bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Residual Networks and Bayesian Data Priors in the Survival Prediction and Classification
Authors: Zhang Min
Published: March 2024

Sepsis is a highly lethal disease in intensive care units, and patient indicators are constantly changing, making accurate prediction of patient mortality crucial for doctors to develop appropriate treatment plans. While machine learning and deep learning have been applied to sepsis research, model generalization performance can suffer from underfitting or gradient issues. To address these challenges, we propose using a deep residual network and a deep residual network incorporating Bayesian data prior to predict patient mortality using the MIMIC-III dataset. The accuracy of the two models on the validation set reached 0.9392 and 0.9329, respectively.
AIGC Technology: Reshaping the Future of the Animation Industry
Authors: Gao Rui
Published: March 2024

This paper explores the transformative role of Artificial Intelligence-assisted Generative Content (AIGC) technology in the animation industry. AIGC's key components, including Generative Adversarial Networks (GANs), Natural Language Processing (NLP), Reinforcement Learning, Virtual Reality (VR), and Augmented Reality (AR) are elucidated. The technology is seen to catalyze innovations in character and scene design, storyline construction, and scriptwriting, enhancing creativity and efficiency. AIGC's application in music and sound effects production, special effects and editing workflows, as well as in rendering and collaboration stages is also discussed, showcasing the improved work efficiency and quality of final products achieved through AI-assisted tools. Through a case study of the pioneering AIGC-assisted animation, "The Dog and the Boy," we demonstrate the potential of AIGC in driving commercial animation. Despite its current limitations, the study concludes that AIGC technology is poised to reshape the animation industry, promising a future marked by enhanced creative expression, increased efficiency, and the successful integration of AI in traditional workflows.
Individualized Movie Recommendation Algorithm Based on Machine Learning
Authors: Liu Bowei
Published: March 2024

With the continuous acceleration of economic growth and the remarkable improvement of people's living standards, people's individualized needs have increased. Moreover, due to the growth of computer technology and network technology, many forms of entertainment and services have been integrated into the network. The recommendation engine actively pushes the information that meets the user's taste and preference to the user by analyzing the user's behavior data on the website when the user's needs are not clear. This article will study how to apply the deep learning algorithm to the field of movie recommendation system to solve the problems existing in the traditional recommendation algorithm based on machine learning (ML), and combine the existing mainstream big data (BD) to improve the execution efficiency of the model. Individualized movie recommendation system saves users the time of searching, viewing and screening, and discovers users' potential movie preferences at the same time. Most users think that the algorithm can help them find the type they want from a large quantity of film and television resources, so as to meet the needs of users and tap their potential interests.
Investigating Corrosion of Low Alloy Steel Detection Method based on Wire Beam Electrode
Authors: Pan Chengqun , Yu Fanbo
Published: March 2024

A wire beam electrode (WBE) method for investigating the local corrosion has been demonstrated by mapping non-unifore corrosion processes occurring on low alloy steel in solution of H3BO3 concentrations. It was found that low alloy steel from the sporadic points at the beginning of the test, the local corrosion range is the largest when it develops for 24 h and obvious local corrosion appeared, then it decreases when it reaches 48 h. To sum up, it is feasible to study the behaviors of low alloy steel localized corrosion with WBE.
Research on vibration suppression of rigid flexible coupling manipulators based on genetic algorithm optimization
Authors: Lin Chong , Liu Jing , Yu Jian
Published: March 2024

Aiming at the problem that the rigid flexible coupling manipulator generates elastic vibration during movement, which reduces the positioning accuracy and work efficiency, we propose a method of end vibration suppression based on cosine function superposition and multi population genetic algorithm. First, we get the relationship between the elastic vibration of the flexible arm and the joint trajectory motion according to the dynamic model of the system. Then, the cosine function superposition is used as the basis function to construct motion parameters of joints and the minimum residual vibration amplitude at the end of the flexible arm is used as the objective function. The undetermined coefficients in the basis function are optimized by multi population genetic algorithm to obtain the optimal trajectory. Finally, the simulation and experiment results show that the residual vibration amplitude is reduced by 60.6%. The proposed method can suppress the residual vibration at the end of the flexible arm and improve the work efficiency.
A distributed framework of student data early warning system
Authors: Qian Chunhong , Xie Zongwu
Published: March 2024

With the deepening of research on big data, various intelligent technologies have been widely used in the digital construction of campus. But the current campus students early warning system can not achieve high accuracy results. It is necessary to collect big data of students and perform online analysis with advanced techniques to timely predict problems of learning performance and activity abnormality. In this paper, the campus big data early warning system is designed based on the distributed framework. It integrates the data collection, storage, mining and analysis, visualization and message notification. The system consists of six layers of data application structure and one security control platform, and uses multiple linear regression algorithm and logistic regression algorithm to predict the abnormal behavior of student, Hadoop and Spark are used to build a distributed framework to monitor student abnormalities.
Tension Control in Unwinding System Based on Nonlinear Parameter Estimation
Authors: Chen Qinzhi , Zhang Xiaofan , Lu Jieying
Published: March 2024

In this paper, two representative nonlinearities in the unwinding system are modelled. One is the rotation-disturbance caused by the rotational change of unwinding roll position; another is the time-varying radius of unwinding roll. Based on the nonlinear mechanism model, the parameters of rotation-disturbance are obtained by the Gauss-Newton algorithm, and the time-varying radius is estimated online by the Kalman filter. Then the feedforward and time-varying feedback controller are designed based on the estimated parameters to compensate for nonlinear effect in unwinding system. The experiment results show that the parameter estimation algorithm can accurately estimate parameters. And the use of estimated parameters to control can effectively solve the problem of tension fluctuation and tension deviation which caused by the nonlinearity of unwinding system.
Research on Health Monitoring System Based on Computer Artificial Intelligence Technology
Authors: Liu Caiyan , Yin Cuijun , Ren Lizhao
Published: March 2024

In this paper, a real-time monitoring system of human health data in a household environment is designed. The system applies Zigbee wireless communication technology to transmit real-time data and establishes family members' health records on the server. After processing by central processing unit(CPU), the system sends the temperature and heart rate data collected by the terminal to the coordinator and realizes real-time data monitoring through the serial port on the PC side. The system can be divided into a system microprocessor, digital system module, human-computer interaction module, signal acquisition module, and wireless communication module, etc., according to the human heart rate, blood oxygen, temperature, and other critical physiological parameters processing and analysis, and then real-time monitoring of the human body. System processor STM32F103C8T6 as the CPU, display module selected OLED; The MAX30102 sensor and Pulse sense sensor were selected for the physiological parameter acquisition system to collect the heart rate of the wrist and finger, respectively. After physiological parameters were collected, A/D transformation was further performed. Through the software and hardware design test of one terminal of the system, the system can accurately measure the physiological information of different patients. By comparing the data, the accuracy can reach 2%. It can be proved that the system has an excellent medical application prospect.
A model for converging tropospheric delay corrections
Authors: Ling Ling , Cai Chenglin
Published: March 2024

In response to the current problem that tropo-spheric delay correction is constrained by the measured meteorological parameters, resulting in low efficiency and limited application, this paper proposes a GE-Sa fusion model combining GPT2w and EGNOS models. The algorithm provides high-precision meteorological data through GPT2w and EGNOS models, and uses the Saastamoinen model to achieve the zenith tropospheric delay (ZTD) correction, the ZTD series of 2017 from 20 stations acquired by the Global Geodetic Observing System (GGOS) Atmosphere product data were used as the standard approximation to calculate the bias and RMS values of the model respectively, and the experimental results showed that the accuracy of the GE-Sa model was higher than that of the EGNOS model, GPT2w+Saastamoinen model, and 68.12% and 16.33%, respectively. The model improves the accuracy of calculating ZTD and expands the scope of application while getting rid of the limitation of measured meteorological parameters.
Research on Integrated application of Advance Mathematics and linear Algebra
Authors: Zhang Tongtong
Published: March 2024

Advanced Mathematics and Linear Algebra are the two most basic and important public subjects of science and engineering in universities. In general, the main content of Advanced mathematics is calculus, which belongs to the type of analysis (analysis and study of the local and global properties of continuous objects). Linear algebra, on the other hand, is a course characterized by algebra (algebra studies the structural properties of discrete objects). From the nature of the two courses, they have great differences in thinking mode and processing methods. Students often to think that Advanced mathematics and Linear algebra are two relatively independent systems. However, after systematically study Advanced mathematics and Linear algebra, I found that Advanced mathematics (mainly calculus) and Linear algebra, two relatively independent courses, also have many methods and ideas that can be borrowed from each other. I shall discuss the relationship between Advanced mathematics (calculus) and Linear algebra in three ways.
Development and Key Technologies of the Cognition system of Autonomous Driving
Authors: Zhang Yuyang , Feng Junliang , Huang Mingni , Liu Qinghao , Fu Wenxuan
Published: March 2024

With the rapid development of automotive electronics technology and artificial intelligence, autonomous driving has become an important solution for future transportation challenges. As an integral component of autonomous driving systems, the intelligent cognitive system has emerged as a prominent area of technological research. This paper focuses on the development and key technologies of autonomous driving cognitive systems, based on published literature. The architecture of cognitive systems for autonomous driving is typically divided into two subsystems: the environmental perception system and the assessment and prediction system. Our study provides a detailed description of the methods used in both subsystems. We then conduct a comparative analysis and discussion to evaluate their strengths and limitations. Overall, we hope that this study will provide timely and valuable information to a rapidly evolving field concerning cognitive technology for autonomous vehicles.
Development and Key Technologies of the Planning and Control System of Autonomous Driving
Authors: Zhang Yuyang , Feng Junliang , Huang Mingni , Liu Qinghao , Fu Wenxuan
Published: March 2024

As autonomous driving technology gradually matures, it may become the most crucial driver assistance system in the near future. As an important part of autonomous driving technology, decision-making, path planning, and control technologies have become mainstream research trends. This paper focuses on the development and key technologies of autonomous driving planning and control system, based on published literature. The architecture of an autonomous driving planning and control system is usually divided into two subsystems: decision-making and planning system and control system. Our study provides a detailed description of the different methods and interconnections of these two subsystems. We then conduct a comparative analysis and discussion to review the advantages and limitations of the various schemes. Overall, we hope that this study provides timely and relevant information in a rapidly evolving field concerning planning and control technology for autonomous vehicles.
Traffic Flow Prediction Based on Explainable Machine Learning
Authors: Zhang Xueting
Published: March 2024

Traffic flow prediction is one of the important links to realize an urban intelligent transportation system. Thanks to the in-depth research of artificial intelligence theories, the machine learning method has been widely used in intelligent transportation engineering. However, due to the “black box” as its characteristics, its application and further development are limited. Exploring the explainability of machine learning models in traffic flow prediction is an important issue to make it more reliable in traffic engineering and other practical applications. Apart from selecting the RandomForest model and the CatBoost model as the objects to research the traffic flow prediction against temporal and spatial changes, this paper makes a comprehensive evaluation and comparison with LightGBM and the other two prediction models through different indicators. Meanwhile, aiming at the low explainability of the models, their feature importance is analyzed and compared with reality. The results show that the RandomForest model and CatBoost model make good predictions, whose feature importance is consistent with the actual situation, verifying their explainability.
Examining The Impacts of Family, Personal, And School Factors on Undergraduate Graduates' Decisions to Continue Their Education or Job Hunting
Authors: Xue Qingxin
Published: March 2024

With the continuous transformation and development of society, the future choice of undergraduate graduates has become a heated issue in society. Since 2017, the number of applicants for the national postgraduate entrance examination has arised. At the same time, the CIER (Employment boom in the Chinese market) index of national college graduates is showing a decreasing trend. This article is based on the "2009 Capital University Student Growth Tracking Survey" and uses logistic regression to analyze the objective effects of school, family, and personal factors on whether undergraduate graduates choose to pursue higher education or employment.Finally, this study found that factors such as learning atmosphere, teacher teaching ability, and family economic status significantly affect college students' graduation choices.Through thorough research and analysis, this article seeks to analyze the elements that affect graduates' decisions regarding their future courses of study and employment. It also offers pertinent advice and recommendations for educational institutions, families, and particular students.
The Current Situation of Energy Trade Between Russia and the EU: From the Perspective of Economic Nationalism
Authors: Li Runyu , Qin Junjie , Yu Lingsu
Published: March 2024

Energy trade has been a long-standing pillar of EU-Russia relations and a critical economic driver, leading to higher interdependence in the energy sector. With the outbreak of the Russia-Ukraine crisis, the energy relations between the EU and Russia and the development of the energy trade have changed, which also generates different energy trade policies and the current state of the energy trade. For a long time, people tend to pay attention to the political nature of nationalism and ignore its economic and cultural nature; they pay attention to the study of energy issues from the economic perspective and neglect the comprehensive study of energy issues from the political, cultural, and even nationalist perspectives. Synthesis, multiple factors, and the interaction of national security in the overall national security situation are significant features of the nationalist-economist perspective. This paper will explore the EU-Russia energy trade dealings from an economic nationalist perspective, focusing on the current state of energy relations and energy policies. Analyzing the current state of cooperation and trade in Russian-EU energy trade from this perspective can fill the gaps in the political and economic interactivity of Russian-EU relations in related studies.
Challenges and Strategies for Cultivating Critical Thinking in Chinese Art Education
Authors: Luo Yilin
Published: March 2024

The evolution of the educational landscape in China has brought heightened recognition to the significance of art education within the country. Nonetheless, the framework of domestic art education remains entrenched in a broader educational paradigm that predominantly emphasizes standardized testing. Consequently, this paper delves into the complex issues impeding the development of critical thinking skills through art education. By examining the current state of domestic art education through the lens of three fundamental components of critical thinking, this study underscores the formidable challenges that hinder the fostering of critical thinking skills within this domain. Drawing from a comprehensive analysis of teaching methodologies and educational practices in art education, this research presents a series of targeted strategies to propel the advancement of domestic art education by prioritizing the cultivation of critical thinking skills. Through the identification of key obstacles and the proposal of strategic interventions, this paper contributes to the discourse surrounding the enhancement of critical thinking abilities within the context of Chinese art education.
Profile of Black Women Presidents at Four-Year Colleges and Universities
Authors:   Colgate University, Jack L. Hazel
Published: March 2024

While women represent the majority of college students, they are underrepresented in positions of leadership in higher education. The presence of Black women in positions of leadership is even less. This article sought to identify how many Black women are presidents of four-year colleges and universities, what types of institutions these women lead, and their path to the presidency. This research identified 83 Black women college presidents, 55 of which represent some type of first for their institution and, in some cases, even the system or state. Their pathway to the presidency was consistent with the literature finding that women’s path to the presidency is most often through academic leadership.
The Relationship Between Lila Size and Mother's Weight Gain During Pregnancy with the Birth Weight of the Baby at the Atikah Sipolu–polu Midwife Clinic, Panyabungan District, Mandailing Regency City Christmas 2023
Authors: Nasution Intan Angriani Ananda , Puspita Rania
Published: March 2024

During pregnancy, the nutritional needs of expectant mothers increase significantly to support both their own health and the development of the fetus. In situations where maternal food intake is limited, the fetus continues to draw from the mother's nutritional resources, potentially leading to maternal malnutrition and imbalanced food consumption. Such conditions can result in adverse outcomes such as premature birth or even fetal demise, as well as prolonged labor during childbirth. Hence, comprehensive maternal health assessments before delivery are crucial. This study aims to investigate the relationship between maternal weight gain during pregnancy, maternal upper arm circumference (MUAC), and newborn weight at the Atikah Sipolu-Polu Midwife Clinic in the Panyabungan District, Mandailing Natal Regency, in 2023. A descriptive research design with a cross-sectional approach was employed. The study population consisted of mothers who delivered at the Atikah Sipolu-Polu midwife clinic, with a total sample size of 45 individuals selected through total sampling. Data were collected using both primary and secondary sources. The analysis utilized the chi-square test with a significance level set at p<0.05. The results indicate a significant relationship between MUAC size, maternal weight gain during pregnancy, and newborn weight, with a calculated p-value of 0.001 (p < 0.05). This study underscores the importance of enhancing nutritional patterns, particularly during pregnancy. It emphasizes the necessity for pregnant women to consume a balanced diet comprising vegetables, fruits, meats, vitamins, and other essential nutrients to ensure optimal maternal and fetal health outcomes.
Subiektywna ocena dźwięku w pomieszczeniach przeznaczonych do wypoczynku pracowników. Wyniki badań własnych, przeprowadzonych wśród użytkowników tych pomieszczeń – osób pracujących w hałasie
Authors:   Główny Instytut Górnictwa - Państwowy Instytut Badawczy, Miterska Magdalena , Kompała Janusz
Published: March 2024

rtykuł przedstawia wyniki badań ankietowych dotyczące subiektywnej oceny dźwięku w pomieszczeniach przeznaczonych do wypoczynku pracowników w czasie przerw. Badania te przeprowadzono wśród osób wykonujących pracę na stanowiskach, na których występuje hałas. Subiektywna ocena jest nie mniej ważna niż ocena dokonywana na podstawie wyników pomiarów parametrów hałasu (zwłaszcza że nie zawsze się z nimi pokrywa) i pomaga zaprojektować pomieszczenia w sposób zapewniający lepsze postrzeganie krajobrazu dźwiękowego (określanego też mianem klimatu akustycznego) w miejscu pracy. Odbywa się to poprzez zmianę dźwięków w pomieszczeniach (a nie przez redukcję hałasu) – tak by uzyskać bardziej przyjazny klimat akustyczny, który wpłynie na poprawę samopoczucia osób pracujących w hałasie.
"Don't Call Me Angel": The Construction of Female Representation from a Postfeminist Perspective
Authors: Hong Qiwen
Published: March 2024

Charlie's Angels (2019) is considered a feminist film, dedicated to promoting women's empowerment and gender equality related discourse. Don't Call Me Angel is the theme song of Charlie's Angels, co-performed by Ariana Grande, Miley Cyrus and Lana Del Rey, which advocates women's control over the body, eliminating gender stereotypes, bold self-expression as well as encouraging sisterhood. This article critically discusses the construction of female images in this pop music video from a postfeminist perspective, which means that this study adopts a qualitative method of social semiotics to draw conclusions by sorting out some scenes, lyrics and performances of Don't Call Me Angel. Firstly, some of the objectified female and sexual expression scenes in this music video reflect complex postfeminist emotions. Secondly, the music video retains the characteristics of the classic Hollywood action movies' femme fatale when constructing female images, especially highlighting the pornographic femininity and the masculinity of violent female images.
Insights into Strengths, Weaknesses, Opportunities, and Threats of Music Streaming GiantSpotify
Authors: Li Longjun
Published: March 2024

Spotify, born in Sweden in 2006, has reshaped the way people consume music in the digital age. As it has grown to become a major player in the music streaming industry, understanding its position and business model becomes crucial. This paper demonstrates the broader Music application market and overview. Employing the SWOT analytical framework, this paper undertakes a comprehensive assessment of Spotify's strengths, weaknesses, opportunities, and threats. Grounded in this analysis, this paper underscores the potential of diversifying into emergent domains such as education and wellness to augment user engagement and streamline operational expenses. Under the fierce market competition, the paper emphasizes the necessity for Spotify to strengthen its differentiation strategy within the music application sphere.
The Impact of Zhongyong Thinking on Employees Mental Health
Authors: Li Xinlu
Published: March 2024

Zhongyong is a moral standard set forth by Confucian culture, meaning maintaining neutrality and moderation. Many people tend to have a misunderstanding of Zhongyong thinking, believing that Zhongyong represents compromise and the middle position. They might consider that such thinking is detrimental to healthy mental development. However, Zhongyong thinking is not the same as compromise, and instead it allows the coexistence of contradictions and opposites. After reviewing a series of previous articles on Zhongyong thinking and mental health, it is shown that Zhongyong thinking plays a moderating role in the employees psychological state. Moreover, the factors affecting the influence of Zhongyong thinking on employees' mental health include psychological resilience and psychological security. Ultimately, the review found that one of the main reasons for the moderation of psychological states in the workplace is because it affects emotional regulation and alleviates symptoms of depression and anxiety. Plus, Zhongyong thinking related training also was shown some benefits. A limitation of previous studies is that subjective questionnaires were used to measure the level of Zhongyong thinking and other psychological variables, which could be less accurate. Future research directions could explore how to accurately demonstrate dynamic Zhongyong thinking processes through experimental and longitudinal design.
The Influence of Teacher-Student Relationship on Students' Learning
Authors: Dai Pinyu
Published: March 2024

As the importance of education is realized by more and more people, people begin to pay attention to the factors that have an important influence on students' studies. As a factor directly related to students' individual and learning outcomes, the teacher-student relationship has been noticed and studied by people. A positive teacher-student relationship can help students in their professional learning in many ways. This paper takes the teacher-student relationship as the starting point, and synthesizes the relevant literature and research on teacher-student relationship and students' learning outcomes. Research has found that a positive teacher-student relationship can regulate students' mental emotions, improve students' participation in class, cultivate students' learning ability, and promote students' academic achievement and goals. In the end, it is suggested that teachers can be considered as listeners in mental health, leaders in academic learning and supporters in creative thinking in the relationship between teachers and students, to establish and promote positive teacher-student relationships and improve students' learning outcomes.
Analysis of the Creation Strategy of Online Humanities Learning Environment from the Perspective of Interactive Psychology
Authors: Dong Xiaoao
Published: March 2024

As online education continues to expand, the need to infuse humanistic values into virtual learning environments becomes paramount. This paper explores the strategies employed in creating online humanistic learning environments, with a particular focus on interaction psychology. Interaction psychology, rooted in understanding how individuals engage with digital interfaces, provides valuable insights into designing educational experiences that prioritize the human element. By examining the interplay between digital interaction and human psychology, this study aims to discuss how interaction psychology can be used to enhance the humanistic value of online education from the perspectives of teachers and students respectively. For educators, the research explores leveraging interaction psychology to establish meaningful connections with students in the virtual realm. Customizing instructional design and communication strategies based on interaction psychology insights can foster a more personalized and empathetic online learning experience. From the student standpoint, the paper examines how an understanding of interaction psychology can enhance motivation, satisfaction, and overall learning outcomes. By considering factors like user interface design and collaborative tools, educators can create online environments that resonate with diverse student needs.
RETRACTED ARTICLE: Ethics in the Age of AI: Research of the Intersection of Technology, Morality, and Society
Authors: Pan Kelin
Published: March 2024

It is an age of artificial intelligence (AI), and the article seeks to explore how one might integrate ethical considerations into AI design and use. It asks how ethics can be integrated into the curriculum of AI, what ethical structures could exist in the design of an algorithm or intelligent machine, and whether big data can help solve these issues. The study uses an interdisciplinary approach, looking at case studies, and the discussion of ethical dilemmas. The study uses the analysis of practical applications in such fields as healthcare or communication to explore AI ethics. This kindles a torch that illuminates a fact: It is necessary to build up one's capacities both technically and morally for AI specialists need an educational curriculum, that stresses the need for all principles (including fairness, transparency, and accountability) to be built into AI design as well as decision-making processes. This requires a multi-disciplinary approach. The paper stresses the significance of ethical dimensions in an increasingly fast-moving AI environment. It seeks to cultivate moral reflection about AI, holding out for technology that is not only advanced but also ethical and more responsible.
The Impact of Mergers and Acquisitions on the Internationalisation Level of Enterprises--The Case of Sany Group
Authors: Jiang Changyuan
Published: March 2024

As the cornerstone of China's engineering construction, the construction machinery industry plays an important role in the process of foreign trade and export. This fully reflects the important demand for China's construction machinery industry, but also shows the important position of the industry in the international market. At present, China's construction machinery market is open to the outside world, the domestic and foreign construction machinery market competition is fierce, many scholars on China's construction machinery export trade situation and ways to improve the enterprise's global market share to do in-depth research. In this paper, we find that Sany Heavy Industry is ranked in the top of the global construction machinery manufacturers, brand, scale, technology than other domestic construction machinery enterprises have advantages, from the perspective of Sany Heavy Industry through cross-border mergers and acquisitions to improve the level of internationalisation, through a combination of quantitative and qualitative methods, to provide assistance to the international development of China's small and medium-sized construction machinery enterprises.
An Empirical Analysis of the Impact of Green Investment on China's Green Development Level: Based on Mediating and Spatial Spillover Effects
Authors: Jing Liu
Published: March 2024

This study, based on panel data from 30 provinces in China from 2007 to 2020, employs the Super Efficiency SBM model to measure the green total factor productivity of each province. By constructing mediating effect models and spatial econometric models, it conducts an empirical investigation into the mechanisms and spatial characteristics of how green investments promote China's green development. The results reveal that, at an overall level, green investments play a positive role in influencing China's green development. Regarding the influence pathways, innovative green technology advancements, both in terms of inventions and improvements, are important mediating channels through which green investments promote green development, with inventions having a stronger mediating effect. In terms of regional heterogeneity, green investments significantly promote green development in the eastern and central regions, while their promotion effect in the western regions is less pronounced. Concerning spatial spillover characteristics, green investments not only drive local green development levels but also stimulate the green development in surrounding areas through spatial spillover effects.
Inflation Forecasting Using a Hybrid LSTM-SARIMA Model Based on Discrete Wavelet Transform
Authors: Li Shizhe
Published: March 2024

The accurate prediction of inflation is of utmost importance in informing economic policy formulation and guiding private investment choices. Enhancing the precision of inflation prediction through the utilization of univariate models continues to present difficulties. Inspired by the hybrid methology that has emerged in the field of time series forecasting, which seeks to enhance forecasting accuracy by integrating linear and nonlinear approaches, the author employs a hybrid model that combines Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) techniques. The objective is to forecast the non-seasonally adjusted monthly US Consumer Price Index (CPI) inflation. The hybrid model employed in this study incorporates the utilization of wavelet transform, namely the discrete wavelet transforms (DWT), for the purpose of breaking the time series data into its constituent components of details and approximation. The subsequent application of LSTM and SARIMA models aims to capture both nonlinear and linear patterns present in the data. The findings of this research indicate that the DWT-based LSTM-SARIMA model outperforms both the solo SARIMA and LSTM models in a one-step rolling window forecast over a period of five years. This superiority is particularly evident during periods characterized by severe levels of inflation.
Research on Tax Management and Tax Risk Prevention Measures
Authors: Zhang Xinyi
Published: March 2024

Firstly, tax management is an important aspect of financial management, which involves the selection and calculation of taxes. However, tax management also carries various risks, and we need to adopt different approaches to avoid these risks. In this article, we will discuss the definition of tax management, the different stages involved in the tax management process, and the differences in tax management in different countries. Secondly, this article also conducted case studies on tax management and tax risk prevention strategies, in order to deepen the understanding of these issues and their impact on the company's financial health. This article first provides the definition of tax management and tax risk prevention strategies, and then delves into the different stages involved in the tax management process. At the same time, this article emphasizes the importance of tax risk prevention strategies in enhancing the financial health of companies, and conducts in-depth analysis of cases.
Value Investing, a Modern Approach
Authors: Guo Yuqin
Published: March 2024

This paper explores the principles and practice of value investing deeply, tracing its origins and analyzing its fundamental ideas. The basic principle of intrinsic value is examined, along with how it influences investment choices, emphasizing the reasons why it can have an effect when investigated and appraised. The limitations of value investment, such as long-term commitment, are also discussed in the article. It is determined that Microsoft, Apple, Pfizer, and Merck's long-term changes are related to the investment technique after integrating their yearly returns, total revenues, and earnings per share over the past four years. After calculating the growth rate of their total revenue and earnings per share, the changes of the four companies are more clearly seen in the chart. It is found that the returns of value investing correspond to the events experienced by the four companies, proving once and for all the usefulness of value investing. Finally, the article considers how value investing, a well-known and established financial technique, may help investors meet their objectives and generate profits.
Predictive model on detecting ChatGPT responses against human responses
Authors: He Zhaokai , Mao Ruolong , Liu Yu
Published: March 2024

The paper investigates the critical differences between AI-generated text and human responses in terms of linguistic patterns, structure, and content. The research makes use of datasets from HC3, collected in 2023. Our results are that ChatGPT with GPT-3.5 is more likely to use words like conjunctions and combinations of words in conversations compared to humans systematically. Our model has high accuracy in identifying AI-generated answers.
Incorporating emotional trend into multi-emotion analysis models for long-text sentiment analysis
Authors: Zhang Yu
Published: March 2024

The role of sentiment analysis is vital in natural language processing(NLP) and has garnered significant attention across different domains. However, multi-emotion analysis in long-text is still a challenging task due to the intricate emotional nuances that are conveyed. In this paper, a novel approach for long-text multi-emotion analysis is proposed by integrating emotional trends. This integration aims to enhance the ability of the model to recognize emotions by including word-level sentiment scores as supplementary features. To achieve this, the ISEAR and IMDB datasets are leveraged to investigate the impact of sentiment scores with varying weights on three models: BiLSTM, CNN, and CNN+BiLSTM. The models are trained for 20 and 50 epochs and evaluated by accuracy, precision, recall, F1 score ROC curve and AUC value. The experimental results indicate that the incorporation can improve the processing speed of the multi-emotion analysis task while maintaining performance with a 66.7% probability. The highlighted improvement over the baseline model reduced the time by 33.42%. In the best case, the accuracy of the model increased by 2.26% and the F1 score increased by 2.16% without affecting the running speed.
Enhancing recommendation with causal embedding: Considering social network influence
Authors: Chu Zhuyiheng , Zhang Pengxiang , Zhou Yibo , Huang Shihan , Guo Yanze
Published: March 2024

In the realm of recommendation models, we consistently rely on observational interaction data. This data encompasses a variety of aspects, such as user conformity and genuine user interests. The key challenge for recommender systems is to extract a user's authentic interests from this interaction data in order to provide accurate recommendations. The current method, DICE, attempts to separate conformity and interest by assigning distinct embeddings for each to users and items. The method ensures that each embedding captures only one causal factor through training with specific causal data. In our research, we've enhanced this existing method by incorporating social networks into the disentanglement of conformity and real interest from observational interaction data. The results from our proposed method surpass those of the prevailing baseline, demonstrating significant improvements across various backbone models using a real dataset. Furthermore, we conducted a sensitivity analysis and provided recommendations for scenarios in which our new model would be most effective.
Teknologi IMPLEMENTASI METODE BACKGROUND SUBTRACTION UNTUK SISTEM PELACAKKAN MUSUH PADA ROBOT PENJAGA POS MILITER
Authors: Radianto Riky , Kasiyanto
Published: March 2024

Kekuatan militer Indonesia yang menduduki peringkat ke-13 secara global tidak serta merta mengecualikan negara ini dari konflik bersenjata, karena Papua menjadi satu-satunya wilayah di Indonesia yang tingkat konflik bersenjatanya paling tinggi. Kelompok Kriminal Bersenjata (KKB) memainkan peran penting dalam meningkatnya konflik bersenjata. Serangan mereka seringkali menyasar warga sipil, personel TNI, polisi, dan pos-pos militer. Serangan yang diprakarsai KKB seringkali sulit dideteksi, dan terkadang serangan ini dilakukan pada malam hari, sehingga menyulitkan personel untuk melacak pergerakan KKB. Apabila terjadi penyerangan terhadap posko militer, maka antisipasi penyerangan tersebut bertumpu pada kesiapan dan kesiapsiagaan personel di lapangan. Saat ini, tidak ada teknologi yang tersedia di pos-pos militer yang dapat membantu personel di sana dalam mengantisipasi serangan; hal ini sepenuhnya bergantung pada keterampilan dan kemampuan personel untuk merespons serangan KKB. Mengingat keadaan tersebut, peneliti bertujuan untuk memperkenalkan Robot yang dapat digunakan untuk mengantisipasi serangan atau mendeteksi pergerakan KKB yang menargetkan pos-pos militer. Robot ini dilengkapi dengan senjata SS2 dan kamera yang memungkinkan pelacakan personel yang mendekati pos terdepan militer. Sistem pelacakan pada robot ini menerapkan teknik pengurangan latar belakang. Hasil dari penelitian ini adalah sistem pelacakan pada robot penjaga yang berhasil diimplementasikan dan berfungsi dengan baik dengan menggunakan teknik pengurangan latar belakang. Pergerakan robot penjaga ke arah senjata menghasilkan penundaan rata-rata 1 detik. Perubahan jangkauan yang paling efektif antara kamera dengan objek musuh atau teman adalah pada jarak 2 hingga 12 meter. Kemampuan deteksi objek terburuk akibat perubahan intensitas cahaya diamati pada 0 Lux
Teknologi Rancang Bangun Sistem Pemantau Indeks WGBT (Wet Bulb Globe Temperature) di Medan Terbuka Untuk Mendukung Pelaksanaan Kegiatan Personel TNI AD
Authors: Rena  , Fajar Kholid
Published: March 2024

Sistem monitoring indeks WBGT adalah suatu alat yang dirancang berfungsi sebagai pengganti alat WBGT (Wet-Bulb Globe Temperature) yang sudah ada. Penelitian ini dilaksanakan dikarenakan adanya beberapa faktor yang pertama yaitu mahalanya alat WBGT dan yang kedua yaitu pengukuran suhu, kelembaban, dan intensitas cahaya pada jajaran TNI AD saat ini masih menggunakan cara manual. Pada alat ini menggunakan 2 macam sensor yang memiliki 4 parameter hasil yang saling berkaitan untuk menghasilkan data indeks seperti pada alat WBGT. Kegunaan dari masing-masing sensor yaitu sensor DHT21 sebagai pengukur suhu dan kelembaban, sensor DS18B20 sebagai alat pengukur suhu bola basah dan dry temperature. Selain menjadi alternatif untuk mengukur indeks WBGT yang sudah ada dan beredar di TNI AD karena dari segi ekonomisnya, alat WBGT yang dirancang ini juga memiliki indikator lampu dan alarm untuk memberikan peringatan apabila indeks WGBT melebihi batas yang telah ditentukan.

Kata Kunci: dry temperature, sensor DHT21, sensor DS18B20, WBGT
Somaesthetics and Methodology: A Dialogue
Authors: Heinrich Falk , Ryynänen Max , Marino Stefano , Alison Aurosa , Romagnoli Elena
Published: March 2024

The article's form is a dialogue among five scholars of somaesthetics. The dialogue's topic is somaesthetics and methodology, addressing questions such as: What are the relevant methods for somaesthetic inquiries and practices? What are the methodological difficulties? Which important dimensions do methods and methodologies exclude? The article consists of five pieces, each addressing questions and propositions presented by other authors. The contributing authors are Falk Heinrich, Max Ryynänen, Stefano Marino, Aurosa Alison, and Elena Romagnoli.
Editorial: Somaesthetic Self-Care and the Politics of Taste and Transformation/Methodologies for Exploring Embodiment and Aesthetics
Authors: Heinrich Falk , Tarvainen Anne
Published: March 2024

Volume 9 (2023) takes the form of a double issue. Its first part is titled: “Somaesthetic Self-Care and the Politics of Taste and Transformation.” The thematic focus of the second issue is “Methodologies for Exploring Embodiment and Aesthetics.”
Pengajian Sabilussalam: Wadah Meningkatkan Spiritualitas dan Moderasi Beragama Umat
Authors: Solechan Solechan
Published: March 2024

This research aims to analyze the role of sabilussalam recitation in improving the spiritual attitudes and religious moderation of residents of Sugiharjo village, Tuban District. This research uses a qualitative approach with a case study type of research. The first research result was that the Sabilussalam recitation was carried out in rotation in the congregation's homes every Friday night. The material presented includes reading yasin and tahlil letters, reading sholawat dibaiyah, and maidhoh hasanah. The method used in recitation is the lecture and question and answer method. Second, the spiritual improvement of Sugiharjo village residents includes increasing faith and piety, religious awareness, and concern. Third, the moderate attitude of Sugiharjo village residents is divided into moderate attitudes in matters of faith and moderate attitudes in matters of worship. A moderate attitude in faith includes tolerating differences in schools of thought, avoiding takfiri attitudes and avoiding fanaticism. A moderate attitude in worship matters is tolerance towards religious practices, active participation in social and religious activities, and respect for differences in understanding of beliefs.
QUALIDADE DE VIDA EM PACIENTES DIAGNOSTICADOS COM ESCLEROSE MÚLTIPLA: REVISÃO DE LITERATURA
Authors: Da Silva Layza Lopes , Coradi Kamilly Santos , Mendes Maria Eduarda Dornelas , Quintino Bianca de Jesus , Uliana Katarina Destro , Silva Emilly Lessa , Saraiva Hugo Severo , Abreu Lorrane Sousa Costa Silva
Published: March 2024

Esclerose Múltipla (EM) é uma doença neurológica crônica de ação desmielinizante. Seu processo fisiopatológico consiste na afecção do sistema nervoso central por mecanismos inflamatórios e autoimunes, de forma que a bainha de mielina, a qual é uma proteína que auxilia na condução do impulso nervoso, é destruída. Sua maior incidência é em pacientes que se encontram na faixa etária de 15 a 40 anos de idade, especialmente no sexo feminino. Suas manifestações clínicas são de caráter autoimune e degenerativo, ocasionando sintomas neurológicos como fadiga, alteração da marcha, parestesia e espasmos musculares. Seu padrão evolutivo é marcado por episódios de exacerbação da atividade inflamatória alternados com períodos de remissão ou com deterioração das funções neurológicas. O estudo foi elaborado por meio de uma revisão sistemática de literatura de natureza descritiva, em que foram consultadas as plataformas eletrônicas Scientific Electronic Library Online (SCIELO), PubMed e Google Acadêmico para análise literária. As informações expostas no trabalham demonstram a importância da atuação de centros de reabilitação e de equipes multidisciplinares no acompanhamento do paciente portador de Esclerose Múltipla.