bluuebunny/crossref_metadata_2025 huggingface dataset:
The concept of successful ageing in wartime: A review of Western scientific sources
Authors: Kolly-Shamne Anzhelika
Published: April 2024

Preserving the full life of an elderly person as the most vulnerable category of the population in times of armed conflict raises many methodological, theoretical, and practical issues for scholars. The article is an attempt at a problematic and reflective analysis of the heuristic nature of the concept of successful ageing for the study of ageing of elderly Ukrainians in the context of the war with Russia. The paper presents a problem-oriented review of contemporary Western literature on successful ageing. In addition to the basic empirical and theoretical works, a significant part of the theoretical analysis is based on the results of the most significant meta-analytical studies on this topic. The methodology of problem-oriented and integrative analysis was used to determine which problem areas of the concept of successful ageing can contribute to its study in wartime. The potential and limitations of the modern concept of successful ageing are considered. Eight problem areas of this concept are presented, which reveal certain contradictions, debatable issues, and gaps, which are simultaneously directions for the development of both the concept itself and its research in the context of war. Among them: vagueness of the criteria for successful ageing and its dilution with similar concepts; interaction of biomedical and psychosocial contexts of successful ageing; limits of responsibility for the success of ageing in the context of the “person – society” dichotomy; socio-structural inequalities between older people and the inability to age equally “successfully”; discrepancies between subjective and objective indicators of successful ageing; dominance of the Western model of successful ageing and levelling of its cultural specificity; integration of individual, social and scientific conceptualization of successful ageing. The author analyses the most promising theoretical and methodological concepts and theories of successful ageing that are most relevant to the problem of ageing in war. The literature review presented in the article will help to draw attention to the problem of improving and preserving the full life of an elderly person in the context of armed conflict. The results of the research will be useful to scholars and practitioners for the further development of programmes to protect the mental and physical health of older people in war
Pedagogical aspects of the development of leadership qualities in cadets of higher military educational institutions
Authors: Melnychenko Sergii , Ovchynnyk Volodymyr , Hudal Sergii , Kulyk Mykhaylo
Published: April 2024

The development of leadership qualities in cadets of military institutions is of particular importance in today's conditions, since the formation of a new generation of professional competent officers-leaders will directly affect the combat capability of the Armed Forces and the defence capability of the state as a whole. The purpose of the study was to identify pedagogical aspects and ways to improve the system of leadership development among cadets of the Military Academy. The study analysed the theoretical foundations of the leadership problem and summarized the best Ukrainian and foreign experience. The methods used were: structural-functional method; analysis, induction. The study also used the following methodological approaches: systemic approach; reductionist and holistic approaches. As a result of the research, it was found that the existing system of training military leaders in higher military educational institutions currently has certain significant drawbacks that significantly reduce its effectiveness, namely: insufficient attention is paid to military-humanitarian disciplines, which shape the socio-communicative qualities of a modern leader; outdated reproductive teaching methodologies dominate instead of innovative interactive ones; individual-psychological characteristics of the cadet's personality and potential are not sufficiently taken into account; the educational potential of the faculty is weakly utilized due to mentoring; the system of cadet self-government is underdeveloped, within which future officers could gain real practical leadership experience. Therefore, constructive ways of improving the existing system were proposed by strengthening the humanitarian component of training, wider use of interactive teaching methods, introducing individual educational trajectories, increasing the role of mentoring from senior command staff, creating an effective system of cadet self-government, and substantiating the feasibility of using innovative pedagogical methods of active learning for the effective targeted formation of necessary leadership qualities and competencies. The results of the study on improving the system of leadership development of cadets of Higher Military Educational Institutions are of great practical importance, as they can be used to improve the effectiveness of training officers as future military leaders
Entrepreneurial Design Thinking and Marketing Performance of Food and Beverage Firms in Port Harcourt, Rivers State
Authors: P. C. Chikere , A. R. Blessing
Published: April 2024

The study examined the relationship between entrepreneurial design thinking and marketing performance of food and beverage firms in Port Harcourt, Rivers State. This study adopted a correlational research design, with the population of the study comprising 25 food and beverage firms in Rivers State. Sequel to the population of the study, which is 25 food and beverage firms, the study adopted a census study with a focus on the managerial staff (production manager, quality control manager, marketing manager, and procurement manager). The questionnaire was distributed in a frame of four (4) copies per firm. A total of one hundred (100) copies of the questionnaire were distributed. The reliability of the instrument was determined using Cronbach's alpha test and it stood at 0.88, higher than the benchmark of 0.7. The data collected for this study were analyzed through descriptive and inferential statistics. The Spearman Rank-Order Correlation Technique was employed to test the various hypotheses formulated. The empirical results of this study confirmed that all the dimensions of entrepreneurial design thinking (creativity, and problem solving) were found to be significant predictors of marketing performance as measured by sales growth. Since all the dimensions of entrepreneurial design thinking are significant predictors of marketing performance, it is therefore concluded that entrepreneurial design thinking has the potential of improving marketing performance of food and beverage firms in Port Harcourt, Rivers State. Based on the findings and conclusions, the study recommends that: food and beverage firms should come up with creative designs, novel products and services that will catch the customer’s attention and improve marketing performance. Also, food and beverage firms, especially those facing different challenges, should adopt a problem solving approach to enable them to improve their sales growth.
Infra-renal aortic intra-mural hydatid cyst: An extremely rare localization
Authors:   Vascular Surgery Specialist, Al-Assad University Hospital, Damascus, Syria, Mohammed Ashraf
Published: April 2024

Hydatid cysts are parasitic infections caused by ecchinococcus granulosus. They usually infect the liver and the lungs in 90% of cases, while the abdominal aortic localization is extremely rare. Aortic infection might lead to serious complications such as ischemia, pseudo-aneurysm formation and anaphylaxis.
Ensemble Residual Network with Iterative Randomized Hyperparameter Optimization for Colorectal Cancer Classification
Authors: S. Vimala Anju T. E.,
Published: April 2024

The analysis of WSI images is widely acknowledged as a method, for identifying stages of cancer and evaluating the spread of cancer cells in tissues. In histopathology image analysis deep learning models are gaining increasing importance. To enhance the effectiveness of these models it is crucial to train and fine-tune Convolutional Neural Network algorithms by adjusting hyperparameters like batch size, convolution depth, and learning rate (LR). However, determining the hyperparameters can be challenging as they significantly impact model performance. This study examines how hyperparameters influence cancer classification, in histopathology images using the CNN architecture. A method called iterative randomized hyperparameter optimization is proposed, which gradually reduces variations over time by adjusting parameters after each network layer. The combination of hyperparameters is applied to version 1 of ResNet18, ResNet50, and ResNet101 models and version 2 of ResNet50, ResNet101, and ResNet151. The results are then combined using the Adaptive Boosting algorithm. The results are quite promising on ensemble version 1 residual networks, achieving an accuracy of 98.7% when tested on nine tissue classes.
The Construction of Higher Education Evaluation Index System Based on Vector Analysis
Authors: Wu Yunyue
Published: April 2024

The construction of a higher education evaluation index system based on vector analysis involves a comprehensive approach to assessing the quality and effectiveness of higher education institutions. By leveraging vector analysis, which considers both magnitude and direction, this method enables the integration of diverse factors and perspectives into the evaluation process. Key considerations in constructing such an index system include academic performance, research output, faculty qualifications, student satisfaction, institutional resources, and societal impact. Each factor is represented as a vector, with its magnitude reflecting its importance and its direction indicating its alignment with the overarching goals of higher education. This paper presents a novel approach for constructing a robust evaluation index system for higher education institutions leveraging vector analysis, supplemented by Periodic Vector Chain Learning (PVCL). the incorporation of PVCL enhances the dynamism and adaptability of the evaluation system. PVCL allows for periodic updates and adjustments to the evaluation index system, ensuring its relevance and effectiveness amidst changing educational landscapes and emerging trends. The integration of vector analysis with PVCL presents a promising avenue for the construction of a robust evaluation index system for higher education institutions.
Intelligent Construction of University Music Education Teaching System Based on Artificial Intelligence Technology
Authors: Li Shu
Published: April 2024

The university music education teaching system plays a vital role in nurturing aspiring musicians and music enthusiasts by providing a comprehensive framework for musical learning and development. This system typically encompasses a diverse range of courses, workshops, and performance opportunities designed to cultivate students' musical talents, theoretical knowledge, and practical skills. Through a combination of classroom instruction, ensemble rehearsals, private lessons, and hands-on experiences, students receive a well-rounded musical education that covers various genres, styles, and traditions. Moreover, university music education teaching systems often incorporate state-of-the-art facilities, including rehearsal rooms, recording studios, and performance venues, to support students' artistic growth and creative expression. This paper presents an innovative approach to the intelligent construction of university music education teaching systems, leveraging artificial intelligence (AI) technology with Intelligent Fuzzy Regression Classification (IFRC). Recognizing the complexity and diversity of music education, this research aims to optimize teaching methodologies and enhance learning outcomes through AI-driven strategies. The proposed approach integrates AI technology, particularly IFRC, into the design and implementation of university music education teaching systems. IFRC combines fuzzy logic with regression analysis and classification techniques to model and predict complex relationships within music education datasets, enabling the system to adapt and respond dynamically to student needs and preferences. The IFRC-enhanced teaching system can generate personalized learning pathways, recommend tailored resources, and provide real-time feedback to students and instructors alike. This intelligent adaptation to individual learning styles and progress fosters a more engaging, effective, and inclusive music education environment. The IFRC-enhanced teaching system achieves an average improvement of 25% in student performance compared to traditional teaching methods. Moreover, specific musical skills, such as sight-reading, ear training, and music theory comprehension, exhibit notable enhancements, with average score increases of 30%, 20%, and 35%, respectively.
An Enhance CNN Model for Brain Tumor Detection from MRI Images
Authors: Yogesh Chaba, Sunila Godara, Sumit Sindhu Jai Bhagwan, Seema Rani, Sanjeev Kumar,
Published: April 2024

A Convolutional Neural Network (CNN) model for the precise classification and detection of brain tumors from Magnetic Resonance (MR) images is presented in this research. Leveraging the power of deep learning, the proposed CNN model demonstrates remarkable accuracy in distinguishing between different tumor types, aiding in effective diagnosis. The network is trained on a diverse dataset, enabling robust generalization to unseen cases. The proposed method not only enhances the classification performance but also facilitates early tumor detection. Operating on 256 x 256 image inputs, the proposed model boasts a complex architecture featuring six Conv2D layers, batch normalization, five MaxPooling2D layers, six dense layers, and an input layer. Throughout these layers, the ReLU activation function dominates with the final dense layer utilizing the sigmoid function. The potential of sophisticated CNN architectures in medical imaging is highlighted by this study. The outcomes validate the effectiveness of the CNN-based method, advancing computer-aided neuroimaging diagnostic tools and offering a useful tool for medical practitioners. The increased accuracy values demonstrate how well the sophisticated CNN model recognizes brain tumors and point to its potential as a more complex and dependable technique. It also presents a viable avenue for accurate and timely brain tumor identification.
Utilizing Machine Learning Techniques for Categorizing Cancer Based on Gene Expression Data: A Review
Authors: Hembrom T., Hazra S., Barman D. Begum S., Sandipan Dey, Chakraborty D.,
Published: April 2024

Cancer is a group of diseases which share one common feature: the growth of abnormal cells, thus ranking as the second leading cause of death globally, after cardiovascular diseases in WHO's report. Evaluating gene expression is based on the fact that it is the genesis of early cancer detection, the concurrence of the molecular and genetic processes. By using DNA microarray techniques and RNA-sequencing approaches, researchers in computational genomics can give quantitative measures of gene expression levels providing very accurate input data for computational evaluation. The current paper is about machine learning technology, which identifies cancer subtypes according to patterns of gene expression. It embraces the two distinct methodologies which are traditional plus deep learning with high proficiency focused on the cancer-related gene. The outline includes the most popular deep neural network designs such as MLPs, CNN, RNN, GNN, and the recently emerged Transformer networks. The review describes common data collection methods used in this field and some of the essential datasets for supervised machine learning. In addition, the specific techniques developed to cover the complicated horizontal spread of gene expression data are also presented. The article explores theoretical possibilities for the promotion of machine learning-based gene expression analysis in cancer classification towards the end.
Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis
Authors: L. K. Vishwamitra Vishakha D. Akhare,
Published: April 2024

An in-depth familiarity with ML and DL models for fraud detection is essential due to the growing frequency and complexity of fraudulent activity across many domains. Despite the abundance of research on the subject, empirical analyses of these models, especially in their real-time implementations, are typically lacking. This study fills that need by meticulously reviewing and analysing ML and DL models developed for fraud detection. We draw attention to the shortcomings of existing approaches, which are crucial in the ever-changing field of fraud detection and include problems with recall, scalability, complexity, precision, and accuracy. By evaluating various ML and DL models using these measures, our evaluation method is based on a rigorous empirical approach. Provided that insights into the practical consequences as well as flexibility of each model in real-time circumstances, they thoroughly assess their performance. There are many ways in which this work will be useful; for example, it will help professionals choose the best models for their fraud detection needs, improve academic knowledge of these models in practice, and open the door to more studies that will concentrate on developing better fraud detection tools. This comprehensive research gives academics and companies a foundation for better, more effective and more scalable fraud detection systems in this period of essential digital security.
Securing Social Media Imagery: GAN-Driven Encryption and CNN Analysis with DEA Protection
Authors: Devendra Kumar, Makarand Upadhyaya, Arvind Kumar Sharma Rupak Sharma, D Prabakar, Aanchal Madaan,
Published: April 2024

Generative adversarial networks (GANs) transform low-dimensional random noise into a photorealistic picture. The use of such misleading pictures laden with irrelevant information on social media platforms might lead to serious and challenging issues. The goal of this study is to create and identify the affected GAN pictures. Additionally, enhanced GAN detection accuracy by preprocessing and segmentation feature extraction. This study examines the effectiveness of several learning-based methods for detecting image-to-image translation. Generative adversarial networks use deep learning approaches like convolutional neural networks for generative modelling and the DEA (Data encryption standard) is used to produce the encryption key. To accurately identify fake images, an effective picture forgery detector is necessary. Recent advancements in generative adversarial networks (GANs) have been focused on producing photorealistic pictures quickly and successfully. However, GANs can complicate visual forensics and model attribution. Data from one source and pictures from another can have a wide range of uses, including in fields like computer vision, video, and language processing. The investigation of some photos reveals that both conventional and deep learning detectors may reach up to 95% detection accuracy. However, only deep learning maintains good accuracy on compressed data. An article that explains how to spot GAN-generated false images on social media also provides context on GANs and the theoretical concepts underlying them.
Deep Generative Models for Data Synthesis and Augmentation in Machine Learning
Authors: Kiran Mayee Adavala
Published: April 2024

The increasing trend in deep generative modelling, which offers data scarcity and diversity solutions in machine learning, is one of the most recent developments in the field. The data-oriented approaches drive the need for the quality and variety of data sets that could be prevented due to privacy issues and limited resources. Generative deep models, basically GANs (Generative Adversarial Networks) and VAEs (Variational Automatic Encoders), appear to be the most reliable approach to the synthesis and augmentation of the data. These models employ deep learning to basically learn all by itself from raw data without anyone teaching it, which is the basis of modern artificial intelligence. Accuracy issues between overfitting and poor generalization emphasize the need for smart solutions to the problem of data shortness. Deep generative modelling works based on the data distribution, which the model learns by itself and enables a realistic sample generator. The study reviews the proficiency and complexity of GANs, VAEs, and WGANs, comparing the WGANs' capabilities with the former two. Techniques of data augmentation, e.g., repositioning, rotation, and adding Gaussian noise to the dataset, will greatly increase the diversity of the data. Regardless of the training time, all models showcase competitive inference performance and, as a result, may be satisfactorily used in real-time operations. The insights obtained shed light on ways to improve machine learning and artificial intelligence through brain data synthesis, model training, and computational efficiency.
Digital Image and Video Processing: Algorithms and Applications
Authors: Kavitha S Patil,
Published: April 2024

Many of the techniques that are used in digital image and video processing were developed in the 1960s at Bell Laboratories. These techniques have applications in a variety of fields, including medical imaging, videophone, character recognition, satellite imagery, and wire-photo standards conversion. Additional applications include enhancement of photographs or vidoes. The early stages of image and video processing were developed with the intention of enhancing the overall quality of the image or video. For the purpose of enhancing the visual effect of humans, it is intended for human beings. When it comes to image and video processing, the input is an image of poor quality, and the output is an image and video of higher quality. Research on algorithms and applications of digital image and video processing is the primary purpose of this study, which aims to investigate these topics extensively. The methodology employed in this study is qualitative research technique. In accordance with the findings of this research, "Image Processing" refers to the process of analyzing images with the objective of determining the significance of objects and identifying them. Image analysts analyze data that has been remotely sensed and attempt to detect, identify, classify, measure, and evaluate the significance of physical and cultural objects, as well as their patterns and spatial relationships. One subcategory of signal processing is known as video processing, and it is distinguished by the fact that the signals that are input and output are video files or video streams. Technology such as television sets, videocassette recorders (VCRs), DVD players, and other devices all make use of video processing algorithms. The processing of images and videos is extremely useful in a variety of contexts.
Machine Learning based Early-detection of Lung Cancer Using CT Scan for the early Identification and Classification
Authors: Jampani Chandra Sekhar
Published: April 2024

The current research is aimed at studying the application of machine learning algorithms for the early prediction and detection of lung cancer taking the images of CT scans and MR scans. In this respect, the dataset from kaggle includes 3200 images from diverse sources, which are pre-processed and applied for the process of feature extraction, using traditional methods and deep learning systems, which include Convolutional Neural Networks, VGG-16, VGG-19, and RNN, while after the training the systems are evaluated in accordance with precision, recall, f1 score, and accuracy. As a result, it is evidenced that the best model is VGG-19 with the highest accuracy of 97.86%, while follows VGG-19 the VGG-16 the CNN model, and the RNN, implying effective implications for clinical practice. Certainly, the results of the research help to create a non-invasive, effective, and fast tool, using by clinicians in their practice. The use of machine learning algorithms for the process of early prediction and detection would be helpful for the timely treatment of patients and personalized treatment plans. In such a way, based on the current research, it could be summarized that the best models are constantly developed and gradually implemented in practice. However, more significant collaboration between the researchers, clinicians, and the industry is needed to have the full implementation of applied methods in practice, having fast results and timely actions taken by clinicians.
FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce
Authors: B. Tarakeswara Rao Noorbasha Zareena,
Published: April 2024

Dynamic Product Recommender systems play a crucial role in e-commerce, guiding users towards products that align with their preferences and enhancing the shopping experience. However, existing systems often struggle to capture the nuances of user preferences, particularly when dealing with multiple features via keyword queries and filters. Additionally, scalability challenges arise as the volume of products grows with features and values. In addition, the static filters of existing recommender systems limit the exploration by strictly adhering to user selections, hindering discovery of potentially better features within the preferred budget or category. This can lead to user dissatisfaction and missed opportunities to discover more desirable options. This research addresses these limitations by introducing FacetDRS, a novel dynamic product recommendation system that leverages flexible feature matching and a scalable architecture. FacetDRS empowers the recommendations with three distinct matching methods to cater to diverse preference nuances. Exact Match ensures precise adherence to user specifications, while High-Impact Partial Match prioritizes core features with flexibility in non-critical aspects. Extended Partial Match suggests relevant options beyond initial preferences, leveraging predefined thresholds. This multi-faceted approach captures the full spectrum of user desires while maintaining accuracy. To evaluate the effectiveness of FacetDRS, a rigorous experimental setup was conducted, comparing its performance across Keyword Match Score, First Selections in Top-K and Average Response Time metrics with established recommender systems such as DeepRec, APGNN, and TADCF. The results demonstrate that FacetDRS achieves superior performance than its counterparts in all comparison aspects. The system's flexible matching methods, combined with its robust architecture, offer a valuable solution for enhancing user satisfaction and driving conversions in the mobile commerce landscape.
Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification
Authors: Akash Saxena Laxmi Upadhyay,
Published: April 2024

This research presents a comprehensive assessment of a sophisticated approach for the accurate detection and classification of various tomato leaf diseases using an improved ResNet-50 based deep learning classifier. The alarming increase in plant diseases has prompted the need for advanced technologies that can promptly identify and categorize these ailments to ensure agricultural sustainability. The proposed method harnesses the potential of deep convolutional neural networks (CNNs) and builds upon the ResNet-50 architecture, renowned for its depth and performance. However, the approach's innovation lies in the incorporation of enhancements such as advanced data augmentation techniques and transfer learning from a vast plant disease dataset. These modifications empower the model to learn intricate disease-specific features and patterns, leading to heightened accuracy and robustness. The evaluation of the approach is conducted on an extensive dataset encompassing high-resolution images of tomato leaves affected by a range of diseases. The dataset is meticulously preprocessed to ensure consistency and quality, followed by a rigorous training regimen that fine-tunes the improved ResNet-50 model.The results underscore the efficacy of the proposed method in accurate disease detection and classification. The improved ResNet-50 based classifier demonstrates exceptional performance, achieving an impressive accuracy exceeding 95%. Notably, the model showcases resilience against variations in lighting conditions, angles, and disease severity, highlighting its applicability in real-world agricultural scenarios. The implications of this research are significant, offering an efficient and reliable tool for early detection and classification of tomato leaf diseases. The integration of advanced deep learning techniques and the enhancements introduced in this work signify a substantial advancement in precision agriculture and sustainable crop management practices. As future work, this approach can be extended to address diseases in other plant species, contributing to a versatile framework that can safeguard global food production and alleviate the challenges posed by plant diseases.
Fuzzy Integrated Optimization-Based College and University Music Education Teaching System
Authors: Du Zhuohui
Published: April 2024

Fuzzy integrated optimization refers to the application of fuzzy logic techniques in the optimization process to handle uncertain or imprecise information effectively. In traditional optimization problems, precise data and deterministic relationships are assumed. This paper proposes the construction of a teaching system for music education in colleges and universities based on fuzzy integrated optimization. Fuzzy logic techniques are applied to handle uncertain or imprecise information inherent in the optimization process. traditional optimization methods that assume precise data and deterministic relationships, fuzzy integrated optimization allows for the modeling of uncertainties in music education settings. By incorporating fuzzy logic into the design of the teaching system, educators can better adapt to the diverse learning needs and preferences of students. The implementation of the teaching system for music education in colleges and universities based on fuzzy integrated optimization yielded significant improvements across various performance metrics. Student engagement levels increased by an average of 25%, with a corresponding rise in satisfaction rates by 30%. Additionally, learning outcomes showed measurable enhancements, with a 20% increase in average test scores compared to traditional teaching methods. The adaptive nature of the teaching system resulted in a 15% reduction in dropout rates, indicating improved student retention and progression through music education programs. Educators reported a 40% increase in efficiency in lesson planning and delivery, attributed to the system's ability to provide personalized recommendations and insights tailored to individual student needs.
Reversible Data hiding based on Three-Level Hierarchical Embedding with Histogram Mapping in Encrypted Images
Authors: Bhuvana J. Neetha S. S.,
Published: April 2024

This paper proposes a reversible data-hiding (RDH) approach based on a three-level hierarchical embedding with histogram mapping in encrypted images for a cloud environment. The proposed approach uses two-level encryption followed by the three-level hierarchical embedding process. The two-level encryption uses an intra-inter block permutation with an exclusive OR operation. In the three-level hierarchical embedding two predictors namely strong predictor and weak predictor are estimated. The strong predictor is used in level 1 embedding, while the weak predictor is used in level 2 embedding. A histogram mapping is then performed on the location map obtained during the level 1 and level 2 embedding process and is then compressed using the Huffman coding. The compressed location map information is then further embedded as the level 3 embedding process. In the extraction phase, the compressed location map information is first extracted, then the level 2 data followed by the level 1 data. The evaluation of the proposed data embedding approach is done using the Bossbase and Bows-2 datasets that result in an embedding rate of 3.806 bpp and 3.513 bpp respectively.
ADM- Road Eye: Advanced Traffic Sign Detection
Authors: Vijaya Kumar C. N.. Suresh Kumar H. S., Rakshitha K. C. Ningappa Uppal,
Published: April 2024

In recent years, a plethora of systems have emerged for recognizing traffic signs. This paper offers a comprehensive overview of the latest and most effective approaches in detecting and categorizing traffic signs. The primary goal of detection techniques is to pinpoint the precise areas containing traffic signs, which are classified into three main categories: color-based, shape-based, and learning-based methods of Alex net, Desnse net, and Mobil net (ADM) models. Moreover, methods of classification are divided into two groups; those relying on manually crafted features such as HOG, LBP, SIFT, SURF, BRISK, and those leveraging deep learning. The paper summarizes various detection and classification methods, along with the datasets utilized, for quick reference. Additionally, it provides suggestions for future research directions and recommendations to enhance traffic sign recognition performance..
Unveiling the submerged secrets: bumblebee queens' resilience to flooding
Authors: Rondeau Sabrina
Published: April 2024

In a previous study, an experimental oversight led to the accumulation of water filling a container housing diapausing bumblebee queens. Surprisingly, after draining the water, queens were found to be alive. This observation raises a compelling question: can bumblebee queens endure periods of inundation while overwintering underground? To address this question, we conducted an experiment using 143 common eastern bumblebee (Bombus impatiens) queens placed in soil-filled tubes and subjected to artificially-induced diapause in a refrigerated unit for seven days. Tap water was then added to the tubes and queens (n=21/treatment) were either maintained underwater using a plunger-like apparatus or left to float naturally on the water’s surface for varying durations (8h, 24h, or 7 days) while remaining in overwintering conditions. Seventeen queens served as controls. After the submersion period, queens were removed from water, transferred to new tubes with soil, and kept in cold storage for 8 weeks. Overall queen survival remained consistently high (89.5 ± 6.4%) across all treatments and did not differ among submersion regimes and durations. These results demonstrate the remarkable ability of diapausing B. impatiens queens to withstand submersion underwater for up to one week, indicating their adaptations to survive periods of flooding in the wild.
Nguy cơ tái phát bệnh võng mạc trẻ sinh non sau điều trị tiêm Aflibercept
Authors: Ivanovich Sidorenko Evgeny , Evgenievich Sidorenko Evgeny , Anatolievich Obrubov Sergey , Thắng Lê Hoàng
Published: April 2024

Nghiên cứu đánh giá các yếu tố nguy cơ gây tái phát bệnh võng mạc trẻ sinh non sau điều trị tiêm nội nhãn aflibercept. Tỉ lệ tái phát sau điều trị là 37/244 mắt (15,16%). Các yếu tố ảnh hưởng đến sự tái phát bệnh võng mạc trẻ sinh non sau tiêm nội nhãn aflibercept bao gồm: tuổi thai, tuổi sau kinh chót tại thời điểm tiêm, cân nặng khi sinh, cân nặng tại thời điểm tiêm, chiều cao khi sinh, chu vi vòng đầu khi sinh, chu vi vòng đầu tại thời điểm tiêm, chu vi vòng ngực khi sinh, tiền sử truyền máu, chỉ số khối cơ thể tại thời điểm tiêm, tiền sử dây rốn quấn cổ. Nhóm cân nặng khi sinh dưới 900g có tỉ lệ tái phát là 28,38%, tiếp theo là nhóm cân nặng khi sinh trung bình (12,20%) và nhóm cân nặng khi sinh trên 1200g (6,82%). Thời điểm tái phát ở ba nhóm lần lượt là 9,82 ± 5,76 tuần, 10,20 ± 3,27 tuần và 12,33 ± 9,24 tuần. Cân nặng khi sinh càng thấp thì tỉ lệ tái phát bệnh càng cao và càng sớm hơn.
Hội chứng dễ bị tổn thương theo thang điểm SEGA ở người cao tuổi mắc đái tháo đường typ 2 tại bệnh viện 19-8
Authors: Lực Trần Viết , Dịu Vũ Thị , Thuỷ Vũ Thu , Thanh Nguyễn Xuân , Anh Nguyễn Trung , Huyền Vũ Thị Thanh
Published: April 2024

Nghiên cứu nhằm đánh giá hội chứng dễ bị tổn thương theo thang điểm đánh giá lão khoa cấp cứu rút gọn (Short Emergency Geriatric Assessment - SEGA) ở người cao tuổi mắc đái tháo đường typ 2 tại bệnh viện 19-8. Nghiên cứu mô tả cắt ngang trên 512 người bệnh ≥ 60 tuổi được chẩn đoán ĐTĐ điều trị tại bệnh viện 19-8. Người bệnh được phỏng vấn theo bộ câu hỏi thống nhất đánh giá tình trạng hội chứng dễ bị tổn thương bằng thang điểm SEGA, các thông tin chung, các đặc điểm bệnh ĐTĐ, biến chứng của ĐTĐ và một số hội chứng lão khoa. Tỉ lệ người bệnh kiểm soát tốt HbA1c ≤ 7,5% chiếm 74,4%. Tỉ lệ người bệnh kiểm soát kém HbA1c > 8,5% chiếm 14,6%. Tỉ lệ hội chứng dễ bị tổn thương theo thang điểm SEGA là 27,5%, hội chứng dễ bị tổn thương nặng là 11,3%. Có mối liên quan có ý nghĩa thống kê giữa tình trạng dinh dưỡng, chức năng hoạt động hàng ngày, chức năng hoạt động hàng ngày có dụng cụ, mức độ hoạt động thể lực, trạng thái tâm thần tối thiểu, trầm cảm, nguy cơ ngã, Sarcopenia, chức năng thể chất với HCDBTT (p < 0,01). Bệnh nhân đái tháo đường có hội chứng dễ bị tổn thương chiếm tỉ lệ không nhỏ khi được sàng lọc bằng thang điểm SEGA - bộ công cụ dễ sử dụng, khách quan, có tính ứng dụng cao trong thực hành lâm sàng có thể áp dụng thường quy để có thể phát hiện sớm hội chứng dễ bị tổn thương ở người bệnh ĐTĐ cao tuổi.
Thực trạng tiêm vắc xin phòng COVID-19 và một số yếu tố liên quan của người bệnh nhiễm SARS-COV-2 nhập viện tại Bắc Ninh và Nghệ An năm 2022
Authors: Vân Hoàng Thị Hải , Hiếu Nguyễn Ngọc , Hương Đặng Thị , Khánh Đỗ Nam , Giang Lê Minh
Published: April 2024

Nghiên cứu được thực hiện nhằm mô tả thực trạng tiêm phòng COVID-19 và một số yếu tố liên quan với tình trạng không tiêm phòng của người bệnh nhập viện do nhiễm SARS-CoV-2 tại Bệnh viện Đa khoa tỉnh Bắc Ninh và Bệnh viện Hữu nghị Đa khoa tỉnh Nghệ An trong sáu tháng đầu năm 2022. Trong 3.242 người bệnh nhập viện, tỷ lệ đã tiêm từ hai mũi vắc xin phòng COVID-19 trở lên chiếm 60,6% và có 30,1% người bệnh chưa tiêm phòng. AstraZeneca, Pfizer là vắc xin được tiêm phổ biến nhất sớm nhất và có 25,1% người tiêm hai mũi trở lên được tiêm phối hợp các loại vắc xin. Nữ giới, người từ 60 tuổi trở lên, nghề nghiệp nghỉ hưu hoặc là học sinh/sinh viên, người có bệnh nền tim mạch mạn tính hoặc phổi mạn tính có khả năng không tiêm phòng COVID-19 cao. Vì vậy, cần tiến hành những biện pháp can thiệp toàn diện nhằm giải quyết vấn đề tỷ lệ bao phủ vắc xin chưa đạt yêu cầu để có miễn dịch cộng đồng và hạn chế hậu quả lâm sàng nặng ở người bệnh.
Khảo sát nồng độ TNF-α và mối tương quan với các chỉ số bạch cầu trên chuột điều trị kết hợp ức chế PD-1 và tế bào CAR-T
Authors: Hạnh Nguyễn Thị Hiền , Mão Cấn Văn , Cường Bùi Khắc
Published: April 2024

Mục đích của nghiên cứu nhằm khảo sát mối tương quan giữa các chỉ số bạch cầu với nồng độ TNF-α của liệu pháp điều trị kết hợp kháng thể đơn dòng ức chế PD-1 với tế bào CAR-T trên thực nghiệm. Phương pháp nghiên cứu thực nghiệm, tiến cứu, can thiệp có so sánh nhóm chứng. 60 chuột nhắt trắng chia 4 nhóm, mỗi nhóm 15 con gồm: nhóm chứng, nhóm điều trị tế bào CAR-T, nhóm điều trị kháng thể đơn dòng ức chế PD-1, nhóm điều trị kết hợp kháng thể đơn dòng ức chế PD-1 với tế bào CAR-T. Sau điều trị, theo dõi tình trạng toàn thân, xét nghiệm các chỉ số bạch cầu và nồng độ TNF-α. Kết quả cho thấy ồng độ TNF-α ở nhóm CAR-T không khác biệt so với nhóm cnhứng. Nồng độ TNF-α ở nhóm kháng thể đơn dòng ức chế PD-1 kết hợp với tế bào CAR-T tăng có ý nghĩa thống kê so với nhóm chứng (p < 0,001). Có mối tương quan thuận giữa nồng độ TNF-α và số lượng tuyệt đối bạch cầu mono ở nhóm điều trị kháng thể đơn dòng ức chế PD-1 kết hợp với tế bào CAR-T (r = 0,578 ; p < 0,05). Qua nghiên cứu, chúng tôi nhận thấy kết hợp điều trị ức chế PD-1 với tế bào CAR-T làm tăng nồng độ TNF-α trên chuột và có mối tương quan thuận giữa nồng độ TNF-α và số lượng bạch cầu mono.
Kết quả điều trị bệnh trào ngược dạ dày thực quản bằng bài thuốc bán hạ tả tâm thang
Authors: Thuân Đinh Thị , Hùng Trần Quốc , Trang Trần Thị Thu , Tú Nguyễn Thị Thanh
Published: April 2024

Nghiên cứu được thực hiện nhằm đánh giá kết quả điều trị bệnh trào ngược dạ dày thực quản bằng bài thuốc “Bán hạ tả tâm thang” và theo dõi tác dụng không mong muốn của bài thuốc trên lâm sàng và cận lâm sàng. Nghiên cứu can thiệp lâm sàng mở, so sánh trước sau điều trị và so sánh với nhóm chứng. 60 bệnh nhân được chẩn đoán bệnh trào ngược dạ dày thực quản được chia thành hai nhóm đảm bảo tương đồng về tuổi, giới, mức độ trào ngược theo thang điểm GerdQ. Nhóm nghiên cứu được sử dụng bài thuốc "Bán hạ tả tâm thang", ngày 01 thang, nhóm đối chứng được sử dụng Omeprazol liều 40 mg/ngày, thời gian điều trị là 28 ngày. Kết quả cho thấy điểm trung bình các triệu chứng của bệnh trào ngược dạ dày thực quản và điểm trung bình GerdQ của hai nhóm đều giảm có ý nghĩa thống kê so với trước điều trị (p < 0,05) và nhóm nghiên cứu cải thiện tốt hơn nhóm chứng (p < 0,05). Chưa thấy tác dụng không mong muốn của bài thuốc trên lâm sàng và cận lâm sàng.
Tình hình kháng kháng sinh của một số vi khuẩn gây nhiễm khuẩn huyết tại Bệnh viện E năm 2023
Authors: Hậu Phan Văn , Hưng Lê Văn , Lượng Vũ Huy , Vinh Nguyễn Thị Hà , Hoa Phạm Quỳnh , My Lê Huyền , An Nguyễn Văn , Hoàng Lê Huy , Việt Nguyễn Hoàng , Vân Phạm Thị , Hiền Trương Thị Thu , Bình Nguyễn Thanh , Hải Lê Hạ Long
Published: April 2024

Nhiễm khuẩn huyết là một nhiễm trùng nặng, mỗi năm gây tử vong cho hàng triệu người trên thế giới. Nghiên cứu này được tiến hành nhằm mục đích đánh giá tỷ lệ nhiễm và tính kháng kháng sinh của một số chủng vi khuẩn gây nhiễm khuẩn huyết tại Bệnh viện E năm 2023. Trong 2094 mẫu cấy máu, nghiên cứu đã phân lập được 275 chủng vi khuẩn, chiếm tỷ lệ 13,1%. Tác nhân gây bệnh hay gặp nhất là E. coli (29,1%), K. pneumoniae (18,5%) và S. aureus (13,5%). Tỷ lệ E. coli và K. pneumoniae sinh men beta-lactamase phổ rộng là 58,1% và 2,4%. E. coli nhạy cảm nhất với imipenem (100%), meropenem (100%) và ertapenem (96.8%). Trong khi đó, K. pneumoniae nhạy cảm cao nhất với amikacin (70,7%) và gentamycin (65,9%). Tỷ lệ S. aureus kháng methicillin là 61,8%, đề kháng cao nhất với benzylpenicillin (94,1%) và chưa phát hiện chủng đề kháng vancomycin. Nghiên cứu chỉ ra tầm quan trọng của việc kiểm soát nhiễm khuẩn và sử dụng kháng sinh hợp lý để giảm thiểu sự lây lan của các chủng vi khuẩn kháng kháng sinh.
Impacts of anti-dumping duties on firm’s performance: Evidence from listed firms in seafood industry in Vietnam
Authors: Phong Nguyen Anh , Diem Pham Cao Kieu , Nhung Ho Thi Hong , Ha Hua Vu Ngan , Trang Nguyen Thi Thuy
Published: April 2024

The study aims to examine the impact of the anti-dumping tax imposed by the United States under the simultaneous influence of financial factors on the performance of enterprises in the seafood export industry. The author selected data including 21 companies listed on the Vietnamese stock market in the period 2017 - 2022, and used GMM estimation technique to retest the influence of familiar factors such as lag of the firm’s performance, size, leverage, liquidity ratio, sales growth rate to the firm’s performance (measured by ROA), in addition, the paper also provides additional evidence on the impact of new factors: anti-dumping tax and export sales ratio. Research results show that (1) 6 out of 7 independent variables including , Size, Liq, g, ADr, Exp.re / Tot.re are statistically significant, (2) anti-dumping tax rate and geographic revenue structure are both recorded negative impacts on performance. The results of this study contribute academically by providing a new insight into the relationship between revenue and profit in the case of exporting to countries that apply anti-dumping tax and contribute practically by proposing some implications for foreign trade policy and corporate financial management strategy.
Building nursing homes for high-income individuals/families in Vietnam
Authors: Hang Le Thi Thuy , Dai Vu Duc , Phuc Tran Luu Khanh , Huyen Nguyen Thi Ngoc , Ngan Nguyen Ngoc Thao
Published: April 2024

This study employs a Bayes model to examine the impact of income groups on the Nursing home usage costs across 10 provinces and cities in Vietnam: Ha Noi, Vinh Phuc, Bac Ninh, Hai Phong, Nam Dinh, Da Nang, Ho Chi Minh City, Can Tho, Dong Nai, and Binh Duong. The study utilizes 8 variables: Average population (DSTB), Average income (TNTB), Income group 1 (TN1), Income group 2 (TN2), Income group 3 (IG3), Income group 4 (TN4), Income group 5 (TN5), Cost of using nursing home (CP), with data collected annually from 2012 to 2022. The results indicate that major cities attract larger populations and a higher proportion of elderly individuals, thus attracting more investment in various forms of nursing homes, thereby improving their competitiveness. Among income groups of residents in provinces and cities, income group 5 has the highest income and shows a positive correlation with cost of using nursing home, which aligns well with reality. In recent years, affluent families have shown a preference for high-end nursing homes offering the best care services, albeit at higher costs than standard ones. However, fundamentally, Vietnamese individuals have not prioritized the choice of living in nursing homes during their elderly years. Research results show that it is necessary to focus on the quality of nursing home services because of increasing demand and the aging population trend of Vietnam in the future.
“Selecting between the dream and the reality”: The Mexican Revolution in All the Pretty Horses
Authors: Cline Brent Walter
Published: April 2024

This article focuses on the relationship of four Mexican characters in Cormac McCarthy’s All the Pretty Horses and their rhetorical use of the Mexican Revolution in conversation with John Grady. Rather than these characters being shaped by the revolution, analysis of their historical embellishments, omissions, and fabrications show them to instead imagine the revolution as a history that validates their individual perspectives. In so doing, they reflect John Grady’s desire to imagine a mythical cowboy past that can be lived in Mexico. These similarities therefore suggest that John Grady’s cowboy fantasies arise from a universal human desire rather than the flaws of an individual person, age, or country. By John Grady’s ultimate removal from Mexico and confession to the Texas judge, he may in fact be the only character in the novel who attempts to resist the universal impulse to turn history into self-justification.
Religion Between the Parody of Faith and the Epiphany of Grace in Cormac McCarthy’s Blood Meridian
Authors: Viganò Paolo
Published: April 2024

The metaphysical inquiry of Blood Meridian is structured in two moments, both involving a reflection on religion. On the one hand, the persistence of violence in the novel can be interpreted as the continuous denial of the Christian idea of “salvation through faith.” Faith, in this book, offers neither salvation for those who suffer nor punishment for evil, as it is immediately stated that “It is not to be thought that the life of darkness is sunk in misery and lost as if in sorrowing.” On the other hand, although the Christian God seemingly disappears from the phenomenal world, the search for a higher instance does not completely vanish from the novel. In fact, fleeting divine occurrences frequently appear throughout the novel in various transient epiphanies of grace. After grace vanishes, we can detect in the last chapters of Blood Meridian an abysmal melancholy—an expression of frustrated attempts to transcend human existence. Although this dark path traveled by the kid in Blood Meridian has led critics to call the book a Gnostic parable, these two possibilities of McCarthy’s metaphysical reflection seem to be closely related to Catholic motifs and ideology.