Search Results (1,120)

This study aims to propose a fault diagnosis method for partial discharge (PD) in high-voltage (HV) potential transformers (PTs) by combining discrete wavelet transform (DWT), scatter plot (SP), and a residual neural network (ResNet) deep learning model for feature extraction and classification. First, models of HV PTs under normal conditions and three internal fault types were established, including coil eccentricity, voids between the primary winding and the core, and voids between the primary and secondary windings. After measuring the PD signals, DWT filtering was applied to process the signals, and the filtered PD signals, together with the fundamental voltage signals, were transformed into an image-based feature SP to represent the characteristics of each fault. Finally, the SPs were trained using the ResNet model to identify four different defect types in HV PTs. Experimental results showed that the proposed method achieves a fault identification accuracy of 98%. Additionally, compared to other deep learning models, the proposed method significantly improves diagnostic efficiency and accuracy. This study also developed an intelligent online fault monitoring and predictive maintenance system for HV PTs to enhance the stability of power grids and equipment. Full article

This paper introduces an iceberg-based digital risk twin (DRT) framework for the health monitoring of complex engineering systems. The proposed model transforms multidimensional sensor and contextual data into a structured, interpretable three-dimensional geometry that captures both observable and latent risk components. Each monitored parameter is represented as a vertical geometric sheet whose height encodes a normalized risk level, producing an evolving iceberg structure in which the visible and submerged regions distinguish emergent anomalies from latent degradation. A formal mathematical formulation is developed, defining the mappings from the risk vector to geometric height functions, spatial layout, and surface composition. The resulting parametric representation provides both analytical tractability and intuitive visualization. A case study involving an aircraft fuel system demonstrates the capacity of the DRT to reveal dominant risk drivers, parameter asymmetries, and temporal trends not easily observable in traditional time-series analysis. The model is shown to integrate naturally into AI-enabled health management pipelines, providing an interpretable intermediary layer between raw data streams and advanced diagnostic or predictive algorithms. Owing to its modular structure and domain-agnostic formulation, the DRT approach is applicable beyond aviation, including power grids, rail systems, and industrial equipment monitoring. The results indicate that the iceberg representation offers a promising foundation for enhancing explainability, situational awareness, and decision support in the monitoring of complex engineering systems. Full article

The 17th-century stucco altar dedicated to St. Michael the Archangel is an interesting, but very damaged, artwork located in the complex of St. Angel in the little town of Barbarano Romano in Central Italy. During the recent and quite necessary restoration carried out by University of Tuscia students on the Conservation and Restoration of Cultural Heritage Master’s program, some problems with the surface coating were encountered in the cleaning phase. Diagnostic and scientific analyses were crucial to better understanding the composition of these materials to perform the safest and most efficient cleaning procedures. The first of many steps required by this approach was an in situ analysis, starting from on-site analysis and diagnostic documentation through X-ray fluorescence spectroscopy and ultraviolet fluorescence photography, followed by laboratory investigations. The latter included µ-Raman and Fourier transform infrared spectroscopies, gas chromatography coupled with mass spectrometry, and scanning electron microscopy equipped with an energy-dispersive detector. Each technique provided useful data to determine the chemical composition of the white surface coating, which was found to be a non-original overpaint containing lead and organic binder. This overpaint had been applied to retouch the white stucco during a previous restoration project. All this new information contributed to achieving the final decision to remove this layer. Full article

Background: Food allergy is a leading cause of severe allergic reactions in children and often results in restrictive elimination diets. The oral food challenge (OFC) remains the diagnostic gold standard but is resource-intensive and carries a risk of adverse reactions. This study aimed to identify epidemiological, clinical, and laboratory predictors of OFC outcomes and reaction severity in children with suspected immediate-type food allergies. Methods: We conducted a retrospective review of 148 children who underwent hospital-based, open OFCs due to suspected immediate-type food reactions. Data on demographics, comorbidities, characteristics of the initial reaction, sensitisation profiles (specific IgE [sIgE], skin prick test [SPT]), and OFC outcomes were analysed. Reactions were graded using the Ring and Messmer scale. Results: OFC was positive in 44 of 148 children (29.7%). However, no clinical or laboratory parameters—including prior reaction severity and the magnitude of allergy test results—were associated with the severity of reactions during OFC. Comorbidities—specifically asthma, atopic dermatitis, and allergic rhinitis—were significantly associated with a positive OFC (p < 0.01), as were elevated sIgE levels and larger SPT wheal diameters (p < 0.01 for both). The optimal thresholds for predicting a positive OFC were 0.73 IU/mL for sIgE and 3.5 mm for SPT. Conclusions: Oral food challenge (OFC) remains essential for confirming food allergies in children. Given that the severity of reactions during OFCs cannot be reliably predicted and that low cut-off values of allergy tests were identified for predicting a positive OFC outcome, OFCs should be performed in a controlled and fully equipped medical setting, particularly in children with atopic comorbidities. Full article

Micro-motors are widely used in industrial applications, which require effective fault diagnosis to maintain safe equipment operation. However, fault signals from micro-motors often exhibit weak signal strength and ambiguous features. To address these challenges, this study proposes a novel fault diagnosis method. Initially, the Jump plus AM-FM Mode Decomposition (JMD) technique was utilized to decompose the measured signals into amplitude-modulated–frequency-modulated (AM-FM) oscillation components and discontinuous (jump) components. The proposed process extracts valuable fault features and integrates them into a new time-domain signal, while also suppressing modal aliasing. Subsequently, a novel Global Relationship Matrix (GRM) is employed to transform one-dimensional signals into two-dimensional images, thereby enhancing the representation of fault features. These images are then input into an Optimized Convolutional Neural Network (OCNN) with an AdamW optimizer, which effectively reduces overfitting during training. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy rate of 99.0476% for multiple fault types, outperforming four comparative methods. This approach offers a reliable solution for quality inspection of micro-motors in a manufacturing environment. Full article

As an emerging threat to global food security, wheat blast necessitates the development of a rapid and field-deployable detection system to facilitate early diagnosis, enable effective management, and prevent its further spread to new regions. In this study, we aimed to validate and improve a Recombinase Polymerase Amplification coupled with PCRD lateral flow detection (RPA-PCRD strip assay) kit for the rapid and specific identification of Magnaporthe oryzae pathotype Triticum (MoT) in field samples. The assay demonstrated exceptional sensitivity, detecting as low as 10 pg/µL of target DNA, and exhibited no cross-reactivity with M. oryzae Oryzae (MoO) isolates and other major fungal phytopathogens under the genera of Fusarium, Bipolaris, Colletotrichum, and Botrydiplodia. The method successfully detected MoT in wheat leaves as early as 4 days post-infection (DPI), and in infected spikes, seeds, and alternate hosts. Furthermore, by combining a simplified polyethylene glycol-NaOH method for extracting DNA from plant samples, the entire RPA-PCRD strip assay enabled the detection of MoT within 30 min with no specialized equipment and high technical skills at ambient temperature (37–39 °C). When applied to field samples, it successfully detected MoT in naturally infected diseased wheat plants from seven different fields in a wheat blast hotspot district, Meherpur, Bangladesh. Training 52 diverse stakeholders validated the kit’s field readiness, with 88% of trainees endorsing its user-friendly design. This method offers a practical, low-cost, and portable point-of-care diagnostic tool suitable for on-site genomic surveillance, integrated management, seed health testing, and quarantine screening of wheat blast in resource-limited settings. Furthermore, the RPA-PCRD platform serves as an early warning modular diagnostic template that can be readily adapted to detect a wide array of phytopathogens by integrating target-specific genomic primers. Full article

In the context of the digital transformation of industrial production, the need for intelligent maintenance and repair systems capable of ensuring reliable operation of machine-tool equipment without operator involvement is growing. This present study reviews the current state and future development of diagnostic and condition-monitoring systems for metalworking machine tools. A review of international standards and existing solutions from domestic and international vendors in vibration diagnostics has been conducted. Particular attention is paid to non-intrusive vibration diagnostics, digital twins, multiparametric analysis methods, and neural network approaches to failure prediction. The architecture of the developed system is presented. The concept of the system is developed in full compliance with Russian and international standards of vibration diagnostics. At its core, the comprehensive digital twin relies on machine learning methods. The proposed architecture is a predictive-maintenance system built on interconnected digital twin realizations: the dynamic machine passport of a unit, operational data, and a comprehensive digital twin of the machine-tool equipment. The potential of neuromorphic computing on a hardware platform is being considered as a promising element for local-condition classification and emergency protection. At the current development stage, the operating principle has been demonstrated along with the integration into the control loop. The system is now at the beginning of laboratory testing. It demonstrates capabilities for comprehensive assessment of the equipment’s technical condition based on multiparametric data, short-term vibration trend forecasting using a Long Short-Term Memory network, and state classification using a Multilayer Perceptron model. The results of the system’s testing on a turning machining center have been analyzed. Full article

In recent years, diagnostic technologies that utilize noninvasively collected sweat have garnered significant interest. However, the concentration of components in sweat is lower than that in blood, making the introduction of a concentration step as a sample pretreatment crucial for achieving highly sensitive detection. In this study, we developed a PDMS-based microchannel filled with silica gel, a desiccant particle, to concentrate liquid samples at room temperature without requiring an external power source or heating. The evaluation of the basic characteristics of the fabricated microchannel confirmed that filling it with silica gel efficiently removed the solvent vapor from the liquid samples. In concentration tests using the fluorescent dye uranine as a model for sweat sugar, a maximum 1.4-fold concentration was achieved in DPBS solution and a 1.2-fold concentration in artificial sweat at room temperature. In contrast, no similar concentration effect was observed in microchannels without silica gel packing. The proposed silica-gel-packed PDMS microchannel features a simple structure and requires no external equipment, making it easily integrable with existing microfluidic devices as a sample pretreatment module. This method is considered useful as a passive and simple sample concentration technique for the analysis of low-molecular-weight components in sweat. Full article

Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour. Full article

Condition-based maintenance strategies using AI-driven health monitoring have emerged as valuable tools for industrial reliability, yet their implementation remains challenging in industries with limited operational data. Class I induction motors (≤15 kW), which power critical equipment in industries such as grain handling facilities, represent a significant portion of industrial assets but lack established healthy vibration baselines for effective monitoring. A fundamental challenge exists in deploying AI-based health monitoring systems when no historical performance data is available, creating a ’cold-start’ problem that prevents industries from adopting predictive maintenance strategies without costly pilot programs or prolonged data collection periods. This study developed a data-driven health monitoring framework for Class I induction motors that eliminates the dependency on long-term historical trends. Through extensive experimental testing of 98 configurations on new motors, a correlation between vibration amplitude at rotational frequency and motor power rating was established, enabling the creation of a synthetic signal generation algorithm. A robust Health Index (HI) model with integrated diagnostic capabilities was developed using the JPCCED-HI framework, trained on both experimental and synthetically generated healthy vibration data to detect degradation and diagnose common failure modes. The regression analysis revealed a statistically significant relationship between motor power rating and healthy vibration signatures, enabling synthetic generation of baseline data for any Class I motor within the rated range. When implemented at an operational grain silo facility, the HI model successfully detected faulty behavior and accurately diagnosed probable failure modes in equipment with no prior monitoring history, demonstrating that maintenance decisions could be made based on condition data rather than reactive responses to failures. This framework enables immediate deployment of AI-based condition monitoring in industries lacking historical data, eliminating a major barrier to adopting predictive maintenance strategies. The synthetic data generation approach provides a cost-effective solution to the data scarcity problem identified as a critical challenge in industrial AI applications, while the successful industrial implementation validates the feasibility of this approach for small-to-medium industrial facilities. Full article

The article presents issues related to fossil fuel energy consumption and CO₂ emissions from motor vehicles. It identifies the main areas of research in this field in the context of motor vehicles, namely driver behavior, fuel consumption, and OBD systems. The research sample consisted of experimental data containing records of a series of test drives conducted with a passenger vehicle equipped with a gasoline-powered internal combustion engine, collected via an OBD diagnostic interface. Three subsets related to engine operation and energy demand patterns were distinguished for the study: during vehicle start-up and low-speed driving (vehicle start-up mode), during urban driving, and during extra-urban driving. Multiple regression models were constructed for the analyzed subsets to predict CO₂ emissions based on engine energy output parameters (power, load) and vehicle kinematic parameters. The developed models were subjected to detailed evaluation and mutual comparison, taking into account their predictive performance and the interpretability of the results. The analysis made it possible to identify the variables with the most substantial impact on CO₂ emissions and fuel energy consumption. The models allow individual drivers to monitor and optimize vehicle energy efficiency in real-time. The extra-urban driving model achieved the highest predictive accuracy, with a mean absolute error (MAE) of 19.62 g/km, which makes it suitable for real-time emission monitoring during highway driving. Full article

Background/Objectives: Evidence-based recommendations are vital in healthcare to standardize care, reduce variability, and improve patient outcomes. In children, anaphylaxis, allergy to antibiotics, and hymenoptera venom allergy are among the commonest reasons for allergological evaluation. This work was intended to optimize the prescriptions for allergological evaluation and for the related diagnostic tests with the aim of improving the management of children with allergic diseases and promoting resource efficiency. Methods: A systematic literature review of the literature was performed to formulate recommendations on the diagnostic management of children with anaphylaxis, drug allergy, and hymenoptera venom allergy. Results: Effective management of anaphylaxis involves rapid assessment and specialist follow-up to identify triggers, prevent recurrence, and ensure patients and caregivers are educated and equipped with an adrenaline auto-injector. Integrating skin testing, specific serological assays, and oral provocation tests into the diagnostic process for children with suspected beta-lactam allergy enhances diagnostic accuracy and minimizes unnecessary avoidance of first-line antibiotics. Children and adolescents with systemic reactions to hymenopteran stings should be referred to an allergy specialist for diagnosis, risk assessment, management education, and adrenaline prescription. Conclusions: These recommendations may enhance care quality, minimize inappropriate prescriptions, and support standardized methods of diagnosis of allergological diseases in children. Full article

To identify and evaluate the failure issues in the livestock manure-to-biomethane supply chain, this study employs a Bayesian network approach with three inference analysis methods: diagnostic analysis, sensitivity analysis, and maximum causal chain inference. First, the main hazard categories affecting the failure of the supply chain are identified, establishing risk indicators for feedstock collection, pretreatment, anaerobic digestion, purification and upgrading, transportation, and biomethane end-use. Then, the half-interval method and possibility superiority comparison are used to calculate and rank the severity of related accidents, obtaining the severity ranking of secondary indicators as well as the severity ranking of work items and risk items. Finally, Bayesian forward inference is applied to investigate the failure probability of the supply chain, combined with backward inference to identify the risk factors most likely to cause supply chain failures and trace the formation of failure hazards. The Bayesian sensitivity analysis method is ultimately applied to determine the key hazards affecting supply chain failures and the correlations between accident hazards, followed by validation. The results show that the failure probability of the supply chain through causal inference is approximately 54.76%, indicating relatively high failure risk. The three factors with the highest posterior probabilities are mechanical stirring failure C₃ (88.11%), corrosion-induced ammonia leakage poisoning D6, and equipment explosion caused by excessive pressure due to overheating during dehumidification heating D₉, which are the hazards most likely to cause failures in the supply chain. Improper operations and the toxicity of related chemicals are key hazards leading to supply chain failures, with the correlation between accident hazards presented as a hazard chain by integrating severity and accident probability, and the key risk points in the supply chain are identified. Full article

Background/Objectives: Percutaneous transthoracic needle biopsy (PTNB) using advanced navigation techniques is increasingly performed; however, pulmonologists’ experience remains limited. This study reports an interventional pulmonologist’s initial experience with cone-beam computed tomography (CBCT) laser-guided PTNB and the diagnostic performance for lesions with diameters greater than or less than 20 mm. Methods: We retrospectively analysed the data of patients who underwent PTNB in a C-arm CBCT-equipped hybrid operating room between July 2020 and March 2024. All patients underwent the biopsy procedure under local anaesthesia. This was preceded by an initial 3D scan for planning of the needle route, followed by coaxial needle insertion. A post-procedural scan was also performed to identify complications. Results: Seventy-seven patients were enrolled in the study. The median distances of the needle path from the skin to the pleura and from the pleura to the lesion were 33.4 mm and 31.7 mm, respectively. The median number of tissue samplings was 4.9 ± 1.8. The median operating room duration was 51.5 ± 25.7 min, respectively. The median total dose area product was 8485.4 ± 5819.9 µGym². The sensitivity and specificity of our study findings were 93.3% (56/60) and 100%, while the accuracy was 94.8% (73/77). The overall complication rate was 13%. Conclusions: PTNB procedure by pulmonologists is a feasible and safe, single-operator workflow in a hybrid operating room. It can be performed under CBCT laser guidance with a similar diagnostic yield, acceptable radiation exposure and procedure duration, and minimal or manageable complications. Full article

This paper explores simulation application functions for the computer monitoring system of a hydro–wind–solar integrated control center, focusing on five core areas: platform management, operational training, performance optimization, exception handling, and emergency drills. Against the “dual carbon” backdrop, multi-energy complementary system simulation faces key challenges including multi-energy coupling, real-time response, and cybersecurity protection. Research shows that integrating digital twin, heterogeneous computing, and artificial intelligence technologies markedly improve simulation accuracy and intelligent decision-making. Dispatch strategies have shifted from single-energy optimization to system-level coordination, while cybersecurity frameworks now provide comprehensive safeguards covering algorithms, data, systems, user behavior, and architecture. Intelligent operation and maintenance with fault diagnosis—powered by big data and deep learning—enables equipment condition prediction, and emergency drill platforms boost response capacity via 3D visualization and scriptless modeling. Current hurdles include absent multi-energy modeling standards, poor extreme-condition adaptability, and inadequate knowledge transfer mechanisms. Future research should prioritize hybrid physical–data-driven approaches, multi-dimensional robust scheduling, federated learning-based diagnostics, and integrated digital twin, edge computing, and decentralized ledger technologies. These advances will drive simulation platforms toward greater intelligence, interoperability, and reliability, laying the technical foundation for unified hydro–wind–solar control centers. Full article