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Isolation stress is a major risk factor for neuropsychiatric disorders such as depressive and anxiety disorders. However, the molecular mechanisms underlying isolation-induced neuropsychiatric disorders remain elusive. In the present study, we investigated the subcellular mechanisms by which long-term isolation elicits depression and anxiety-related behaviors in mice. First, we found that long-term isolation induced depression-related behaviors in the forced swimming test (FST) and the sucrose preference test, as well as anxiety-related behaviors in the elevated zero maze test (EZMT) and the open field test. Next, we showed that intracentral amygdala (CeA) injection of oxytocin (OXT), but not intracerebroventricular injection, attenuated isolation-induced depression and anxiety-related behaviors via oxytocin receptor (OXTR), not vasopressin-1a receptor (V1aR), in the FST and EZMT, respectively. Quantitative real-time polymerase chain reaction analysis revealed that after 5 weeks of isolation, mRNA transcription of OXTR in the CeA, but not that of V1aR, significantly decreased, whereas OXT and vasopressin mRNA transcription in the paraventricular nucleus of hypothalamus did not change significantly. Whole-cell patch clamping of acute brain slices demonstrated that the frequency of miniature inhibitory postsynaptic currents (mIPSCs) in CeA neurons, but not their amplitude, was lower in isolated mice than in group-housed mice. Notably, OXT treatment increased the mIPSC frequency in the CeA neurons, but to a lesser extent in the case of isolated mice than in that of group-housed mice via OXTR. Taken together, our findings suggest that long-term isolation down-regulates OXTR mRNA transcription and diminishes OXT-induced inhibitory synaptic transmission in the CeA and may contribute to the development of depression and anxiety-related behaviors in isolated mice through the enhancement of CeA activity.

In previous work from this laboratory, it has been found that the urban heat island intensity (UHI) can be scaled with the urban length scale and the wind speed, through the time-dependent energy balance. The heating of the urban surfaces during the daytime sets the initial temperature, and this overheating is dissipated during the night-time through mean convection motion over the urban surface. This may appear to be in contrast to the classical work by Oke (1973). However, in this work, we show that if the population density is used in converting the population data into urbanized area, then a good agreement with the current theory is found. An additional parameter is the “urban flow parameter,” which depends on the urban building characteristics and affects the horizontal convection of heat due to wind. This scaling can be used to estimate the UHI intensity in any cities and therefore predict the required energy consumption during summer months. In addition, all urbanized surfaces are expected to exhibit this scaling, so that increase in the surface temperature in large energy-consumption or energy-producing facilities (e.g., solar electric or thermal power plants) can be estimated.

Prognostics and health management (PHM) is an enabling technology essential for the safe operation and conditional maintenance of any engineering system. Over the past two decades, the use of fiber-reinforced polymer (FRP) composites has increased to develop high-strength, lightweight, and durable composite structures. PHM technology has been widely adopted in the composites industry to address challenges such as damage detection, localization, remaining useful life (RUL) prediction, and maintenance scheduling. However, implementing PHM technologies for FRPs necessitates a thorough understanding of their failure modes and the non-destructive testing (NDT) techniques commonly used to assess their health condition. Therefore, this review provides a comprehensive discussion on the failure modes of FRP composites based on practical loading conditions, examines several NDT techniques employed on FRP composites, detailing their advantages and limitations, and highlights the current challenges while providing future directions for the use of NDT techniques in PHM of composite structures. Thus, the review aids practitioners, the scientific community, and new researchers in understanding the failure modes of FRP composites and exploring recent advancements in NDT techniques to address the current challenges and limitations in PHM of composite structures.

This study addresses the challenges of data scarcity and class imbalance in structural health monitoring (SHM) of composite structures. Data-driven SHM techniques that benefit from non-destructive evaluation (NDE) are used in various composite structures. However, the lack of damaged state data causes data scarcity and class imbalance problems that prevent robust diagnostics of composite structures. This study introduces a novel data-driven multi-class data augmentation method for composite structures, employing a multi-class generative adversarial network (MC-GAN) for the first time to generate synthetic data for multiple classes without the need for excessive experimentation or simulation. Additionally, the MC-GAN model is integrated with the convolutional neural network (CNN) to develop the MC-GAN-CNN model for autonomous data augmentation and delamination detection. The approach has been validated using experimentally obtained vibrational data for laminated composites. The damage detection using manual feature extraction showed overfitting and very high standard deviation during 10-fold cross-validation for various machine learning models. However, the proposed method suggested a more rigorous assessment with a mean accuracy of 99.72 ± 0.08 %. In addition, the proposed framework assists in handling the delamination detection problem autonomously without requiring hand-crafted statistical features with a good generalization capability.

This paper aims to comprehensively evaluate the shear strength of various junctions between diamond chips and double copper bonded ceramic substrates. The study involves several stages, beginning with the deposition of Ti/Ni/Ag metallization system on diamond chips using a chemical vapor deposition (CVD) process. These metallization systems are selected to determine their suitability as ohmic contacts on diamond. In parallel, Ni/Au and Ni/Ag layers are electroplated onto the thick copper metallization of the ceramic substrate. Following these depositions, junctions are created for both Cu/Cu and C/Cu assemblies using different techniques: reflow process for AuGe solder joints, reflow-diffusion process for Ag-In joints, and a thermomechanical process for Ag nanoparticle joints. To assess the effectiveness of these techniques, the shear strength of the junctions is measured using a micro-shear test, which is analogous to a classical micro-scratching test. Additionally, the mechanical behaviors of the layers adjacent to the assemblies are analyzed through nano-indentation tests. Finally, the fracture surfaces are examined using scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) spectrometry to identify the microstructure and damage modes. The work described in the paper exhibits several innovative aspects. It introduces the novel combination of Ti/Ni/Ag metallization system on diamond chips, evaluated for its suitability as ohmic contacts, potentially leading to improved electrical and thermal performance in diamond substrate applications. The study employs diverse junction creation techniques, such as reflow for AuGe solder joints, reflow-diffusion for Ag-In joints, and a thermomechanical process for Ag nanoparticle joints, allowing for a thorough comparison and identification of the most effective method for different scenarios. We have also introduced an integrated testing methodology, which combines micro-shear tests and nano-indentation tests and provides a robust framework for assessing both the shear strength and mechanical properties of the junctions and adjacent layers, enhancing the reliability and depth of the evaluation. The specific focus on metallization system and junctions suitable for diamond, known for its exceptional thermal and electrical properties, positions this work as highly relevant for advanced electronic and thermal management applications.

In this paper, we present mathematical geometric models and recursive algorithms to generate and design complex patterns using fractal structures. By applying analytical, iterative methods, iterative function systems (IFS), and L-systems to create geometric models of complicated fractals, we developed fractal construction models, visualization tools, and fractal measurement approaches. We introduced a novel recursive fractal modeling (RFM) method designed to generate intricate fractal patterns with enhanced control over symmetry, scaling, and self-similarity. The RFM method builds upon traditional fractal generation techniques but introduces adaptive recursion and symmetry-preserving transformations to produce fractals with applications in domains such as medical imaging, textile design, and digital art. Our approach differs from existing methods like Barnsley’s IFS and Jacquin’s fractal coding by offering faster convergence, higher precision, and increased flexibility in pattern customization. We used the RFM method to create a mathematical model of fractal objects that allowed for the viewing of polygonal, Koch curves, Cayley trees, Serpin curves, Cantor set, star shapes, circulars, intersecting circles, and tree-shaped fractals. Using the proposed models, the fractal dimensions of these shapes were found, which made it possible to create complex fractal patterns using a wide variety of complicated geometric shapes. Moreover, we created a software tool that automates the visualization of fractal structures. This tool may be used for a variety of applications, including the ornamentation of building items, interior and exterior design, and pattern construction in the textile industry.

Ensuring operational reliability and efficiency in steam power plants requires advanced and generalized fault detection methodologies capable of addressing diverse fault scenarios in boiler and turbine systems. This study presents an autonomous fault detection framework that integrates deep feature extraction through Convolutional Autoencoders (CAEs) with the ensemble machine learning technique, Extreme Gradient Boosting (XGBoost). CAEs autonomously extract meaningful and nonlinear features from raw sensor data, eliminating the need for manual feature engineering. Principal Component Analysis (PCA) is employed for dimensionality reduction, enhancing computational efficiency while retaining critical fault-related information. The refined features are then classified using XGBoost, a robust ensemble learning algorithm, ensuring accurate fault detection. The proposed model is validated through real-world case studies on boiler waterwall tube leakage and motor-driven oil pump failure in steam turbines. Results demonstrate the framework’s ability to generalize across diverse fault types, detect anomalies at an early stage, and minimize operational downtime. This study highlights the transformative potential of combining deep feature extraction and ensemble machine learning for scalable, reliable, and efficient fault detection in power plant operations.

Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require tedious manual feature extraction, while deep learning models require large training datasets, which may not be feasible. To overcome these limitations, this study presents a hybrid deep transfer learning (HTL) framework to identify delamination in composite laminates. The proposed framework enhances SHM performance by utilizing pre-trained EfficientNet and ResNet models to allow for deep feature extraction with limited data. EfficientNet contributes to this by efficiently scaling the model to capture multi-scale spatial features, while ResNet contributes by extracting hierarchical representations through its residual connections. Vibration signals from piezoelectric (PZT) sensors attached to the composite laminates, consisting of three health states, are used to validate the approach. Compared to the existing transfer learning approaches, the suggested method achieved better performance, hence improving both the accuracy and robustness of delamination detection in composite structures.

Due to their precision, compact size, and high torque transfer, Rotate vector (RV) reducers are becoming more popular in industrial robots. However, repetitive operations and varying speed conditions mean that these components are prone to mechanical failure. Therefore, it is important to develop effective health monitoring (HM) strategies. Traditional approaches for HM, including those using vibration and acoustic emission sensors, encounter such challenges as noise interference, data inconsistency, and high computational costs. Deep learning-based techniques, which use current electrical data embedded within industrial robots, address these issues, offering a more efficient solution. This research provides transfer learning (TL) models for the HM of RV reducers, which eliminate the need to train models from scratch. Fine-tuning pre-trained architectures on operational data for the three different reducers of health conditions, which are healthy, faulty, and faulty aged, improves fault classification across different motion profiles and variable speed conditions. Four TL models, EfficientNet, MobileNet, GoogleNet, and ResNET50v2, are considered. The classification accuracy and generalization capabilities of the suggested models were assessed across diverse circumstances, including low speed, high speed, and speed fluctuations. Compared to the other models, the proposed EfficientNet model showed the most promising results, achieving a testing accuracy and an F1-score of 98.33% each, which makes it best suited for the HM of robotic reducers.

The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy by replacing the computationally intensive ResNet backbone with MobileNet and leveraging depthwise separable convolutions. The modified RetinaNet achieves an average precision (AP) of 32.1, surpassing state-of-the-art models in small tumor detection (APS: 14.3) and large tumor localization (APL: 49.7). Furthermore, the model significantly reduces computational costs, making real-time analysis feasible on low-power hardware. Clinical relevance is a key focus of this work. The proposed model addresses the diagnostic challenges of small, variable-sized tumors often overlooked by existing methods. Its lightweight architecture enables accurate and timely tumor localization on portable devices, bridging the gap in diagnostic accessibility for underserved regions. Extensive experiments on the BRATS dataset demonstrate the model robustness across tumor sizes and configurations, with confidence scores consistently exceeding 81%. This advancement holds the potential for improving early tumor detection, particularly in remote areas lacking advanced medical infrastructure, thereby contributing to better patient outcomes and broader accessibility to AI-driven diagnostic tools.

Lateral flow immunoassays (LFIAs) are widely used for their low cost, simplicity, and rapid results; however, enhancing their reliability requires the meticulous selection of ligands and nanoparticles (NPs). SiO2@QD@SiO2 (QD2) nanoparticles, which consist of quantum dots (QDs) embedded in a silica (SiO2) core and surrounded by an outer SiO2 shell, exhibit significantly higher fluorescence intensity (FI) compared to single QDs. In this study, we prepared QD2@PEG@Aptamer, an aptamer conjugated with QD2 using succinimidyl-[(N-maleimidopropionamido)-hexaethyleneglycol]ester, which is 130 times brighter than single QDs, for detecting carbohydrate antigen (CA) 19-9 through LFIA. For LFIA optimization, we determined the optimal conditions as a 1.0:2.0 × 10−2 ratio of polyethylene glycol (PEG) to aptamer by adjusting the amounts of PEG and aptamer, phosphate-buffered saline containing 0.5% Tween® 20 as a developing solution, and 0.15 μg NPs by setting the NP weight during development. Under these conditions, QD2@PEG@Aptamer selectively detected CA19-9, achieving a detection limit of 1.74 × 10−2 mg·mL−1. Moreover, FI remained stable for 10 days after detection. These results highlight the potential of QD2 and aptamer conjugation technology as a reliable and versatile sensing platform for various diagnostic applications.

Carbon fiber reinforced polymer (CFRP) composites have been continuously replacing conventional metallic materials due to their excellent material properties. The orthotropic nature of CFRP composites makes them vulnerable to various types of damage. Among these, delamination stands out as the most common and severe form of damage. Therefore, deep learning based structural health monitoring (SHM) which performs autonomous health monitoring from sensor data have gained wide attention for delamination detection of CFRP composites. However, limited training data often restricts the application of these models for autonomous health monitoring. Therefore, the present research proposes convolutional neural network (CNN)-based pre-trained transfer learning method using ResNetV2 (RNV2) model to solve the data scarcity problem. The use of RNV2 model eliminated the need for developing the model from scratch and only required fine-tuning on the target composites dataset. The target dataset contained multi-class wavelet-transformed vibrational data obtained from CFRP specimens. The efficacy of the proposed approach is determined using various evaluation metrics on unseen dataset. The results of the validation demonstrated that the pre-trained RNV2 model can effectively perform SHM of CFRP composites even under limited data conditions.

This comprehensive review explores data-driven methodologies that facilitate the prognostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data. This review investigates common fault types in CPs, while placing a specific emphasis on artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) techniques, for fault diagnosis and prognosis. A key innovation of this review is its in-depth analysis of cutting-edge methods, such as adaptive thresholding, hybrid models, and advanced neural network architectures, aimed at accurately predicting the remaining useful life (RUL) of CPs under varying operational conditions. This review also addresses the limitations and challenges of the current AI-driven methodologies, offering insights into potential solutions. By synthesizing these methodologies and presenting practical applications through case studies, this review provides a forward-looking perspective to empower industry professionals and researchers with effective strategies to ensure the reliability and efficiency of centrifugal pumps. These findings could contribute to optimizing industrial processes and advancing health management strategies for critical components.

The detection of boiler water-wall tube leakage in steam power plants is essential to prevent efficiency loss, unexpected shutdowns, and costly repairs. This study proposes a hybrid deep learning approach that combines convolutional neural networks (CNNs) with a support vector machine (SVM) classifier to allow early and accurate leak detection. The methodology utilizes temperature data from multiple sensors positioned at critical points in the boiler system. The data of each sensor are independently processed by a dedicated CNN model, allowing for the autonomous extraction of sensor-specific features. These features are then fused to create a comprehensive feature representation of the system’s condition, which is analyzed by an SVM classifier to accurately identify leakages. By utilizing the feature extraction capabilities of CNNs and the classification strength of an SVM, this approach effectively identifies subtle operational anomalies that are indicative of potential leaks. The model demonstrates high detection accuracy and minimizes false-positives, providing a robust solution for real-time monitoring and proactive maintenance strategies in industrial systems.

Wildfires pose a severe threat to ecological systems, human life, and infrastructure, making early detection critical for timely intervention. Traditional fire detection systems rely heavily on single-sensor approaches and are often hindered by environmental conditions such as smoke, fog, or nighttime scenarios. This paper proposes Adaptive Multi-Sensor Oriented Object Detection with Space–Frequency Selective Convolution (AMSO-SFS), a novel deep learning-based model optimized for drone-based wildfire and smoke detection. AMSO-SFS combines optical, infrared, and Synthetic Aperture Radar (SAR) data to detect fire and smoke under varied visibility conditions. The model introduces a Space–Frequency Selective Convolution (SFS-Conv) module to enhance the discriminative capacity of features in both spatial and frequency domains. Furthermore, AMSO-SFS utilizes weakly supervised learning and adaptive scale and angle detection to identify fire and smoke regions with minimal labeled data. Extensive experiments show that the proposed model outperforms current state-of-the-art (SoTA) models, achieving robust detection performance while maintaining computational efficiency, making it suitable for real-time drone deployment.

In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures. In contrast, this study aims to perform damage detection, severity assessment, and localization using independent DL models. Three DL models, namely the artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU), are compared. To assess their damage detection and localization capabilities. Moreover, zero-mean Gaussian noise is introduced as a data augmentation technique to address the variability and noise inherent in LW signals, improving the generalization capability of the DL models. The proposed framework is validated on a composite plate with four piezoelectric transducers, one at each corner, and achieves high accuracy in both damage localization and severity assessment, offering an effective solution for real-time structural health monitoring. This dual-function approach provides a scalable data-driven method to evaluate composite structures, with applications in predictive maintenance and reliability assurance in critical engineering systems.

Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. Laminated composites, characterized by their anisotropic behavior, multi-layered structures, and intricate interlayer interactions, pose significant challenges for traditional computational methods. PINNs address these issues by embedding governing physical laws directly into neural network architectures, enabling efficient and accurate modeling. This review provides a comprehensive overview of PINNs applied to laminated composites, highlighting advanced methodologies such as hybrid PINNs, k-space PINNs, Theory-Constrained PINNs, optimal PINNs, and disjointed PINNs. Key applications, including structural health monitoring (SHM), structural analysis, stress-strain and failure analysis, and multi-scale modeling, are explored to illustrate how PINNs optimize material configurations and enhance structural reliability. Additionally, this review examines the challenges associated with deploying PINNs and identifies future directions to further advance their capabilities. By bridging the gap between classical physics-based models and data-driven techniques, this review advances the understanding of PINN methodologies for laminated composites and underscores their transformative role in addressing modeling complexities and solving real-world problems.

This paper presents an enhanced approach to kidney segmentation using a modified CLAHE preprocessing method, aimed at improving image clarity and CNN performance on the KiTS19 dataset. To assess the impact of the modified CLAHE method, we conducted quality evaluations using the BRISQUE metric, comparing the original, standard CLAHE and modified CLAHE versions of the dataset. The BRISQUE score decreased from 28.8 in the original dataset to 21.1 with the modified CLAHE method, indicating a significant improvement in image quality. Furthermore, CNN segmentation accuracy rose from 0.951 with the original dataset to 0.996 with the modified CLAHE method, outperforming the accuracy achieved with standard CLAHE preprocessing (0.969). These results highlight the benefits of the modified CLAHE method in refining image quality and enhancing segmentation performance. This study highlights the value of adaptive preprocessing in medical imaging workflows and shows that CNN-based kidney segmentation accuracy may be greatly increased by altering conventional CLAHE. Our method provides insightful information on optimizing preprocessing for medical imaging applications, leading to more accurate and dependable segmentation results for better clinical diagnosis.

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of laminated composite structures. Their favorable strength-to-weight ratio renders them ideal for the automotive, marine, and aerospace industries. Among various non-destructive testing (NDT) methods, ultrasonic techniques have emerged as robust tools for detecting and characterizing internal flaws in composites, including delaminations, matrix cracks, and fiber breakages. This review concentrates on recent developments in ultrasonic NDT techniques for the SHM of laminated composite structures, with a special focus on guided wave methods. We delve into the fundamental principles of ultrasonic testing in composites and review cutting-edge techniques such as phased array ultrasonics, laser ultrasonics, and nonlinear ultrasonic methods. The review also discusses emerging trends in data analysis, particularly the integration of machine learning and artificial intelligence for enhanced defect detection and characterization through guided waves. This review outlines the current and anticipated trends in ultrasonic NDT for SHM in composites, aiming to aid researchers and practitioners in developing more effective monitoring strategies for laminated composite structures.

The purpose of this study is to develop an optimized system for predicting Knee Adduction Moment (KAM) using wearable Inertial Measurement Unit (IMU) sensors and Long Short-Term Memory (LSTM) RNN. Traditional KAM measurement methods are limited by the need for complex laboratory equipment and significant time and cost investments. This study proposes two systems for predicting Knee Adduction Moment based on wearable IMU sensor data and gait patterns: the Multi-model Gait-based KAM Prediction System and the Single-model KAM Prediction System. The Multi-model system pre-classifies different gait patterns and uses specific prediction models tailored for each pattern, while the Single-model system handles all gait patterns with one unified model. Both systems were evaluated using IMU sensor data and GRF data collected from participants in a controlled laboratory environment. The overall performance of the Multi-model Gait-based KAM Prediction System showed an approximately 20% improvement over the Single-model KAM Prediction System. Specifically, the RMSE for the Multi-model system was 6.84 N·m, which is lower than the 8.82 N·m of the Single-model system, indicating a better predictive accuracy. The Multi-model system also achieved a MAPE of 8.47%, compared with 12.95% for the Single-model system, further demonstrating its superior performance.

Pneumonia is a form of acute respiratory infection affecting the lungs. Symptoms of viral and bacterial pneumonia are similar. Rapid diagnosis of the disease is difficult, since polymerase chain reaction-based methods, which have the greatest reliability, provide results in a few hours, while ensuring high requirements for compliance with the analysis technology and professionalism of the personnel. This study proposed a Concatenated CNN model for pneumonia detection combined with a fuzzy logic-based image improvement method. The fuzzy logic-based image enhancement process is based on a new fuzzification refinement algorithm, with significantly improved image quality and feature extraction for the CCNN model. Four datasets, original and upgraded images utilizing fuzzy entropy, standard deviation, and histogram equalization, were utilized to train the algorithm. The CCNN’s performance was demonstrated to be significantly improved by the upgraded datasets, with the fuzzy entropy-added dataset producing the best results. The suggested CCNN attained remarkable classification metrics, including 98.9% accuracy, 99.3% precision, 99.8% F1-score, and 99.6% recall. Experimental comparisons showed that the fuzzy logic-based enhancement worked significantly better than traditional image enhancement methods, resulting in higher diagnostic precision. This study demonstrates how well deep learning models and sophisticated image enhancement techniques work together to analyze medical images.

Hypophosphatasia (HPP) is a rare condition characterized by abnormal bone mineralization. The manifestations of HPP vary from no symptoms to intrauterine fetal death; short stature is another indication of HPP. A 3 ½-year-old boy presented with short stature, transient hypercalcemia, and mild gait disturbance without definite bony deformity. Laboratory examination revealed transient hypercalcemia, normal phosphorous and 25-hydroxy vitamin D levels, and mildly low alkaline phosphatase levels. A targeted next-generation sequencing panel associated with inborn errors of metabolism revealed a pathogenic heterozygous mutation in the ALPL gene, c.979T>C (p.Phe327Leu). When a child visits a hospital with short stature, decreased height velocity, and low alkaline phosphatase level, clinicians should consider the possibility of HPP even if definite skeletal dysplasia is not evident.

Excessive medical waste is generated in various medical facilities, especially post-Covid. Recently, sterilization-based shredding systems are being widely used to treat medical waste; however, such systems are commonly over-designed, due to load variation among various treatment facilities. To overcome this challenge, a data-driven surrogate model framework is proposed to perform sensitivity analysis and design optimization based on different loading environments and capacity requirements. The stress estimation surrogate model was generated using the Latin hypercube sampling (LHS), which can represent the overall information of the design area with a limited sample. Furthermore, the data-driven model significantly reduced the computational time as increased numbers of samples were generated from the data-driven surrogate model, instead of finite element analysis (FEA). Two distinct design capacities of the shredding system were used for assessing the effectiveness of the present framework. The results demonstrated that surrogate model-based sensitivity analysis is an efficient approach to developing system designs based on various input and output conditions. The proposed approach mitigates the tremendous potential of the surrogate model, significantly reduces computational costs associated with sensitivity analysis, and yields promising accuracy for the optimization process. This method suggests a computationally efficient optimization method for different shredding capacities. Additionally, the proposed method does not remain at simply optimizing the existing system but provides optimization values for various capacity systems using the data of the existing design, therefore it can be applied to any mechanical system for designing an optimized and compact system.

Metal–oxide–semiconductor field-effect transistors (MOSFETs) are critical in power electronic modules due to their high-power density and rapid switching capabilities. Therefore, effective thermal management is crucial for ensuring reliability and superior performance. This study used finite element analysis (FEA) to evaluate the electro-thermal behavior of MOSFETs with copper clip bonding, showing a significant improvement over aluminum wire bonding. The aluminum wire model reached a maximum temperature of 102.8 °C, while the copper clip reduced this to 74.6 °C. To further optimize the thermal performance, Latin Hypercube Sampling (LHS) generated diverse design points. The FEA results were used to select the Kriging regression model, chosen for its superior accuracy (MSE = 0.036, R2 = 0.997, adjusted R2 = 0.997). The Kriging model was integrated with a Genetic Algorithm (GA), further reducing the maximum temperature to 71.5 °C, a 4.20% improvement over the original copper clip design and a 43.8% reduction compared to aluminum wire bonding. This integration of Kriging and the GA to the MOSFET copper clip package led to a significant improvement in the heat dissipation and overall thermal performance of the MOSFET package, while also reducing the computational power requirements, providing a reliable and efficient solution for the optimization of MOSFET copper clip packages.

The reliability of electric vehicles (EVs) is crucial for the performance and safety of modern transportation systems. Electric motors are the driving force in EVs, and their maintenance is critical for efficient EV performance. The conventional fault detection methods for motors often struggle with accurately capturing complex spatiotemporal vibration patterns. This paper proposes a recurrent convolutional neural network (RCNN) for effective defect detection in motors, taking advantage of the advances in deep learning techniques. The proposed approach applies long short-term memory (LSTM) layers to capture the temporal dynamics essential for fault detection and convolutional neural network layers to mine local features from the segmented vibration data. This hybrid method helps the model to learn complicated representations and correlations within the data, leading to improved fault detection. Model development and testing are conducted using a sizable dataset that includes various kinds of motor defects under differing operational scenarios. The results demonstrate that, in terms of fault detection accuracy, the proposed RCNN-based strategy performs better than the traditional fault detection techniques. The performance of the model is assessed under varying vibration data noise levels to further guarantee its effectiveness in practical applications.

Oil paintings represent significant cultural heritage, as they embody human creativity and historical narratives. The preservation of these invaluable artifacts requires effective maintenance practices to ensure their longevity and integrity. Despite their inherent durability, oil paintings are susceptible to mechanical damage and chemical deterioration, necessitating rigorous conservation efforts. Traditional preservation techniques that have been developed over centuries involve surface treatment, structural stabilization, and gel-based cleaning to maintain both the integrity and aesthetic appeal of these artworks. Recent advances in artificial intelligence (AI)-powered predictive maintenance techniques offer innovative solutions to predict and prevent deterioration. By integrating image analysis and environmental monitoring, AI-based models provide valuable insights into painting preservation. This review comprehensively analyzes traditional and AI-based techniques for oil painting maintenance, highlighting the importance of adopting innovative approaches. By integrating traditional expertise with AI technology, conservators can enhance their capacity to maintain and preserve these cultural treasures for future generations.

Due to their excellent strength-to-weight ratio, composite laminates are gradually being substituted for traditional metallic materials in a variety of industries. However, due to their orthotropic nature, composite laminates are prone to several different types of damage, with delamination being the most prevalent and serious. Therefore, deep learning-based methods that use sensor data to conduct autonomous health monitoring have drawn much interest in structural health monitoring (SHM). However, the direct application of these models is restricted by a lack of training data, necessitating the use of transfer learning. The commonly used transfer learning models are computationally expensive; therefore, the present research proposes lightweight transfer learning (LTL) models for the SHM of composites. The use of an EfficientNet–based LTL model only requires the fine-tuning of target vibration data rather than training from scratch. Wavelet-transformed vibrational data from various classes of composite laminates are utilized to confirm the effectiveness of the proposed method. Moreover, various assessment measures are applied to assess model performance on unseen test datasets. The outcomes of the validation show that the pre-trained EfficientNet–based LTL model could successfully perform the SHM of composite laminates, achieving high values regarding accuracy, precision, recall, and F1-score.

The reuse of waste materials has recently become appealing due to pollution and cost reduction factors. Using waste materials can reduce environmental pollution and product costs, thus promoting sustainability. Approximately 95% of calcium carbonate-containing waste eggshells end up in landfills, unused. These eggshells, a form of bio-waste, can be repurposed as catalytic electrode material for various applications, including supercapacitors, after being converted into CaO. Similarly, used waste battery electrode materials pose environmental hazards if not properly recycled. Various types of batteries, particularly lithium-ion batteries, are extensively used worldwide. The recycling of used lithium-ion batteries has become less important considering its low economic benefits. This necessitates finding alternative methods to recover and reuse the graphite rods of spent batteries. Therefore, this study reports the conversion of waste eggshell into calcium oxide by high-temperature calcination and extraction of nanographite from spent batteries for application in energy storage fields. Both CaO and CaO/graphite were characterized for their structural, morphological, and chemical compositions using XRD, SEM, TEM, and XPS techniques. The prepared CaO/graphite nanocomposite material was evaluated for its efficiency in electrochemical supercapacitor applications. CaO and its composite with graphite powder obtained from used lithium-ion batteries demonstrated improved performance compared to CaO alone for energy storage applications. Using these waste materials for electrochemical energy storage and conversion devices results in cheaper, greener, and sustainable processes. This approach not only aids in energy storage but also promotes sustainability through waste management by reducing landfills.

Abstract In order to evaluate the in vivo thrombogenicity of injectable agents, a suitable animal model is needed. We introduce an ultrasound-guided non-selective cerebral artery occlusion model via the common carotid arteries of rabbits. A total of 30 rabbits were assigned to an experimental group (n = 20) and a control group (n = 10). Each group received 2 mL suspension of embolic agent or 2 mL of normal saline, respectively, under ultrasound guidance. The animals were observed for immediate reaction and underwent magnetic resonance imaging (MRI) scan. Follow-up neurologic examination was conducted 24 h following the procedure. In 7 of the 30 rabbits, 2 in the control group and 5 in the experimental group, the administration of either normal saline or the embolic agent failed. Among the successfully injected 15 experimental animals, 14 showed neurologic impairment or deceased, whereas 1 animal did not show significant neurologic deficit. The MRI of 4 experimental animals showed detectable cerebral infarction on diffusion-weighted imaging. None of the 8 control animals showed neurologic abnormality and their brain MRI was normal. Our minimally invasive model is technically feasible and competent to show thrombogenecity of an injectable agent and consequent in vivo neurologic outcome.

Abstract Nitrogen-doped multiwalled carbon nanotubes (N-MWCNTs) have been used to fabricate nanostructured materials for various energy devices, such as supercapacitors, sensors, batteries, and electrocatalysts. Nitrogen-doped carbon-based electrodes have been widely used to improve supercapacitor applications via various chemical approaches. Based on previous studies, CuO@MnO2 and CuO@MnO2/N-MWCNT composites were synthesized using a sonication-supported hydrothermal reaction process to evaluate their supercapacitor properties. The structural and morphological properties of the synthesized composite materials were characterized via Raman spectroscopy, XRD, SEM, and SEM–EDX, and the morphological properties of the composite materials were confirmed by the nanostructured composite at the nanometer scale. The CuO@MnO2 and CuO@MnO2/N-MWCNT composite electrodes were fabricated in a three-electrode configuration, and electrochemical analysis was performed via CV, GCD, and EIS. The composite electrodes exhibited the specific capacitance of ~ 184 F g−1 at 0.5 A g−1 in the presence of a 5 M KOH electrolyte for the three-electrode supercapacitor application. Furthermore, it exhibited significantly improved specific capacitances and excellent cycling stability up to 5000 GCD cycles, with a 98.5% capacity retention.

Background/Aims We evaluated the feasibility and long-term efficacy of the combination of cytarabine, idarubicin, and all-trans retinoic acid (ATRA) for treating patients with newly diagnosed acute promyelocytic leukemia (APL). Methods We included 87 patients with newly diagnosed acute myeloid leukemia and a t(15;17) or promyelocytic leukemia/retinoic acid receptor alpha (PML-RARα) mutation. Patients received 12 mg/m2/day idarubicin intravenously for 3 days and 100 mg/m2/day cytarabine for 7 days, plus 45 mg/m2/day ATRA. Clinical outcomes included complete remission (CR), relapse-free survival (RFS), overall survival (OS), and the secondary malignancy incidence during a 20-year follow-up. Results The CR, 10-year RFS, and 10-year OS rates were 89.7%, 94.1%, and 73.8%, respectively, for all patients. The 10-year OS rate was 100% for patients that achieved CR. Subjects were classified according to the white blood cell (WBC) count in peripheral blood at diagnosis (low-risk, WBC < 10,000/mm3; high-risk, WBC ≥ 10,000/mm3). The low-risk group had significantly higher RFS and OS rates than the high-risk group, but the outcomes were not superior to the current standard treatment (arsenic trioxide plus ATRA). Toxicities were similar to those observed with anthracycline plus ATRA, and higher than those observed with arsenic trioxide plus ATRA. The secondary malignancy incidence after APL treatment was 2.7%, among the 75 patients that achieved CR, and 5.0% among the 40 patients that survived more than 5 years after the APL diagnosis. Conclusions Adding cytarabine to anthracycline plus ATRA was not inferior to anthracycline plus ATRA alone, but it was not comparable to arsenic trioxide plus ATRA. The probability of secondary malignancy was low.

Due to its simplicity, accuracy, and adaptability, Crimp Force Monitoring (CFM) has long been the standard for fault detection in wiring harness manufacturing. However, it necessitates frequent reconfigurations based on the variability in materials, dependency on operator skill, and high costs of implementation, and thus reconfiguration presents significant challenges. To solve these problems, this paper introduces a fault detection system that employs an Artificial Intelligence (AI) classification model to enhance the performance and cost-efficiency of the quality control process of wiring harness manufacturing. Since there are no labeled data to train the classification model at the onset of manufacturing, a small number of normal data from each production run are manually extracted to train the model. To address the constraint of the limited available data, the system generates synthetic data from normal data, simulating potential defects by using Regional Selective Data Scaling (RSDS). This innovative method performs upscaling or downscaling on specific regions of the original data to produce synthetic abnormal data, which enables the fault detection system to efficiently train its classification model with a dataset consisting solely of normal operation data.

Antarctic continental margin sediments are eroded from the shelf and transported to the slope/rise in association with changing ice sheet configuration. Understanding the dynamics of this transport pathway is important for utilizing distal deep-sea sedimentary archives to determine past changes in the Antarctic ice sheet. However, these connections are poorly understood. Here we present multi-proxy records of two sediment cores (BS17-GC01 and BS17-GC02) from the Bellingshausen Sea continental rise, to explore relationships between depositional regime and ice sheet dynamics. Two cores show depositional/sedimentological variations on glacial-interglacial scales. Biogenic sediments were deposited during MIS 1, 5, and 7 under open ocean conditions. Glacial to deglacial sediments were laminated as a result of varying intensity of bottom currents. Terrestrially derived sediments are inferred to be transported from shelf both as grounded ice advanced during glacial expansion, and as ice retreated during deglacial periods. Sediment color shifted to brown after deglacial periods with high Mn/Ti and occurrence of bioturbation, indicating increasing bottom water oxygenation in the study area. Since surface water production started to increase from deglacial periods, we infer increased bottom water oxygenation in this setting is due to ventilation (i.e., Antarctic Bottom Water (AABW) formation), implying that AABW formation was increased during interglacial periods from deglacial period whereas was decreased during glacial periods. Thus, sedimentary/depositional changes in BS17-GC01 and BS17-GC02 are closely linked to ice sheet dynamics during the late Quaternary.

Abstract Background Quantum dots (QDs) have been used as fluorophores in various imaging fields owing to their strong fluorescent intensity, high quantum yield (QY), and narrow emission bandwidth. However, the application of QDs to bio-imaging is limited because the QY of QDs decreases substantially during the surface modification step for bio-application. Results In this study, we fabricated alloy-typed core/shell CdSeZnS/ZnS quantum dots (alloy QDs) that showed higher quantum yield and stability during the surface modification for hydrophilization compared with conventional CdSe/CdS/ZnS multilayer quantum dots (MQDs). The structure of the alloy QDs was confirmed using time-of-flight medium-energy ion scattering spectroscopy. The alloy QDs exhibited strong fluorescence and a high QY of 98.0%. After hydrophilic surface modification, the alloy QDs exhibited a QY of 84.7%, which is 1.5 times higher than that of MQDs. The QY was 77.8% after the alloy QDs were conjugated with folic acid (FA). Alloy QDs and MQDs, after conjugation with FA, were successfully used for targeting human KB cells. The alloy QDs exhibited a stronger fluorescence signal than MQD; these signals were retained in the popliteal lymph node area for 24 h. Conclusion The alloy QDs maintained a higher QY in hydrophilization for biological applications than MQDs. And also, alloy QDs showed the potential as nanoprobes for highly sensitive bioimaging analysis. Graphical Abstract

Background Hemophagocytic lymphohistiocytosis (HLH) is a rare but severe, life-threatening inflammatory condition if untreated. We aimed to investigate the etiologies, outcomes, and risk factors for death in children and adults with HLH. Methods The medical records of patients who met the HLH criteria of two regional university hospitals in Korea between January 2001 and December 2019 were retrospectively investigated. Results Sixty patients with HLH (35 children and 25 adults) were included. The median age at diagnosis was 7.0 years (range, 0.1–83 years), and the median follow-up duration was 8.5 months (range, 0–204 months). Four patients had primary HLH, 48 patients had secondary HLH (20 infection-associated, 18 neoplasm-associated, and 10 autoimmune-associated HLH), and eight patients had HLH of unknown cause. Infection was the most common cause in children (14/35, 40.0%), whereas neoplasia was the most common cause in adults (13/25, 52.0%). Twenty-eight patients were treated with HLH-2004/94 immunochemotherapy. The 5-year overall survival (OS) rate for all HLH patients was 59.9%. The 5-year OS rates for patients with primary, infection-associated, neoplasm-associated, autoimmune-associated, and unknown cause HLH were 25.0%, 85.0%, 26.7%, 87.5%, and 62.5%, respectively. Using multivariate analysis, neoplasm-induced HLH (p=0.001) and a platelet count

This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity.

The main challenge with rotating machine fault diagnosis is the condition monitoring of machines undergoing nonstationary operations. One possible way of efficiently handling this situation is to use the deep learning (DL) method. However, most DL methods have difficulties when the issue of imbalanced datasets occurs. This paper proposes a novel framework to mitigate this issue by developing an area-metric-based sampling method. In the proposed process, the new sampling scheme can identify which locations of the datasets can potentially have a high degree of surprise. The basic idea of the proposed method is whenever significant deviations from the area metrics are observed to populate more sample points. In addition, to improve the training accuracy of the DL method, the obtained sampled datasets are transformed into a continuous wavelet transform (CWT)-based scalogram representing the time–frequency component. The dilated convolutional neural network (CNN) is also introduced as a classification process with the altered images. The efficacy of the proposed method is demonstrated with fault diagnosis problems for welding robots. The obtained results are also compared with existing methods.

Total knee arthroplasty (TKA) is a surgical technique to replace damaged knee joints with artificial implants. Recently, the imageless TKA has brought a revolutionary improvement to the accuracy of implant placement and ease of surgical process. Based on key anatomical points on the knee, the software guides the surgeon during the TKA procedure. However, the number of revision surgeries is increasing due to malalignment caused by registration error, resulting in imbalanced contact stresses that lead to failure of the TKA. Conventional stress analysis methods involve time-consuming and computationally demanding finite element analysis (FEA). In this work, a machine-learning-based approach estimates the contact pressure on the TKA implants. The machine learning regression model has been trained using FEA data. The optimal preprocessing technique was confirmed by the data without preprocessing, data divided by model size, and data divided by model size and optimal angle. Extreme gradient boosting, random forest, and extra trees regression models were trained to determine the optimal approach. The proposed method estimates the contact stress instantly within 10 percent of the maximum error. This has resulted in a significant reduction in computational costs. The efficiency and reliability of the proposed work have been validated against the published literature.

The availability of computational power in the domain of Prognostics and Health Management (PHM) with deep learning (DL) applications has attracted researchers worldwide. Industrial robots are the prime mover of modern industry. Industrial robots comprise multiple forms of rotating machinery, like servo motors and numerous gears. Thus, the PHM of the rotating components of industrial robots is crucial to minimize the downtime in the industries. In recent times, deep learning has proved its mettle in different areas, like bio-medical, image recognition, speech recognition, and many more. PHM with DL applications is a rapidly growing field. It has helped achieve a better understanding of the different condition monitoring signals, like vibration, current, temperature, acoustic emission, partial discharge, and pressure. Most current review articles are component- (or system-)specific and have not been updated to reflect the new deep learning approaches. Also, a unified review paper for PHM strategies for industrial robots and their rotating machinery with DL applications has not previously been presented. This paper presents a review of the PHM strategies with various DL algorithms for industrial robots and rotating machinery, along with brief theoretical aspects of the algorithms. This paper presents a trend of the up-to-date advancements in PHM approaches using DL algorithms. Also, the restrictions and challenges associated with the available PHM approaches are discussed, paving the way for future studies.