Search Results (4,104)

Climate concerns are driving the automotive industry to adopt advanced manufacturing technologies that aim to improve energy efficiency and reduce vehicle weight. In this context, lightweight structural materials such as aluminium alloys have gained significant attention due to their favorable strength-to-weight ratio. Laser welding plays a crucial role in assembling such materials, offering high flexibility and fast joining capabilities for thin aluminium sheets. However, welding these materials presents specific challenges, particularly in controlling heat input to minimize distortions and ensure consistent weld quality. As a result, numerical simulations based on the Finite Element Method (FEM) are essential for predicting weld-induced phenomena and optimizing process performance. This study investigates welding-induced distortions in laser butt welding of 1.5 mm-thick Al 6061 samples through FEM simulations performed in the SYSWELD 2024.0 environment. The methodology provided by the software is based on the Moving Heat Source (MHS) model, which simulates the physical movement of the heat source and typically requires extensive calibration through destructive metallographic testing. This transient approach enables the detailed prediction of thermal, metallurgical, and mechanical behavior, but it is computationally demanding. To improve efficiency, the Imposed Thermal Cycle (ITC) model is often used. In this technique, a thermal cycle, extracted from an MHS simulation or experimental data, is imposed on predefined subregions of the model, allowing only mechanical behavior to be simulated while reducing computation time. To avoid MHS-based calibration, this work proposes using thermal cycles acquired in-line during welding via infrared thermography as direct input for the ITC model. The method was validated experimentally and numerically, showing good agreement in the prediction of distortions and a significant reduction in workflow time. The distortion values from simulations differ from the real experiment by less than 0.3%. Our method exhibits a slight decrease in performance, resulting in an increase in estimation error of 0.03% compared to classic approaches, but more than 85% saving in computation time. The integration of real process data into the simulation enables a virtual representation of the process, supporting future developments toward Digital Twin applications. Full article

Cassava is the sixth most important food crop and is cultivated in more than 100 countries. The crop tolerates low soil fertility and drought, enabling it to play a role in climate adaptation strategies. Cassava generally requires careful preparation to remove toxic hydrogen cyanide (HCN) before its consumption, but HCN concentrations can vary considerably between varieties. Climate change and low inputs, particularly carbon and nutrients, affect agriculture in Pacific Island countries where cassava is commonly grown alongside traditional crops (e.g., taro). Despite increasing popularity in this region, there is limited experimental data about cassava crop management for different local varieties, their relative toxicity and nutritional value for human consumption, and their interaction with changing climate conditions. To help address this knowledge gap, three field experiments were conducted at the Koronivia Research Station of the Fiji Ministry of Agriculture. Two varieties of cassava with contrasting HCN content were planted at three different times coinciding with the start of the wet (September-October) or dry (April) seasons. A time series of measurements was conducted during the full 18-month or differing 6-month durations of each crop, based on destructive harvests and phenological observations. The former included determination of total biomass, HCN potential, carbon isotopes (δ¹³C), and elemental composition. Yield and nutritional value were significantly affected by variety and time of planting, and there were interactions between the two factors. Findings from this work will improve cassava management locally and will provide a valuable dataset for agronomic and biophysical model testing. Full article

Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R² average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Full article

The use of aerial robots for inspection and maintenance in industrial settings demands high maneuverability, precise control, and reliable measurements. This study explores the development of a fully customized unmanned aerial manipulator (UAM), composed of a tilting drone and an articulated robotic arm, designed to perform non-destructive in-contact inspections of iron structures. The system is intended to operate in complex and potentially hazardous environments, where autonomous execution is supported by shared-control strategies that include human supervision. A parallel force–impedance control framework is implemented to enable smooth and repeatable contact between a sensor for ultrasonic testing (UT) and the inspected surface. During interaction, the arm applies a controlled push to create a vacuum seal, allowing accurate thickness measurements. The control strategy is validated through repeated trials in both indoor and outdoor scenarios, demonstrating consistency and robustness. The paper also addresses the mechanical and control integration of the complex robotic system, highlighting the challenges and solutions in achieving a responsive and reliable aerial platform. The combination of semi-autonomous control and human-in-the-loop operation significantly improves the effectiveness of inspection tasks in hard-to-reach environments, enhancing both human safety and task performance. Full article

Non-destructive concrete strength prediction faces limitations in validation scope, methodological comparison, and interpretability that constrain deployment in safety-critical construction applications. This study presents a machine learning framework integrating polynomial feature engineering, AdaBoost ensemble regression, and Bayesian optimization to achieve both predictive accuracy and physics-consistent interpretability. Eight state-of-the-art methods were evaluated across 4420 concrete samples, including statistical significance testing, scenario-based assessment, and robustness analysis under measurement uncertainty. The proposed PolyBayes-ABR methodology achieves R² = 0.9957 (RMSE = 0.643 MPa), showing statistical equivalence to leading ensemble methods, including XGBoost (p = 0.734) and Random Forest (p = 0.888), while outperforming traditional approaches (p < 0.001). Scenario-based validation across four engineering applications confirms robust performance (R² > 0.93 in all cases). SHAP analysis reveals that polynomial features capture physics-consistent interactions, with the Curing_age × Er interaction achieving dominant importance (SHAP value: 4.2337), aligning with established hydration–microstructure relationships. When accuracy differences fall within measurement uncertainty ranges, the framework provides practical advantages through enhanced uncertainty quantification (±1.260 MPa vs. ±1.338 MPa baseline) and actionable engineering insights for quality control and mix design optimization. This approach addresses the interpretability challenge in concrete engineering applications where both predictive performance and scientific understanding are essential for safe deployment. Full article

Post-tensioned (PT) reinforced concrete bridges are particularly vulnerable structures, as the deterioration of internal tendons is often difficult to detect using conventional inspection methods or visual assessments. This paper introduces a practical framework for ranking non-destructive testing (NDT) techniques employed to assess PT systems. The ranking is based on four performance categories: measurement accuracy, ease of use, cost, and impact of disruption to bridge operations on traffic. For each NDT technique, a score is assigned for each evaluation category, and the final ranking is determined using the weighted sum model (WSM). This approach enables the final assessment to reflect the priorities of different decision-making contexts defined by the end-user such as accuracy-oriented, cost-oriented, and impact-oriented scenarios. The proposed method is then applied to an existing bridge in order to practically demonstrate its effectiveness and the flexibility of the proposed criteria. Full article

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production. Full article

This research presents the development and management principles of mobile resonant electrical systems designed for high-voltage generation, intended for non-destructive diagnostics of insulation in high-power electrical equipment. The core of the system is a series inductive–capacitive (LC) circuit characterized by a high quality (Q) factor and operating at high frequencies, typically in the range of 40–50 kHz or higher. Practical implementations of the LC circuit with Q-factors exceeding 200 have been achieved using advanced materials and configurations. Specifically, ceramic capacitors with a capacitance of approximately 3.5 nF and Q-factors over 1000, in conjunction with custom-made coils possessing Q-factors above 280, have been employed. These coils are constructed using multi-core, insulated, and twisted copper wires of the Litzendraht type to minimize losses at high frequencies. Voltage amplification within the system is effectively controlled by adjusting the current frequency, thereby maximizing voltage across the load without increasing the system’s size or complexity. This frequency-tuning mechanism enables significant reductions in the weight and dimensional characteristics of the electrical system, facilitating the development of compact, mobile installations. These systems are particularly suitable for on-site testing and diagnostics of high-voltage insulation in power cables, large rotating machines such as turbogenerators, and other critical infrastructure components. Beyond insulation diagnostics, the proposed system architecture offers potential for broader applications, including the charging of capacitive energy storage units used in high-voltage pulse systems. Such applications extend to the synthesis of micro- and nanopowders with tailored properties and the electrohydropulse processing of materials and fluids. Overall, this research demonstrates a versatile, efficient, and portable solution for advanced electrical diagnostics and energy applications in the high-voltage domain. Full article

Sickle cell disease (SCD) is a prevalent genetic disorder caused by a mutation in the beta-globin gene. Hyperhemolysis (HS) is a severe complication involving the rapid destruction of both transfused and endogenous red blood cells, commonly found in SCD. This literature review explores the clinical presentation, diagnosis, pathogenesis, and management of HS in SCD. HS can manifest acutely or in a delayed manner, complicating diagnosis due to overlapping symptoms and varying reticulocyte responses. Immunohematological assessments often reveal delayed positivity in direct antiglobulin tests and antibody screens. HS typically presents severe anemia, jaundice, hemoglobinuria, and hemodynamic instability. Diagnostic markers include elevated bilirubin and lactate dehydrogenase levels alongside a reduced reticulocyte count. The management of HS is primarily empirical, with no clinical trials to support standardized treatment protocols. First-line treatments involve steroids and intravenous immunoglobulins (IVIG), which modulate immune responses and mitigate hemolysis. Refractory cases may require additional agents such as rituximab, eculizumab, tocilizumab, and, in some instances, plasma exchange or erythropoietin-stimulating agents. Novel therapeutic approaches, including bortezomib and Hemopure, have shown promise but require further investigation. Current management strategies are empirical, underscoring the need for robust clinical trials to establish effective treatment protocols that ultimately improve outcomes for SCD patients experiencing HS. Full article

Tomatoes are among the most widely consumed and economically significant fruits in the world. However, the extensive use of pesticides in their cultivation has led to the contamination of the peels, posing potential health risks to consumers. As one of the top global producers, consumers, and exporters of tomatoes, Mexico requires rapid, non-destructive, and real-time methods for pesticide monitoring. In this study, a detailed characterization of six pesticides using Raman and Fourier Transform Infrared (FT-IR) spectroscopies was carried out to identify their characteristic vibrational modes. The pesticides examined included different chemical classes commonly used in tomato cultivation: organophosphorus (dichlorvos and methamidophos), pyrethroids (lambda-cyhalothrin and cypermethrin), and carbamates (methomyl and benomyl). Tomato peel samples were examined both before and after pesticide application. Prior to treatment, the peel exhibited a well-organized polygonal structure and showed the presence of carotenoid compounds. After pesticide application, no visible structural damage was observed; however, distinct vibrational bands enabled the detection of each pesticide. Organophosphorus pesticides could be identified through vibrational bands associated with P-O and C-S bonds. Pyrethroid detection was facilitated by benzene ring breathing modes and C=C stretching vibrations, while carbamates were identified through C-N stretching contributions. Phytotoxicity testing in the presence of pesticides indicates no significant damage during the germination of tomatoes. Full article

Introduction/Objectives: Osteoarthritis (OA) is a chronic systemic disease that affects the entire array of joint structures. It is one of the most common chronic, socially significant diseases, associated with a decline in the quality of life of patients and constantly increasing the cost of treatment. Clinical trial outcomes are largely inconclusive, and OA remains one of the few musculoskeletal diseases without an established disease-modifying therapy. One potential explanation is the use of ineffective tools for OA classification, patient stratification, and the assessment of disease progression. There is growing interest in musculoskeletal ultrasonography (MSK US), as it enables the dynamic visualization of the examined structures and gives information about both inflammatory and structural changes that have occurred. Determining the leading ultrasound phenotype, which depends on the most damaged tissue at a given time (bone, cartilage, synovial membrane, joint capsule, ligaments, tendons, menisci, etc.), can rationalize therapy use by selecting patients more suitable for specific treatments. This article aims to evaluate and summarize the potential of MSK US in the process of determining the clinical phenotype of OA and to emphasize the importance of this imaging modality in evaluating further therapeutic strategies. Method: A single-center prospective study conducted in the period of September 2023–June 2024 enrolled 259 consecutive patients with proven OA. The statistical program Minitab version 22.2.1 (2025) was used to analyze the data. The predominant and secondary phenotypes were tabulated for each OA localization and were presented numerically and as relative proportions (%). The rate of the most frequently occurring phenotypes was compared against that of the less frequent ones through paired z-tests. The initially acceptable type I error was set at 5%; it was further adjusted for the number of comparisons (Bonferroni). Results: The most frequent and predominant US phenotype for patients with knee OA was intra-articular effusion (n = 47, 37.90%). It was significantly higher compared to the rest of the US phenotypes: synovial proliferation (n = 22, 17.70%; p < 0.001), cartilage destruction (n = 26, 21%; p = 0.001), altered subchondral bone (n = 8, 6.50%; p < 0.001), extra-articular soft tissue changes (n = 12, 9.70%; p < 0.001), crystal deposits (n = 6, 4.8%; p < 0.001), and post-traumatic (n = 3, 2.40%; p < 0.001). The most common US phenotype for hip OA was altered subchondral bone (n = 32, 47.1%), with significant differences from intra-articular effusion (n = 12, 17.60%; p = 0.001), synovial proliferation (n = 5, 7.40; p = 0.001), cartilage destruction (n = 12, 17.60%; p = 0.001), extra-articular soft tissue changes (n = 3, 4.40%; p = 0.001), crystal deposits (n = 3, 4.40%; p = 0.001), and post-traumatic (n = 0). Altered subchondral bone was also the leading US phenotype for hand OA (n = 31, 55.40%), with significant differences compared to intra-articular effusion (n = 1, 1.80%; p < 0.001), synovial proliferation (n = 7, 12.50%; p < 0.001), cartilage destruction (n = 11, 19.60%; p < 0.001), extra-articular soft tissue changes (n = 2, 3.60%; p < 0.001), crystal deposits (n = 3, 5.40%; p < 0.001), and post-traumatic (n = 1, 1.80%, p < 0.001). For shoulder OA, extra-articular soft tissue changes were the most frequent (n = 8, 46.20%), followed by post-traumatic (n = 4, 30.70%), as the rate of both phenotypes was significantly higher compared to that of intra-articular effusion (n = 0), synovial proliferation (n = 0), cartilage destruction (n = 1, 7.70%; p = 0.003), and crystal deposits (n = 0). Conclusions: The therapeutic approach for OA is a dynamic and intricate process, for which the type of affected joint and the underlying pathogenetic mechanism at a specific stage of the disease’s evolution is essential. MSK US is one of the options for the clinical phenotyping of OA. Some of the suggested ultrasound subtypes may serve as the rationale for selecting a particular treatment. Full article

This study introduces and validates a comparative experiment-based method for determining the effective thickness of composite glass, including polymeric laminated glass (with polyvinyl butyral (PVB) and SentryGlas^® (SGP) interlayers) and vacuum glazing. This method employs comparative four-point bending tests, defining effective thickness by equating the bending stress of a composite specimen to that of a reference monolithic glass specimen under identical loading and boundary conditions. Specimens with varying configurations (glass thicknesses of 5 mm, 6 mm and 8 mm) were tested using non-destructive four-point bending tests under a multi-stage loading protocol (100 N–1000 N). Strain rosettes measured maximum strains at each loading stage to calculate bending stress. Analysis of the bending stress state revealed that vacuum glazing and SGP laminated glass exhibit superior load-bearing capacity compared to PVB laminated glass. The proposed method successfully determined the effective thickness for both laminated glass and vacuum glazing. Furthermore, results demonstrate that employing a 12 mm monolithic reference glass provides the highest accuracy for effective thickness determination. Theoretical bending stress calculations using the effective thickness derived from the 12 mm reference glass showed less than 10% deviation from experimental values. Conversely, compared to established standards and empirical formulas, the proposed method offers superior accuracy, particularly for vacuum glazing. Additionally, the mechanical properties of the viscoelastic interlayers (PVB and SGP) were investigated through static tensile tests and dynamic thermomechanical analysis (DMA). Distinct tensile behaviors and differing time-dependent shear transfer capacities between the two interlayer materials are found out. Key factors influencing the reliability of the method are also discussed and analyzed. This study provides a universally practical and applicable solution for accurate and effective thickness estimation in composite glass design. Full article

The integrity of bolted components primarily relies on the quality of interfacial contact, which is achieved by maintaining prescribed bolt torque levels. However, challenges arise from corrosion-induced bolt head damage, potentially compromising the bolt preload, and quantifying such effects remains unanswered. Many studies often compare bolt corrosion’s effects to bolt loosening as both affect the interfacial contact stresses to some extent. This technical study aimed to investigate whether a correlation exists between the impact of bolt head damage and the different levels of bolt torque. Guided wave ultrasonic testing (UT) was implemented for this investigation. Laboratory experiments were conducted to monitor the transmission of ultrasonic signals across the bolted interface first during the bolt-tightening process. Once the highest bolt torque was achieved, the process was repeated for a simplified corrosion scenario, simulated by artificially damaging the bolt head in a controlled manner. The analysis focused on studying the transmission of signal energy for both scenarios. The findings revealed different trends for the signal energy transmission during bolt tightening, which are subjective to the inspection frequency. On the contrary, even at an advanced level of bolt head damage corresponding to 16% mass loss, no clear or monotonic trend was observed in the total transmitted energy. While the total energy remained relatively stable across all inspection frequencies, distinct waveform changes, such as energy redistribution and the emergence of additional wave packets, were observed. The findings emphasize the need for more advanced waveform-based analysis techniques to detect and interpret subtle changes caused by bolt degradation. Full article

Concrete is the most important construction material, and improving its durability properties is a topic in constant development owing to the economic costs that the degradation of concrete implies. Different nanoparticles have been reported to improve concrete durability, although the positive results are not a generality. Among these nanomaterials, graphene oxide stands out as an option for improving concrete properties, such as its compressive strength, which could increase the useful life of concrete infrastructure. This study addresses the effects of graphene oxide on the durability properties of concrete, with the aim of obtaining data on the viability of graphene oxide as an additive in concrete. The incorporation of graphene oxide into concrete was carried out through graphene oxide suspensions that were incorporated into concrete mixtures with a high water/cement ratio. The characterization of concrete was done using non-destructive testing such as ultrasonic pulse velocity, electrical resistivity, porosity, capillary absorption, chloride ion permeability, and other characterization methods such as compressive strength, XPS, SEM, and EDS. Together, these tests provided an overview of the concrete durability properties that are improved, affected, or unchanged by the presence of graphene oxide. In this study, a chemical analysis was also carried out on concrete modified with graphene oxide. The results show that graphene oxide improves the compressive strength of concrete, but the effect on durability properties is negligible; however, there are indications that, in combination with other additives, improvements can be achieved, so it is advisable to continue with these studies. Full article

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is capable of capturing the transient stress waves induced by leakage, thereby furnishing an effective means for the real-time monitoring and quantitative assessment of internal leakage within the valve body. This paper conducts a systematic review of the theoretical foundations, signal-processing methodologies, and the latest research advancements related to the technology for detecting internal leakage in the valve body based on acoustic emission. Firstly, grounded in Lechlier’s acoustic analogy theory, the generation mechanism of acoustic emission signals arising from valve body leakage is elucidated. Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. Moreover, this paper recapitulates the current challenges encountered by this technology and delineates future research orientations, such as the fusion of multi-modal sensors, the deployment of lightweight deep learning models, and integration with the Internet of Things. This study provides a systematic reference for the engineering application and theoretical development of the acoustic emission-based technology for detecting internal leakage in valves. Full article