bluuebunny/crossref_metadata_2025 huggingface dataset:
Empowering women through reducing unpaid work
Authors: Robu Mariana
Published: July 2023

Unpaid work is recognized as a critical barrier to gender equality and women’s economic and social empowerment. Formally categorized as non-market work, unpaid work is not included in gross domestic product (GDP) calculations and remains invisible to decision- and policymakers. Social and cultural gendered norms related to unpaid care work remain stubbornly entrenched. While the ‘men as breadwinners’ and ‘women as caregivers’ model may not be universal, it is still the normative construct for gender relations in the Republic of Moldova. Similar to other countries across the world, women in the Republic of Moldova undertake the majority of unpaid work; in the country the unpaid work is not equally shared by women and men. Moreover, while men work longer hours in paid employment, women continue to work longer total hours than men do overall, bearing a ‘double burden’ or ‘second shift’. The actuality of the subject does exist, for example, when women have access to paid work, they may be able to earn their own income, which can give them a greater degree of independence and autonomy. Additionally, they may be able to gain a greater level of social and economic empowerment, which can help them to improve their lives and the lives of their families. The purpose of the research it is focused on rethinking the care economy and empowering women. An in-depth desk review of the available studies in the Republic of Moldova and worldwide was conducted in order to analyze the empowering women through reducing unpaid work.
Entrevista a Cristina Fernández Valenzuela, diseñadora de moda nupcial y alta costura
Authors: Fernández de Alarcón Roca Belén
Published: July 2023

Cristina Valenzuela es de los últimos diseñadores que aman la confección bien hecha, la perfección por el detalle y la elaboración de trajes muy bien depurados, impecables y que sientan muy bien a las prometidas en un día tan especial. Ella y su equipo realizan un trabajo artesanal con remates en bordados, e incluso actualizando piezas que les llegan como complemento, con un esmerado trabajo de confección, como lo han hecho siempre los grandes maestros de la Alta Costura. Su lugar en el mundo de la moda se lo ha ganado a base de un gran esfuerzo y sacrificio, muchas horas de taller entre agujas, junto a su gran equipo, al que admira, y sabe que, sin su trabajo, los diseñadores no son nadie…  Se abre también paso entre las celebrities de la alfombra roja, y reivindica una mejor obra cualificada de carácter artesanal para que no se pierda este patrimonio inmaterial tan importante como es dar forma a una creación de la Alta Costura española.
El patrimonio, base de la realidad social
Authors: Martínez Paricio Jesús Ignacio
Published: July 2023

La realidad social se construye mediante un proceso donde lo objetivo es transformado de manera subjetiva por el individuo. A partir de ese momento lo subjetivo termina condicionando las opiniones y también las conductas de ese individuo. En la dimensión objetiva de la sociedad está el presente, pero el presente es consecuencia del pasado, y ese presente determinará el futuro. La dinámica social no es lineal, es cíclica. En esa tendencia no cabe la ruptura real. Se puede imponer de manera temporal la ruptura del ciclo en términos administrativos, interesados, ideológicos. En términos realistas no existe la hora cero para empezar a contar desde donde interesa. Ante esta evidencia el patrimonio cultural y natural, tangible e intangible es uno más de los soportes de la construcción social de la realidad. Razón por la que se impone como obligación y compromiso con las generaciones pasadas, presente y futuras su conservación, defensa, protección y restauración llegado el caso. Aunque el patrimonio se localiza en un espacio político administrativo, ese patrimonio es un bien colectivo cuya propiedad supera las fronteras de las naciones. El patrimonio es un bien de la Humanidad. La defensa del patrimonio viene de lejos. Existe voluntad y decisiones para su conservación. Organizaciones públicas y privadas se empeñan en su defensa. Lamentablemente realidad se impone poniendo en peligro ese bien. Su destrucción se utiliza como táctica para destruir la esencia y resistencia de los pueblos. En este sentido los daños son muchos e irreparables, pero no cejan los empeños por reducir sus efectos dañinos.
Aspek Geoteknik Dalam Perencanaan Fasilitas Landfill Mineral Ikutan Radioaktif
Authors:   Direktorat Pengaturan Pengawasan Fasilitas Radiasi dan Zat Radioaktif, BAPETEN, Jakarta, Indonesia, Puji Yuwana Hermawan
Published: July 2023

Radioactive by-products (MIR) can be produced from non-nuclear activities. It is crucial to ensure proper management of MIR from the moment they are created. The issue of permanent disposal in landfill facilities has recently been resolved. Design planning, land availability, financing, public acceptance, and other factors are essential considerations in this process. This paper provides an overview of the role and influence of geotechnical engineering in planning MIR landfill facilities. The methodology involves a literature study of regulations, research results, and recommendations from international organizations. The Government Regulation No. 52 of 2022 outlines the general landfill criteria. However, the regulation of MIR and Hazardous and Toxic Waste (B3) differs in concept and approach. Geotechnical studies the properties of rocks and soil. For MIR landfills, the characteristics of the MIR to be disposed of must be considered during planning and design. Physical, chemical, and activity concentration elements of the MIR affect the planning and design process. Geotechnical aspects, such as soil and rock type, groundwater level (MAT), pore water pressure, grain size, water content, density, shear strength, compression, permeability, and chemical testing, are necessary considerations. Several geotechnical tests are carried out to prevent infrastructure failure. The first step in planning and design is to understand the characteristics of the MIR material to be disposed of in landfills. Some geotechnical aspects include slope stability analysis, soil carrying capacity, and foundation suitability. Although the current regulations do not explicitly mention the geotechnical aspects that must be met, the general criteria for the acceptability of landfill locations are used in designing landfill designs. Keywords: radioactive by-products, planning, landfill, geotechnic
Extraction and Characterization of Watermelon (Citrullus lanatus) Seed Oil
Authors: Keke Mabel , Okpo Samson Onoriode , Anakpoha Oghenekome Cyril
Published: July 2023

This study focuses on employing solvent extraction to extract and characterize watermelon (Citrullus lanatus) seed oil. The physicochemical properties of the oil were investigated to assess its potential applications in the food, cosmetic, and pharmaceutical industries. The extraction process yielded an oil content of 43%. The oil exhibited a pH value of 4.02, refractive index of 1.452 at 25 °C, and specific gravity of 0.934 at 15 °C. The boiling point, cloud point, flash point, and melting point of oil were determined as 326 °C, 7.3 °C, 289 °C, and 2 °C, respectively. The oil's viscosity was measured as 0.04072 Pa.s, and it demonstrated a non-sooty flame nature and solubility in ether. The free fatty acid contents and acid value were determined at 3.339 % and 6.678 mg KOH/g, respectively. The saponification value (S.V) and iodine value (I.V.) were 147.6315 mg/KOH/g and 88.526 mg Iodine/g, respectively. Additionally, peroxide value was 16.40 meq peroxide/g, and the oil exhibited a congealing temperature range of -14 °C to 22 °C. The oil's retention factor during chromatography was determined as 1.6 cm. Fourier-transform infrared (FTIR) analysis revealed the presence of functional groups such as hydroxyl, amine, aliphatic hydrocarbon, carbonyl, carbon-nitrogen bond, and sulfoxide groups in the oil. These findings suggest that the presence of functional groups, such as hydroxyl, amine, aliphatic hydrocarbon, carbonyl, carbon-nitrogen bond, and sulfoxide groups in watermelon oil, indicate its potential suitability for a wide range of applications in the food, cosmetic, and pharmaceutical industries.
Synergetic Interfacial Tension Reduction Potential of Silica Nanoparticles and Enzyme
Authors: Udoh Tinuola Hannah , Orodu Oyinkepreye David
Published: July 2023

The co-existence of multiphase fluids in the hydrocarbon reservoir rock pores plays a fundamental role in oil recovery processes because of the strong effect of interfacial forces that exist at the interface of these immiscible fluids. In this study, the effects of enzyme and silica nanoparticles on crude oil-brine interfacial tension were investigated under varied brine salinities and brine compositions. The results showed that the application of silica nanoparticles alone in brines of varied compositions and salinities does not significantly modify the crude oil-brine IFT. The use of enzyme and combined enzyme-nanoparticles however significantly reduced crude oil-brines IFT but the contribution of silica nanoparticles to the IFT reduction was not significant. The result of this study is relevant to the design and applications of enzyme and nanoparticles enhanced oil recovery processes.
The Development of Modern Chinese Education System Since 1949 and the Key Factors That Impact It
Authors: Wang Junzhuo
Published: July 2023

Modern Chinese education system underwent profound changes since the establishment of the People's Republic of China in 1949, over the past few decades. In 2021, for students enrolled in compulsory education, the Chinese government has proposed a strategy to lessen the weight of excessive homework and off-campus tutoring. This so-called Double Reduction policy has laid a hard restriction on the decentralization and the capitalized operation of education system. It is a milestone for Chinese education system reformation. The educational industry in China has also received a knockout, especially for those private-owned education and training institutions focusing on primary and secondary education. Through literature review, this paper examines the trajectory of the Chinese educational system's growth and identifies a number of crucial elements that have shaped the sector, including societal trends, political upheaval, and global and economic trends. These elements are tremendously affecting Chinese education policymaking. Due to the unique influence of the factors, policy-makers should attach more significance to them, which would make the impact more suitable for the society.
Analysis of Ethereum Ghost Protocol under Blockchain Framework
Authors: Wu Baixi
Published: July 2023

This article discusses Ethereum Ghost Protocol, a technology that promises faster mining speeds. The Ghost protocol enables different crypto miners to create valid blocks concurrently. The article explains why the protocol reduces the delay in traditional blockchains through stale or uncle blocks. It explains why the Ghost Protocol is faster than Bitcoin. It also introduces areas in which Ethereum Ghost Protocol can work, such as insecure environments and Internet of Things (IoT). Besides, the paper defines centralization bias and explains how the Ghost Protocol solves the problem. Since the Ghost Protocol is decentralized and stores information in multiple nodes, it is more secure. Besides, it is scalable and saves storage space. Despite these benefits, this paper discusses two significant challenges, including slow transactions and difficulties in coding. However, the Ghost protocol will enhance cryptocurrency due to the ease of mining, enhanced security, and other benefits to miners. Overall, the paper suggests the need to embrace the Ghost protocol in the blockchain.
Logistic Regression Model based on heart disease and Its Potential Influencing Factors
Authors: Qiu Yu
Published: July 2023

Heart disease is without doubt getting more and more popular in human society. According to the statistics of the World Heart Federation, one person dies of heart diseases for every 3 deaths in the world, and the number of deaths due to heart disease and stroke is as high as 17.5 million in the world every year. In this paper, 5 potential influencing factors and their data are selected to construct a logistic regression model to predict the possibility of one catching a heart disease so that early prevention may be achieved in time. During the construction of the model, some transformations are applied to the predictors to optimize the model. In the end, cross-validation method is used to test the final model, and the results show that the accuracy of the model is over 73%. In conclusion, this model can briefly predict the possibility of one catching a heart disease, and also reveal that the factors chosen do have some significant impacts on heart disease prediction.
The Impact of ChatGPT on Education and Future Prospects
Authors: Tan Xin
Published: July 2023

ChatGPT is a large language model based on the GPT series models developed by OpenAI, which has gained widespread attention in the education sector due to its ability to generate natural language, demonstrate powerful understanding capabilities, and support multi-turn dialogue. Analysis results suggest that the emergence of ChatGPT has fundamentally changed the way people connect with information. It has made information that was previously expensive and difficult to obtain more accessible and greatly reduced communication barriers between different fields. This marks an unprecedented information revolution that could potentially alter human lifestyles. ChatGPT is also expected to have a profound impact on education, serving as a double-edged sword that brings both innovation and risks to the education sector. This paper summarizes existing literature and materials to analyze the essence of ChatGPT, its impact on education, and prospects for its future application. This paper aims to provide valuable insights for educators and researchers interested in understanding the implications of ChatGPT and its potential role in advancing education.
The Development of Medical Robotics in the Context of Artificial Intelligence
Authors: Guo Jiajun
Published: July 2023

The emergence of artificial intelligence (AI) technology has once again promoted the development of robots by making them more efficient, independent, and intelligent. With the progress of the times, the two promote each other and develop together in many fields, especially in the field of medical and health. This paper describes the strengths and limitations of surgical robots and intelligent medical robots as well as their possible development trend in the future from both hardware and software aspects. Conclusions can be drawn that, on the one hand, surgical robots can improve the safety and reliability of the surgery and intelligent medical robots can help achieve drug research and development, intelligent diagnosis and treatment, intelligent image recognition, etc. These can help doctors make diagnoses and treatments more accurately; on the other hand, it is difficult to install an electronic sensor on the mechanical arm at present, the current positioning technology is not perfect, and the rotation angle of the mechanical arm is limited. These limitations improve the difficulty for doctors to use surgical robots. However, with the continuous progress of robot and AI technology, the limitations of many surgical robots can be improved and more targeted AI technology can be developed and applied in specific fields.
Application of Deep Learning in Algorithm Design
Authors: Jiang Yi
Published: July 2023

Deep learning has recently subverted algorithm design ideas in one and one fields, such as speech recognition, image classification, and text notification, by forming a model that begins with training data, goes through an end-to-end model, and then a new mode for direct output to obtain the final result. This not only makes everything easier, but because each layer in deep learning can tune itself for the final task, eventually cooperating between the layers, it can greatly improve task accuracy. This paper primarily introduces what deep learning is, the deep learning principle, and the application of deep learning in people's lives. This paper analyzes and clarifies the future development direction of deep learning and its practical application in many different fields, so as to offer some references for future studies.
Application of AI in Construction
Authors: Wu Renjiang
Published: July 2023

At present, the application of BIM is in the ascendant, and 5G, the Internet of Things, and big data are also rising. AI is gradually influencing and innovating the construction field, and releasing the productivity of the industry from BIM, Internet of Things, big data and other aspects. The paper starts from the current situation of the construction industry, focusing on the integration of building construction, and tells how AI helps design in the early stage of architectural design to intelligent construction assistance in the later stage of the building. The use of AI can bring about the liberation of labor, improve the efficiency of the building from the beginning of the design to the final construction of the process and other advantages, and there are shortcomings such as excessive costs. In the future, AI applications may develop further when the time benefits can outweigh the cost benefits.
Research and Analysis of Floating-Point Adder Principle
Authors: Yang Fengyuan
Published: July 2023

With the development of the times, computers are used more and more widely, and the research and development of adder, as the most basic operation unit, determine the development of the computer field. This paper analyzes the principle of one-bit adder and floating-point adder by literature analysis. One-bit adder is the most basic type of traditional adder, besides bit-by-bit adder, overrun adder and so on. The purpose of this paper is to understand the basic principle of adder, among them, IEEE-754 binary floating point operation is very important. So that the traditional fixed-point adder is the basis of the floating-point adder, which can have a new direction in the future development of floating-point adder optimization. This paper finds that the floating-point adder is one of the most widely used components in signal processing systems today, and therefore, the improvement of the floating-point adder is necessary.
Exploiting Neural Network for Heart Disease Probability Prediction
Authors: Liu Songwei , Miao Zhonghui , Zhang Anqiao
Published: July 2023

In general, doctors determine the presence of heart disease through clinical evaluation and pathological data, and the diagnosis process is complex and inefficient. Based on the above situation, professionals are committed to researching efficient and accurate methods for predicting heart disease. After studying many literatures, this paper found that the existing heart disease prediction system has high requirements for clinical data. Based on the reality of the shortage of medical resources under the COVID-19 epidemic, this paper develops a simple heart disease prediction system, which predicts heart disease through simple and easy-to-measure data of patients, and then prevents heart disease. The method consists of two steps. First, collect the characteristics related to heart disease, and then select the most important 10 characteristics through correlation analysis and literature research, namely gender, age range, body mass index (BMI), smoking status, physical health index, walking difficulty, stroke status, skin cancer, diabetes, kidney disease. Second, an algorithm for heart disease based on artificial neural networks classification based on these features is developed. The prediction accuracy is close to 92%. In the future, the proposed model could be leveraged for heart disease recognition.
Basic Technologies to Build a Complete Metaverse
Authors: Liu Zhekai
Published: July 2023

Since the development of computers and screens capable of displaying a wide range of colors, people are starting to look at the bigger picture, such as 3D projection, online society, virtual reality, and so on. Nowadays, more and more technology companies are entering the field of virtual reality, they call the ideal project a metaverse. This paper will introduce the basic concepts and technologies of the metaverse, from the fundamental construction to different areas connections and a higher demand for services with advantages. So that there should have a comprehensive impression for people to understand how far from the final goal on the metaverse, how about the feasibility nowadays and development methods in the future. Although nowadays it still seems far from the ideal opinion, under the situation that many huge companies are making investment on it, we could presume the potentials of metaverse through its basics and have a better understanding of its benefits and limitations. Overall, the metaverse will change peoples social lives a lot, so its important to have a detailed understanding of this.
A Survey of Blockchain IoT Integration
Authors: Fang Chuanwang
Published: July 2023

The Internet of Things (IoT) brings people increasingly more convenience nowadays. However, due to the simple structure of most IoT devices and the limited budget when designing and building IoT systems, there are often security vulnerabilities in IoT systems. In addition, the accelerated growth of IoT devices and the data they generate also puts greater pressure on traditional centralised computing centers. Due to its openness, transparency, secure communication, and decentralisation, blockchain technology may solve these problems. This paper mainly studies the research trends of blockchain, IoT, and their integrated systems in recent years and puts forward issues worthy of attention in future development. This paper aims to help beginners get started with blockchain and IoT by providing an overview of blockchain, IoT, and their integration through reviewing and analysing literature. It recommends focusing on chain-chain communication security, new information compression and extraction methods, multi-dimensional evaluation of blockchain IoT systems, information security mechanisms against quantum computers in blockchain IoT integrated systems, and so on.
Image Detection and Segmentation using YOLO v5 for surveillance
Authors: S Mohanapriya , S Mohana Saranya , T Kumaravel , P Sumithra
Published: July 2023

Segmentation an advancement of object detection where bounding boxes are placed around object in object detection whereas segmentation is used to classify every pixel in the given image. In Deep Learning, Yolov5 algorithm can be used to perform segmentation on the given data. Using YOLOv5 algorithm objects are detected and classified by surrounding the objects with the bounding boxes. Compared to the existing algorithms for segmentation, YOLOv5 algorithm has improved time complexity and accuracy. In this paper YOLOv5 algorithm is compared with the existing CNN algorithm.
The Style Transfer of Photos and Landscape Paintings Based on CycleGAN Combined with Nested Edge Detection Module
Authors: Jin Yueling , Li Zeyang , Lu Tiantian
Published: July 2023

Chinese landscape paintings, especially Chinese landscape ink paintings have always been the treasure of the Chinese, or even the world culture. Due to the advancement of the science and knowledge, there are always interests for finding ways to produce Chinese landscape paintings through the way of the technology. In this paper, based on the original CycleGAN model, we are trying to transfer a real-world photograph to a typical Chinese landscape painting. To be specific, the brushwork in Chinese ink paintings contains enormous numbers of distinct and technical brush strokes which makes it extremely hard for CycleGAN to recognize and transfer Chinese ink painting. We improved the brushstroke effect of Chinese ink painting still by incorporating the Holistically Nested Edge Detection (HED) method into the CycleGAN model. The HED module puts the source image and the corresponding generated image into the convolution layer of five stages to generate their respective edge maps. The balanced cross-entropy loss calculated with edge maps is added to the total loss of CycleGAN to train the generator which is confronted with discriminator to improve output results. The addition of HED enables the extraction of edge features from the images, preserving the structural information and enhancing the accuracy of the style transfer, so the detailed of the Chinese ink painting can be transferred better in this sense. Experimental process with model building, training, validating, and predicting concludes that, complementing HED method into the original CycleGAN models well preserved distinct features in the conversion processes from real-world photographs to traditional Chinese landscape paintings.
Emojis Generation Based on Deep Convolution Generative Adversarial Network
Authors: Tang Kejia
Published: July 2023

With the development of information technology and mobile communication, people's usage of emoji is increasing. However, designing emojis by artists can be time-consuming and costly. Therefore, this study attempts to use the Deep Convolution Generative Adversarial Network (DCGAN) method in deep learning to automatically generate emojis. DCGAN is a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). It introduces convolutional networks into the generative model for unsupervised training, which can improve the learning effect of the generator network. DCGAN consists of two different models: a generator model and a discriminator model. The generator's role is to produce fake images that are similar to the training images, and the discriminator's role is to judge whether the fake image is the same as a real image. During training, the generator continuously tries to defeat the discriminator by producing better and better fake images, while the discriminator tries its best to distinguish between real and fake images. Through continuous optimization of the generator and discriminator, this study can finally obtain clear new emojis. Overall, the images generated by DCGAN can largely fulfill our purpose. Many novel emojis could be obtained to replace the work of human artists. However, the quality of the generated emojis has some defects, and it is still necessary to screen out images with strange shapes that are not suitable for daily life.
Review on Traffic Light Control Systems Based on Single-Chip Microcomputers and Smart Traffic Light Control Systems
Authors: Gordon Matthew Robert
Published: July 2023

With the development of the economy and the improvement of living standards, the number of private cars is increasing, which brings about traffic congestion and increased traffic safety hazards. Although some cities have widened some urban roads, traffic congestion and traffic safety hazards have not been effectively alleviated, and the speed of road construction and urban construction is far from meeting the needs of citizens. Therefore, traffic congestion is the primary problem that needs to be solved at present. This paper introduces single-chip microcomputers with their functions and applications as well as designs for traffic light control systems based on microcomputers. By comparing previous research designs and models, this paper points out the comparisons between different models and provides research suggestions and future prospects.
Multiple SOTA Convolutional Neural Networks for Facial Expression Recognition
Authors: Huang Yuming
Published: July 2023

Facial Expression Recognition (FER) has been a popular topic in the field of computer vision. Various and plentiful facial expression datasets emerged every year for people to train their models and compete. ImageNet, as a massive database for image classification, became a standard benchmark for new computer vision models. Many excellent models such as VGG, ResNet, and EfficientNet managed to excel and were regarded as state-of-the-art models (SOTAs). This study aims to investigate whether SOTA models trained on ImageNet can perform exceptionally well in FER tasks. The models are categorized into three groups based on different weight initialization strategies and then trained and evaluated on the FER-2013 dataset. The results indicate that models with weights trained on ImageNet can be fine-tuned and perform well in FER-2013, particularly when compared to other groups. Finally, simpler models with less computational costs are promoted considering the need for real-time application of facial expression recognition.
Food Supply Chain Traceability System Based on Blockchain Technology
Authors: Tang Jiawei
Published: July 2023

In recent years, with the Internet industrys development, more and more industries are developing in a more informative and transparent direction, especially traceability system. The databases of traditional supply chain traceability systems are all relational (RDBMS). The data is easy to be lost and tampered with. This shortcoming is particularly obvious in the food processing product supply chain traceability system. The data of each supply chain sector is stored in different databases. When regulatory authorities conduct information investigation and traceability, they must search multiple databases. The steps could be more convenient and easier to hold accountable by using the blockchain technology. A supply chain traceability system based on blockchain technology is designed to respond to these problems. The supply chain traceability system for processed food products is taken as an example to implement. This system is developed on the hyperledger fabric (HLF) platform, which realizes decentralization and tamper-proof, one-click traceability of the complete information and other functions. Consumers can not only query the whole process information of the food supply chain but also lodge complaints and defend their rights in this system.
An Overview of Machine Learning Applications in the Football Field
Authors: Sun Dasheng
Published: July 2023

Machine learning has become one of the hottest research directions in computing today and is widely used in various fields, including the field of football, which is considered the worlds number one sport. The application of machine learning in the football field has brought great convenience to players, coaches, club managers, spectators, and various professions engaged in the football industry, driving the development of the football industry. As the applications of machine learning are continuously developed and explored, machine learning has a broad development prospect in the field of football. This paper outlines various applications of machine learning in the match process, football odds, and team management, analyzes the shortcomings of previous research, and explores the future directions for the development of machine learning in the field of football.
Secure and Privacy-Preserving Voting System Using Zero-Knowledge Proofs
Authors: Miao Yizhuo
Published: July 2023

Electronic voting systems have the potential to improve the efficiency and accessibility of elections, but they also introduce unique challenges in terms of security, privacy, and voter anonymity. In this paper, we propose a secure and privacy-preserving voting system based on zero-knowledge proofs and homomorphic encryption. Our system ensures the integrity, confidentiality, and authenticity of votes while preserving the anonymity of voters. We present the system architecture, design, and implementation, along with a detailed analysis of the cryptographic techniques employed. The evaluation of our proposed system demonstrates its effectiveness, efficiency, and scalability, making it suitable for use in large-scale elections. This work contributes to the ongoing efforts to develop more secure, transparent, and accessible electronic voting systems for the future.
Blockchain and Classification of Mammograms and Histopathology Images in Breast Cancer Lesions
Authors: Barthwal Archana , Joshi Kapil , Kumar Adarsh , Memoria Minakshi , Kumar Rajiv , Mahajan Shubham , Abualigah Laith
Published: July 2023

This work provides an overview of the research that has been done on using Computer-Aided Detection (CAD) for the diagnosis of breast cancer. The focus is on mammographic and histopathology images and the different techniques used for image pre-processing, segmentation, and classification. The accuracy of the different algorithms was evaluated on different datasets, including MIAS, IRMA, DDSM, and CBIS-DDSM, and the results showed that deep learning models such as Convolutional Neural Networks (CNNs) and Random Forest, along with Multi-Layer Perception and Nave Bayes, were effective in detecting and classifying breast cancer. The results of these studies show the potential of CAD in making the diagnosis of breast cancer easier and more accurate. In the segmentation stage, the U-Net model has been modified to perform better in terms of accuracy. The DDSM database has been found to have a higher accuracy percentage compared to the MIAS and CBIS-DDSM databases. This indicates that the modified U-Net model has performed well on the DDSM database in terms of accurately segmenting the images. The use of Computer Aided Diagnosis (CAD) has improved the accuracy of breast cancer diagnosis in mammography and histopathology images. The four-step process of preprocessing, segmentation, feature extraction, and classification has proven to be effective in detecting malignant and benign tumors. Different algorithms like Naive Bayes, Multilayer Perception, Random Forest, and Convolutional Neural Network (CNN) have been used with varying accuracy levels. The best results were obtained by using the modified U-Net model for segmentation and the Random Forest algorithm for classification, as they showed higher accuracy compared to other methods and databases. In this study it has been analyzed that the use of Computer-Aided Diagnosis (CAD) in mammography and histopathology images has shown promising results in the diagnosis of breast cancer. Different algorithms and techniques have been used to improve the accuracy of the classification of malignant and benign tumors. The MIAS database has shown the best results when using a Convolutional Neural Network approach. However, histopathology images have some limitations in comparison to radiologist images, but the use of CAD is still an important tool in cancer diagnosis.
Review of Architecture and Model for Parallel Programming
Authors: Xia Zihan
Published: July 2023

Parallel programming has become a critical aspect of modern computer systems and applications. With the increasing demand for faster and more efficient computing, it is essential to have multiple parallel architectures and parallel models to choose from. Different parallel architectures, like SISD and MIMD, perform differently and offer different benefits. It is essential to have an understanding of these architectures to select the appropriate one for the specific application being developed. Similarly, different parallel models such as OpenMP allow developers to write parallel programs using high-level constructs. By leveraging these models, developers can focus on the algorithmic aspects of the program rather than giving attention to low-level details. In conclusion, understanding the different parallel architectures and parallel models available is necessary to develop efficient parallel programs and improve the performance of modern computer systems.
Analysis on the Accuracy of Different Classification Prediction Models in the Field of the Impact of Online Classes on Students During the COVID-19
Authors: Qian Yucen
Published: July 2023

Some students are pleased, while others are sad, as a result of the introduction of online education. Different attitudes, levels of physical health, family economic conditions, and so on have resulted from online classes. Consequently, it is necessary to determine what has caused various students to have different states and whether or not parents of school-aged children took the appropriate response measures. In this paper, the author preprocesses relevant datasets from Kaggle, then uses naive Bayesian, random forest, K-neighbor, SVC, logistic regression, and neural networks to classify and predict the dataset, analyses their accuracy and confusion matrices to determine the best classification and prediction model, and conducts importance analysis based on the best classification and prediction model. Therefore, it is possible to determine the significance of each parameter and to make suggestions based on the significance of various parameters. This can be used to determine whether there is room for development and errors in the next plans implemented in the era of online classes, and in the future, it can be used to reduce the health problems of students caused by the next pandemic in the era of online classes.
Handwritten Digit Image Generation Using Improved Generative Neural Network
Authors: Xiao Furui
Published: July 2023

Generative adversarial network (GAN) is a new and effective neural network model. GANs are used in a lot of areas such as images and visual, speech and language, composing music, and creating 3D shapes. Although the GAN has a lot of applications, there are still some problems with it. Because not only the generator but also the discriminator needs to be trained, this model is easy to be unstable. And the GAN may not generate different images in human vision to make the generated images can safely pass the discriminator. Some methods will be shown to solve the problems of unstable and lack of diversity in this article by an example that generates handwritten digits. The methods are changing the optimizer from SGD to Adam or SGD with Adam, adding a Batchnorm, and combining GAN with CNN or RNN, each of the improvements will be contrasted with others by the chart of loss rate and the generated images. The research finds that each of the improvements has some advantages over the original GAN in the area of speed or stability. Especially, GAN with the optimizer Adam and DCGAN have the most stable consequence according to the image from the generator.
A Comparative Study of Clinical Risk Prediction Using Limited Patient Electronic Health Records
Authors: Li Ziyu
Published: July 2023

Predictive modeling of clinical risk using patient electronic health records (EHRs) has the potential to enhance healthcare outcomes by enabling early detection and intervention for high-risk patients. However, dealing with sparse, irregular, and temporal EHR data presents significant challenges. This paper presents a comparative study of clinical risk prediction with limited patient electronic medical records. The related literature is categorized and compared based on research objectives, methods and experimental analysis. Additionally, potential research opportunities for future work in this area are discussed. Meta-learning-based algorithms have the ability to overcome data scarcity challenge by learning shared feature representations. Nevertheless, further research is necessary to address limitations such as the interpretability and generalizability of the model across different patient populations.
Using Convolutional Neural Network for Detection of Face Mask
Authors: Yang Jiajin
Published: July 2023

The COVID-19 pandemic has had a sweeping impact across the globe, resulting in enormous economic losses and significant changes to people's way of life. Despite the World Health Organization's (WHO) assertion that the COVID-19 pandemic will conclude by 2023 and people's lives will begin to settle down, the possibility of a resurgence of the virus cannot be overlooked. In crowded public places, it is essential to have a system that can rapidly and accurately detect whether people are wearing masks and adhering to proper usage protocols. Therefore, it is crucial to make necessary preparations for such a system. Fortunately, there have been notable advancements in facial recognition technology, which can aid in this endeavor. This paper aims to build a model for face-mask-detection with convolutional neural network to help perform a rapid mask-wearing check and carry out the system model training with a final accuracy reaching 0.95. In the end, high accuracy is observed in the classification of correctly wearing masks and incorrectly wearing masks, demonstrating that the model is capable to identify whether people wear masks and wear correctly with the final accuracy reaching 0.97.
Review of Object Detection Challenges in Autonomous Driving
Authors: Cao Shenxuan
Published: July 2023

This paper presents a comprehensive review of object detection in autonomous driving applications. The classical object detection network is presented, along with several well-known online resources and benchmark methods. A thorough review of the challenges in object detection for autonomous driving is provided, along with potential solutions to these challenges. By exploring the current state of object detection in autonomous vehicles, this paper aims to contribute to the ongoing efforts to improve the safety and efficiency of autonomous driving technology.
FACTORS INFLUENCING SALES DECISIONS AND THEIR IMPACT ON MICRO, SMALL AND MEDIUM ENTERPRISES IN BRAVO PHOTOCOPY BUSINESS BLANG PULO VILLAGE
Authors: Wahyuni Yuyun , Khairani Husna Fadhila , Haway Fitra , Aulia Ramadhan Hafidz , Sinta Irada
Published: July 2023

This study aims to find out the sales decisions and their impact on micro, small and medium enterprises in the blang pulo village photocopy business. The research method used is the survey method. The survey research method uses data collection and analysis techniques in the form of opinions / facts from the subject under study (respondents) through question and answer which is directly communicated with the respondents, and interviews (oral questions) conducted with face-to-face communication. This research is qualitative which means descriptive research and tends to use analysis with an inductive approach.
ANALYSIS OF THE COMMUNITY FUND MANAGEMENT FOR PREVENTION AND HANDLING OF COVID-19 DISEASE IN BENER MERIAH ACEH, INDONESIA
Authors: Mustaqim  , Hizir Sofyan  , Heru Fahlevi
Published: July 2023

The impact of COVID-19 on national finances and the national economy is that the management of the government's budget becomes the focus and vital. Losses in the economic aspect such as a large number of unemployed due to the dismissal of workers that is affecting the economic and social aspects later on. As a result, the role and function of the government are needed to deal with the COVID-19 pandemic. The purpose of this study was to find out the planning process for using village funds in preventing and handling COVID-19, and also the budgeting process for using village funds in preventing and handling COVID-19, and the process of monitoring and evaluating the use of Village Funds in prevention. and handling of COVID-19. The type of research used in this study is qualitative. The data collection technique is using the interview method. This study used a purposive sampling technique, the samples in this study were DPMK (Village Community Empowerment Service) secretaries, BPBD (Regional Disaster Management Agency) secretaries, Subdistrict headsectaries, and Village Government. Data analysis in this study is the data collection, data reduction, data presentation, and summing up the conclusions. The first results of the research are the planning process for using village funds in the prevention and handling of COVID-19  which is conducted from the Regency, District, and Village levels. The second result is the process of budgeting for the use of village funds in the prevention and handling of COVID-19, activities include: refocusing activities and budget reallocation, funding in emergencies. Moreover COVID-19 funding policy is also reallocating and refocusing APBK, and management mechanisms finance for handling the impact of COVID-19 at the village level. The third result is the monitoring process and evaluating of the use of Village Funds in the prevention and handling COVID-19 which is carried out through regional leadership plus communication forum meetings, based on activities that have been and will be carried out by the Regional Work Unit (SKPK) and village government.
Nutrition practices in COVID-19 patients
Authors: Saseedharan Sanjith , Gupta Roma , Pramanik Chandrima
Published: July 2023

The COVID-19 pandemic posed a huge challenge to the healthcare system worldwide. India being a developing country, was pushed to the brim of its capabilities due to exhaustion of manpower and financial resources. Currently there are very few studies linked to nutrition in COVID-19 in Indian scenario. In this study we aim to study the nutrition practices among hospitalised Covid-19 patients from tier I and tier II cities of India. An online survey was conducted among 70 COVID hospitals of India from both government and private sector. The data was collected by the use of cloud-based spreadsheet function in googledocs of the selected 70 hospitals. 63 out of 70 respondents used height, weight and BMI criteria to calculate calorie protein requirement of the patients. With the remaining 7, two respondents from government and private hospitals used direct calorimetry and three private hospitals used indirect calorimetry (IC) method. It was observed that the number of patients on EN was higher than PN in wards as well as in ICU. 38 respondents initiated EN within 24 hours, 18 respondents did within 24-36 hrs, of patient’s ICU admission. 39 respondents checked Gastric Residual Volume in patients in prone position on ventilator as part of monitoring. As nutrition is considered equally important in treating COVID-19 patients, it is suggestive that one common government body can be formed which can formulate an India specific standardised nutrition protocol for all the practicing dieticians and health care workers.
A study of auditory acuity in patients with essential hypertension using pure tone audiometry
Authors: D Chaitra , Veeraiah Shivkumar
Published: July 2023

Background: The ability to communicate is a crucial aspect of human life and auditory sense is very important for communication of any kind. It has been hypothesized that hypertension is an independent risk factor for hearing loss is arterial hypertension. It is hypothesised that hypertension causes the degeneration of inner ear due to alteration in microcirculation. This study is undertaken to identify the role of hypertension in auditory acuity.

Aims and Objectives: The aim of the study was to assess the association between high blood pressure and hearing function, after excluding the influence of noise pollution on hearing impairment and to see the association of duration of hypertension with hearing impairment, by performing pure tone audiometric tests on normotensives and hypertensives.

Materials and Methods: 50 hypertensives visiting Hesaraghatta PHC and 50 normotensives residing in the same area were chosen for the study. After selecting the subjects from appropriate inclusion-exclusion criteria, informed consent was taken. Then blood pressure was measured followed by audiometric assessment for hearing acuity in each using standard guidelines. The results were compiled and statistically analysed.

Results: Hypertensives have a higher threshold in all frequencies for both air conduction and bone conduction. Significant P-values were found in both ears for all frequencies in both air conduction and bone conduction in both ears. Thresholds are increased but were within normal limits since cases are on treatment, resulting in mild hypertension.

Conclusions: There was a significant increase in hearing thresholds at all frequencies in hypertensives than normotensives. In the current study, no significant correlation was found between duration of hypertension and auditory thresholds.