suggested and tested, comprising seven audio frequencies and three angles of incidence. It is concluded that a practical, new operational tool of similar design should be readily manufactured. This tool would be inexpensive, compact, low-power, and non-intrusive to either the soil or the surrounding environment. Audio processing can be conducted within the scope of, say, mobile phones. The practical application is to be able to easily map regions of an agricultural space in some detail and to use that to guide land treatment and mitigation. Full article

Tall-pier, long-span continuous rigid-frame bridges are prone to wind-induced vibration due to their large spans and pier heights; during cantilever erection, the maximum double-cantilever stage has reduced stiffness and buffeting becomes more evident. Accordingly, a time-domain framework driven by three-component aerodynamic coefficients and their angle-of-attack derivatives is adopted. Code-based target spectra are used to synthesize multi-point fluctuating wind time histories via harmonic superposition, followed by statistical and spectral consistency checks. Buffeting forces are then computed under the quasi-steady assumption, mapped to finite-element nodes, and integrated in time to obtain global responses (displacement and acceleration). In parallel, static six-component wind tunnel tests provide mean force and moment coefficients and their derivatives for comparison. The results indicate that the three-component time-domain approach captures the buffeting features dominated by vertical and torsional responses. When pronounced along-span sectional variation and high angle-of-attack sensitivity are present, errors associated with the strip assumption increase, whereas the force–moment coupling revealed by the six-component data helps explain discrepancies between simulation and tests. These response patterns and error characteristics delineate the applicability and limits of the three-component time-domain evaluation for variable-depth continuous rigid-frame bridges, offering a reference for wind resistance assessment and construction-stage checking of similar bridges. Full article

The growing demand for automated production systems is driving continuous innovation in smart and data-driven manufacturing technologies. In the field of production metrology, the trend is shifting from using measurement laboratories to integrating measurement systems directly into production processes. This has led the Institute of Manufacturing Technology at TU Vienna together with its partners to develop a roughness measurement device that can be directly integrated into machine tools. Building on this foundation, this study tries to find applications beyond mere surface roughness assessment and demonstrates how the device could be applied in broader contexts of manufacturing process monitoring. By linking surface measurements with tool wear monitoring, the study establishes a correlation between surface roughness and wear progression of indexable inserts in milling. It demonstrates how in situ data can support predictive maintenance and the real-time adjustment of cutting parameters. This represents a first step toward integrating in situ metrology into closed-loop control in machining. The experimental setup followed ISO 8688-1 guidelines for tool life testing. Indexable inserts were operated throughout their entire service life while surface roughness was continuously recorded. In parallel, cutting edge conditions were documented at defined intervals using focus variation microscopy. The results show a consistent three-phase pattern: initially stable roughness, followed by a steady increase due to flank wear, and an abrupt decrease in roughness linked to edge chipping. These findings confirm the potential of integrated roughness measurement for condition-based monitoring and the development of adaptive machining strategies. Full article

Background: The second-to-fourth digit ratio (2D:4D) serves as a non-invasive proxy for prenatal androgen exposure. While its relationship with adult athletic ability is well documented, evidence for its association with childhood physical fitness remains inconsistent, and links between 2D:4D and objective fitness measures in prepubertal children are unclear. Methods: In this cross-sectional study, 338 prepubertal children (181 girls, 157 boys; aged 5–12 years) underwent precise measurement of right- and left-hand 2D:4D ratios (intra-class correlation coefficient = 0.94). Physical fitness was evaluated using standardized tests: the Illinois agility run, bent-arm hang, and standing long jump. Results: Among boys, higher 2D:4D ratios were modestly associated with prolonged bent-arm hang performance (β = 0.19, q = 0.04) and shorter Illinois agility times (β = −0.19, q = 0.04). No significant associations were observed in girls. All effect sizes were small, suggesting subtle, sex-dependent influences rather than robust predictors of performance. Conclusions: These findings indicate that prenatal hormonal environment may exert a limited, sex-specific influence on early physical fitness characteristics. Although biologically informative, the observed associations are insufficient for direct application in talent identification in sports. Longitudinal research incorporating direct hormonal measurements and broader populations is recommended to clarify developmental mechanisms and causal pathways. Full article

Electrochromic windows (ECWs) are promising smart façade technologies that can enhance indoor comfort and reduce energy demands, yet their performance in university classrooms remains underexplored in cold regions. This study evaluates the applicability of ECWs in classrooms in Xi’an, a representative cold-climate city, through dynamic simulations of three classroom types. Three control strategies—based on outdoor temperature, illuminance, and solar radiation—were tested under different thresholds. The results show that compared with static windows, ECWs can increase the annual mean indoor temperature by up to 1.4 °C, extend thermal comfort time ratio by 4.5%, improve visual comfort duration by 6.3%, and reduce heating and cooling demands by 11.6 and 14.3 kWh/m², respectively. These findings demonstrate both the feasibility and the differentiated benefits of ECWs in educational buildings, filling the research gap on their performance across different classroom types and offering practical guidance for sustainable classroom design and operation in cold climates. Full article

(1) Background: This research investigates the application of Artificial Intelligence (AI), particularly biomimetic convolutional neural networks (CNNs), for the automatic classification of gastrointestinal (GI) polyps in endoscopic images. The study combines AI and Transfer learning techniques to support early detection of colorectal cancer by enhancing diagnostic accuracy with pre-trained models; (2) Methods: The Kvasir dataset, comprising 4000 annotated endoscopic images across eight polyp categories, was used. Images were pre-processed via normalisation, resizing, and data augmentation. Several CNN architectures, including state-of-the-art optimized ResNet50, DenseNet121, and MobileNetV2, were trained and evaluated. Models were assessed through training, validation, and testing phases, using performance metrics such as overall accuracy, confusion matrix, precision, recall, and F1 score; (3) Results: ResNet50 achieved the highest validation accuracy at 90.5%, followed closely by DenseNet121 with 87.5% and MobileNetV2 with 86.5%. The models demonstrated good generalisation, with small differences between training and validation accuracy. The average inference time was under 0.5 s on a computer with limited resources, confirming real-time applicability. Confusion matrix analysis indicates that common errors frequently occur between visually similar classes, particularly when reviewed by less-experienced medical physicians. These errors underscore the difficulty of distinguishing subtle features in gastrointestinal imagery and highlight the value of model-assisted diagnostics; (4) Conclusions: The obtained results confirm that Deep learning-based CNN architectures, combined with Transfer learning and optimisation techniques, can classify accurately endoscopic images and support medical diagnostics. Full article