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Physical exercise is increasingly recognized as an important compo-nent in the neuroscience related field. What is the targeting of exercise and what accounts for the exercise’s benefits observed in neurosci-ence? Several types of exercise have been studied in various fields across physiological, psychological, and biochemical experiments of neuroscience. However, more clarity is needed to unveil optimal exer-cise conditions such as frequency, intensity, type, and time. In this re-view, we briefly highlight the positive effects of exercise on promoting brain function. Key areas relate to exercise neuroscience are as follow: structural level with synaptic plasticity and neurogenesis, functional level with behavioral development, and molecular level with possible mechanisms that involved in exercise-induced brain plasticity. Overall, we provide the importance of understanding the exercise neuroscience and highlight suggestions for future health research.

Two field experiments were conducted to improve the conception rate of Hanwoo cow. The first experiment aimed to investigate the physiological condition of Hanwoo cows on estrus, including metabolic profiles and body condition score (BCS). The second experiment investigated the effect of a novel estrus detector on the artificial insemination (AI) conception rate for Hanwoo cows. For the first experiment, 80 Hanwoo cows (2.5 ± 0.10 of parity), approximately one month before estrus, were housed in 16 pens and offered the experimental diets twice daily with free water access. The BCS were recorded, and blood was collected from the jugular veins just before AI. The collected blood was used to measure physiological conditions, such as metabolite and hormone levels. For the second experiment, each cow was equipped with a neck-mounted estrus detector collar, which had a sensor connected through the internet. Approximately one month before estrus, three hundred sixty Hanwoo cows (2.4 ± 0.21 of parity) were assigned into groups with or without W-Tag collar treatments. The animals were managed the same as in the first experiment. The pregnancy rate reached 55% in the first experiment. The concentration of luteinizing hormone (LH) was higher (p < 0.012; 1.56 vs. 1.08 ng/mL) in cows that were not pregnant (NPG) than in cows that were pregnant (PG) after AI. The BCS and other concentrations of metabolites and hormones in the blood were not different in both NPG and PG cows. The ranges of estrogen, LH, and follicle-stimulating hormone for PG cows were 11.9 to 39.0 pg/mL, < 0.25 to 1.98 ng/mL, and < 0.50 to 0.82 ng/ mL, respectively. In the second experiment, cows with the estrus detector had lower days open (p < 0.001; 78.1 vs. 84.8 d), insemination frequency (p < 0.001; 1.26 vs. 2.52), and return of estrus (p < 0.001; 70.9 vs. 79.1 d) than those in cows without the estrus detector. In conclusion, the present study indicated that lower LH concentration just before AI potentially increased the pregnancy rate of Hanwoo cows. Furthermore, the application of estrus detectors to Hanwoo cows could improve the conception success rate for AI.

Objective To evaluate the effect of extracorporeal shock wave therapy (ESWT) in myofascial pain syndrome of upper trapezius with visual analogue scale (VAS) and pressure threshold by digital algometer.Method Twenty-two patients diagnosed with myofascial pain syndrome in upper trapezius were selected. They were assigned to treatment and standard care (control) groups balanced by age and sex, with eleven subjects in each group. The treated group had done four sessions of ESWT (0.056 mJ/mm2, 1,000 impulses, semiweekly) while the control group was treated by the same protocol but with different energy levels applied, 0.001 mJ/mm2. The VAS and pressure threshold were measured twice: before and after last therapy. We evaluated VAS of patients and measured the pressure threshold by using algometer.Results There were two withdrawals and the remaining 20 patients were three men and 17 women. Age was distributed with 11 patients in their twenties and 9 over 30 years old. There was no significant difference of age, sex, pre-VAS and pre-pressure threshold between 2 groups (p>0.05) found. The VAS significantly decreased from 4.91±1.76 to 2.27±1.27 in the treated group (p<0.01). The control group did not show any significant changes of VAS score. The pressure threshold significantly increased from 40.4±9.94 N to 61.2±12.16 N in the treated group (p<0.05), but there was no significant change in the control group.Conclusion ESWT in myofascial pain syndrome of upper trapezius is effective to relieve pain after four times therapies in two weeks. But further study will be required with more patients, a broader age range and more males.

금속 PLA 3D 프린팅 소결 기법을 이용한 근현대 금속 문화유산의 복원 재료 특성을 파악하고자 소결 금속 PLA 시편에 관한 과학적 분석 및 비교를 진행하였다. 금속 PLA의 소결 전⋅후를 기준으로 형태를 비교한 결과, 소재에 따라 약 10∼26% 범위 내 수축이 발생하였고, 중량은 최대 25% 감소하였다. 소결 후 미세조직 및 물성 평가 수치를 종합한 결과, 금속 입자 경계 면적이 감소하여 단위 면적당 금속분말의 밀도가 증가하였고, 미상의 미세조직이 발생하 여 소결 전과 비교되는 미세조직 형태가 관찰되었다. 또한, 소결 후 금속 PLA의 표면 경도 및 비중이 증가하였다. 이를 표준 금속 측정값과 비교한 결과, 표면 경도는 유사하거나 높았으 며, 작은 비중값이 확인되었다. 이러한 금속 PLA의 소결 특징을 통해 경량화가 이루어진 재료 임을 확인하였으며, 근현대 금속 문화유산의 부분복원 및 복제, 기능 복원 및 전시⋅활용에 적합할 것으로 판단된다.

Herein, sintered metal PLA samples were scientifically analyzed to evaluate their suitability for restoring metal cultural heritage. After sintering, sample shrinkage was 10∼26%, with up to 25% weight reduction. In addition, microstructure analysis revealed reduced particle boundaries, increased particle density, and new microstructures. Furthermore, surface hardness and density increased; however, the sample density remained lower than those of standard metals. Compared to standard metals, the metal PLA samples showed comparable or higher hardness and lightweightness. These results highlight the potential of metal PLA for restoring and replicating modern metal heritage, particularly for their functional restoration and exhibition.

Anomaly recognition in visual and audio data has gained increasing significance in computer vision, as it plays a crucial role in protecting human lives and property. In this work, we developed a semi-supervised multimodal framework for anomaly recognition that combines audio and visual data for better performance. The proposed framework employs a hybrid network consisting of a convolutional neural network, Bi-Directional Long Short-Term Memory, a multi-head attention module, and a fully connected layer for anomalous pattern recognition. We created a novel real-time visual-audio anomaly recognition dataset and evaluated our framework on it, achieving promising results.

Recently, a graph neural network has played a crucial role across various fields. In this paper, we designed a Graph Convolutional Network (GCN) to analyze population movement at the city level. It consists of four Graph Convolution (GC) layers, with each layer responsible for aggregating knowledge from its neighboring nodes and updating the feature representation for each city. We utilized population mobility data from China, which includes daily city-to-city movement data. GCN estimates the strength of relationships among all cities. Experimental results demonstrate that the proposed GCN achieves improved performance in estimating city-to-city migration flow relationships.

In recent years, the anticipation of human mobility flow has significant applications in various domains ranging from urban planning to public health. This study proposes a hybrid Graph Neural Network and Long Short Term- Memory network-based model for nationwide human mobility prediction, effectively capturing inter-urban movement patterns. We validate the feasibility and effectiveness of our model using the Korean internal-city mobility dataset, which captures real-world population movement patterns across various urban regions. Our experimental results accurately predict inter-city mobility, advancing urban planning, health, and transport.

The identification of key feature selection plays a significant role in accurate population projection, which is an essential aspect of demographic statistics. The goal of this paper is to investigate the importance of the different features in population projection by using four advanced feature analysis techniques i.e. Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA) Fast Independent Component Analysis (FICA) and Principal Component Analysis (PCA). This analysis is important to determine the major factors that affect population change. The identification and ranking of these predictors can enhance demographic forecasting and policy planning. We utilized Koran population data from the UN Population Division dataset and evaluated the above four methods. The experimental results reveal that LDA achieved the lowest performance in selecting the most appropriate features, while PCA is the most efficient in selecting an effective feature with the highest variance. These insights build up the knowledge of population change and refine the projection models.

Recent advancements in data-driven methodologies have brought significant attention to the computational prediction of material properties. Traditional machine learning (ML) approaches have struggled to achieve high accuracy due to the complex relationships between a material's structure and its properties. To address this challenge, in this work, we present an ML framework for predicting the stability of silicon (Si) and Si-based alkaline metal alloys with reduced error. This emphasizes the model transferability to discover new silicon alloys with diverse electronic configurations and structures. We explore the effectiveness of two atomic structural descriptors including X-ray diffraction (XRD) and sine coulomb matrix (SCM). The dynamic ensemble learning (DEL) model is trained and evaluated using 750 Si alloys from the materials project database (MPD) and optimized via ensemble learning. The results indicate that the XRD descriptor with DEL performs most reliably for formation energy, total energy and packaging fraction prediction, showing the model robustness and transferability for ultimate efficient silicon anode’s material synthesis.

The battery market is experiencing rapid growth due to advancements in technology and increased recycling efforts. Verifying the suitability of developed batteries through rate capability experiments, which measure capacity based on charging and discharging speeds, is essential but resource-intensive and time-consuming. This research proposes a method to predict battery rate capability using a polynomial regression model based on similar data groups, aiming to shorten these experiments. The research was conducted in two main stages, namely the construction of the dataset and the development of the predictive model. Data was collected from experimental graphs in existing literature and new experiments on Coin Cell batteries. Through preprocessing steps including deduplication, interpolation, and extrapolation, a comprehensive dataset was created. A combined Quadratic and Linear Piecewise Interpolation method was developed to handle missing data efficiently. In the model development stage, polynomial regression models were created for groups of similar battery data, allowing accurate predictions for partial rate capability experiments. Experimental results demonstrated high accuracy, significantly reducing the need for extensive testing. The proposed method offers substantial time and resource savings, enhancing the efficiency of the battery development process.

Recognizing anomalies in surveillance is crucial for public safety to identify events that deviate from normal patterns. Visual information is essential for effective anomaly recognition; however, audio data can enhance recognition accuracy by providing additional context. Despite this, existing systems only utilize visual information, overlooking the potential of audio modalities in anomaly recognition. This paper introduces a multi-modal framework for anomaly recognition through active learning, integrating audio and visual modalities to enhance anomaly prediction. The framework extracts features using a pretrained ResNet-50 convolutional neural network (CNN) model from the visual and audio data. The extracted features are then forwarded to the Bi-Directional Long Short-Term Memory (Bi-LSTM) network for temporal feature learning. These features are then fused and fed into a classification layer for final prediction. The proposed framework's performance is assessed on a benchmark dataset and yields promising results.

In recent years, anomaly recognition using audio has attracted the attention of the research community, due to the increasing number of abnormal situations day by day. In the past, researchers have mainly focused on video-based anomaly recognition. However, occlusion is one of the most important factors due to which the anomalous object is unidentifiable. Therefore, in this paper, we proposed a modified vision transformer that utilized the Shifted Patch Tokenization (SPT), and Local Self-Attention (LSA) mechanism and reduced the number of multilayer perceptrons in the head, enabling the model to capture rich spatial information within the spectrogram of anomalous data. The proposed model is implemented using the Sound Events for Surveillance Applications (SESA) dataset and obtained 87% testing accuracy. Thus, the proposed model is an efficient and effective solution for audio-based anomaly recognition.

Nowadays, surveillance systems play a pivotal role in monitoring various sectors to ensure public safety and security. These systems generate massive amounts of video data. Therefore, effective analysis of these streams is an important research area with multiple applications. Several methods have been reported for the automatic recognition of abnormal activities, but these techniques show limited performance while learning complex temporal dependencies of real-world surveillance of abnormal activities. We introduce a Deep Learning (DL)-assisted framework that is mainly divided into two parts. First, the surveillance video stream is preprocessed, and then the BoTNeT-152 is employed to extract spatial features. Secondly, a Residual Deep Bidirectional Long Short-Term Memory (RBLSMT) Network is introduced to learn the complex temporal dependencies across multiple frames for abnormal activity recognition. To assess the effectiveness of our proposed method, we evaluated its performance on the benchmark real-world UCFCrime2Local dataset, achieving an accuracy of 86% reveals a significant improvement of up to 2% compared to existing methods which shows the superiority of the suggested technique in addressing the challenges posed by complex surveillance environments.

This survey explores electricity theft detection in smart grids, where traditional power systems meet modern technology. Smart grids, designed for efficient energy management and continuous integration of renewables, face a pressing challenge electricity theft, costing utility companies over $96 billion annually. The survey traces the evolution from conventional to smart grids, emphasizing their core components. It underscores the economic impact of theft, driving researchers to explore Artificial Intelligence (AI) and Deep Learning (DL) techniques for detection. A comprehensive literature review reveals various approaches, with a focus on DL's growing influence. Public datasets are explored as invaluable resources, and methods for theft detection, including advanced AI and DL, are dissected. Performance metrics like accuracy and precision are discussed, and challenges, including imbalanced data and privacy concerns, are highlighted. In conclusion, the survey emphasizes the need for diverse AI and DL approaches, data sources, and features to create robust theft detection systems for smart grids, ensuring their secure and efficient operation.

This paper introduces a comprehensive approach to dataset standardization aimed at enhancing the effectiveness and reliability of solar power forecasting models. Leveraging multiple datasets, this study incorporates additional attributes such as atmospheric pressure and sunshine duration. These enrichments bridge critical gaps in meteorological and environmental data, facilitating more robust and precise solar power forecasting. The paper underscores the significance of these attributes, furnishes detailed equations for their computation, and presents the outcomes of their integration. It underscores their pivotal role in enabling solar energy stakeholders to make informed decisions and optimize energy production effectively.

Sustainable power systems should include solar energy generation. However, for effective grid management and the integration of renewable energy sources, accurate solar power generation predictions are essential. Therefore, this study compares the prediction of solar power forecasting in Italy and Bulgaria. These are two countries that have alike latitudes but different populations and solar energy production. The historical solar power generation and meteorological data from these countries are preprocessed and then used to apply four different deep learning models including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results are analyzed to gain insights into how the proximity of geographical locations and the quality and quantity of data impact the precision of prediction algorithms.

The identification of anomalies in industrial settings poses a significant challenge, especially when there is a lack of negative samples and when the anomalous regions are small. Although existing computer vision methods have automated this task to some extent, these approaches struggle to extract salient features for inspecting defective chips. To tackle this problem, a deep learning-based framework is proposed for detecting anomalies in industrial settings. The framework utilizes a fine-tuned backbone convolutional neural network model and incorporates an enhanced attention mechanism. The attention module generates discriminative feature maps along two dimensions: channel and spatial. This is achieved by processing intermediate features obtained from the backbone model. These attention maps are then multiplied with the input feature map to dynamically enhance the relevant features. Extensive experiments demonstrate the effectiveness of our proposed method in maintaining a high level of detection accuracy for industrial product inspections. Consequently, our results conclude a suitable solution for optical chip inspection systems in industrial settings.

The analysis, recognition and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem to recognize animal species, which has numerous applications in zoology, ecology, biology, and entertainment. Researchers used different machine learning approach for animal species recognition, however the researchers mostly used image data for this purpose and ignore the importance of audio data. In this work, our focus is to process multi modality (image and voice) dataset for animal species recognition. We proposed two different networks for animals’ audio and visual representation to recongize animals’ species. First network for animals’ audios classification that extract MFCC features, and these features is passed from four VGG style blocks while the second network extract visual features from images to classify according to their species. The experimental results demonstrated the effectiveness of the proposed model of achieved better performance in terms of classification accuracies.

Deep learning models achieved a lot of success due to the availability of labeled training data. In contrast, labeling a huge amount of data by a human is a time-consuming and expensive solution. Active Learning (AL) efficiently addresses the issue of labeled data collection at a low cost by picking the most useful samples from a large number of unlabeled datasets However, current AL techniques largely depend on regular human involvement to annotate the most uncertain/informative samples in the collection. Therefore, a novel AL-based framework is proposed comprised of proxy and active models to reduce the manual labeling costs. In the proxy model, VGG-16 is trained on chunks of labeled data that later act as an annotator decision. On the other hand, in the active model, unlabeled is passed to Inception-V3 using the sampling strategy. The uncorrected predicted samples are then forwarded to the proxy model for annotation and considered those data have a high confidence score. The empirical results verify that our proposed model is the best in terms of annotation and accuracy.