bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison Of 6 Machine Learning Models in Estimating Population Growth
Authors: Huang Yitong
Published: April 2024

With the rapidly increasing population globally, it is essential for policymakers to be able to accurately predict or gain an idea of the forecast of population growth to be able to make effective regulations that can benefit the general public. Therefore, the development of a machine learning model to estimate future population growth is crucial. In this article,  various machine learning models such as linear regression, logistic regression, decision trees, random forest, neural networks, and support vector machines are discussed and the benefits and downsides of each are considered. Factors impacting population growth are also discussed to conclude the qualities needed for a model to most suitably perform the task of population prediction. In the end, it is shown that random forest is the best model for this job as it can give a generalized pattern for its results as well as handle complex data types. This paper provides predictions and insights based on machine learning to predict future demographic trends, which can provide useful information for policymakers, researchers, and society in various fields.
Review of Predicting Stock Prices Based on Machine Learning and Stock Learning
Authors: Cao Yi
Published: April 2024

Predicting stocks is to help investors make decisions and help them minimize risks as much as possible, thereby obtaining investment returns. If we can accurately predict the rise and fall of stock market prices, we can buy or sell stocks at higher prices in the market, thereby obtaining higher profits. This article is based on machine learning and deep learning algorithms such as support vector machine (SVM), principal component analysis (PCA), random forest (RF), and recurrent neural network (RNN), long short-term memory network (LSTM), and convolution neural network (CNN). The conclusion is that machine learning requires manual intervention, is faster to train, more interpretable, and usually performs better on data sets with simpler structures and smaller sizes. Deep learning has lower requirements for feature extraction and discovery, has stronger expression and fitting capabilities, and can better capture the correlation between data in larger and more complex data sets.
A Survey of Deep Reinforcement Learning Based on Multi-Particle Environments
Authors: Weng Chuhao
Published: April 2024

This paper extensively explores the application of deep reinforcement learning within multi-agent environments, a domain marked by intricate challenges and burgeoning opportunities. Multi-agent environments inherently entail the presence of multiple interacting intelligent agents, thereby amplifying the complexity of decision-making and control. Traditional reinforcement learning methods, when confronted with these intricate settings, manifest limitations that have prompted a compelling shift towards the fusion of deep learning and reinforcement learning. The journey commences by elucidating the multifaceted challenges and intricacies pervasive in multi-agent environments. It systematically delineates the constraints of conventional reinforcement learning techniques, underscoring their inadequacies in effectively addressing the multifarious complexities presented by these environments. As the narrative unfolds, it places a spotlight on the transformative potential of deep learning in surmounting the formidable challenges associated with decision-making in multi-agent settings. A critical examination ensues, encompassing a thorough review of pioneering applications of methodologies such as value functions, policy gradients, and actor-critic approaches within the realm of multi-agent systems. Through an exhaustive exploration of existing literature, this article endeavors to unveil the vanguard developments and persisting challenges within the domain of deep reinforcement learning in multi-agent environments. Furthermore, this paper conducts a comparative analysis of the performance of two prominent algorithms, namely Q-learning and the Deep Q Network (DQN) algorithm. This analysis serves as a compass, elucidating the trajectory of development within the sphere of deep reinforcement learning. Ultimately, this paper acts as a compass, guiding future research and innovation in the dynamic and promising field of deep reinforcement learning within multi-agent environments.
Secure Transmission in the Digital Age: Exploring Exchange Encrypted Watermarking Technology
Authors: Feng Qingyang
Published: April 2024

In an era defined by the pervasive nature of digital data and the persistent specter of cyberattacks, the imperative to safeguard sensitive information and intellectual property has ascended to a position of paramount importance. This essay embarks on a comprehensive exploration of exchange encrypted watermarking technology, a cutting-edge and highly secure methodology for data protection that seamlessly amalgamates the foundational tenets of digital watermarking and encryption. By delving deep into the concept underpinning this technology, elucidating its core principles, and elucidating its multifaceted applications, this essay seeks to illuminate the transformative potential it holds within our data-driven world. The convergence of digital watermarking and encryption not only fortifies the defenses against data breaches but also paves the way for a new era of secure data sharing, digital rights management, and content protection. This innovative approach offers a dynamic response to the evolving landscape of cyber threats, establishing itself as a potent guardian of our digital assets and a catalyst for the secure exchange of information in an increasingly interconnected global environment.
Bidirectional Flow-Based Image Representation Method for Detecting Network Traffic Service Categories
Authors: Jiang Ziyu
Published: April 2024

Network traffic identification is crucial for network resource management and improving service quality. Traditional methods, such as port-based and deep packet inspection approaches, face challenges due to the increasing complexity of network environments, privacy concerns, and the emergence of encrypted traffic. This paper aims to address the issues of low accuracy and slow operation speed in encrypted traffic classification while ensuring the protection of user privacy. We propose a data processing method that transforms network traffic into images representing bidirectional flow packet arrival timestamps and packet sizes. By employing this data processing approach and utilizing deep recognition algorithms, the study conducts service analysis on network traffic. Experimental results demonstrate that the bidirectional flow-based image representation method achieves a 90.9% accuracy rate for the traffic analysis task on a TOR-encrypted imbalanced dataset, surpassing the accuracy of the unidirectional flow image method. Furthermore, the method also shows improvements in operation speed, enabling online network traffic detection.
Application of Machine Learning in Vegetable Classification and Recognition
Authors: Chen Hongyi
Published: April 2024

The rapid development of artificial intelligence plays an important role in People's Daily life today. Vegetable classification is closely related to life. With the continuous advancement of artificial intelligence technology, similar applications in the agricultural and retail industries are expected to play a more significant role. This paper aims to envision a vegetable identification system based on a convolutional neural network to provide intuitive and accurate vegetable classification information for supermarket managers and customers. This study mainly uses the convolutional neural network model as the main research model to carry out the design of a vegetable classification system. In this paper, the usefulness and disadvantages of the CNN model are summarized, how to use the model for deep learning and big data is learned, and the problems that may be encountered after the model is run are also predicted. Furthermore, the future development direction of the project is predicted too. As technology continues to evolve, it is expected that more innovative solutions will emerge, delivering enhanced benefits to society.
Evolution and Emerging Trends in Musical Information Retrieval: A Comprehensive Review and Future Prospects
Authors: Ding Yuxin
Published: April 2024

The rapid development of the digital music industry has brought challenges for music lovers and researchers. In response to these challenges, the field of Music Information Retrieval (MIR) emerged in the mid-1960s to capture the complicated and multi-layered nature of music. Among the various approaches explored, machine learning methods have shown promise in overcoming the complexity of this interdisciplinary field. This paper is primarily centered on conducting an in-depth review of the current state of research regarding the utilization of machine learning within the field of Music Information Retrieval (MIR). Additionally, it aims to forecast the potential future directions within the MIR industry. Upon extensive literature review, it becomes evident that various machine learning techniques, including Neural Networks (NN), Support Vector Machines (SVM), and K-nearest neighbors (KNN), have found common applicability in this field. Furthermore, this review highlights Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) as potential algorithms poised to shape the future landscape of MIR. The findings of this paper serve to elucidate the direction in which MIR is progressing, offering valuable guidance for forthcoming research and development endeavors. In doing so, it contributes to the continued progress and maturation of the field MIR.
BP neural network predicts the exchange rate of US dollar against gold
Authors: Chen Luopu , Liu Tianhao , Zhang Yifan
Published: April 2024

Gold and the US dollar are important currencies, and their price changes reflect changes in the world and North American economies. The gold market has become a financial investment market of equal importance to markets such as stocks, foreign exchange, and bonds. In such a situation, the price change of gold has become an important predictive research object. However, due to the changing world landscape, the randomness of gold price changes, and the existence of exchange rate and other influencing factors, it is very difficult to predict. It is crucial to establish a prediction model for exchange rate changes over time. This topic establishes a neural network model for exchange rate changes over time using the newly developed Back Propagation Neural Network in recent years. Finally, after several experiments, the parameters are adjusted to the optimal state to obtain the minimum error result. Through this study, we can determine that the prediction function of the BP artificial neural network can be applied in certain fields of life, but it also has significant limitations, to make it a complete tool for our daily life or work, it is necessary to further improve this technology.
Research On Compliance Risk Identification and Prevention Mechanism of Enterprises in The ESG Field
Authors: Ni Jiahao
Published: April 2024

With the continuous progress of the global society and the enhancement of the awareness of corporate responsibility, environmental, social and governance (ESG) issues have become the core considerations for the sustainable development of enterprises. This paper provides an in-depth study of corporate compliance risks in ESG from a legal perspective. By integrating the theories of jurisprudence, management and risk management, it systematically analyses the legal framework, regulatory trends and relevant standards of ESG compliance, and explores the challenges and opportunities for corporations in this area. This paper not only expands the research field of corporate legal management, but also provides jurisprudence support for the sustainable development of enterprises, and calls on enterprises to establish a comprehensive risk awareness in ESG compliance, actively respond to challenges, and contribute to global social responsibility.
Deep Learning Methods for EEG Applications: Focusing on CNN and RNN
Authors: Xiang Jinfan
Published: April 2024

In the field of neuroscience, the electroencephalogram (EEG) study aims to discover patterns of various human brain activities in an efficient and accurate manner, which plays an important role in treating brain diseases and other fields. The emergence and development of deep learning networks bring end-to-end approaches towards processing and classifying human brain signals. In the first section of the study, two typical networks used in EEG applications—the convolutional neural network (CNN) and the recurrent neural network (RNN)—are introduced, then the study measures the performance of each network in relation to their specific characteristics in temporal and spatial domain respectively and analyzes how noise and other interference towards EEG signals affect the training of the two networks. To make an integral view of how to take advantage of both networks mentioned above for EEG applications, the paper finally introduces a general system framework using a combination of CNN and RNN. This study will bring important value to the research and application of deep learning methods for EEG applications.
Word2Vec and SVM Fusion for Advanced Sentiment Analysis on Amazon Reviews
Authors: Wang Haili
Published: April 2024

As a matter of fact, sentiment analysis plays a crucial role in different kinds of reviews for online shopping. With this in mind, this study explores the application of Word2Vec and Support Vector Machine (SVM) in sentiment analysis of Amazon reviews. To be specific, initially, this study conducted preprocessing and feature extraction on a large-scale review dataset, revealing deep semantic relationships between words through the Word2Vec model. Subsequently, this study utilized SVM for model training and optimization, achieving efficient and accurate sentiment classification. According to the analysis, preliminary experimental results indicate that this combined method can effectively capture complex patterns and relationships in the text, demonstrating significant advantages in enhancing the accuracy as well as efficiency of sentiment analysis compared to traditional methods. Overall, these results shed light on providing a powerful tool for e-commerce platforms to understand better and analyze consumer opinions and feelings as well as guiding further exploration.
Delving into and Implementing Blockchain-related Technologies
Authors: Zhang Ye
Published: April 2024

Blockchain, birthed with the advent of Bitcoin, stands as a revolutionary decentralized digital ledger technology, casting light on the pathways of transparency, security, and efficiency across diverse sectors. Rooted in a blend of sophisticated technologies – from P2P network technology and encryption to timestamping – blockchain ensures secure, immutable transactions spanning its distributed nodes. Beyond its rudimentary role in Bitcoin, the technology has matured, fueling other digital currencies and leading to the emergence of private blockchains tailored for specific organizational processes. In its architectural framework, blockchain is layered with components like data, network, and consensus. Moreover, its versatile application layer introduces functionalities such as digital currencies and the dynamism of smart contracts. Yet, with all its promise, the foray of blockchain into domains such as commercial banking is still in its infancy, grappling with hurdles related to regulatory acceptance and broad market recognition. At the heart of blockchain lie its blocks: repositories of transactional data linked in a sequential chain. This design ensures paramount data integrity, upheld by stringent cryptographic principles. As industries gradually awaken to its potential, blockchain's evolutionary journey is bound to unfold further nuances, driving transformation in ways yet to be fully realized.
Exploring factors in Human Cognition that affect Human Performance during Takeover in Autonomous Driving
Authors: Liu Zijing , Zhang Jiayi
Published: April 2024

Driver's mental load is an important influence on the design of the human-machine interface (HMI) for self-driving cars. During the autopilot-forced takeover process, aspects of the user's cognition, decision-making, attention, and emotional state affect the takeover performance. Therefore, the cognitive and psychological states of drivers must be considered in the HMI design of self-driving cars. And a good HMI design will reduce the user's mental burden and improve the takeover performance. In this study, a model affecting drivers' takeover performance was designed through human factors. And we refine the analysis from three aspects of cognitive load, emotional load, and environmental load by making subjective likert-style-questionnaires, derive the main influencing factors through the reliability analysis of the data, factor analysis, and other methods. This work finds that the clarity, comprehensiveness, and level of humanization of takeover messages, as well as the driver's arousal level when receiving the message, have the greatest impact on the driver's workload.
Vaccine Hesitancy Prediction Based on Machine Learning Algorithms
Authors: Zhao Yike
Published: April 2024

Modern medical technology has led to the development and implementation of vaccines for various infectious or malignant diseases, offering active acquired immunity to those who receive them. Despite extensive research and validation confirming vaccine safety, a segment of the population exhibits reluctance or refusal to get vaccinated, known as vaccine hesitancy. This hesitancy posed challenges during the COVID-19 pandemic, hindering the attainment of herd immunity and contributing to a higher number of preventable deaths. It is imperative to accurately predict vaccine hesitancy by analyzing various population characteristics to distinguish between those with and without vaccine hesitancy. Subsequently, different communication strategies and incentives can be adopted. In this article, the author utilizes private data from a survey that includes demographic features about the participants, as well as their behaviors and beliefs about the COVID-19 pandemic and vaccination. To identify the most significant features, the author employed the feature importance method to eliminate features that were less influential and narrowed to the 17 most influential features. Machine learning techniques, including non-ensemble, ensemble, and neural networks, were employed to predict the data set and then calculate testing errors to compare each method on prediction accuracy. The authors concluded that neural networks have superior prediction accuracy and are mostly immune to overfitting. Although neural networks take a long time to train, the test error of less than 5.5% is significantly lower than the 6.5% average error of other methods.
Development And Challenges of Generative Artificial Intelligence in Education and Art
Authors: Yang Junpeng , Zhang Haoran
Published: April 2024

Thanks to the rapid development of generative deep learning models, Artificial Intelligence Generated Content (AIGC) has attracted more and more research attention in recent years, which aims to learn models from massive data to generate relevant content based on input conditions. Different from traditional single-modal generation tasks that focus on content generation for a particular modality, such as image generation, text generation, or semantic generation, AIGC trains a single model that can simultaneously understand language, images, videos, audio, and more. AIGC marks the transition from traditional decision-based artificial intelligence to generative artificial intelligence, which has been widely applied in various fields. Focusing on the key technologies and representative applications of AIGC, this paper identifies several key technical challenges and controversies in the field. These include defects in cross-modal and multimodal generation, issues related to model stability and data consistency, privacy concerns, and questions about whether advanced generative models like ChatGPT can be considered general artificial intelligence (AGI). While this dissertation provides valuable insights into the revolution and challenge of generative AI in art and education, it acknowledges the sensitivity of generated content and the ethical dilemmas it may pose, and ownership rights for AI-generated works and the need for new intellectual property norms are subjects of ongoing discussion. To address the current technical bottlenecks in cross-modal and multimodal generation, future research aims to quantitatively analyze and compare existing models, proposing practical optimization strategies. With the rapid advancement of generative AI, we anticipate a transition from user-generated content (UGC) to artificial intelligence-generated content (AIGC) and, ultimately, a new era of human-computer co-creation with strong interactive potential in the near future.
Recommendation Approach Based on TwinBERT Model
Authors: Luo Zheng
Published: April 2024

In order to provide more personalized recommendation services for users as well as improve the platform's traffic and user satisfaction, a recommendation method based on the TwinBERT model has been proposed. As a matter of fact, this method combines not only the basic information of users and items but also user comments or item text information to predict users' behaviors and make more accurate recommendations. The text information in the dataset is encoded in sentences using the BERT model, and the encoded text features are fused with other features to obtain a representation of user and item embedding vectors. These vectors are then used to predict the user's rating of the item and recommend relevant items for the user. The proposed method was tested on the Amazon review dataset and the MovieLens dataset, and the experimental results showed that it improved over traditional recommendation algorithms in terms of accuracy and efficiency.
Gold Prediction Based on XGBoost and OLS
Authors: Jiao Baixi
Published: April 2024

As a matter of fact, the value of gold is not only reflected in how much it is worth, but its unique nature also makes it determine economic and political policies. In reality, Gold is often seen as a hedge against inflation, influencing the direction of countries and investors. Gold prices from 2012-2018 from various websites were chosen as the dataset, including opening and closing prices and so on. With this in mind, using XGBoost and linear regression models successfully predicted the price of gold more accurately and it is not much different from the actual price. However, these methods are outdated, and geopolitical factors, national economic policies, and so on are some of the major factors affecting the price of gold in an era of increasingly up-to-date information. According to the analysis, the usage of integrated forecasting models combining Random Forests, MLR, SVM and ANN is a more futuristic way to predict the price of gold.
Research Advanced in Image Generation Based on Diffusion Probability Model
Authors: Huang Yuhan
Published: April 2024

Image generation has been a popular research task in the computer vision community, which aims to learn a distribution from a specific dataset and generate realistic images obeying this distribution. Thanks to the rapid development of deep learning technology, image generation models based on convolutional neural networks, especially generative adversarial networks (GANs) and variational autoencoders (VAEs), have become mainstream frameworks for image generation. However, in recent years, with the gradual deepening of the research on the denoising diffusion probability model (DDPM), the image generation technology based on DDPM has made new breakthroughs in accuracy and speed. Around the Diffusion Model, this paper introduces its latest research progress in image generation and derivative tasks. Specifically, this paper reviews the key techniques and basic theories of the diffusion model in detail. Then, the main research work, improvement mechanism and characteristics of the DDPM-based image generation method are summarized and summarized. This paper focuses on the basic structure and related applications of diffusion models, and evaluates some basic functions. Finally, the current problems and future development directions of image generation technology based on diffusion model are analyzed and summarized.
Water Quality Monitoring Research Analysis Based on the Remote Sensing Technology
Authors: Lin Zuyi , Yang Yinuo , Yang Yue
Published: April 2024

At present, supply and demand contradictions in water resources around the world are imminent and monitoring and governance of the water environment is urgent. This paper aims to combine space and aerospace remote sensing technology and water quality monitoring fields, fully consider the impact of current water body optical polarization on the accuracy of remote sensor optical signals, to identify the lower cost, more accurate measurement data, and easier to operate detection methods. The main analysis and study of three common water quality variables in water quality testing methods and the combination and synthesis of aerospace remote sensing data are proposed. In addition, this paper also selects the relevant water quality testing cases and the impact of optical polarization on the same type of parameter monitoring and performs a parameter analysis, estimating the financial cost that can be saved annually using remote sensing technology for water quality monitoring. Finally, the importance of the combination of space remote sensing data and aerial remote sensing data to improve the accuracy of water quality monitoring data is summarized, and the development prospect of the integration of air remote sensing and space remote sensing technology to overcome the impact of water multi-angle polarization on the accuracy of remote sensing data has prospected.
The Application of VR in Films and Games
Authors: Fang Shenghan
Published: April 2024

Virtual Reality (VR) technology has come a long way in recent years, and the current research in VR games and films is aimed at making VR experiences more immersive and realistic. This paper explores the current application of VR technology in film, and its potential impact on the industry and audience engagement. The study examines the different types of VR experiences that are currently available, such as interactive and non-interactive experiences. It also explores the ways in which VR technology is being used to enhance storytelling and audience engagement in film. The research also examines the impact of VR technology on the film industry, and the challenges that filmmakers face when creating VR content. At the end of the article, the paper summarizes the full text and looks forward to the relevant content.
Studies Advanced in Development and Application of Facial Expression Recognition
Authors: Guo Piao
Published: April 2024

Facial expression is an important way to convey people's inner emotional changes, as the basis of emotional understanding and the prerequisite for computers to understand human emotions, expression recognition has attracted more and more research attention from academia and industry in recent years. Most of the early expression recognition methods relied on manual features such as texture, geometry, and contour. Thanks to the powerful feature representation capability of convolutional neural networks, deep learning-based face recognition technology has made breakthroughs in recognition accuracy and speed. This paper details the latest research progress in the field of face expression recognition, including the design ideas, key steps, advantages and disadvantages of representative methods. This paper also introduces the common face expression recognition datasets and quantitatively compares the results of different methods on these datasets. Finally, this paper discusses the problems in the face expression recognition research field and look forward to the future development.
POWER OF NURSING EDUCATION TO INFLUENCE THE ATTRIBUTES OF STUDENTS IN A PRIVATE COLLEGE OF NURSING
Authors: RASHEED K , ZAFAR M , FARID B , RASHEED B , ARSHAD H
Published: April 2024

This methodological study addresses a critical gap in the research landscape by exploring the influence of nursing education on the attributes of nursing students in Pakistan. Despite existing studies in countries such as Iran, Egypt, and Finland, satisfactory research on this topic remains elusive. The study aims to develop and validate an instrument tailored for the Pakistani context, specifically focusing on the power of nursing education and its capacity to shape nursing students' attributes. A questionnaire-based approach was employed to gather data, focusing on the extent to which nursing education influences the attributes of student nurses. The analysis of the data revealed encouraging findings regarding the impact of nursing education on various attributes. Notably, improvements were observed in areas such as professional communication, fluidity, and the integration of human values and professional excellence into nursing practice. This study sheds light on the nuanced concept of the power of nursing education from the perspective of nursing professionals. The findings underscore the complexity inherent in this concept, emphasizing its significance within the context of the nursing profession's ethos of compassion, ethics, and care. Importantly, the study highlights the notion of professional influence through nursing education as a valuable concept, vital for fostering the professional growth and development of nursing students. Crucially, the results demonstrate that nursing education plays a pivotal role in enhancing nursing students' professional knowledge and overall development, thereby positively impacting their attributes. By equipping students with the necessary skills and knowledge, nursing education cultivates professionalism and establishes a strong foundation for future nursing practice. This study contributes to the international discourse on nursing education and underscores the importance of tailored research initiatives to understand its impact on nursing students' attributes across diverse cultural and contextual settings.
Neuromonitoring in neurocritical care for traumatic brain injury in the Thai context
Authors: Boontoterm Panu , Sakoolnamarka Siraruj , Nakla-or Peera , Phontien Prateep , Fuengfoo Pusit
Published: April 2024

The purposes of this review were to identify the incidence and types of traumatic brain injury (TBI) in Thailand, and recommend neuro-monitoring for limited resources in Thai patients. The monitoring methods focus on the targeted and personalized management in severe TBI such as intracranial pressure (ICP), pressure reactivity index (PRx), regional cerebral saturation (rSO2), cerebral autoregulation (CA), as well as noninvasive methods for example near-infrared spectroscopy (NIRS) and optic nerve sheath diameter (ONSD). These monitors are aimed to optimize brain oxygen delivery and prevent further neurologic deterioration in terms of increased ICP and decreased cerebral perfusion pressure (CPP). Some of these were implemented in neurological monitoring protocol and clinical practice guidelines for severe TBI. However, cost - effectiveness is concerned. Even though considering CA and the advance monitoring methods in continuous assessment are widely used, current therapeutic interventions which appear entirely to bedside approach for correct dysregulated CA are limited. In addition, understanding of basic molecular and cellular pathways involved in cerebral homeostasis ( brain oxygen delivery, cerebral blood flow, CA and cerebral vascular reactivity to oxygen and carbon dioxide) as well as secondary brain injury prevention are still necessary for improving TBI outcomes. In summary, further study in Thailand is required to determine optimal cerebral physiologic-based technology, monitoring parameters, and individualized thresholds to optimize CA and potentially improve neurologic outcomes across a spectrum of TBI patients, which focus in Thai rural areas where invasive monitoring is not routinely performed due to resources limitation. Encourage and training of non invasive methods might solve these issues.
Optimalisasi Media Musikalistik Terapis Sebagai Penyembuhan Diri Pada Anak Keluarga Tak Utuh
Authors: Nindya Annisa Salzabila  , Sudaryanto Memet
Published: April 2024

A harmonious family greatly influences a child in carrying out his development both physically and psychologically. However, not all children are born and live in a complete family environment, many children also live and are born from broken homes. This of course has an impact on the lives of broken home children such as lack of affection, not being good at socializing, lack of a support system, and the possibility of experiencing problems in the academics they take. Therefore, a solution is needed for children by applying therapy using musicality media or media results from a combination of therapy by listening to music that forms a person's psyche accompanied by poetry writing activities. The purpose of this study is to explain the optimization of the therapist's musicality media as a self-healing solution for broken home children. This research method uses clinical language therapy with magical musik sung by Indonesian singers. To answer this problem, this study uses a qualitative approach by examining the tone content of Indonesian songs and examining the meaning of the songs. After that, clinical testing and interviews were conducted on children with intact families to include the resulting poems. The interview relates to (1) the use of media used to convey the hearts of children with incomplete families; and (2) the form of interaction produced by children in conveying their feelings in writing or verbally. The results showed that optimizing the use of the therapist's musicality media can be used as a self-healing therapist for children from broken homes because music therapists and writing poetry can influence children to express more emotions so that they can ease the psychological burden.
Faktor-Faktor yang Berhubungan dengan Status Gizi Balita di Desa Baumata Timur Kabupaten Kupang Tahun 2022
Authors: Yufen Lorens Ati  , Aspatria Utma , Boeky Daniela
Published: April 2024

Nutritional status is a picture of the balance of nutritional intake that enters the body with what the body needs. The result of the balance of nutritional intake is optimal growth and development. Toddlers are an age group that is prone to nutritional problems, so toddlers really need optimal nutritional intake. Nutritional problems such as poor nutrition, malnutrition and nutrition occur more if the nutritional intake is unbalanced. The purpose of this study is to analyze the relationship between the number of family members, household income, household expenditure, household food security, feeding patterns, energy intake and protein intake as well as a history of infectious diseases with the nutritional status of toddlers in East Baumata Village, Kupang Regency in 2022. Types of analytical survey research or quantitative research with cross-sectional research designs. The study population, namely all mothers in Posyandu who have toddlers, totaled 256 toddlers. The sample was 72 toddlers, determined by a systematic random sampling technique. Data collection with questionnaires was then carried out chi-square tests with the help of SPSS. The results showed a significant relationship between the number of family members (0.029), household income (0.008), household expenditure (0.000), household food security (0.000), feeding patterns (0.000), energy intake (0.004) and protein intake (0.001) with the nutritional status of toddlers in East Baumata Village, Kupang Regency in 2022. Meanwhile, what is not related is a history of infectious diseases (0.091). It is recommended for mothers of toddlers to increase visits to posyandu so that toddlers get health services such as weight weighing, PB/TB measurements, immunizations and mothers obtain health information that is able to support the growth and development process of toddlers.
Faktor-Faktor Yang Berhubungan Dengan Kejadian Ikterus Pada Bayi Baru Lahir Di RSU UMMI
Authors: Elsi Rahmadani  , Marlin Sutrisna
Published: April 2024

Jaundice is one of the contributors to infant morbidity in Indonesia because it can cause the baby's body to become weak and refuse to suck, increased muscle tone, stiff neck, muscle spasms, seizures, sensory disturbances, mental retardation, disability and even death. The purpose of this study was to determine Factors Related To Icterus Incident On Newborn Baby At Ummi Hospital. The method used is an analytical research design with a cross sectional approach. The sample in this study are 55 mothers who have newborn babies and chosen by using accidental sampling technique. This study uses SPSS with a chi-square bivariate statistical test. The results of the univariate analysis showed that most of the respondents were not LBW, most were not ABO incompatibility, the frequency of breastfeeding was more than 8 times and most were not jaundiced. The results of the bivariate analysis showed that there was a relationship between low birth weight, breastfeeding frequency, ABO incompatibility with the incidence of jaundice in newborns (p 0.000). Researchers suggest that Public Health Center is expected to provide information about health, especially about jaundice.
Hubungan Asupan Zat Gizi Besi Dengan Kadar Hemoglobin Remaja Putri
Authors: Natalia Kristin  , Lewi Jutomo  , Daniela L.A Boeky
Published: April 2024

Iron nutrition is the main element in the formation of hemoglobin. Consumption of insufficient iron nutrient intake can be at risk of anemia which causes oxygen transport to be reduced, resulting in low energy production which has an impact on the work productivity of young women. This study aims to determine the relationship between iron nutrient intake and hemoglobin levels of students of the Faculty of Public Health, Nusa Cendana University. This type of research is  an analytical survey and is used in a cross sectional design. The total population of 595 female students and a sample of 86 female students consisting of semesters II, IV, and VI were obtained. Sampling is carried out by the stratified sampling  method  purposively.   Statistical analysis using the Pearson Product Moment test.  The results showed that out of 86 respondents, the average consumption of iron intake was 11.5 mg and 76.7% of female students' hemoglobin levels were in the normal category. The results of the Pearson Product Moment  analysis showed that the p-value= 0.965>0.05 had no significant relationship between the level of iron nutrition adequacy and hemoglobin levels in young women. The need to provide education about the importance of consuming iron nutrition intake to prevent the risk of anemia.
Analisis Pemanfaatan Sistem Informasi JKN Mobile Di Kota Makassar
Authors: Narmansyah Senja , Indar  , Rahmadani Suci , Arifin Muhammad Alwy , M Thaha Ridwan
Published: April 2024

JKN Mobile is an application presented by BPJS Kesehatan to make it easier for users to register, change and find membership data, obtain First Level Health Facility services and Advanced Level Referral Facility and make it easier for users to provide criticism or suggestions. BPJS participants are still lacking in utilizing the JKN Mobile application service, even though the formation of this application is expected to make it easier for the public to access health services from BPJS Kesehatan. This study aims to determine the utilization of the JKN Mobile information system in Makassar City. The type of research used is quantitative research with an analytical survey method and using a cross sectional study design. The population in this study were all BPJS Kesehatan participants in Makassar City who used the JKN Mobile application as many as 245,341 users. Sampling using nonprobability sampling technique by accidental sampling with a total sample of 100 people used. The results showed that 86% of respondents felt that the use of JKN Mobile was sufficient. The results of statistical analysis using Chi Square showed that the system quality (p=0.000), information quality (p=0.000) and service quality (p=0.000). The p-value of all independent variables <0.05 so that there is a relationship between system quality, information quality and service quality on the utilization of JKN Mobile in Makassar City. All variables studied in this study indicate that there is a relationship with the use of JKN Mobile. With this, it is hoped that BPJS Kesehatan will improve and develop the JKN Mobile application, especially on the registration and registration to make it more easily accessible to all groups of people and further increase socialization and education to BPJS participants regarding the procedures for using and the importance of using the JKN Mobile application.
Karakteristik Persalinan Sectio Caesarea (SC) di RSU Bahagia Makassar
Authors: Surmayanti  , Fitriana Jufri.P  , Sainah
Published: April 2024

Sectio Caesarea (SC) is generally performed when there are certain medical indications, as a termination action with complications. In addition, CS is an alternative to childbirth without medical indications because it is considered easier. As many as 25% of the number of births were carried out to mothers who did not have a high risk for normal delivery or childbirth complications. In 2020, at Happy Makassar Hospital, mothers who gave birth with SC 34.5% were 147 out of 426 deliveries. The purpose of this study was to determine the characteristics of mothers who experienced cesarean delivery at RSU Bahagia Makassar in 2020. This type of research was descriptive analytic with a retrospective approach. The population in this study was data on mothers who experienced cesarean delivery at the Happy Makassar General Hospital in 2020 as many as 147 people. The sample used in as many as 87 respondents with systematic random sampling sampling technique. The instrument used is a checklist. Data analysis consists of univariate analysis. The results showed that the highest number was obtained in the age group of 20-35 years with 56 respondents (64.4%), Bugis ethnicity with a total of 80 respondents (92%), higher education (high school and university) 63 respondents (72.4%). ), working mothers as many as 70 respondents (80.5%), BPJS payment system as many as 63 respondents (72.4%), primipara parity and grandemulti (high risk) with 72 respondents (82.8%), pregnancy intervals more than 2 years as many as 46 respondents (52.9%), good history 56 respondents (64.4%), sc with indications of pre-eclampsia in the mother as many as 28 respondents (32.2%) SC with indications of fetal distress 25 respondents (28.7%). The results of the above study indicate that SC is not always caused by indications but from sociodemographic and medical obstetric factors that must be considered.
Penanggulangan Tindak Pidana Perjudian dalam Tradisi Ma’pasilaga Tedong di Upacara Adat Rambu Solo’ Toraja Utara
Authors: M. Yusuf  , Amir Faisal  , Eriani Rensa Malino  , Hijriani  , Winner A. Siregar  , St. Fatmawati. L
Published: April 2024

Tradisi Ma’Pasilaga Tedong di Toraja awalnya memiliki nilai sakral sebagai bagian dari upacara adat Rambu Solo, namun kini telah dimanfaatkan untuk kegiatan perjudian, dengan kerbau yang sebelumnya digunakan dalam adu kini menjadi sarana taruhan. Penelitian ini menganalisis upaya penanggulangan tindak pidana perjudian dalam tradisi Ma’Pasilaga Tedong pada upacara adat Rambu Solo’ di Toraja Utara. Menggunakan pendekatan yuridis empiris, data diperoleh melalui observasi dan wawancara. Hasil penelitian menunjukkan upaya preventif dan represif oleh kepolisian, pemerintah, tokoh agama, dan tokoh adat. Langkah preventif melibatkan perbaikan ekonomi, penyuluhan hukum, perlindungan kebudayaan, dan peningkatan kesadaran moral. Upaya represif melibatkan penegakan hukum dengan penangkapan, penyelidikan, penyidikan, dan penuntutan terhadap pelaku. Tantangan utama meliputi faktor ekonomi, pluralisme budaya hukum, dan kendala operasional. Kesimpulannya, penanggulangan perjudian membutuhkan kerjasama holistik untuk mempertahankan tradisi dan moralitas masyarakat Toraja Utara.
Peran Pemerintah dalam Mencegah Pengambilalihan Harta Yayasan oleh Ahli Waris Pendiri
Authors: Suriani Bt. Tolo  , Muh. Fitriadi  , Mustahul Jannah  , La Ode Bariun  , Winner A. Siregar  , La Ode Munawir
Published: April 2024

Penelitian ini membahas peran pemerintah dalam mencegah pengambilalihan harta yayasan oleh ahli waris pendiri di Indonesia. Meskipun undang-undang yayasan telah ada sejak tahun 2001, masih terdapat tantangan dalam mengatasi isu-isu seperti konflik internal, penggelapan dana, kesalahan administratif, dan keterbatasan sumber daya yang mempengaruhi fungsi yayasan. Metode penelitian yang digunakan adalah penelitian hukum normatif dengan pendekatan kualitatif melalui studi kepustakaan. Hasil penelitian menunjukkan bahwa yayasan memiliki peran penting dalam mencapai tujuan sosialnya, tetapi sering kali mengalami masalah terkait pengambilalihan harta oleh ahli waris pendiri. Pengawasan pemerintah, baik melalui Kementerian Hukum dan HAM maupun Kementerian Sosial, menjadi kunci dalam mencegah pengambilalihan tersebut. Proses pengawasan meliputi tahap pendirian yayasan hingga pengumuman resmi, dengan langkah konkret berupa pemberian akreditasi oleh Kementerian Sosial. Dapat disimpukan bahwa pengawasan negara terhadap yayasan penting untuk memastikan keberlangsungan dan integritas yayasan dalam memberikan kontribusi positif bagi masyarakat serta memastikan bahwa harta yayasan tetap menjadi milik yayasan sebagai badan hukum yang terpisah.
PENANGANAN KAWASAN KUMUH MELALUI REDEVELOPMENT HUNIAN VERTIKAL DI KOTA SEMARANG
Authors: Binta Usfuroh Dinar , Istijanto Suko , Rolalisasi Andarita
Published: April 2024

Salah satu kota dengan populasi penduduk yang cukup padat yaitu Kota Semarang. Dari kejadian tersebut, kebutuhan lahan untuk permukiman di perkotaan semakin meningkat, sehingga mengakibatkan nilai tanah yang relatif tinggi dan tidak semua penduduk mampu membeli tanah dengan nilai tanah yang tinggi. Akibatnya masyarakat lebih memilih untuk tinggal di kawasan yang memiliki nilai lahan yang rendah, dan menimbulkan permukiman kumuh akibat penurunan kualitas permukiman. Tingkat kekumuhan di wilayah ini menentukan bagaimana program pemerintah untuk meningkatkan kualitas permukiman di Kota Semarang diimplementasikan. Menurut rencana pemerintah, pembangunan kembali permukiman Tambak Lorok merupakan solusi dari penurunan luas lahan di pesisir utara yang berakibat penurunan kualitas permukiman. Pendekatan penelitian eksploratif dilakukan dalam penelitian ini untuk mengidentifikasi masalah atau subjek yang memerlukan investigasi lebih lanjut. Tujuan dari penelitian ini adalah cara penanganan masalah menurunanya kualitas permukiman dengan adanya penataan kembali dengan hunian vertikal dikawasan Tambak Lorok Semarang.
KRITERIA PEMILIHAN TAPAK PADA PERANCANGAN FASILITAS KEBUGARAN FISIK DAN MENTAL REMAJA DI KOTA SURABAYA
Authors: Aprilina Sasa , Wilfritz Soaduon Panjaitan Tigor , Rolalisasi Andarita
Published: April 2024

Masalah kesehatan mental pada remaja diperlukan perhatian khusus dalam penanganannya. Lingkungan mempunyai pengaruh paling besar terhadap penyembuhan manusia, baik alami maupun buatan. Dalam artapakktur lingkungan binaan, baik ruangan, bangunan, lingkungan hingga skala kota, faktor lingkungan memegang peranan yang sangat penting dalam perancangan fasilitas remaja. Oleh karena itu, pemilihan tapak sangat berperan penting dalam menciptakan lingkungan binaan. Artikel ini bertujuan untuk menganalisis tapak agar dapat digunakan sebagai data dasar dalam perancangan fasilitas kebugaran fisik dan mental remajadi Kota Surabaya. Metode yang digunakan adalah analisis kondisi eksisting tapak sehingga menghasilkan output konsep perancangan tapak. Lokasi yang dipilih berdasarkan kriteria tapak yang sesuai dalam perancangan fasilitas remaja. Kriteria tapak bertujuan agar lahan mampu dimanfaatkan secara efektif dan efisien.