bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Neutrosophic Cognitive Maps (NCM) for the Evaluation and Ranking of the Main Causes of the Appearance of Fruit Fly Pests
Authors: Emerson Emerson ,   Technical University of Cotopaxi, Ecuador,   Technical Partner of the Union of Agroecological Farmers Organizations of the Province of Tungurahua PACAT. Ecuador,   Technical University of Cotopaxi, Ecuador,   Tashkent State University of Economics, Uzbekistan, Morales Pablo , Jiménez-Jácome Cristian Jiménez-J� , Abidova Dilfuza
Published: July 2024

The development of Neutrosophic Cognitive Maps (NCM) for the evaluation and ranking of the main causes of the appearance of fruit fly pests represents a significant advance in the field of agriculture and entomology ̣This innovative approach allows for a holistic and integrated view of the complex and often interdependent factors that contribute to the proliferation of these destructive pests ̣Using neutrosophic theory, which incorporates degrees of truth, falsehood, and indeterminacy, NCMs offer a powerful tool for identifying and prioritizing critical variables ̣In this way, a more nuanced and precise understanding of the phenomenon is facilitated, enabling the design of more effective and sustainable management strategies ̣The methodology applied in the construction of the NCM is characterized by its ability to manage the uncertainty and ambiguity inherent to ecological and agricultural systems ̣Through the participation of experts and the analysis of empirical data, maps can be outlined that reflect the real complexity of the problem ̣These maps not only highlight direct causes, such as weather conditions and poor agricultural practices, but also address underlying and systemic factors ̣Thus, the use of NCM provides a robust conceptual framework for informed decision making, improving the efficiency of interventions and contributing significantly to crop protection and global food security.
The Effect of Changing Convolutional Neural Nets Parameters on EEG Signals Recognition Ratio
Authors: Khaled Khaled ,   University of Mosul, department of computer science and mathematics, Mosul, Iraq
Published: July 2024

Brain Computer Interface (BCI), especially systems for recognizing brain signals using EEG (Electroencephalography), is one of the important research topics that arouse the interest of many researchers currently. Convolutional Neural Nets (CNN) is one of the most important deep learning classifiers used in this recognition process, but the parameters of this classifier have not yet been precisely defined so that it gives the highest recognition rate and the lowest possible training and recognition time. This research proposes a system for recognizing EEG signals using the CNN network, while studying the effect of changing the parameters of this network on the recognition rate, training time, and recognition time of brain signals, as a result the proposed recognition system was achieved 76.38 % recognition rate, And the reduction of classifier training time (3 seconds) by using Common Spatial Pattern (CSP) in the preprocessing of IV2b dataset, and a recognition rate of 76.533% was reached by adding a layer to the proposed classifier.
The Governance Effect of China's Green Finance Policy from an International Perspective
Authors: Liu Yuan
Published: July 2024

Most of the previous studies on the utility of green financial policies have focused on provincial data, exploring the differences between different provinces at the same time; few have focused on China as the main body, focusing on the positive effects of national-level green financial policies on the rapid development of the economy and society effect. Based on the macro data of 43 countries, this paper uses the SBM model of undesired output to measure the main characteristics of social and economic development. It estimates the net effect of China's green financial policy through the non-parametric synthetic control method. The study found that China's green financial policy has a significant positive effect on the improvement of factor productivity. Further exploration of the predictor variables shows that China's industrial structure and technical level are relatively low, and the use efficiency of green financial funds and government green finance need to be optimized.
What Cause RV Insurance? A Case Study on TIC Company
Authors: Xu Xinzhuo
Published: July 2024

With the vigorous development of digital technology, more and more enterprises realize the importance of digital transformation. Data has also become one of the enterprise assets. Through modern information technology, enterprises have improved their ability to collect and integrate data. Then the comprehensive data information is analyzed to contribute to business forecasting and enterprise management. This paper takes the customer data from the TIC insurance company as an example. It uses statistical analysis methods to mine the data, such as descriptive statistics, the Pearson chi-square test, the nonparametric test, K-means clustering, and binary logistic regression prediction. Analyze and forecast customers who may purchase RV insurance.
Equity Research of Teslas Company
Authors: Ji Xinyi
Published: July 2024

The primary purpose of this research is to conduct an equity research for Tesla Inc and to check whether the stock is recommended for investment. Tesla Inc. has been one of the most profitable automobile companies in the world. Both qualitative and quantitative methods are used for the analysis of Tesla Inc. The qualitative way would be to analyze the companys position in the industry, for instance, the prospect of the electric vehicle industry. The quantitative way would be to analyze the financial metric using the financial statements of Tesla Inc. and also calculate the beta value which is the risk value of the company and finally give a valuation to the companys cost of equity by using CAPM. The data used for financial statement would be collected from Yahoo Finance. The beta value would be calculated using Excel. The report has shown that Tesla has good financial performance and a potential for high returns because it has a beta value greater than 1. At the end of the paper, limitations of CAPM have been discussed and a comparison between CAPM and APT has been listed. In conclusion, APT seems to be a better model than CAPM.
Analysis of Influencing Factors of Stock Return Rate in China
Authors: Shi Kewei
Published: July 2024

In recent years, the decline of China's stock market prices, as reflected in the decline of the Shanghai Composite Index and Shenzhen Composite Index in the late 2020s, has led to an economic downturn in China. The article analysis the statistics from 2016 to 2021 and introduces the possible reasons and political background and the reasons why these factors lead to the share value. Through the method of linear regression, the possible factors were simulated on the statistical system SPSS, and a more reliable data conclusion was obtained. To analyze the causes of the economic downturn by factoring the relationship between the consumer consumption index, per capita GDP growth, Gini coefficient, the Exchange rate of RMB against the US dollar, and the Shenzhen Index. In addition, it focuses on the impact of real-life factors on the stock market, focusing on the relationship between fixed asset investment, Hong Kong factors, the economic cycle, and the stock market.
The Exploration of the Reasons for Alibaba to List on the New York Stock Exchange (NYSE) in 2014
Authors: Liu Yimo
Published: July 2024

Alibaba made an important announcement on August 8 in 2022, showing that its requirement about applying to the Hong Kong Stock Exchange for a dual primary listing in Hong Kong and New York had been confirmed and approved by the Hong Kong Stock Exchange. Unquestionably its such a crucial action that was done by Alibaba to expand its market and finance further after its first controversial large-scale Initial Public Offerings (IPO) on the New York Stock Exchange (NYSE) in 2014. The listing process of Alibaba created a sensation in the world that gave rise to the discussion about the reasons and effects of this decision for the company itself and the IPO market in China in recent years. The current study summarized the previous professional articles on these discussions to explore the key reasons for Alibaba to choose to list on the NYSE and the changes in the thoughts of this decision due to the changing times. Although this decision let Alibaba slip into a more competitive global business environment and resulted in the query for its unique governance structure, listing on the NYSE was still more beneficial for Alibaba at that time. The proper unprecedented determination brought greater growth opportunities for Alibaba and provided guidance for future companies. With the development of the times and policy reform in China, the methods of Initial Public Offerings in the companies certainly tend to be more varied and exoteric that need to be innovated and dialectically treated.
Feds Monetary Policy and Dynamic Changes in Stock Prices of Large Supermarket Chain
Authors: He Jiabei
Published: July 2024

The U.S. is experiencing the highest inflation in 40 years, and prices have been rising. The Fed adjusts this rate to achieve one goal - to maintain maximum employment and price stability in the United States. But the Fed's sharp rate hikes also raise the risk of a sharp slowdown, especially as new challenges emerge, such as Russia's war with Ukraine and China's capacity problems due to Covid lockdowns. This article assesses the impact of Fed rate hikes on the supermarket stock market. This paper builds a VAR model and an ARMA-GARCH model to analyze changes in stock prices from the perspective of value and volatility. This paper finds that Fed rate hikes unsurprisingly negatively impacted stock returns and increased their volatility, leaving stock prices in an unpredictable situation. The research paper can give the Fed a reference data. When the Fed decides to raise interest rates, it needs to consider the current world economic situation. For example, flu and war will affect the world economy, and will also have some impact when the Fed raises interest rates.
The Influence of Cognitive Bias on People's Online Shopping Decision
Authors: Xu Chuyi
Published: July 2024

Since China initially established a simple online shopping model in 1999 and experienced 20 years of a long development, people should have accumulated a lot of online shopping experience. However, people will still make many irrational decisions. From the perspective of behavioral economics, this paper considers more factors regarding the assumption of a "rational person" in traditional economics, breaks the shortcomings of conventional assumptions, and more systematically explains many economic phenomena. It enables people to avoid the same mistakes and maintain relative rationality after understanding the economic phenomena contained in the irrational decision-making of online shopping. In order to achieve this goal, this paper will list several representative cognitive biases and link them with various irrational online shopping behaviors so that people can understand how these irrational behaviors produce and affect people. Finally, this paper finds that when people make decisions, they are often affected by the "field dependence" of external information and the "field independence" of people's hearts. What people can do is accumulate knowledge reserves and keep relatively rational when making similar decisions in the future.
Development of a smart health monitoring system for elderly care
Authors: Wang Jing
Published: July 2024

This paper describes an intelligent health monitoring system developed for detecting the health of the elderly. This paper introduces an intelligent health monitoring system used to detect the health status of the elderly. With the progress of mobile communication technology and the increasing demand for personal intelligence, wearable devices have become more and more popular products. The system can provide personalized services for the elderly, and also provide more comprehensive and accurate data support for medical staff, which is expected to play an important role in the future care of the elderly.
A survey of text generation models
Authors: Liang Wenhan
Published: July 2024

In this article, I propose four model classifications to summarize the characteristics and analyze the advantages and disadvantages of text generation models that have emerged in recent years, so as to give researchers an overall overview. The models based on the decoder only use the decoder for text extraction, and its output only depends on the previous output. The models based on the encoder-decoder, on the other hand, refer to both the encoder's output and the previous prediction. I've deliberately categorized prefix models and ensemble models to highlight their differences. I also present the current state of the text generation field and compare the advantages and disadvantages of several of these models. Finally, I summarize the difficulties encountered in the field of text generation and provide a research direction for the field. In the module Challenges, I focused on the problem of scarcity regarding datasets. The current solutions are given, as well as the efforts made by relevant workers on domain-specific datasets.
Auxiliary prediction of cardiovascular diseases based on deep learning
Authors: Zhang Zhongzheng , Zhu Junnan
Published: July 2024

Assisting in the prediction of cardiovascular diseases is beneficial for both individuals and medical institutions, especially for cardiovascular diseases, which are prevalent worldwide and easily lead to death and health issues. Although many scholars have discussed various datasets from different regions, understanding their internal decision-making process is challenging. This paper aims to discuss and verify data features by collecting data from Kaggle, as well as use the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance issues. The research used multiple machine learning, ensemble learning, and deep learning models for analysis, compared the accuracy of these models, and chose the best model to better assist doctors and help individuals in disease prediction. The implemented models in this paper include K Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting Decision Trees (GBDT), and Deep Neural Network (DNN). The primary focus on XGBoost, GBDT, and DNN yielded AUC scores in the test set of 0.823, 0.825, and 0.832, respectively. The established DNN model demonstrates a good auxiliary prediction capability for cardiovascular diseases.
An investigation on strategies for optimizing consumer trust in chatbots
Authors: Luo Xi Ning
Published: July 2024

The advancement of artificial intelligence (AI) gave rise to chatbots, which is a type of AI-powered software that communicates via natural language. Chatbots have been used in diverse contexts, delivering significant convenience to the consumers. Nonetheless, this technology encounters ambivalent attitudes from consumers. Some aspects of the chatbot technology are evoking distrustful attitudes among consumers, while the others are cultivating a sense of trust. Thus, the objective of the current paper is to outline and analyze key factors that affect consumer trust and elucidate strategies that firms can adopt to optimize trust. According to recent studies, consumer distrust primarily stems from algorithmic bias, privacy and security concerns, and the lack of algorithmic transparency; on the other hand, consumer trust is formed due to anthropomorphic attributes of chatbots, particularly warmth and competence. To reduce consumer distrust, companies are advised to first identify and minimize existing real risks in their products, then deliver transparency to the public to establish a trustworthy image. To increase trust, companies are suggested to improve upon the anthropomorphic attributes of chatbots. Contributions and limitations of the paper are also discussed to highlight areas that require further investigation in the field of chatbots as well as AI in general.
Risk prediction of acute myocardial infarction based on deep neural network
Authors: Chen Guanyu
Published: July 2024

The cardiovascular disease situation in China is becoming more and more serious, among which the prevalence and mortality rate of acute myocardial infarction (AMI) have been increasing in recent years, so it is urgent to prevent cardiovascular disease. Based on the personal behavior data and basic physical data covering 30,000 respondents within 30,000 days, a deep learning algorithm was used to predict whether the respondents would have acute myocardial infarction in the near future. The prediction accuracy and generalization ability of different algorithms, including logistic regression, decision tree, XGBoost, and GNB algorithms, are compared. The results show that it is feasible to predict the short-term AMI disease split line based on the patient's personal basic information, and the DNN algorithm can achieve the highest prediction accuracy. The research results can assist clinical medical staff in the prevention and treatment of acute myocardial infarction to a certain extent, and also have good civilian characteristics, which can serve medical insurance and other industries.
Performance comparison of deep learning-based image classification algorithms on ImageNet
Authors: Liu Zhelin
Published: July 2024

The rapid evolution of Artificial Intelligence (AI) technology has propelled image recognition to the forefront of computational advancements. Since the inception of Convolutional Neural Networks (CNNs), the field has expanded into a multitude of sophisticated models and their derivatives, each tailored to address specific challenges and applications. Image recognition's landscape encompasses foundational tasks such as object and face detection, extending to more specialized applications like emotion analysis, optical character recognition, and complex interpretation of biological imagery. This domain's historical perspectives trace back to models like AlexNet, which set benchmarks with accuracy rates of around 70%. Fast forward to contemporary times, and advanced algorithms consistently achieve accuracy figures beyond the 90% threshold on benchmark datasets like ImageNet. Moreover, the diversification of AI applications has led to the development of models like MobileNet, which are intricately designed for streamlined efficiency on mobile devices, balancing performance with resource constraints. This discourse will navigate the intricate maze of image recognition, primarily leveraging insights from the ImageNet dataset as a canonical reference. By the end of this exploration, this work will discuss several cost-efficient models. Finally, this work will also cover some complex algorithms with high accuracy. All these algorithms use different approaches and obtain good performance in either cost-efficiency or accuracy. This discourse will provide an overview of these algorithms, detailing their novelty, implementation, and experimental results for accuracy and cost-efficiency.
Prediction of second-hand house prices in Ames city based on data mining
Authors: Zhou Chucheng
Published: July 2024

As the secondary housing market continues to play a vital role in the global real estate industry, there is an increasing demand for accurate and efficient prediction of secondary housing prices. In this study, transaction data from the second-hand housing market in Ames, Iowa, USA, available on the Kaggle website, was utilized. A total of 79 influential feature variables, including Lot Area, Overall Quality, and Year Built, were selected to construct seven different models for predicting secondary housing prices: Linear Regression, Lasso Regression, Ridge Regression, LassoCV, RidgeCV, Random Forest, and Neural Network models. To assess the effectiveness of these models, we utilized a range of evaluation metrics including the R2 score, Mean Absolute Error (MAE), and Mean Squared Error (MSE). The results indicated that the RidgeCV model stood out among the others, displaying the most robust predictive performance with a goodness-of-fit of 90.3%. This discovery holds significant implications for improving the accuracy of secondary housing valuation.
Angle calculation method based on Cognex binary image processing and edge tool positioning
Authors: Zhong Hangkai
Published: July 2024

Aiming at the problem of no suitable measurement method for the angle between a product's upper and lower cylinder axes in a specific horizontal rotation position, a calculation method based on the Cognex vision system for automatic angle measurement was proposed. This algorithm uses binary image processing technology to reduce interference in product implementation caused by variations in surface roughness and resulting inconsistencies in the reflection effect. The ability to perform robust image feature searching is thereby built upon. It utilizes edge tools to locate the points on either side of a cylindrical product and compute the axial coordinate for averaging the measured axes at these positions to determine each product elements upper and lower axial angle measurement. Simulation results show that this algorithm utilizes binary image processing to effectively filter product differences, which plays a role in capturing product image features continuously and reliably. The edge tool based on feature location can accurately locate product edges and complete target angle calculations. It has certain production field application capabilities regarding image processing effectiveness and computational logic accuracy.
Administrative punishment likelihood forecasting based on deep learning
Authors: Zhang Chunyi
Published: July 2024

As the economic situation becomes increasingly complex, the need for investors to avoid risks grows rapidly. For instance, the investors may encounter companies that have potential to incur violations. Once the violation is exposed, companies may face fines, warnings, or even expulsion from the stock market. Such punishments damage the companys reputation and erode trust from stakeholders and investors. This paper proposes a deep learning-based model to predict the likelihood of a company facing administrative penalties in the future, aiming at assisting investors in making informed decisions. This study considered both financial and linguistic variables. To counter data imbalance, the under-sampling and normalization methods were employed to the dataset. For modelling, a Neural Network model was applied due to its competence in hierarchical learning. The fully connected layer incorporated a rectified linear unit activation function to address gradient explosion, while the output layer utilized the sigmoid activation function for binary classification to forecast potential violations. The model achieved a final testing accuracy of 0.9701.
Metadata and the way of encoding of the collections in Shaanxi History Museum
Authors: Liu Yangzeyu
Published: July 2024

The contemporary advancement of digital museum studies has emerged as a significant subfield within the broader discipline of museum studies and is poised to have a profound influence on the future trajectory of museology. The Shaanxi History Museum is a provincial museum located in Shaanxi, China. It has the distinction of being the first national museum in China to have contemporary facilities on a big scale. Situated in Xi'an, a city with a historical legacy spanning over a thousand years as the capital of thirteen dynasties, the location has a significant cultural and historical heritage, as well as abundant archaeological riches. This paper explores the qualities and limitations of metadata encoding in the context of the Shaanxi History Museum. It does so by analyzing the collection metadata from two perspectives: page search and collection data files. The study also suggests further encoding options about the collection circulation, museum data administration, and display requirements, considering the future trajectory of digitizing museum collections.
Terahertz communication for 6G networks: Opportunities and challenges
Authors: Yao Shutong , Teng Junyi
Published: July 2024

This paper introduces terahertz communication technology, the core component of future 6 g wireless communication. Terahertz band communication can expand the spectrum range of communication, increase its capacity limit, and can be effectively utilized and realized in many fields. This paper systematically describes the background, development, difficulties and challenges of terahertz communication, as well as the possible application fields and development prospects in the future, and introduces in detail the key technologies of terahertz communication, including the simulation processing of terahertz signal processing and modulation technology and the research data of the system. In this paper, I use the ten-ray model modeling to help me understand the propagation characteristics of terahertz waves in different environments, and provide reference and guidance for the application of terahertz waves and system design. Finally, the future application field of terahertz are expanded and prospected. It describes new and predictable technologies that combine the power of terahertz technology with other technologies, such as exploring hybrid terahertz/optical wireless links, terahertz automotive applications, terahertz communications to enhance data center performance and terahertz 3D beamforming technology.
Analysis and applications of prominent indoor positioning techniques
Authors: Zhang Jiashuo
Published: July 2024

The limitations of traditional positioning methods have become increasingly evident, prompting a shift towards indoor positioning. Indoor positioning is particularly suitable for scenarios where outdoor positioning services, such as GPS, fail to provide accurate or reliable data. When exploring the nuances of indoor positioning technology, several key characteristics emerge that differentiate it from its outdoor counterparts. A variety of indoor positioning technologies currently exist, each with its unique set of advantages and drawbacks. For instance, some might offer exceptional accuracy but may be limited by infrastructure requirements, while others might boast broad coverage but compromise on precision. Given these trade-offs, there's growing interest in the potential of multi-sensor fusion sites. By integrating data from various sensors, it's believed that these fusion sites can amplify the strengths and mitigate the weaknesses inherent to individual technologies. Experimental evidence has started to validate the feasibility and benefits of such a multi-sensor approach. In today's interconnected world, the application domains of indoor positioning span a broad spectrum, ranging from retail environments and healthcare facilities to large-scale manufacturing plants and entertainment venues. However, despite the advances, there remain gaps and challenges in the current indoor positioning landscape. As technology continues to evolve, there is an anticipation of further refinements and breakthroughs that will address these shortcomings, paving the way for more robust, comprehensive, and adaptive indoor positioning solutions in the future.
Research on algorithm accuracy and the application of facial recognition technologies
Authors: Yang Wenjie
Published: July 2024

Research on face recognition systems began in the 1960s. It is a biometric technology that is based on some features as well as areas of the face for recognition. After collecting the images with the help of model acquisition, algorithms are used to process the recognized images. Nowadays, face recognition technology is being used all around people's lives and is very relevant to people's lives. The article will introduce the use of face recognition technology in different fields. It submits the application of face recognition in different backgrounds and application scenarios. It will introduce some representative applications, and analyse the implementation of the core algorithm of the technology, and the results. The paper will also provide a dialectical discussion of face recognition to gain a deeper understanding of the technology. Face recognition has become one of the most popular technologies nowadays and has a great prospect in the future.
Uber stock prediction: A comparative study between three models
Authors: Dong Xinyi
Published: July 2024

These days, people are paying greater attention to the stock market, precising stock prediction can considerably boost the rate of return. This article will utilize Uber Inc.'s stock forecast as an exploratory objective to conduct a study on its stock price, specially, this article mainly compares three models: Linear Regression, Random Forest Regressor and Long Short-Term Memory (LSTM) and uses four parameters (MSE, RMSE, MAE and R2) to evaluate and determine which model has the most accuracy in predicting a stock's closing price. In light of the experimental findings, the results of linear regression yield a R^2 of 0.98, RMSE of 1.27, MSE of 1.62, and MAE of 0.94; the results for random forest regression are R^2 equal to 1.00, RMSE is 0.52, MSE amount as 0.27, and MAE up to 0.38; and the LSTM results are R^2 come up to 0.97, RMSE is 1.68, MSE equal to 2.82, and MAE is 1.25. Based on the result, this article concludes that Random Forest is the most accurate in predicting Uber's stock price.
A review of face recognition technologies based on deep learning
Authors: Deng Chong
Published: July 2024

Face recognition is a widely used technology in the field of security, with the goal of identity verification or recognition by analyzing and comparing facial images. In recent years, the rapid development of deep learning technology has brought significant breakthroughs to face recognition. Face recognition technology mainly involves four modules: face detection, face alignment, face representation, and face matching. Among them, face representation is the most important module in face recognition technology, which directly affects the accuracy of face recognition. This article summarizes the application of deep learning technology in the field of face recognition in the past decade and introduces the technologies as well as their development involved in the above four modules. According to analysis, it is concluded that deep learning models can learn and capture rich features in face images and thus achieve more accurate and stable face recognition. Besides, feature extraction is a critical link in deep learning face recognition, and model training and optimization are key to achieving high-performance face recognition.
Three-Dimensional Synergies: The Impact of Intelligent Investment, Industrial Environment, and Human Capital on the Employment Structure of High-Tech Manufacturing
Authors: Guo Jing , Nie Weikang
Published: July 2024

This study investigates the impact pathway and mechanisms of multi-factor artificial intelligence investment on the proportion of high-tech manufacturing labor force, drawing upon the theory of skill-biased technological changes (SBTC). Employing a mixed qualitative and quantitative research design, we identify key resource factors influencing changes in the employment structure within the high-tech manufacturing industry. Our analysis employs fuzzy-set qualitative comparative analysis (fsQCA) on a provincial dataset from China, spanning the years 2018 to 2020. Findings reveal the following insights: (1) There exists a configurational effect among antecedent factors influencing changes in the employment structure of the manufacturing industry. Specifically, investments in AI-related industries, industrial robots, industry transformation and upgrading, industrial intelligence, working-age population proportion, and educational attainment of the workforce are not individually necessary conditions. (2) There are three paths that drive the increase of the proportion of high-tech manufacturing labor force, including comprehensive development type, capital-led type, and education-led type. (3) Investments in industrial robots and advancements in industrial intelligence emerge as pivotal factors driving the increase in the proportion of high-tech manufacturing labor force. This research contributes to advancing a multidimensional perspective on biased technological change, laying the groundwork for regional management strategies to invest in AI for fostering the healthy development of high-tech manufacturing.
The Impact of Affordable Housing on Community Health and Well-Being in China
Authors: Xia Jingnan , WOOK TENGKU KASMINI TENGKU
Published: July 2024

This paper explores the impacts of affordable housing on community health and well-being in China, focusing on urbanization's role in providing affordable housing. The study uses qualitative methodology and use systemic review and meta-analysis to analyze health indicators and socioeconomic improvements directly linked to access to affordable housing. Among the findings is that the mental health implication is that affordable housing reduces stress and, therefore, encourages social stability and economic resilience. This study, thus, allows regional housing policy diversification, innovative housing solutions, and the subjective experiences of the residents. This results in an overall understanding of the effects that affordable housing brings to one's health and well-being. The insights are critical to the development of practical policies regarding housing and effective policies that are centered on community development towards the improvement of life quality.
The Impact of Executive Compensation Performance Sensitivity on Corporate Innovation in an Uncertain Environment
Authors: Yue Xueying , Qi Huarui
Published: July 2024

Enhancing corporate innovation capabilities in complex and uncertain environments is a focal point of societal concern. This study examines the impact of executive compensation performance sensitivity on corporate innovation against the backdrop of environmental uncertainty, using a sample of listed companies on the Shanghai and Shenzhen A-shares market from 2015 to 2020. Empirical results indicate that executive compensation performance sensitivity significantly inhibits corporate innovation; however, this inhibitory effect varies with the characteristics of the environment and the industry in which the company operates. As environmental uncertainty increases, this inhibitory effect becomes insignificant. High-tech enterprises, due to their strong motivation for innovation, can offset the inhibitory effect of executive compensation performance sensitivity on corporate innovation. Nevertheless, in highly uncertain environments, where resource constraints and risks are heightened, the inhibitory effect of executive compensation performance sensitivity on corporate innovation in high-tech enterprises becomes significant. The conclusions of this study contribute to the understanding of the motivational effects of executive compensation contracts in uncertain environments, provide a reference for evaluating the effectiveness of performance-based compensation from the perspective of corporate innovation, and offer insights for companies in designing and refining executive compensation incentive plans according to the times and industry characteristics.
SEJARAH EMPAT MADZHAB ISLAM DAN EKSISTENSINYA DI INDONESIA
Authors: Alfatoni Muhammad Aqil , Ni’matul Wasih Imelda , Hikmal Akbar Mohammad , Nur Niba Nadia Oktavia
Published: July 2024

AbstractIslamic law as we know and understand it has undergone several phases. Starting at the time of the Prophet untilnow. The Prophet has laid the foundation of the law which is upheld by the companions. This research the authorwants to know why many Indonesian people prefer to apply or use the Shafi'i Madzhab. This paper uses empiricallegal methods, with a qualitative approach and historical approach. The data collection technique that the authoruses is a questionnaire distributed to 33 respondents regarding why the general public applies more teachings from the Shafi'i madhhab and through journals and books, so that in this paper using qualitative research. The four main Sunni madhhabs have characteristics that distinguish each other in their interpretation of Islamic law. Despite their differences, the four madhhabs share the same goal of providing legal guidance to Muslims and maintaining the unity of the ummah. The methodology used by the four madhhab imams is certainly different in determining Islamic law. In Indonesia, the Shafi'i madhhab is the most dominant. The majority of scholars, Islamic boarding schools, and Islamic religious education institutions in Indonesia follow the Shafi'i madhhab in interpreting Islamic law. This is influenced by the history of Islam in Indonesia, where most of the spreaders of Islam in Indonesia were of the Syafi'i school of thought.Keyword: Law, Madzhab, Islam
 
Abstrak
Hukum Islam seperti yang kita ketahui dan pahami telah mengalami beberapa fase periode. Dimulai pada zaman Nabi hingga sekarang. Nabi telah meletakkan dasar hukum yang dipegang teguh oleh para sahabat. Penelitian ini penulis ingin mengetahui mengapa banyak masyarakat Indonesia lebih memilih menerapkan atau menggunakan Madzhab Syafi'i. Penulisan ini menggunakan metode hukum empiris, dengan pendekatan kualitatif dan pendekatan historis. Teknik pengambilan data yang penulis gunakan adalah kuisioner yang disebarkan kepada 33 responden mengenai mengapa khalayak umum lebih banyak menerapkan ajaran dari madzhab syafi'i dan melalui jurnal dan buku-buku, sehingga dalam tulisan ini menggunakan penilitian kualitatif,. Keempat madzhab utama Sunni memiliki ciri-ciri yang membedakan satu sama lain dalam intrepertasi hukum Islam. Meski memiliki perbedaan, keempat madzhab tersebut berbagi tujuan yang sama yaitu memberikan pedoman hukum kepada umat Islam dan menjaga persatuan umat. Metodologi yang digunakan oleh empat imam madzhab tentunya berbeda beda dalam menentukan hukum Islam. Di Indonesia, madzhab Syafi'i adalah yang paling dominan. Mayoritas ulama,pesantren,dan lembaga pendidikan agama islam di Indonesia mengikuti madzhab Syafi'i dalam penafsiran hukum Islam. Hal ini, dipengaruhi oleh sejarah Islam di Indonesia, dimana sebagian besar penyebar agama Islam di Indonesia bermadzhab Syafi'i. Kata kunci: Hukum, Madzhab, Islam
Wealth/Poverty Opposition in English and Kazakh: A Comparative Study
Authors: Kerimbayeva Kamila , Beisenbai Ardak
Published: July 2024

This research employs linguistic concept modeling to scrutinize the semantic variations between notions of wealth and poverty in English and Kazakh, aiming to discern their contrasting characteristics. The corpora in English and Kazakh encompassing varied genres, styles, and thematic content were examined through word frequency and co-occurrence to elucidate divergent contextual frames for the terms in both languages, unveiling cultural nuances and cognitive associations. The results indicate that in English, wealth is majorly associated with financial prosperity, real estate ownership, and the availability of resources for a comfortable lifestyle. Conversely, in Kazakh-language writings, the concept of wealth extends to encompass elements like family connections and devotion to the homeland, reflecting cultural priorities and lifestyles. A comparable distinction is evident when examining the contextual usage of the term poverty. The findings underscore the significance of exploring linguistic concepts within distinct cultural and cognitive frameworks, opening avenues for further comparative linguistic research.
The Reality and Effects of Using Duolingo to Develop English Language Skills for EFL Learners in Jordan
Authors: Olimat Kholod Naser
Published: July 2024

This research attempted to know the reality and effects of using Duolingo to develop English language skills for EFL learners in Jordan. The descriptive analytical approach was used to describe and analyze the phenomenon being investigated. Moreover, the sample included 500 first year EFL learners who were randomly selected from the Jordanian universities. They were asked to respond to the questionnaire items, by selecting the option that better describes the degree of their agreement or disagreement regarding each item of the questionnaire. The results reflected that the Jordanian EFL learners are aware of the effectiveness of Duolingo in developing their English language skills.  The arithmetic means related to the effects of using Duolingo to develop English listening, reading, writing and speaking skills ranged between 3.09 and 4.01.
Understanding the Future Posthumanities: An Analytical Study of William Gibson’s Spook Country
Authors: M.C Aquinas Mikki , V Bhuvaneswari
Published: July 2024

William Gibson, who introduced the term ‘cyberpunk’ is a distinguished American Canadian science fiction writer. Gibson’s Spook Country, published in 2007, is a popular science fiction political thriller. With three interesting and intersecting plots, the novel is set against the background of the post-September 11, 2001 incident. Gibson portrays a future society that is beyond human capabilities using philosophical and imaginative posthumanist concepts such as Post-Humanism, Post-Anthropocentrism, and Post-Dualism intertwined with science fiction themes like interactive media, cyberspace, locative art, espionage and the art of virtual reality. This article analyzes the novel Spook Country based on Synthetic Theoretical Posthumanism, a typology framed by Matthew E. Gladden. The Posthuman theorists use philosophy and science fiction as a resource to re-examine the notion of “human” in a future techno cultural context. In addition, this study uses the theoretical framework of Francesca Ferrando, Rosi Braidotti, and other posthumanist theorists to substantiate how the novel represents future posthumanities.
Otonom Silah Sistemlerinin ve Doğabilecek Sorumluluk Meselesinin Uluslararası İnsancıl Hukuk Bağlamında Değerlendirilmesi
Authors: Dülger Kenan , Tanyeli Sökük Özge
Published: July 2024

Bilgi ve iletişim teknolojilerinin yaygın olarak kullanımına bağlı olarak her geçen gün teknolojik alt yapılarını daha fazla geliştiren devletlerin söz konusu teknoloji bağımlılığı, aynı zamanda kendileri açısından ayrı bir risk alanını da beraberinde getirmektedir. Teknolojiden her alanda yararlanıldığı gibi devletlerin kendilerine en fazla katkıyı savunma ve güvenlik alanlarında elde edebilmeleri, birinci derecede önem arz etmektedir. Zira gerek sıcak çatışmalarda gerekse caydırıcılık gücü bağlamında, her devlet kendi askeri alt yapısını sürekli yenileme ihtiyacı içindedir. Teknoloji ve bilimin gelişimi sürecinde ortaya çıkan en önemli sonuç ise insan varlığının ve katkısının giderek azalmaya başlamış olmasıdır. Savaş ve/veya silahlı çatışmalar alanında insan faktörünün giderek yerini teknolojiye bırakmaktadır. Teknolojik gelişmeler sürecinde devletlerin bahsi geçen alanlarda otonom silah sistemleri kullanımı, her geçen gün artmaktadır. Lakin otonom silahların kullanımı ile ilgili çoğu uluslararası örgüt ve devlet nezdinde uluslararası düzenleme boşlukları doğduğu düşünüldüğü gibi bu boşlukların ne şekilde doldurulacağı da açık değildir. Bu çalışmada, belirtilen hukuki boşlukların doldurulup doldurulamayacağı, hangi tür uluslararası insancıl hukuk kurallarının uygulanacağı ve bu çerçevede ortaya çıkabilecek sorumluluk sorunu ele alınmaktadır.
Prevalência de disfunção temporomandibular em N servidores da superintendência da Zona Franca de Manaus
Authors: Santos Paulo Henrique Soles dos , Lucena Raimundo de Oliveira , Santos Maria Eduarda Cavalcante dos , Santos Luis Geovane Guedes dos
Published: July 2024

O objetivo deste trabalho de pesquisa, transversal, em seres humanos, foi avaliar a Prevalência de Sintomas de Disfunção Temporomandibular (DTM) nos Servidores da Superintendência da zona Franca de Manaus. A amostra inicial foi de 177 indivíduos, embora, 07 tenham sido excluídos, passando para 170, sendo, 76 do gênero feminino e 94 do masculino; nas faixas etárias de 18 anos aos 35, 36 – 52 e 53 – 69. Para avaliar a Prevalência de Sintomas de DTM foram utilizados uma ficha Sociodemográfica e dois Questionários: o questionário recomendado pela Academia Europeia de Desordem Crâniomandibular (EADC); composto de quatro perguntas, e o segundo o Questionário de Gonzales, “Instrumento de Rastreamento de Dor para Desordens Temporomandibulares” (TMD – Pain Screeneer), versão curta, composto por três perguntas. Os resultados mostraram que 75 (44, %) indivíduos responderam, afirmativamente, pelo menos uma das questões. A amostra foi dividida por gênero masculino e feminino, sendo que o gênero feminino apresentou maior prevalência de sintomas de DTM e DOF (23,5%) em relação ao gênero masculino (20,5%). Quanto à prevalência dentro dos gêneros, separadamente, (52,6%) do gênero feminino respondeu, afirmativamente, a pelo menos uma questão, enquanto que no gênero masculino a porcentagem positiva foi de 37,2% com diferença estatística significante (P = 0,044). A análise de dados foi realizada pelo software Bioestat 5.3 e as tabelas pelo teste quí-quadrado. Conclusão: Conclui-se que à DTM tem uma maior Prevalência no gênero feminino, o sintoma mais prevalente foi a Cefaleia (Dor de cabeça).
A IMPORTÂNCIA DA CULTURA ORGANIZACIONAL NAS EMPRESAS: O PAPEL FUNDAMENTAL DA CONTABILIDADE
Authors: Leitão Alexsandro Lagoa , Albarelli Bruno de Andrade , Azevedo Magno Santana
Published: July 2024

A atualidade do ambiente empresarial é caracterizada por uma competição intensa, onde as empresas estão constantemente procurando estratégias não só para se manterem, mas também para prosperarem no mercado. A contabilidade, por sua vez, atua como um instrumento fundamental na mensuração e no monitoramento dos elementos que compõem a cultura organizacional, proporcionando dados tangíveis que orientam as decisões e ações empresariais. Este artigo propõe o objetivo geral de fazer uma análise aprofundada sobre a importância da cultura organizacional nas empresas, destacando o papel central desempenhado pela contabilidade nesse processo. Os objetivos específicos são: trazer conceitos de cultura organizacional; tratar de cultura do controle financeiro; expor a cultura a excelência/serviço e; por fim, o papel do contador em meio a esse contexto. A metodologia aplicada está ancorada na pesquisa bibliográfica do tipo descritiva, estabelecendo uma análise qualitativa, utilizando-se de dados secundários. Neste cenário complexo e interdependente, fica claro que a contabilidade não deve ser vista isoladamente, mas integrada como parte integral da cultura organizacional. O desafio reside em aproveitar essa interação para fortalecer a integridade, a resiliência e a eficácia das empresas, alinhando-as a um futuro empresarial sustentável e orientado por valores.
KREATIVITAS KOMUNIKASI SALES DALAM MENJUAL PRODUK SMARTPHONE PASCA PANDEMI COVID-19
Authors: Mardiansyah Iis , Kusumawardhani Trisnawati
Published: July 2024

Penelitian ini menyoroti evolusi strategi personal selling di Perusahaan retail smartphone X di Kabupaten Bekasi selama pandemi COVID-19 dan New Normal. Menggunakan pendekatan kualitatif dan metode studi kasus, studi ini mengeksplorasi bagaimana integrasi kreativitas dalam komunikasi personal selling dan pemanfaatan teknologi digital seperti media sosial dan aplikasi komunikasi telah menjadi kunci dalam menjual produk di era pasca pandemi. Fokus penelitian meliputi empat tahapan strategi penjualan utama: perencanaan, penyampaian kebijakan, implementasi, dan evaluasi, serta analisis distribusi sales persons di lokasi berbeda. Hasil penelitian menunjukkan bahwa pemanfaatan kreatif media sosial, khususnya Instagram, TikTok, Facebook, dan WhatsApp, telah berhasil menjaga keterlibatan konsumen dan meningkatkan visibilitas brand dalam lingkungan yang kompetitif dan digital. Temuan ini memberikan wawasan penting tentang pentingnya adaptasi strategi, profesionalisme dalam konten digital, dan pelatihan tim penjualan untuk menghadapi tantangan pasar yang terus berubah. Penelitian ini merekomendasikan peningkatan dalam pelatihan tim penjualan, optimalisasi penggunaan media sosial, dan adaptasi terhadap perubahan pasar sebagai kunci keberhasilan penjualan di masa mendatang.