bluuebunny/crossref_metadata_2025 huggingface dataset:
EFFECT OF SEVERITY AND ETIOLOGY OF CHRONIC KIDNEY DISEASE IN PATIENTS WITH HEART FAILURE WITH MILDLY REDUCED EJECTION FRACTION
Authors: RAMZAN HS , RAWAT A , MUSTAFA G , SATTAR S , SHAHEEN A , ABDELGANI AAA , LODHI N , RAMZAN Z
Published: June 2024

This study explores the impact of chronic kidney disease (CKD) severity and etiology on patients with heart failure with mildly reduced ejection fraction (HFmrEF). Understanding these relationships is crucial for optimizing management strategies and improving patient outcomes. Methods: A cross-sectional study was conducted involving 550 patients diagnosed with HFmrEF. Patients were categorized based on CKD severity (stages 1 to 5) and etiology (diabetic nephropathy, hypertensive nephrosclerosis, glomerulonephritis, and others). Data on demographics, clinical characteristics, laboratory findings, and echocardiographic parameters were collected and analyzed. Results: Data were collected from 550 patients according to the study's criteria. The mean age of the patients was 62.5 ± 10.8 years. Of 550, 320 (58.2%) were male, and 230 (41.8%) were female. According to the NYHA classification, 40 (7.3%) belong to Class I, 290 (52.7%) to Class II, 200 (36.4%) to Class III, and 20 (3.6%) to Class IV. Advanced CKD stage (OR 2.5, 95% CI 1.6-3.8), diabetic nephropathy (OR 1.8, 95% CI 1.1-3.0), and lower eGFR (OR 2.2, 95% CI 1.5-3.2) were all associated with increased risk of mortality and hospitalizations. Conclusions: It is concluded that the severity and etiology of chronic kidney disease significantly impact the outcomes of patients with heart failure with mildly reduced ejection fraction. Advanced CKD stages and diabetic nephropathy are associated with higher mortality rates and more frequent hospitalizations.
Evaluation of phytochemical analysis and antibacterial activity of Cassia roxburghii DC. and Hyptis suaveolens L.
Authors: Murugan M. , Mohan V.
Published: June 2024

Phytochemicals from the leaves and stem bark of Cassia roxburghii and leaves and stem of Hyptis suaveolens were extracted using different solvents of various polarities such as petroleum ether, chloroform, methanol and water. The antibacterial activity was carried out against Bacillus subtilis, Escherichia coli, Klebsiella pneumoniae and Staphylococcus aureus by disc diffusion method. Among the solvent extracts, methanol extract was most effective against the tested microorganisms. Phytochemical analysis revealed the presence of alkaloids, coumarin, flavonoids, tannins and terpenoids.
A Study on Decision Making and Teaching Competency: Processing Self Perception and Cognitive Schema through Neutrosophic Science
Authors:   Liberal Arts Department, American University of the Middle East, Kuwait, Saziye Saziye
Published: June 2024

The objective of this research is to examine the decision-making processes of teachers and explore their self-assessments of teaching competency levels based on the competency indicators proposed by the Ministry of National Education (MoNE) in Turkey. The study adopts a constructivist perspective, offering a fresh look at the cognitive levels of teachers and their decision-making mechanisms. Additionally, it integrates neutrosophic science principles to address the uncertainties and indeterminacies present in teachers' self-evaluation and decision-making processes. Data were gathered using the General Competencies for Teaching Profession (GCTP) scale, which was developed according to the competencies defined by the MoNE. This new scale, featuring 15 Likert-type items, was validated and tested for reliability before being administered to a sample of 320 volunteering teachers from various disciplines in Turkey. The scale measures data within the Professional skills domain and captures teachers' self-perceived competency beliefs related to their professional skills, considering factors such as years of teaching experience, gender, subjects taught, and the type of school (primary or secondary) where they are employed. SPSS 16.0 was used for data analysis and to obtain descriptive statistics for the item results. The analysis revealed that primary school teachers scored higher on the GCTP scale compared to high school teachers. By incorporating neutrosophic science, the study effectively navigates the uncertainties in assessing teaching competencies, offering a more nuanced understanding of the factors that influence teachers' decision-making processes.
ESG Information Disclosure in China: Institutional "Localization" in a Developing Country
Authors: Wang Yimeng
Published: June 2024

Over the years, the concept of ESG, introduced from Western developed countries, has become increasingly implemented in China, gradually forming a mature system. However, as a developing country, China's national conditions and social realities differ significantly from those of Western developed nations, leading to maladaptation in the process of ESG information disclosure by listed companies. This maladaptation manifests as incomplete disclosure content, inconsistency in the scope of disclosure, and "greenwashing." This study, from the perspective of a developing country, summarizes the social issues and relevant policy improvements of other developing countries to propose recommendations for ESG information disclosure that are more suited to China's current national conditions. This aims to promote a high-quality, sustainable development that is internationalized yet distinctly Chinese.
Green Credit Policy and the Debt Scale of Heavily Polluting Enterprises Evaluation of the Mediating Effect Based on Corporate ESG Performance
Authors: Lin Zhenhe
Published: June 2024

With the gradual development of low-carbon and environmental actions, the implementation of policies such as green credit has been effective. Based on the dataset of A-share listed companies spanning from 2009 to 2023, this paper focuses on the relationship between green credit policies and the debt scale of heavily polluting enterprises, and explores the role of ESG performance in this relationship through the evaluation of intermediary effects. To facilitate identification, we used the parallel trend test, dividing the data before and after the policy publication into two groups. This is more conducive to observing the effects and effects of policies. The findings indicate a clear suppression of the debt scale among heavily polluting enterprises following the enactment of relevant policies in 2012, highlighting the discernible impact of these policies. Finally, the enlightenment and suggestions are put forward based on the conclusion.
Prediction of Stock Price by Neural Network Based on CNN, LSTM, ANN
Authors: Wen Hanqing
Published: June 2024

The stock market's complexity and volatility have long posed significant challenges for prediction models. This study delves into the comparative efficacy of three machine learning models, i.e., Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN), in forecasting stock prices. Empirical evidence from a range of research papers is synthesized in the study, which emphasizes the advantages and limitations of each model when applied to the analysis of financial time series. CNNs, while adept at feature extraction, may not fully leverage the temporal dynamics of stock data. LSTMs, with their ability to capture long-term dependencies, have shown a propensity for enhanced prediction accuracy, yet they are computationally intensive. ANNs, despite their simplicity, offer a robust baseline for comparison but often fall short in capturing intricate market patterns. Based on the analysis, LSTMs generally outperform the other models, suggesting their suitability for complex financial forecasting tasks. The decision to select a model should be dictated by the demands of the forecasting endeavor, encompassing available computational assets and the preferred equilibrium between precision and intricacy. This investigation emphasizes the critical role that model choice plays in achieving dependable prognostications of stock prices and lays down a groundwork for future studies aiming to merge these models with a broader spectrum of financial metrics.
A Brief Analysis of Hong Kong Public Housing Allocation Mechanism: From the Perspective of Market Design
Authors: Huang Zihao
Published: June 2024

Housing policy be identified that the prescriptive measures established by the government for addressing housing problems and interventions nowadays. By formulating and implementing housing policies tailored to the country's specific characteristics, these policies serve to guide the development of the housing industry, manage the housing resources, and ensure a fair resolution of the housing problem. However, research and professional analysis of housing demand within a particular city is a challenging task. Our report explains Hong Kong's housing allocation mechanism from a market design perspective by analyzing arguments and historical digital evidence from relevant and reliable literature. And then we have modeled and evaluated the mechanisms of Hong Kong public rental housing allocation. We conclude that although the mechanism itself can be improved, but the housing issues in Hong Kong is more about other external factors. So, this report provides a more detailed view of Hong Kong's public housing policy compared with previous papers.
A Review of the Development of Portfolio Theory and Its Application in the Chinese Securities Market
Authors: Gao Haoxuan
Published: June 2024

In the field of financial investment, portfolio theory has been widely studied as an important risk management and asset allocation tool. Generally speaking, investors will always aim for low risk and high yield. The portfolio theory fully accounts for investor psychology and introduces the concept of diversified investment, which focuses on finding ways to diversify investments to minimize non-systematic risk and maximize returns when people's expected income is impacted by a variety of uncertain factors. Portfolio theory, as one of the most important theories in the modern financial field, aims to seek the best balance of risk and return by optimizing the allocation of assets, so as to achieve the optimization of investment portfolios. This paper overviews the development of portfolio theory and its application in the Chinese securities market, and discusses its application effects as well as limitations in practice. Through continuous theoretical innovation, method improvement and practice deepening, it is expected to further enhance the ability of portfolio theory to solve problems in the market with Chinese characteristics, and provide more scientific and reasonable investment guidance for investors.
Driving Growth Amidst Challenges: Chinas Economic Focus for 2024
Authors: Li Chao
Published: June 2024

In 2023, China's economic growth exhibited an unexpected fluctuating pattern, marked by relative insufficiency in growth across the demand side, encompassing investment, consumption, and exports. Moreover, challenges stemming from geopolitical and global trends posed hurdles for China's international cooperation, exports, and foreign investment. To compound matters, the accumulation of domestic cyclical and structural contradictions created bottlenecks in the overall economic cycle, further hindering the nation's recovery. In light of these economic challenges and weaknesses, this thesis analyzes the reasons behind the sluggish economic recovery in China from both international and domestic perspectives. Based on this analysis, it delves into the direction and key points of the central economic policies for 2024, and further clarifies three policy measures to achieve economic development goals, including the feasibility of political means and the superiority of institutional characteristics. The conclusions drawn are as follows: It is expected that the economic growth rate in 2024 will exceed 5%, with the GDP growth target for the 14th Five-Year Plan period expected to be around 5.5% annually. Under an active fiscal policy, it is anticipated that the growth rate of fixed asset investment in 2024 will significantly increase, while the consumption growth rate is slowing down, requiring strong policy measures to restore expectations. Additionally, influenced by geopolitical factors, national security has become an important consideration, and exports may continue to face challenges. The analysis and conclusions of this paper provide a deep insight and theoretical development experience support for the better development of the Chinese economy.
Artificial Intelligence as Personal Financial Advisor in the Future? - A Case Study Based on Algorithmic Innovation Strategies
Authors: Liu Zechen
Published: June 2024

This paper mainly introduces the development prospects and challenges of personal financial advisors driven by artificial intelligence (AI), as well as the advantages of combining artificial intelligence with financial management. With the rapid development of artificial intelligence technology, the personal financial consulting industry is undergoing profound changes. Through deep learning and big data analysis, taking JIMI, JD.COM as an example, it is demonstrated that artificial intelligence financial consultants can grasp the market trend more accurately and provide users with more accurate and personalized financial advice. It shows in detail that in the artificial intelligence environment, it can provide all-weather service for financial users, and users can log in to the client at any time to consult financial related issues, and artificial intelligence will restore to users. The capital market has been in a dynamic change, and there are many uncertainties in the development of the whole financial market, but investors don't know enough about financial treatment. Therefore, the application of artificial intelligence in financial management has won the favor of many financial users, and it also has a certain role in promoting the development of the capital market.
Research on AAPL Stock Price Prediction Using ARIMA Model
Authors: Xia Zewei
Published: June 2024

On February 2, 2024, Apple officially launched a new generation-changing product: Vision Pro. For a leading technology company like Apple, every technological advancement will affect changes in its stock price. Stock prediction has always been a direction that data scientists and economists pay attention to and explore. As a result, to achieve this goal, people continue to develop new models and algorithms. Among them, the ARIMA model is a typical data prediction model. This paper uses the ARIMA model to make a simple prediction of Apple's stock closing price in the past five years. The optimal model is used to make the final prediction by processing the Apple stock data in the past five years. Although the short-term forecast is very optimistic, the time series data of the stock itself contains non-linear trends or seasonal changes. For long-term predictions, the results are not accurate. Apple's technology iterations are seasonal and regular. It is not advisable to use the ARIMA model alone to make long-term predictions of its stock closing price.
Analysis of LV Stocks Based on Linear Regression, Random Forest and XGBoost
Authors: Zhao Yubing
Published: June 2024

As a matter of fact, the outbreak of the global phenomenon of large-scale infectious disease in early 2020 (i.e., Covid-19) has brought great impact to the global economic market. This study will investigate LV stock prices during Covid-19 based on linear regression, random forest and XGBoost models. As one of the most severely affected industries, this study selects the most representative and socially influential brand Louis Vuitton as investigation target. Different machine learning models have been used in stock price prediction at the beginning of the epidemic outbreak, combining the eigenvalues of the market panic index and the epidemic related situation when the time series is close to a random walk. To evaluate the models, MSE and R-squared error are used to compare the degree of fit of the model. According to the analysis, the stock price prediction models will have better performance on the basis of considering the market sentiment and the epidemic situation.
The Transformation and Strategic Research of China's Rural Areas under the Conditions of Urban-rural Imbalance
Authors: Su Ziran
Published: June 2024

Currently, under the imbalance between urban and rural areas in China, there is a lack of transition strategies for most rural areas. In the context of vigorously promoting the rural revitalization strategy, it is necessary to focus on optimizing the urban-rural governance mechanism and constructing a more efficient market system and division of labor system to effectively address the economic imbalance between urban and rural areas. At the same time, it is important to emphasize the development of collective economy and improve the investment and financing mechanism to meet its funding needs, as well as to pay attention to the role of incoming population in promoting urban-rural integration development. This article focuses on the problems in these three aspects, elaborates on the significance of coordinated development in rural areas, and provides corresponding solutions, and incorporated analysis of development cases from some excellent countries. Aiming to play a positive role in China's rural transformation and development.
The Role of Digital Technology in Rural Revitalization Development: A Case Study of the Digital Strawberry Demonstration Garden in Changfeng County
Authors: Wan Yuhan
Published: June 2024

On October 18, 2017, Chinese President Xi Jinping proposed the rural revitalization strategy in the report of the 19th National Congress of the Communist Party of China. In May 2019, the General Office of the Communist Party of China Central Committee and the General Office of the State Council issued the "Outline of the Development Strategy for Digital Villages," which clearly stated that digital villages are an endogenous process of agricultural and rural modernization and transformation accompanying the application of networking, informatization, and digitization in the development of agricultural and rural economy and society, as well as the improvement of farmers' modern information skills. Against this background, this paper aims to explore the experience and strategies of empowering rural industrial revitalization through the successful case of the Digital Strawberry Demonstration Garden in Changfeng County, thereby providing reference strategies for relatively poor areas across China to address their challenges. The analysis concludes that the digital economy can significantly promote the revitalization of rural industries by optimizing the production process of rural industries, improving production efficiency, and enhancing the risk resilience of agriculture.
Monetary Policies in Japan: Structural Challenges and Evolutions from The Lost Decades to Post-COVID Dynamics
Authors: Chen Shihua
Published: June 2024

Navigating a distinctive economic landscape characterized by long persistent deflationary mindsets and a set of unique monetary policy challenges, Japan's experience with inflation and deflation starkly contrasts with the inflationary pressure observed in many other major economies around the world. Through literature review and data collection methodologies, this paper examines the Bank of Japan's (BOJ) implementation of diverse monetary policies spanning recent decades since the 1990s, with a specific emphasis on its strategies in response to the economic conditions post-COVID-19. By integrating theoretical frameworks, this paper critically assesses the rationale and efficacy of these policies, while delving into the discrepancies between anticipated and realized outcomes. The research identifies three key structural problems underpinning the disparityage, debt and risk structures. Emphasizing the decisive role of real economy, it further predicts forthcoming supply-side structural transformations in Japan, offering valuable insights into the latest significant policy adjustments in Japan and intricate world economy.
Pathways and Innovative Mechanisms for Achieving Urban Carbon Neutrality
Authors: Liu Jiahao , Xi Xi
Published: June 2024

This paper discusses the problems of the traditional carbon emissions trading mechanism, such as failing to effectively incorporate carbon sinks into the carbon market and failing to adequately incentivise the economic and ecological development of cities, and proposes a new urban carbon neutral trading system to solve these problems. The new urban carbon neutral trading system is based on the city as a unit, and by fully integrating carbon sinks into the trading system, it adopts the market mechanism to regulate the balance between carbon emissions and carbon sinks, so as to realise the coordinated development of the urban economy and the green environment. In addition, this study develops a comprehensive evaluation index system for urban carbon neutrality, and uses big data and intelligent analysis techniques to improve the accuracy and real-time performance of urban carbon management. These innovations not only help strengthen urban environmental management, but also promote urban economic transformation and provide theoretical support for sustainable regional economic growth.
Enhancing Energy Management in New Energy Vehicles and Energy Storage Systems Through Advanced Data Analysis and Machine Learning
Authors: He Jiahong
Published: June 2024

This paper explores the pivotal role of data analysis and machine learning in advancing energy management strategies for New Energy Vehicles (NEVs) and Energy Storage Systems (ESS). Focused on the comprehensive journey from data collection and preprocessing to the application of dynamic programming, reinforcement learning, and genetic algorithms, our study underscores the transformational impact of these technologies on optimizing energy utilization and prolonging battery life. Initial stages involve meticulous data gathering and preprocessing to ensure the quality and usability of information derived from operational parameters. Subsequently, feature selection and engineering refine this data into meaningful insights, laying the groundwork for predictive modeling. These models forecast energy demands and system behavior, facilitating proactive maintenance and system efficiency improvements. We delve into optimization strategies, highlighting dynamic programming's role in decision-making, reinforcement learning's adaptability to environmental changes, and genetic algorithms' exploration of optimal charging/discharging strategies. These methodologies collectively contribute to sustainable energy practices and resource conservation, marking significant advancements in the field. The integration of machine learning not only enhances predictive maintenance and charging protocol optimization but also addresses challenges related to data scarcity, model generalizability, and interpretability. This paper provides a comprehensive analysis of current methodologies and future prospects, advocating for a multidisciplinary approach to further enrich the research landscape in energy management for NEVs and ESS.
Ethical IT Decision Making and Data Governance
Authors: He Kuang
Published: June 2024

This paper explores the importance of data governance in influencing ethical decision-making in the context of information technology (IT). Data governance is used as a management technique to ensure the quality of organisational data by establishing and implementing policies, procedures and standards. Three main types of data governance are discussed: silo-oriented data governance, functional data governance, and platform data governance. The impact of each type on ethical IT decision-making is analysed, highlighting the challenges and benefits associated with each approach.
Ethical IT decision-making hinges on robust data governance. Silo-oriented governance fosters fragmentation, conflicting standards, and security vulnerabilities. Functional Data Governance tailors policies to departments, enhancing accountability and risk mitigation but may lack unified ethics. Platform Data Governance builds on this, ensuring clearer accountability, data privacy, and unified ethical standards. To foster ethical IT decision-making, prioritize Platform Data Governance, establish a data platform for security, cultivate a unified ethical culture, and enforce organizational standards. These steps promote accountability, mitigate risks, and safeguard data integrity, crucial for ethically sound IT decisions in today's complex landscape.
Production Location Selecting and Subsequent Production Decision Making for Sport Obermeyer
Authors: Cui Cheng
Published: June 2024

This paper revolved around two core research issues: production location selection and subsequent production decisions. The aim of the study was to provide a detailed analysis of Sport Obermeyer's data from 1992 to 1995 in a new way, hoping to assist companies facing similar challenges. The paper took Cost, Quality, Lead time and Minimum order quantity as bridges to the four main factors affecting the choice of production location. It detailed the advantages and disadvantages of the two production locations: Hong Kong and China. Then, with the aid of the Newsvendor model, the mismatch cost ratio for the 10 styles was determined. Ultimately, using the coefficient of variance for comparison and confirmation of the mismatch cost ratio results, the paper helped Sport Obermeyer analyze which styles of parkas had low-risk and low-uncertainty demand, and which had high-risk and high-uncertainty. The paper then combined the characteristics of the two production locations and assisted Sport Obermeyer in making subsequent production decisions. Five styles of parkas suitable for production in China and five styles suitable for production in Hong Kong were identified, ensuring stable profits and deliveries for Sport Obermeyer.
Binning of biological image based on unsupervised semantic segmentation and no-reference image quality assessment
Authors: Chen Feiyang
Published: June 2024

Accurate and efficient species classification is crucial for various applications, and machine-learning approaches have been widely employed for image-based taxonomic identification. However, challenges arise due to the variability in image quality and the need for extensive dataset preparation. In this study, we propose a model that combines unsupervised semantic segmentation by distilling feature correspondences (STEGO) and a no-reference image quality assessment model to assess the quality of images for taxonomic identification. STEGO enables the counting of objects in an image, while the image quality assessment model evaluates subject articulation. The model is applied to a dataset of Bombus hypnorum images from the Global Biodiversity Information Facility (GBIF) after data cleaning. The effectiveness of the model is demonstrated through the segmentation and scoring of images, providing a valuable tool for image recognition and quality assessment in taxonomic studies.
A literature review on the application of neural networks in the time series prediction
Authors: Pan Junya
Published: June 2024

With the coming of the big data era, the demand for analysis and processing of mass data has become bigger and bigger. Prediction by using mass data has become an indispensable part of peoples daily lives and work, among which time series prediction has received widespread attention and been widely researched. Based on neural networks and recent years research on time series prediction, this article discusses the improvement and application of neural networks in time series prediction, including BP Neural Network, Long Short-term Memory Network (LSTM) and Neural Networks based on cluster analysis. By analyzing the purposes and consequences of these improved methods, this article clarifies the necessity and superiority of the (improved) neural networks in time series prediction. For example, through research and discussion, it finds that using improved neural networks for prediction can not only dramatically increase the accuracy of prediction, but also improve the anti-interference ability of the model. In other circumstances, improved neural networks can also use various methods to make the data match the model better in specific problems and so on. In the end, this article discusses the current problems and difficulties in neural networks in time series prediction, clarifying the problems that prediction needs a high match between model and data, the demand for the choice of model is high and so on. Then it makes prospects for future research, expecting the appearance of more models and improved methods.
Data job requirement characteristics analysis based on large language model and K-means clustering
Authors: Zhou Jie , Chen Yan , Guo Yiling , Zhang Chong
Published: June 2024

This paper aims to analyze the characteristics of data-related job requirements using a Large Language Model (LLM), providing decision support for the construction of data-related courses in universities. Using Zhaopin.com as the data source and employing a key attribute extraction method based on ChatGLM3 along with prompting engineering techniques, this study extracts and clusters key attributes from job descriptions. It analyzes the characteristics of data-related job requirements in terms of competencies, fields of expertise, and tools. The research found that the quality of key attribute extraction using ChatGLM3 improved by 54.1% compared to dictionary-based methods, significantly reducing the workload of manual annotation. Additionally, the analysis revealed commonalities and differences in the demand features of data analysis, data engineering, and data operations roles across six dimensions: soft skills, tools, specialties required, educational background, experience, and salary.
Analysis of the implementation of ChatGPT in Shenzhen tourism
Authors: Zhang Haoxiang
Published: June 2024

ChatGPT is currently a highly popular subject of discussion. In recent years, there has been a significant advancement in artificial intelligence technology, leading to revolutionary breakthroughs. As a result, AI has gained the capability to successfully pass the Turing test. ChatGPT has revolutionized the inflexible and formal manner in which voice assistants used to communicate, approaching the level of human cognitive capabilities. The worldwide tourism industry has been greatly affected by the COVID-19 epidemic in 2020, resulting in a substantial decline in both tourist arrivals and revenue for China's tourism sector. In 2021, with the normalization of epidemic prevention and control and prompt policy adjustments, the Chinese tourism industry is gradually transitioning into the recovery phase. Many tourist sites are starting to incorporate advanced technology to improve their visibility. Furthermore, there is a consistent increase in demand within the domestic tourism sector, and the Chinese government is actively facilitating the advancement of the tourism industry. This essay will examine the influence of ChatGPT on the growth of the tourism sector in Shenzhen. This research analysis aims to investigate the potential of ChatGPT in fostering long-term growth and creating novel prospects for the tourism industry in the future.
Study on the renovation and reuse strategy of the first batch of high-quality residential communities in china at the end of the 20th century: A Case study of Chongqing Jinguoyuan community
Authors: Lv Zhaojiang
Published: June 2024

At the end of the 20th century, brick and concrete structures were commonly used in residential buildings. These types of buildings were cheap to construct and easy to build, but their disadvantages were also very apparent. They had poor seismic resistance, heavy structural weight, and poor fire resistance, among others. The Jinguoyuan residential community in Chongqing, discussed in this paper, adopted a frame structure building strategy, solving the problem of planning high-quality residential buildings and addressing the different needs for functional division among homeowners. During a time when brick and concrete structures were commonly used for commercial housing, the adoption of brick and concrete mechanisms improved the high-quality nature of the community. It enhanced the accommodation of different needs for rooms, ultimately achieving the effect of positioning as a high-quality residential community
Risk Factors for Leaving Against Medical Advice in Patients Admitted for Upper Extremity Orthopedic Procedures
Authors: Momtaz David , Ghilzai Umar , Okpara Shawn , Ghali Abdullah , Gonuguntla Rishi , Kotzur Travis , Zhu Kai , Seifi Ali , Rose Ryan
Published: June 2024

Introduction:

Patients who leave against medical advice (AMA) face increased risks of negative health outcomes, presenting a challenge for healthcare systems. This study examines demographic and hospital course factors associated with patients leaving AMA after an upper extremity (UE) orthopaedic procedure.

Methods:

We analyzed 262,912 patients who underwent UE orthopaedic procedures between 2011 and 2020, using the Healthcare Cost and Utilization Project database. We then compared demographic and hospital course factors between patients who left AMA and those who did not leave AMA.

Results:

Of 262,912 UE orthopaedic patients, 0.45% (1,173) left AMA. Those more likely to leave AMA were aged 30 to 49 (OR, 5.953, P < 0.001), Black (OR, 1.708, P < 0.001), had Medicaid (OR, 3.436, P < 0.001), and were in the 1st to 25th income percentile (OR, 1.657, P < 0.001). Female patients were less likely to leave AMA than male patients (OR, 0.647, P < 0.001). Patients leaving AMA had longer stays (3.626 versus 2.363 days, P < 0.001) and longer recovery times (2.733 versus 1.977, P < 0.001).

Conclusion:

We found that male, Black, younger than 49 years old, Medicaid-insured, and lowest income quartile patients are more likely to leave AMA after UE orthopaedic treatment.
FAKTOR-FAKTOR YANG MEMPENGARUHI AUDIT REPORT LAG
Authors: Wahyuningsih Devi , Melania Krispina
Published: June 2024

Penelitian ini bertujuan untuk menganalisis pengaruh Profitabilitas, Solvabilitas, Likuiditas, Komite Audit dan Ukuran Perusahaan terhadap Audit Report Lag pada Perusahaan Property dan Real Estate yang terdaftar di Bursa Efek Indonesia (BEI) Periode 2021 sampai 2023. Sampel yang digunakan dalam penelitian ini adalah sebanyak 47 perusahaan, jumlah data yang digunakan adalah sebanyak 141 data penelitian yang diperoleh dengan menggunakan Teknik Purposive Sampling. Analisis data dilakukan dengan menggunakan Teknik analisis regresi linear berganda dengan menggunakan SPSS 26. Hasil penelitian ini menyatakan bahwa Profitabilitas dan Solvabilitas berpengaruh terhadap Audit Report Lag, sedangkan Likuiditas, Komite Audit dan Ukuran Perusahaan tidak berpengaruh terhadap Audit Report Lag.
 
This research aims to analyze the influence of Profitability, Solvency, Liquidity, Audit Committee and Company Size on Audit Report Lag in Property and Real Estate Companies listed on the Indonesia Stock Exchange (BEI) for the period 2021 to 2023. The sample used in this research was 47 company, the amount of data used was 141, research data obtained using purposive sampling techniques. Data analysis was carried out using multiple linear regression analysis techniques using SPSS 26. The results of this study stated that Profitability and Solvency had an effect on Audit Report Lag, while Liquidity, Audit Committee and Company Size had no effect on Audit Report Lag.
Đánh Giá ChatGPT trong việc Giải Đề Thi Minh Họa Kỳ Thi Tốt Nghiệp Trung Học Phổ Thông 2023 tại Việt Nam
Authors: Xuan-Quy Dao  , Bac-Bien Ngo  , Xuan-Dung Phan  , Ngoc-Bich Le
Published: June 2024

Bài báo giới thiệu kết quả thử nghiệm ChatGPT (phiên bản 13 tháng 2) giải đề thi minh họa kỳ thi tốt nghiệp Trung học Phổ thông năm (THPT) 2023 do Bộ GDĐT công bố. Thực hiện thử nghiệm cho các môn học: Toán, Văn, Anh, Lý, Hóa, Sinh, Sử, Địa và GDCD. Kết quả thử nghiệm cho thấy ChatGPT có khả năng vượt qua đề thi minh họa THPT 2023 trong cả hai tổ hợp xét tốt nghiệp: tự nhiên và xã hội. Đối với các môn trắc nghiệm: Toán, Lý, Hóa, Sinh, Sử, Địa, GDCD và Anh, ChatGPT đạt được điểm bài thi tương ứng là 5.4, 5.75, 4.75, 6.00, 7.75, 6.75, 7.75 và 7.80. Đối với môn Văn, ChatGPT có thể đạt điểm 6.47. Kết quả cũng thể hiện điểm số ChatGPT đạt được đối với Toán, Lý, Hóa và Sinh (5-6) thấp hơn số điểm đạt được đối với Văn, Anh, Sử, Địa và GDCD (6-8). Dữ liệu về câu hỏi và câu trả lời của ChatGPT được công bố công khai. Hơn nữa, dựa vào cấu trúc đề thi, có thể dự đoán rằng ChatGPT hoàn toàn có khả năng vượt qua kỳ thi tốt nghiệp THPT 2023.
Nhận Diện Ngôn Ngữ Độc Hại Tiếng Việt
Authors: Đông Trần , Phước Huỳnh Minh , Nhật Văn Mai Thanh , Quang Trần Nhật , Tân Lê Minh
Published: June 2024

Trong những năm gần đây, thế giới trực tuyến đã chứng kiến sự bùng nổ của các nền tảng giao tiếp và chia sẻ. Mạng xã hội, diễn đàn và vô số trang web đã tạo ra một không gian trực tuyến rộng lớn và đa dạng. Mặc dù sự phong phú về nội dung này thú vị và có thể hữu ích, nhưng nó cũng mang đến những thách thức mới, đặc biệt là về vấn đề bảo vệ trẻ em. Việc dễ dàng truy cập internet có thể khiến trẻ em tiếp xúc với các rủi ro tiềm ẩn, chẳng hạn như gặp phải ngôn ngữ độc hại và bắt nạt trực tuyến. Các phương pháp giảm thiểu truyền thống, chẳng hạn như chặn kết nối hoặc hạn chế thời gian sử dụng màn hình, có thể không thực sự hiệu quả. Bài báo này đề xuất một giải pháp sử dụng sức mạnh của học sâu. Bằng cách đào tạo các mô hình học sâu để xác định các cụm từ độc hại, các mô hình của chúng tôi có thể nhận dạng các dạng ngôn ngữ không phù hợp khác nhau, bao gồm cả từ nhạy cảm và những từ có vẻ vô hại được sử dụng với mục đích gây hại. Hệ thống lọc thông minh này có thể được triển khai trên cả phía máy chủ và phía máy khách của các nền tảng trực tuyến, cung cấp một lớp bảo vệ tốt hơn cho người dùng trong thế giới kỹ thuật số.
Impact of the Charging Station on the Quality of Voltage and Frequency in the Microgrid
Authors: Hong Nhung Le Thi  , Trieu Tan Phung  , Hoang Minh Vu Nguyen  , Ngoc Thuong Huynh Thi  , Mi Sa Nguyen Thi
Published: June 2024

This paper presents the impacts of Electric Vehicle Charging Stations (EVCSs) on voltage and frequency quality in the Microgrid. It addresses security analysis, voltage and frequency quality, as well as issues related to EVCSs. In this paper, modeling and simulation method is utilized to evaluate the impact of Charging Stations (CSs) in Microgrid power systems using PowerWorld software. Potential attacks may occur when malicious actors intrude and control the operating states of CSs in the power grid, as well as potential vulnerabilities in the process of using EVs in Microgrid, are simulated and analyzed. Based on this, the paper proposes power grid attack scenarios based on charging/discharging operations with a large number of EVs on generator buses. Simulation results show the negative impacts of charging/discharging operations on the grid through frequency and voltage fluctuations. Specifically, from the simulation results, it can be observed that Bus 8 is not suitable for installing a charging station due to its weak stability at all power levels. Furthermore, the remaining generator buses can only accommodate charging stations with power levels starting from 30% of the proposed initial capacity to maintain system stability.
Breaking Down the Barrier: Understanding How Magnesium Deficiency Contributes to Periodontitis
Authors:   School of Stomatology, Zunyi Medical University, Zunyi, Guizhou, China, Chen Yujiong , Yang Minzhi ,   School of Stomatology, Zunyi Medical University, Zunyi, Guizhou, China, Wu Mingsong ,   School of Medicine & Nursing, Huzhou University, Huzhou, China
Published: June 2024

A common disturbing dental malaise which ultimately results in low quality of life is periodontitis, a chronic bacterial infections disease that is caused by the dental plaque biofilm. Both tissues and blood need enough magnesium, this is a natural and one of essential minerals that is highly abundant in the human body. Different diseases due to a magnesium deficiency can be formed. Results from clinical and nutritional data supported this hypothesis. However, The relationship between magnesium deficiency and periodontitis requires further research. This paper examines some possible ways in which these two are connected. This article focuses on presenting a valuable and resourceful material for those who are eager to acknowledge the role that magnesium level plays in periodontal disease as well as providing potential means to the creation of advanced and effective periodontitis prevention and treatment approaches.
CONHECIMENTO SOBRE FATORES DE RISCO E PRÁTICAS DE PREVENÇÃO DO CÂNCER DO COLO DO ÚTERO ENTRE MULHERES ATENDIDAS EM UM CENTRO DE SAÚDE
Authors: Xavier Jéssica Rodrigues Dantas , Cândido Guilherme Felinto , Cassimiro Kamilly Trajano da Silva , Monteiro Bruno Tavares , Pires Maria Lívia Cândido da Silva , Lima Eloise de Oliveira , Braga Marieliza Araújo , Alexandrino Kedma Anne Lima Gomes
Published: June 2024

O câncer do colo do útero (CCU) é uma das principais causas de mortalidade no mundo. O conhecimento adequado e o comportamento preventivo são fundamentais para reduzir sua incidência e mortalidade. Este estudo objetivou avaliar o conhecimento sobre fatores de risco e práticas de prevenção do CCU entre mulheres atendidas em um centro de saúde na Paraíba. Os objetivos específicos incluíram identificar o nível de informação das participantes sobre os fatores de risco, examinar a compreensão e realização das práticas de prevenção, investigar a influência de variáveis sociodemográficas no acesso à informação e à prevenção e analisar a relação entre conhecimento e práticas de prevenção. Tratou-se de uma pesquisa de campo transversal de natureza quantitativa. Um questionário estruturado foi aplicado a uma amostra selecionada por conveniência em um centro de saúde, resultando em 30 participantes. A maioria das participantes possuía conhecimento limitado sobre os fatores de risco e práticas preventivas do CCU; 76% das mulheres realizavam o Papanicolau regularmente. Não foi encontrada associação significativa entre o acesso a informações sobre o CCU e variáveis sociodemográficas, tampouco entre conhecimento sobre os fatores de risco e a prevenção (p > 0,05). No entanto, houve uma associação entre a faixa etária e a realização do Papanicolau (p < 0,05). Embora haja um nível razoável de conhecimento sobre alguns fatores de risco e práticas preventivas, os achados apontam a necessidade de ações educativas focadas em aumentar a conscientização sobre a prevenção e os fatores de risco contra o HPV.
Báo cáo lâm sàng ca bỏng hô hấp mức độ nặng, ngạt khói
Authors: Nguyễn Hải An , Trần Đình Hùng , Ngô Tuấn Hưng
Published: June 2024

Bỏng hô hấp gây ra các tổn thương đường khí đạo, giảm ô xy hoá máu, làm tăng thêm 20% nguy cơ tử vong và là yếu tố liên quan độc lập với tử vong trên bệnh nhân bỏng nặng. Điều trị bỏng hô hấp còn nhiều thách thức đặt ra cho các nhà lâm sàng. Chúng tôi báo cáo một trường hợp bỏng hô hấp mức độ nặng, ngộ độc khí CO điều trị tại Khoa Hồi sức Cấp cứu, Bệnh viện Bỏng Quốc gia Lê Hữu Trác.
Reverse Mixed-Ownership Reform and Ambidextrous Innovation Investment in Private Enterprises: Evidence from China
Authors: Wang Jinfeng , Xiao Min
Published: June 2024

Amid the country's vigorous promotion of mixed-ownership reform, optimizing the layout of state-owned capital and guiding private enterprises in innovation are crucial for achieving high-quality economic development. This study, adopting the perspective of "reverse mixed-ownership reform," which involves the introduction of state-owned capital into private enterprises, analyzes the impact of state-owned equity and its shareholding level on the ambidextrous innovation investment of private enterprises using A-share private listed companies from 2007 to 2021 as samples. The findings reveal that the introduction of state-owned capital significantly enhances the ambidextrous innovation investment of private enterprises. Furthermore, there is an inverted U-shaped relationship between the level of state-owned equity shareholding and developmental innovation investment, while a linear positive correlation exists with exploratory innovation investment. Additionally, financing constraints play a mediating role in the relationship between state-owned equity participation and innovation investment, while agency costs negatively moderate the relationship between the introduction of state-owned capital and ambidextrous innovation investment. Private enterprises undergoing direct privatization exhibit stronger organizational inertia, exhibiting a positive impact on both types of innovation investment. This study provides theoretical support for further advancing mixed-ownership reform and rationalizing the layout of state-owned capital, while also offering empirical evidence for promoting high-quality development among private enterprises.
PERAN LITERASI DIGITAL BAGI MASYARAKAT DALAM MENGURANGI DAMPAK KEJAHATAN TRANSASKI ELEKTRONIK ILLEGAL
Authors: Romadhonia Annisa , Nahdliyin Sukma Hidayatun , Janah Miftakhul
Published: June 2024

Penelitian ini dilatar belakangi sebab akibat majunya perkembangan teknologi terutama akibat arus globalisasi. Salah satu bidang yang terkena imbas dari majunya teknologi adalah bidang keuangan. Saat ini industri Financial Technology (Fintech) mengeluarkan produk peer to peer lending (P2P) berupa pinjaman online. Namun, permasalahan yang kemudian muncul ialah adanya pinjaman online illegal yang secara jelas tidak terdaftar dalam Otoritas Jasa Keuangan (OJK). Fenomena seperti itu merupakan bentuk pelanggaran hukum dimana keadaan saat ini semakin marak dan banyak memakan korban. Oleh sebab itu pentingnya menjadi individu yang melek akan digital dimulai dengan faham akan literasi digital. Pentingnya literasi digital bagi masyarakat, terutama bagi golongan masyarakat yang susah mengikuti isu kemajuan teknologi. Penelitian ini menggunakan penelitian hukum yurudis normatif dengan pendekatan perundang-undangan, aturan  hukum dan permasalahan isu pelanggaran hukum.
Modified ElGamal signature with secret key pair and additional random number
Authors: Chande Manoj
Published: June 2024

ElGamal digital signature has numerous applications in real time situations. Due to its utility and popularity, security threats and challenges are increasing day by day for this signature scheme. This paper introduces a variant of ElGamal signature scheme, mainly after analyzing the existing weaknesses of the scheme where the security depends on one private key only. To overcome this, our scheme consists of a private key pair and an additional random number which makes the relation between the secret key and the randomly chosen number used more complicated. The cryptographic security of the proposed scheme is relatively much higher than the existing ElGamal signature scheme in literature.
experiência mística em Clarice Lispector:
Authors: Moraes Marcella Assis de
Published: June 2024

Este artigo se dedica a investigar a maneira como a ascese mística na obra de Clarice Lispector não se desvincula da experiência concreta e cotidiana da existência. Lê-se aqui a imbricação entre o sentido da visão, tradicionalmente associado à capacidade humana de contemplação, e o do paladar, associado à necessidade animal de nutrição. Assim, a autora avança para o desfazimento das fronteiras entre humano e animal, problematizando a categoria filosófica de sujeito e valorizando o estatuto de coisa.
Dossiê Mística e Subjetividade
Authors: Losso Eduardo Guerreiro Brito , Barreira Marcelo Martins
Published: June 2024

Este número da Revista Terceira Margem foi dedicado ao tema “Mística e subjetividade”. Os artigos que o compõem debatem um conjunto diverso de desafios e perspectivas teóricas acerca da interface entre mística, arte, religião e problematização do “eu”. Assim, desde uma pluralidade de referenciais teóricos e campos de saber, o eixo temático do dossiê – graças ao caráter paradoxal do mistério e à condição da subjetividade diante do Outro, evocado e inferido pela “experiência mística” – proporciona um amplo arco de percursos hermenêuticos.