bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Heart Disease Through the Application of Machine Learning Techniques Using the Python
Authors: Fu Zihang
Published: April 2024

This paper is mainly about using machine learning methods to predict cardiovascular disease. Because cardiovascular disease now ranks first in mortality and the methods and costs of curing heart disease are very small and expensive, so why not take a step earlier? As for discovering diseases, there are not many projects that use machines to predict diseases, and it costs a lot of money to predict diseases in hospitals. Using machines can be faster and more accurate. Another point is that society has entered the era of artificial intelligence, and the medical industry It should also usher in an improvement and should keep pace with other industries, so I want people to experience predicting diseases, such as performing cardio-tracheal examinations faster and more accurately with less money. This helps a lot of families save a lot of money and get the same services as a hospital, which is not a good thing and that's why I did this research.
Improved Sliding Mode Variable Structure Control of Robot Manipulators with Flexible Joints based on Singular Perturbation
Authors: Wang Chenxu , Yi Dajian , Zhang Wenhui , Huang Xiaochen , Chen Rui , Wu Maojun , Ma Jing
Published: April 2024

This paper discusses the kinematics analysis method of six link mechanism based on virtual prototype technology and Adams. Firstly, the kinematics mathematical model of the six-bar linkage is deduced. Then, based on the design and development process of virtual prototype technology, the virtual prototype model of the six-bar press is established in ADAMS software. Finally, the kinematics simulation analysis is carried out for the model. The proposed method provides a reference for the parametric and optimal design analysis of the multi bar linkage of the mechanical press.
Optimized Queuing Modeling in Multimodal Sentiment Analysis: An Examination and Application
Authors: Lv Zhetong
Published: April 2024

This paper introduces an innovative queuing model tailored for multimodal sentiment analysis, designed to swiftly generate a preliminary score by prioritizing influential factors from the most valuable modalities. In a bid to yield reliable results in a compressed timeframe, this model meticulously processes data from the modality possessing the paramount effect factor, concurrently trimming down processing time by omitting data from lesser impactful modalities. This discerning approach allows for a dynamic adjustment of the weights assigned to various modalities based on their respective impact variables, culminating in precise and credible sentiment analysis outcomes. In an era where expediency and accuracy are at a premium, the proposed model stands out by ensuring that the sentiment analysis is not just rapid, but also robust and reliable, reflecting the true emotional tonality with a high degree of reliability. The model’s adaptability to fluctuating modalities, ensures its utility across diverse platforms and applications, making it a valuable tool for contemporary sentiment analysis tasks. Amidst a digital landscape that continually evolves, this queuing model for sentiment analysis exemplifies a significant leap forward, proffering enhanced efficiency, accuracy, and reliability in sentiment analysis endeavors.
Sales Forecasting Based on Transformer-LSTM Model
Authors: Liu Yi
Published: April 2024

As a matter of fact, sales forecasting becomes a hot topic in recent years. The LSTM model is a classic model in the field of sales forecasting. Recently, Transformer has gained a lot of popularity, and many researchers are applying it to time series forecasting. To be specific, this study proposes a novel Transformer-LSTM framework for the one-step sales prediction task. This study retained the classical Transformer structure, using it as the model's encoder, and then combined attention mechanisms with the LSTM model in the decoder section. This hybrid approach allows the model to leverage the advantages of Transformer in handling long sequences while benefiting from LSTM's unique strengths in processing time series data. Ultimately, the model's performance was evaluated using prediction curve comparisons and RMSE as validation metrics, achieving significant results. In conclusion, these results present a novel model for sales forecasting and time series analysis, contributing to improved prediction accuracy in these domains.
A Comprehensive Survey on Blockchain Technology and Its Applications
Authors: Liu Jiajun , Wu Junhao
Published: April 2024

This survey paper provides a comprehensive and in-depth overview of blockchain technology and its wide-ranging applications. It begins by introducing the fundamental characteristics and structure of blockchain, with a particular focus on the five major consensus mechanisms and their unique features. The article emphasizes the crucial role of smart contracts and cryptography in the construction and operation of blockchain networks. Furthermore, the paper explores the specific applications of blockchain in three key areas: cryptocurrencies, supply chains, and healthcare security. It highlights the numerous advantages that blockchain brings to these domains, including enhanced security, transparency, and efficiency. The paper also offers valuable insights into the future potential of blockchain technology in these areas, providing a glimpse into the possibilities that lie ahead. Additionally, the article addresses the challenges posed by the "impossible triangle" of decentralization, security, and high performance in blockchain. It discusses the emerging research trends aimed at tackling these challenges, such as cross-chain protocols, privacy protection mechanisms, blockchain expansion strategies, and advanced data storage solutions. The paper presents recent advancements and breakthroughs in each of these research directions, showcasing the ongoing efforts to overcome the limitations of blockchain technology.
Enhancing Cancer Prediction Accuracy Through Real-Time Monitoring and Artificial Intelligence Analysis for Patients
Authors: Wang Zichang
Published: April 2024

The integration of Artificial Intelligence (AI) into clinical medicine has recently garnered significant attention, particularly in the context of digital pathology and precision medicine. Real-time monitoring and analysis of patients play a pivotal role in the overall treatment process. This includes monitoring basic body detection indices, analyzing the patient's overall condition, and predicting future possibilities for their medical status. In addition to fundamental machine learning (ML), both deep learning (DL) and digital twin technology have demonstrated considerable potential in enhancing the accuracy of analysis and prediction in clinical cancer patients' condition monitoring. This study employs a comparative analysis approach to assess and compare how DL and digital twin technology contribute to improving accuracy in monitoring and analyzing clinical cancer patients' conditions. ML is well-suited for training models with large datasets, while DL excels in tackling more intricate problems. To enhance the precision and accuracy of analysis and future prediction, digital twin technology is leveraged to explore various patient scenarios, with basic twins providing real-time monitoring of the patient's current conditions. Markov Decision Progress (MDP) is employed when predicting potential outcomes. However, it's essential to consider the impact of the number of times the model is trained when making comparisons. The evaluation criterion centers on whether the use of DL and digital twin technology improves accuracy and precision compared to using ML alone. Examining the results reveals that using only ML or DL yields accuracies of 97.01% and 94.73%, respectively. However, when ML is combined with DL and digital twin technology, accuracy significantly improves to 99.73%.
Algorithmic Composition of Music Utilizing the Digits of Pi
Authors: Wu Jingtian
Published: April 2024

As a matter of fact, algorithmic composition, the fusion of computational methods with music creation, has a rich history dating back to Pythagoras. On this basis, this paper explores the innovative intersection of mathematics, algorithms, and music, focusing on the translation of Pi's digits into a musical composition. Through a combination of manual and automated techniques, the research successfully crafts a melody using the minor pentatonic scale and traditional Chinese instruments, enriched by an algorithmically generated counter-melody. According to the analysis, the study reveals both the potential and limitations of this approach, with the fixed nature of the accompaniment and strict adherence to the pentatonic scale constraining musical diversity. Nevertheless, the research offers a novel perspective on mathematical representation in music, contributing to the broader field of algorithmic music composition. The findings open new avenues for personalized music creation, educational tools, and artistic exploration, highlighting the limitless possibilities when art and science converge.
The Investigation of Deep Learning Models Utilized in Vector Graphics Manipulation
Authors: Yang Chen
Published: April 2024

Vector graphics, 2D or 3D, hold paramount significance across various professional domains, including graphic design, web design, architecture, and engineering. However, traditional methods of creating vector graphics are characterized by low efficiency. This review explores the integration of some deep learning models designed for 2D and 3D vector graphics generation and manipulation, summarizing their main tasks and methods. In terms of 2D vector graphics, this review examines advanced models, including Convolutional Neural Networks, Generative Adversarial Networks, and more, for diverse tasks such as font or icon generation and image manipulation. For 3D vector graphics, this paper assesses the progress achieved in models tailored for point cloud and image reconstruction, as well as 3D shape generation, using approaches such as Variational Autoencoders, Multi-Layer Perceptrons, and Transformers. This review also assesses their progress and limitations, acknowledging a comprehensive overview of deep learning models in vector graphic manipulation, and emphasizing their potential impact on the design industry while recognizing the challenges ahead.
Music Generation Based on Bidirectional GRU Model
Authors: Zhou Yuchen
Published: April 2024

Lately, substantial advancements in the realm of deep learning have given rise to new approaches for autonomously generating music. This study has devised a generative framework intended to produce musical melodies. This framework capitalizes on bidirectional gated recurrent units (GRU) as its foundational architecture. To impart knowledge to the model, a collection of classical piano compositions in MIDI format has been employed as the training dataset. One implements a stacked architecture of bidirectional GRU layers to capture long-term musical patterns. The addition of dropout regularization prevents overfitting. Generated samples are evaluated both quantitatively through model loss, as well as qualitatively by manual listening tests. According to the analysis, the approach can produce coherent musical melodies with reasonable structure. This demonstrates the potential for deep bidirectional models to learn musical syntax and generate new compositions. Limitations include lack of long-term musical form and repetitive patterns. Future work should explore architectures to improve coherence over longer time spans, as well as integrate other modalities like rhythm and harmony. These results provide a strong foundation for automated music generation systems.
Several Efficient Alternative Solutions for RS Codes in RAID 6
Authors: Xue Xinran
Published: April 2024

The RAID-6 specification requires a storage system with multiple storage devices to be able to endure the failure of any two parities. RAID-6 can be implemented using a variety of erasure coding methods, but each has drawbacks, like low fault tolerance, bad writing performance low decoding speed, and so on. In this paper, we research a variety of materials, explore RAID 6 disks to comprehend their purpose and prerequisites, and understand how to decode RS codes. In order to help people find a decoding method that is more appropriate to their own systems and actual needs, we research and compare numerous high-quality alternating decoding techniques, and the RAID 6 Liberation codes and Row-Diagonal Parity emerge as the winners. We introduce their decoding methods briefly and list their advantages and disadvantages in this paper for people to request based on their needs. Finally, we compare different decoding methods, propose practical application scenarios and future development directions for these more efficient decoding methods, and also point out their existing shortcomings.
Analysis Of One-Class and Binary Classification For COVID-19 Image Data
Authors: Cui Huihan
Published: April 2024

With the recent Coronavirus Disease 2019 (COVID-19) outbreak, there has been a growing interest in the utilization of machine learning models to aid in medical diagnostics. However, during the initial phases of the pandemic, the available datasets were severely imbalanced due to a scarcity of positive cases. These datasets predominantly comprised normal cases, with only a limited number of disease instances. This inherent data imbalance restricted the effectiveness of binary classification, prompting an exploration of one-class classification. This study aims to compare the performance of one-class and binary classification models during the early stages of COVID-19 development. The objective is to provide healthcare professionals with the most effective machine learning-assisted solutions for handling future outbreaks of a similar nature. The experiment incorporates three distinct models: One-Class Support Vector Machine (OCSVM) for one-class classification, alongside Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) for binary classification. These models undergo a rigorous tuning process employing grid search, using training data composed of features extracted from compressed chest X-ray images. In summary, the research findings unequivocally demonstrate the superior performance of one-class classification over binary classification in all early stages, achieving an impressive F1 score of 0.46, compared to binary classification's modest score of 0.063. Consequently, it is strongly recommended to employ a one-class classification model to enhance real-life diagnostic processes. However, it is anticipated that as more positive cases are incorporated into the dataset, achieving a more balanced distribution, binary classification may exhibit greater potential. Thus, medical practitioners should reconsider the applicability of binary classification in later stages when the dataset attains improved balance.
Research on the Interrelationship of Sustainable Development Goals Based on Multilayer Network Modeling
Authors: Zhu Xiaotian , Xu Xiaoxiao , Gu Fengru
Published: April 2024

The Sustainable Development Goals are not just a call to action, but a vision of hope for a better world. To achieve sustainable development, it is critical to study relationship between SDGs and select the most effective priority goals. A multi-layer network approach was employed to establish a model for analyzing the interrelationships and impacts of SDGs.The ffnal model was derived by computing the Pearson correlation coefffcient matrix and network connections. The network models for 10 regions were visualized, revealing that in the WLD region, SDG8 had a positive impact on other SDGs, particularly on SDG4, while SDG17 had a negative impact on 14 SDGs.We have concluded that our model has strong robustness, high stability, and good interpretability. This provides scientiffc guidance and support for achieving the SDGs.
Core Technology, Typical Applications, And Future Development Prospects of ChatGPT
Authors: Hu Jingyu
Published: April 2024

Natural language processing is an important branch of artificial intelligence, which studies how to enable computers and humans to communicate in a natural language way. Thanks to the rapid development of deep learning technology, natural language processing algorithms based on artificial neural networks have been making breakthroughs in both accuracy and speed. As a representative technology in recent years, generative pre-trained transformer (ChatGPT) has been widely used in many fields such as chatbots, Q&A systems, machine translation, etc. ChatGPT is mainly based on the Transformer architecture and uses a large amount of text data for training to realize the understanding and generation of natural language. Around ChatGPT, this paper describes in detail the key technologies behind it, such as the ATTENTION mechanism. In addition, it also introduces typical applications of ChatGPT in medical, programming, education and other fields. By summarizing the problems that still exist in the technical development and practical application of ChatGPT, this paper further discusses the future development direction of ChatGPT
Comparison of Data Visualization, Outlier Detection and Data Dimensionality Reduction Methods
Authors: Zhao Xingyu
Published: April 2024

With the deepening of the digital age of information, people's daily data is getting larger and larger, and it is more and more difficult to quantify and process. At this time, the data processing means becomes particularly important. This paper compares and analyzes some methods from data visualization to data dimensionality reduction to outlier detection. In this paper, two different types of datasets, ModelNet40, and red wine quality, are used to introduce the visualization method of the Farthest Point Sampling (FPS). This method can have a clear visual effect on the data dimension and scale, and allow users to observe the structure, type, and scale of the data. In data dimensionality reduction, the study uses Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (t-SNE), Triplets Manifold Approximation and Projection (TriMAP), Uniform Manifold Approximation and Projection (UMAP), Pairwise Controlled Manifold Approximation Projection (PaCMAP), and Autoencoder to compare their dimensionality reduction effects. Through these methods, this paper finds that different methods have different effects on different datasets. Therefore, in data dimensionality reduction, it can get twice the result with half the effort by choosing the appropriate method. Finally, this paper also detects outliers. Outliers in datasets will make it difficult for people to process data and make subsequent results inaccurate, so it is necessary to identify outliers. This paper involves methods such as isolation forest and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Through this paper, the methods of different datasets are analyzed and summarized.
Efficient Cloud-Assisted Protocol for Private Set Intersection Computation
Authors: Qin Jihan
Published: April 2024

As cloud computing capabilities have advanced, coupled with the rise of cloud-assisted computing, innovative solutions for private set intersection computation have emerged. Traditionally, protocols designed for private data security entail extensive computational demands. This can be a burden for those involved in the privacy set seeking intersection. In many real-world scenarios, some participants might not possess the necessary computational power mandated by these protocols, leading to potential data vulnerabilities. However, the advent of cloud-assisted computing has introduced a transformative approach. Participants, rather than being limited by their local computational constraints, can now offload tasks to a cloud server. The server, equipped with vastly superior computing resources, undertakes the computation and subsequently returns the results. This not only alleviates the pressure on individual participants but also makes the process more adaptable to varied scenarios. In the discussed paper, a protocol is presented that adeptly harnesses cloud assistance to facilitate efficient private set intersection. This protocol not only ensures that data remains secure but also broadens the range of potential applications by accommodating different computational capacities. The marriage of cloud resources and private set computation offers a promising direction for the future of data security in a cloud-dominated era.
Optimizing Brain-Computer Interfaces through Spiking Neural Networks and Memristors
Authors: Xu Jiahong
Published: April 2024

Brain-computer interfaces (BCIs) have emerged as a transformative conduit bridging the human brain's intricate realms and computing systems' capabilities. However, numerous challenges remain in improving BCI accuracy, efficiency, and adaptability. This paper investigates the integration of spiking neural networks (SNNs) and memristors to optimize BCI performance. SNNs offer exceptional potential to enhance BCI accuracy through biomimetic modeling of biological neural networks. By emulating the brain's spatio-temporal signaling patterns, SNNs may significantly improve neural decoding precision. Meanwhile, memristors can simulate synaptic plasticity and potentially enable real-time adaptive learning in BCIs. Preliminary studies demonstrate substantially improved signal processing, feature extraction, and classification capabilities when using SNNs and memristors in BCIs. This neuroinspired integration offers a compelling vision for personalized BCIs that continuously adapt to individual users. However, realizing the full potential relies on addressing lingering technical hurdles as well as emerging ethical considerations around user autonomy, privacy, responsibility, and access. Ultimately, interdisciplinary collaboration remains imperative to harness the promising trajectory of optimized BCIs while navigating the multifaceted challenges ahead.
Integrating Multi-Agent Deep Deterministic Policy Gradient and Go-Explore for Enhanced Reward Optimization
Authors: Liu Muchen
Published: April 2024

The field of Multi-Agent Reinforcement Learning (MARL) continues to advance with the development of new and effective methods. This research is centered on two prominent approaches within this field: Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Go-Explore. The study explores the synergistic potential of combining these two methodologies to enhance rewards for individual agents as well as for agent groups. In the course of this research, MADDPG is introduced into the experimental environment, providing agents with both actor networks (policy networks) and critic networks (Q networks) to implement the actor-critic model. Additionally, each individual agent is equipped with a Go-Explore network, empowering them to conduct deeper explorations of the environment and accumulate rewards at an accelerated rate, often resulting in higher overall rewards. This novel approach emphasizes achieving a balance between individual and collaborative rewards, offering a promising avenue for optimizing multi-agent systems. The results of this study demonstrate that the combined method exhibits notable advantages in certain scenarios. Specifically, it showcases a higher rate of reward accumulation and improved overall performance. This research contributes to the MARL domain by highlighting the potential of combining MADDPG and Go-Explore to enhance the efficiency and effectiveness of multi-agent systems.
Mainstreaming Climate Change Adaptation at the National Level in the Caribbean
Authors:   Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualien 974301, Taiwan, Lewis Clint
Published: April 2024

Small Island Developing States have been identified as some of the most vulnerable countries to the impacts of climate change due to inherent environmental, economic, and demographic characteristics. The cross-cutting reach of climate change impacts has led to the conversation of mainstreaming and its practicality. The study uses a qualitative research design that focuses on interviews with senior officials in the Caribbean at the national and regional levels. The study aims to identify the drivers of, barriers to adaptation mainstreaming into national policies and development plans in the Caribbean, and to derive actions needed to achieve mainstreaming at a national level. The main drivers of mainstreaming are the region’s vulnerability, institutional arrangement, and the government budget, while the major barriers include poor planning and governance, insufficient human resources, and competing development priorities. The paper proposes several key initiatives and actions needed at a national level that can help the region to achieve adaptation mainstreaming. To this end, mainstreaming adaptation at the national level is an essential strategy for building resilience to the impacts of climate change within the region. It cannot be a “one size fits all” approach but one that is tailored by countries to fit the countries’ circumstances and cultures.
IBA promotes adventitious Rhizogenesis in juvenile shoot cuttings of Albizia procera
Authors: Singh Sanjay , Kumar Pramod , Ansari S.
Published: April 2024

Albizia procera is a multipurpose tree of immense potential in forestry pursuits. In this very difficult-to-root species, lack of adventitious rhizogenesis in shoot cuttings creates problem in clonal propagation and mass multiplication. However, significant adventitious rooting has been achieved in softwood juvenile cuttings taken from seedlings. Low concentration (100 ppm) of IBA enhances induction and growth of adventitious roots. The technique would help in clonal propagation of Albizia procera especially in case of half-sib progenies of selected superior genotypes and hybrids of known parentage.
Impact of Ascorbic acid in artificial diet on the growth and development of Shisham defoliator, Plecoptera reflexa Guen.
Authors: Prasad Lalji , Ansari Israr , Chandra Subash
Published: April 2024

The effect of ascorbic acid in artificial diet on the growth and development of Plecoptera reflexa from larva to adult has been studied. For want of ascorbic acid, the moths obtained from such diets were relatively small in size, malformed body and short life-span.
Studies on the Algal plant diversity of Pong Dam Wetland
Authors: Srivastava S. , Gupta R.
Published: April 2024

The paper presents a preliminary account of the vegetation pattern and floristic diversity in the catchment area of the Pong Dam wetland in Himachal Pradesh. Brief description of 41 species of algae with line drawings of ca. 22 species have been provided, which include one new record for Indian territory. Some significant observations related to the habitat and their ecology have been made, with a note on their status of occurrence and distribution in India.
From the Photosynthesis to Hormone Biosynthesis in Plants
Authors: Choi Hyong Woo
Published: April 2024

Land plants produce glucose (C6H12O6) through photosynthesis by utilizing carbon dioxide (CO2), water (H2O), and light energy. Glucose can be stored in various polysaccharide forms for later use (e.g., sucrose in fruit, amylose in plastids), used to create cellulose, the primary structural component of cell walls, and immediately metabolized to generate cellular energy, adenosine triphosphate, through a series of respiratory pathways including glycolysis, the tricarboxylic acid cycle, and oxidative phosphorylation. Additionally, plants must metabolize glucose into amino acids, nucleotides, and various plant hormones, which are crucial for regulating many aspects of plant physiology. This review will summarize the biosynthesis of different plant hormones, such as auxin, salicylic acid, gibberellins, cytokinins, ethylene, and abscisic acid, in relation to glucose metabolism.
Faktor yang Berhubungan Dengan Pemanfaatan Pelayanan Posyandu Lansia di Wilayah Kerja Puskesmas Antara Kota Makassar
Authors: Icha Aisyah Nur Rahma , Amran Razak  , Suci Rahmadani
Published: April 2024

The coverage of health services for the elderly at the Antara Public Health Center is 62.1%. Posyandu for the elderly is an effort made to increase health coverage for the elderly, the elderly who utilize Posyandu are 558 people out of 1152 elderly. This study aims to determine the factors related to the use of Posyandu for the elderly in the work area of ​​the Antara City Health Center in Makassar. The type of research used is quantitative research design, analytical descriptive study, with a cross sectional approach. The sample is set at 90 samples with stratified random sampling technique. The instrument used is a structured questionnaire. Data analysis using Chi-square test. The results showed that age (p=0.093), education (p=0.000), knowledge (p=0.000), family support (p=0.000), distance (p=0.000), and perception of pain (p=0.000) has a relationship with the use of Posyandu for the elderly in the work area of ​​the Makassar City Health Center. There was no relationship between gender (p=0.147) and occupation (p=0.117) on the utilization of the Posyandu for the elderly in the work area of ​​the Antara City Health Center in Makassar.  most of the variables studied in this study showed a significant relationship. It is hoped that health workers/posyandu cadres will further increase the socialization/counseling activities given to the elderly regarding the importance of conducting health checks at the posyandu for the elderly.
Gambaran Kepatuhan Menggunakan Alat Pelindung Diri (APD) Perawat Guna Mencegah Infeksi NosokomialDi Balai Besar Kesehatan Paru Kota Makassar
Authors: Andi Nailah Amirullah  , Mudrika  , Salki Sasmita
Published: April 2024

Personal protective eqiutment (PPE) is a set of safety equitmen used by workers to protect all or part of its body from the possibility of exposure to potential hazards in the work environtment agains aoccupational accidents and disease. Nurse are required to use personal protetive aquipment to reduce accopational safety and health risks in hospitals in providing nursing action. The purpose of this study was to know the description of knowledge of attitude and actions on the nurse with using personal protective aquitment in terms to prevent nosocomial infection in Central Pulmonary Health Makassar City. This research was conducted at Central Pulmonary Healt Makassar which an descrptif analytical research. The number of samples was 52 respondents who worked at inpatient care room. The sampling technique was made by total sampling technique. The results of the study demonstrating statistic test result obedience the nurse to use PPE show suficient results is 30 peopel (80,3%) and less 22 peopel (19,7 %). Knowledge of nurse using PPE, enough is 14 people (19,4%) and less 38 peopel (80,6%). Based on attitude, which is enough 19 people (29,1%) and less 33 peope (70,9%). Based on action, enough as much 38 people (78,3%) and less 14 people (21,7%) at Balai Besar Kesehatan Paru Kota Makassarin inpatient care room at Central Pulmonary Health Makassar. From the results of this study is expected to perform various activities and improving the quality of human resources, especially nurses to better understand the PPE, and in addition to knowing and understanding well carry out and properly use the Personal Protective Equipment in order to avoid all threats of danger.
Hubungan Panjang Badan Lahir dan Berat Badan Lahir Dengan Terjadinya Stunting Pada Balita Didaerah Lokus dan Non Lokus Stunting Dikabupaten Sidrap
Authors: Devi Akib Resky , Syahriani  , St. Nurbaya
Published: April 2024

Stunting is one of the problems that occur in toddlers today. Globally, stunting in children under five reached 165 million in 2011. According to the World Health Organization (WHO) in the Global Nutrition Targets 2025, stunting is an irreversible growth disorder that is influenced by mostly inadequate nutritional intake and is caused by repeated infections for 1000 days. first life. Therefore, it is very important for children aged 0-2 years to ensure optimal nutritional intake. This study aims to determine how the relationship between birth length and birth weight occurs in stunting loci and non locus areas. The purpose of this study was to determine whether there was a relationship between the incidence of birth length and birth weight with the incidence of stunting. This study used a correlation analytic survey method with a cross-sectional time approach. with sampling using total sampling, data analysis using Chi Square. Research results There is a relationship between the incidence of birth length and birth weight with the incidence of stunting in locus and non-locus areas in Sidrap district.
Tinjauan Pelaksanaan Controlling (Pengawasan) Sistem Penjajaran Rekam Medis di Rumah Sakit Annisa Cikarang
Authors: Putrikama Nurhaliza , Rosmala Dewi Deasy , Fannya Puteri , Aula Rumana Nanda
Published: April 2024

Medical records are one of the important files in health care facilities, both in clinics, health centers and hospitals because they contain a patient's medical history. In order to keep these medical records available when needed, supervision is needed. Thus, from the supervision of the implementation of medical records in the filing room of hospital institutions, the process can be known, whether there are deviations or errors and the extent of errors that occur in the process. Research on the implementation of the Controlling (Supervision) medical record alignment system was carried out at the Annisa Cikarang Hospital. This study aims to determine how the supervision of the medical record alignment system at the Annisa Cikarang Hospital. This study uses a qualitative method with informants 1 medical record coordinator, 1 person in charge of filing and 6 officers in the filing room. The results of the research obtained are that there is no SOP for alignment and supervision that has not been carried out using an expedition book. Suggestions for the Annisa Cikarang Hospital are to add procedures in the alignment to be more detailed, and to have an expedition book to make it easier to track medical records.
Gambaran Tingkat Pengetahuan Kader Tentang Manfaat Posyandu Pada Ibu Hamil
Authors: Harahap Ummi Irmadani , Hasibuan Evi Erianty
Published: April 2024

Cadres are human figures who attract public attention, the percentage of active cadres nationally is 69.2%, so the drop out rate for cadres is around 30.8%. The benefits of posyandu for pregnant women are that pregnant women will be monitored for their weight, receive 1 tablet of added blood every day, pregnant women check themselves regularly and want to give birth assisted by health workers, pregnant women and fertile women (WUS) receive Tetanus Toxoid (TT) immunization . The purpose of this study was to find out how the level of knowledge of cadres about the benefits of posyandu for pregnant women in the Padangmatinggi Public Health Center, Padangsidimpuan City in 2013. This type of research was quantitative with a descriptive design using a questionnaire. The population in this study were 110 cadres in the Padangmatinggi Public Health Center with a total sample of 22 people, the sampling method was systematic random sampling. The results of the study based on knowledge the majority had sufficient knowledge of 11 people (50%), based on the age of the majority aged 25-35 years as many as 17 people (77,27%), based on education the majority had high school education as many as 17 people (77,27%), based on occupation the majority of housewives as many as 14 people (63.63%), based on the majority of information sources from Electronic Media as many as 9 people (40.90%). Based on the discussion, the conclusions drawn from the study are a description of the level of knowledge of cadres about the benefits of posyandu for pregnant women influenced by age, education, occupation, and sources of information and the conclusion that the level of knowledge of cadres about the benefits of Posyandu for pregnant women is still quite knowledgeable. Suggestions for respondents to know more about the benefits of posyandu for pregnant women are clearer than health workers.
KAJIAN PENERAPAN PRINSIP ARSITEKTUR BIOKLIMATIK MENURUT KEN YEANG DALAM PERANCANGAN FASILITAS AGROINDUSTRI JAGUNG
Authors: Prayogo Adi , Murti Farida
Published: April 2024

Tanaman jagung merupakan salah satu komoditas unggulan setelah padi dan termasuk salah satu komoditas tanaman palawija utama di Indonesia yang manfaatnya relatif luas terutama digunakan sebagai alternatif karbohidrat yang dikonsumsi manusia serta dapat menjadi bahan pakan ternak hewan, sehingga perlunya membangun sebuah agroindustri yang dapat mengembangkan olahan jagung menjadi suatu produk unggulan, agroindustri juga menjadi peran utama dalam perkembangan sector pertanian. Pada umumnya bangunan industri banyak yang kurang mengoptimalkan energi alami sebagai energi alternatif pada bangunan dan kurangnya memperhatikan lingkungan sekitar seperti pembuangan limbah industri yang memberi dampak buruk bagi lingkungan, maka dengan menerapkan prinsip arsitektur bioklimatik yang dipakai oleh Ken Yeang bisa dapat digunakan untuk merespon permasalahan tersebut, arsitektur bioklimatik merupakan metedologi untuk memberikan arahan kepada seorang arsitek untuk memperoleh susunan rencana solusi desain yang memperhatikan hubungan antara bentuk bangunan dengan iklim lingkungan sekitar. Metode penelitian yang digunakan pada penelitian ini ialah deskriptif kualitatif dengan melakukan pengumpulan data kondisi eksisting lingkungan serta studi literatur, tujuan dari penelitian ini ialah untuk mendapatkan strategi penerapan prinsip arsitektur bioklimatik menurut Ken Yeang pada perancangan fasilitas agroindustri jagung.
The Socio-economic impact of decarbonising geographical Islands' energy systems
Authors: Agu Nkiru , Agu Nkiru , Pillai Gobind , Abi Ghanem Dana , Vakalis Stergios , Crosbie Tracey , Chen Xihui Haviour , Haralambopoulos Dias
Published: April 2024

The impact of the transition to energy autonomy on two geographical island’s local economies, through maximising renewable energy generation and storage, is assessed. The different sectors and activities that impact employment and income generation in the local economies of each of the islands are described. An empirical assessment approach based on the Keynesian Income Multiplier (KIM) is developed and applied using Analytical Hierarchy Process (AHP). Data for AHP was collated through interviews with local experts and stakeholders on each island. Gender employment and wage data was used to calculate the impact on female waged employment within the islands’ economic sectors. The analysis conducted showed that the induced local economic impact per unit of electrical energy due to the new RES-based autonomy in all sectors for male waged employment for both islands, exceeds its unit cost (LCOE). While the profits from per unit of electrical energy for the female waged employment only exceeded the unit cost (LCOE) in tourism for La Graciosa, and three other sectors in Gotland. The local economic impact from decarbonisation and 100% energy autonomy is significantly influenced by how the income from this renewable energy is recirculated within the island’s economic sectors, most essentially, tourism. Our findings suggest that strategies for community ownership and training local people to manage renewable energy facilities is necessary to maximise the benefits of the transition to energy autonomy on local communities.
Characteristics of household energy consumption in the shadow of the Russia-Ukraine war - a case study from Hungary
Authors: Tóth Géza , Jáger Viktor , Kovalszky Zsolt , Bóday Pál , Ádám Dénes , Kincses Áron , Szép Tekla
Published: April 2024

This study aims to identify the factors affecting household energy consumption by applying spatial econometric models. Our findings suggest that sharp energy price growth is followed by significant reductions in household energy consumption, but it is difficult to distinguish specific consumer groups. Weather conditions are of particular importance (compared to the EU average). The results highlight two things: a) the problem of obsolete housing stock and the need for energy efficiency improvements and b) low energy awareness of households, especially before the energy crisis. Furthermore, hotspots with higher per capita gas consumption are also identified in Hungary. The geographical dimension is crucial, and it is not possible to make general decisions on energy efficiency issues. Effective results can be achieved through spatially concentrated interventions.
Peningkatan Kosakata Peserta Vorbereitungskurs (Kursus Persiapan) Onset Deutsch melalui Penerapan Metode Lesebaum di Yayasan Indonesia Jerman
Authors: Sari Dwi Januar Mustika
Published: April 2024

Penelitian ini merupakan Penelitian Tindakan Kelas (PTK). Penelitian Tindakan Kelas ini dilaksanakan di Vorbereitungskurs (Kursus Persiapan) Yayasan Indonesia Jerman. Subyek penelitian ini adalah peserta Vorbereitungskurs (Kursus Persiapan). Onset Deutsch yang berjumlah 25 siswa (1 kelas). Penelitian ini dilakukan sebanyak 2 siklus. Kegiatan pembelajaran ini menggunakan metode Lesebaum, dengan cara siswa menuliskan kata-kata yang sudah mereka ketahui dengan bermacam jenis kata-kata. Dalam satu siklus terdiri dari 4 tahap, yaitu: a) Perencanaan (Planning), b) Pelaksanaan (Acting), c) Pengamatan (Observing) dan d) Refleksi (Reflecting). Hasil Penelitian Tindakan Kelas yang didapat yaitu terjadinya peningkatan kosakata peserta Vorbereitungskurs (Kursus Persiapan) Onset Deutsch melalui penerapan metode Lesebaum. Hal tersebut dilihat dari rata-rata perolehan score siklus I sebesar 69,36 dan menjadi meningkat pada rata-rata perolehan score siklus II sebesar 78,4 sehingga terjadi peningkatan sebanyak 9,04 %.
Kesantunan Bahasa Sunda dalam Novel Rasiah nu Goréng Patut karya Joehana
Authors: Ainina Alifia Nur , Sobarna Cece , Kadir Puspa Mirani
Published: April 2024

Penelitian ini berjudul “Kesantunan Bahasa Sunda dalam Novel Rasiah nu Goréng Patut”. Penelitian ini merupakan kesantunan bahasa Sunda sebagai tata krama berbahasa menyangkut bidang sosiopragmatik. Kesantunan bahasa Sunda dikenal dengan istilah undak-usuk basa Sunda atau tata krama berbahasa Sunda. Undak-usuk basa Sunda meliputi bahasa Sunda kasar dan bahasa Sunda lemes. Tujuan penelitian ini adalah mendeskripsikan bagaimana kesantunan bahasa Sunda pada novel Rasiah nu Goréng Patut. Metode yang digunakan adalah kualitatif. Objek penelitian ini adalah novel Rasiah nu Goréng Patut karya Soekria/Joehana. Hasil penelitian menunjukkan bahwa kesantunan bahasa Sunda terbagi berdasarkan penutur teman usia sebaya, penutur suami/istri, penutur anak/orang tua, penutur majikan dan pekerja, dan penutur orang tidak dikenal. Dari data tersebut, dapat disimpulkan kesantunan bahasa Sunda berdasarkan tuturan cenderung menggunakan undak-usuk bahasa Sunda kasar dan undak-usuk bahasa Sunda campuran kasar-lemes.
Perbandingan Model Kebangkrutan Grover, Springate, Zmijewski dan Ohlson pada Perusahaan Properti dan Real Estate Tahun 2020-2022
Authors: Haloho Charlina Rosenna , Setyawan Surya
Published: April 2024

Efek pemantauan khusus yang diterbitkan oleh Bursa Efek Indonesia (BEI) menjadi peringatan dini terjadinya kebangkrutan pada perusahaan, sehingga tujuan penelitian ini memprediksi kebangkrutan pada perusahaan yang terdaftar dalam efek pemantauan khusus di tahun 2020-2022, mendeteksi perbedaan keempat model kebangkrutan Grover, Springate, Zmijewski, dan Ohlson, dan meninjau tingkat akurasi dari keempat model tersebut. Penelitian eksplanatif ini, mendeteksi 15 sampel perusahaan properti dan real estate dengan menggunakan uji One-Way Anova. Hasil penelitian ini menjelaskan terdapat dua perusahaan yang terhindar dari prediksi keempat model kebangkrutan yaitu kode emiten BAPI dan REAL. Terdapat perbedaan yang signifikan pada model kebangkrutan Zmijewski, sedangkan model Ohlson yang tidak terdeteksi perbedaan dengan ketiga model lainnya. Tingkat akurasi menunjukkan keakuratan tertinggi pada model Zmijewski sebesar 87 persen, dilanjutkan Grover, Ohlson, dan Springate.
Jinas Al-Asy’ar Fi Al-Kitab Ta’lim Al-Muta’allim Li Asy-Syaikh Az-Zarnujy (Dirasah Tahliliyyah Balaghiyyah)
Authors: Yuniar Siti Elisa , Hikmah Khizanatul
Published: April 2024

Penelitian ini bertujuan untuk mengetahui unsur-unsur al-muhassinat al-lafdziyyah berupa jinas yang merupakan cabang dari ilmu badi’- bagian dari sub pembahasan dalam ilmu balaghah. Objek dalam penelitian ini ialah syair-syair yang terkandung dalam kitab ta’lim al-muta’allim yang ditulis oleh syaikh Az-Zarnujy, yang di dalamnya membahas mengenai tata cara menuntut ilmu. Penelitian ini menggunakan metode kualitatif deskriptif, dengan metode pengumpulan data berupa studi kepustakaan, peneltian ini temasuk dalam jenis penelitian teori murni, teknik dalam pengumpulan data menggunakan teknik dokumentasi, sedangkan sistem analisis dan pengolahan data yang digunakan ialah dengan mengunakan analisis isi dengan beberapa tahapan, yaitu pengumpulan data, reduksi data, pengklasifikasian, dan penarikan kesimpulan. Berdasarkan analisis yang telah dilakukan, diketahui bahwa secara keseluruhan, terdapat 57 bait syair yang mengandung 61 jenis jinas yang terbagi menjadi 3 jinas tam, 44 jinas ghayru tam, dan 14 mulhaq bil jinas (yang diserupakan dengan jinas). 3 jinas tam yang dimaksud yaitu 2 jinas mumatsil dan 1 jinas murakkab mafruq. 44 jinas ghayru tam yang dimaksud terdiri dari 6 jinas mudhari’, 17 jinas lahiq, 6 jinas muharraf, 8 jinas mutharraf, 6 jinas mudzayyal, dan 1 jinas qalb ba’d. Sedangkan 14 mulhaq bil jinas yang dimaksud ialah 14 jinas isytiqaq.
A Pembelajaran Teks Persuasi dengan Pendekatan Multimodal Berbasis Poster untuk Meningkatkan Kreativitas Siswa Kelas XI
Authors: Naibaho Aurora Stevani , Wahyuni Lilik
Published: April 2024

Kreativitas merupakan hal yang harus dimiliki siswa, agar dapat menyelesaikan kendala belajar, serta mampu menemukan hal baru yang dapat diaplikasikan didalam kehidupan terutama dalam bersaing di perkembangan zaman yang sudah sangat maju. Dalam upaya mewujudkan SDGs point keempat yaitu membuat pendidikan di Indonesia berkualitas, harapannya pendidik dapat memberi siswa bekal untuk mengembangkan keterampilan kreatif, berpikir kritis, berkomunikasi, serta kemampuan kolaborasi yang kuat atau yang dikenal dengan keterampilan 4C. Salah satu yang dapat dilakukan adalah menerapkan pembelajaran yang lebih interaktif, inovatif dan efektif, seperti penerapan pembelajaran multimodal dengan metode ceramah dan project based learning yang dipadukan dengan metode kerja kelompok antar siswa. Tujuan penelitian ini adalah untuk menganalisis bagaimana kreativitas siswa serta respon siswa terhadap penerapan pembelajaran multimodal sebagai upaya meningkatkan kreativitas siswa berbasis poster persuasi digital. Penelitian ini merupakan Penelitian Tindakan Kelas (PTK), dengan melakukan dua siklus, dengan masing-masing siklus dilakukan perencanaan, pelaksanaan, observasi, serta refleksi. Populasi penelitian ialah siswa XI-8 SMAN 6 Malang yang melibatkan sebanyak 34 siswa. Data dikumpulkan dengan kuesioner yang disebar, beserta penilaian kreativitas siswa berdasdarkan indikator kreatif. Hasil penelitian menujukkan peningkatan kreativitas siswa dan respon yang baik oleh siswa terhadap proses pembelajaran yang telah dilakukan.