bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Genetic Algorithm and Neural Network-Based Cyber Security Approach for Enhanced Detection of DDoS and Malware Attacks in Wide Area Networks
Authors: Anusooya Anusooya ,   Assistant Professor (Selection Grade) /ECE, B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India,   Assistant Professor, Department of ECE, Nehru Institute of Engineering and Technology, Coimbatore, India,   Assistant professor, Department of ECE, United Institute of Technology, Coimbatore, India,   Assistant Professor, Department of ECE, Kings Engineering College, Irungattukottai, India,   Professor, Department of ECE, Mahendra Institute of Technology, Namakkal, India, Revathi N. , P Sivakamasundari , A.N.Duraivel A.N , S.Prabu S
Published: July 2024

This study addresses the growing threat of network attacks by exploring their types and analyzing the challenges associated with their precise detection. To mitigate these threats, we propose a novel cyber security approach that integrates Genetic Algorithm (GA) and neural network architecture. The GA is employed for the selection and optimization of attributes that represent DDoS and malware attack features. These optimized features are then fed into a neural network for training and classification. The effectiveness of the proposed approach was evaluated through precision, recall, and F-measure analyses, demonstrating superior detection capabilities for DDoS and malware attacks compared to existing methods. Furthermore, we introduce a hybrid approach that combines Swarm Intelligence (SI) and nature-inspired techniques. The GA is utilized to select features and reduce the dataset size, followed by the application of Discrete Wavelet Transform (DWT) with Artificial Bee Colony (ABC) to further filter irrelevant features. The results show that this hybrid approach significantly enhances the accuracy and efficiency of network attack detection in wide area networks.
The effect of Article 354.1 of the Criminal Code of the Russian Federation in space and in a circle of persons
Authors: Советова М.В.
Published: July 2024

В статье анализируются уголовно-правовые основания действия статьи 354.1 УК РФ в пространстве и по кругу лиц. Автор приходит к выводу о невозможности экстратерриториального применения ст. 354.1 УК РФ ввиду необеспеченности уголовно-правовых оснований действия нормы необходимыми экстратерриториальными юрисдикционными полномочиями; формулируются предложения для обеспечения такой возможности.

The article analyzes the criminal law grounds for the operation of Article 354.1 of the Criminal Code of the Russian Federation in the space and in the circle of persons. The author concludes that the extraterritorial application of Article 354.1 of the Criminal Code of the Russian Federation is impossible due to the lack of security of the criminal law grounds for the operation of the norm with the necessary extraterritorial jurisdictional powers, and formulates proposals to ensure this possibility.
On the essence and significance of search investigative actions aimed at detecting and seizing electronic storage media
Authors: Шолохов С.В.
Published: July 2024

В статье рассматриваются сущность и значение следственных действий, направленных на обнаружение и изъятие электронных носителей информации. Анализируются существующие основные классификации следственных действий, определяются основные критерии, по которым следует группировать поисковые следственные действия в отдельную категорию. В обоснование своих умозаключений автор приводит мнения ученых, посвятивших исследования указанной проблематике. Автор предлагает рассматривать осмотр, обыск и выемку как систему поисковых следственных действий, направленных на отыскание, восприятие и закрепление криминалистически значимой информации. Обозначается значимость грамотно спланированных поисковых следственных действий в связи с развитием технологий удаленного доступа к информации, а также облачных сервисов хранения и обработки данных. Отмечается, что в работе следственно-оперативной группы возникают дополнительные технические сложности, когда изъятие электронного носителя информации провоцирует цепь событий, приводящих к шифрованию информации, блокировке доступа к содержимому носителя, активации предупредительных сигналов сообщникам.

The article examines the essence and significance of investigative actions aimed at the detection and seizure of electronic media. The existing basic classifications of investigative actions are analyzed, the main criteria by which search investigative actions should be grouped into a separate category are determined. In substantiation of his conclusions, the author cites the opinions of scientists who have devoted research to this issue. The author suggests considering inspection, search and seizure as a system of search investigative actions aimed at finding, perceiving and securing forensically significant information. The importance of well-planned investigative search actions in connection with the development of technologies for remote access to information, as well as cloud services for data storage and processing is indicated. It is noted that additional technical difficulties arise in the work of the investigative task force when the seizure of an electronic data carrier provokes a chain of events leading to the encryption of information, blocking access to the contents of the media, and activation of warning signals to accomplices.
Crimes against the peace and security of mankind: no statute of limitations
Authors: Федоров А.В.
Published: July 2024

Статья посвящена преступлениям против мира и безопасности человечества. Рассматриваются становление международно-правового понятия «преступления против мира и безопасности человечества»; определение преступлений против мира и безопасности человечества как преступлений без срока давности; признание судебными решениями преступлений нацистов против человечества, совершенных в период Великой Отечественной войны, геноцидом. Особое внимание обращено на «персонализацию» ответственности за преступления против мира и безопасности человечества, в связи с чем проанализированы положения законодательства Республики Беларусь, регламентирующие производство по уголовным делам в случае смерти лица, подлежавшего привлечению к уголовной ответственности за совершение преступлений, не имеющих сроков давности. С учетом белорусского опыта предлагается дополнить российское уголовно-процессуальное законодательство положениями о возможности «посмертного» осуждения лиц, совершивших преступления без срока давности. Отмечена актуальность такого решения в связи с преступлениями киевского режима против мира и безопасности человечества.

The article is devoted to crimes against the peace and security of mankind. The article considers the formation of the international legal concept of crimes against the peace and security of mankind; the definition of crimes against the peace and security of mankind as crimes with no statute of limitations; the recognition by judicial decisions of Nazi crimes against humanity committed during the Great Patriotic War as genocide. Particular attention is paid to the “personalization” of responsibility for crimes against the peace and security of mankind, in connection with which the provisions of the legislation of the Republic of Belarus regulating criminal proceedings in the event of the death of a person subject to criminal liability for committing crimes that have no statute of limitations are analyzed. Taking into account the Belarusian experience, it is proposed to supplement the Russian criminal procedural legislation with provisions on the possibility of “posthumous” conviction of persons who have committed crimes with no statute of limitations. The relevance of such a decision is noted in connection with the crimes of the Kyiv regime against the peace and security of mankind.
On the procedural powers of the head of the inquiry body and the head of the inquiry unit in pre-trial criminal proceedings
Authors: Оленев П.А.
Published: July 2024

В статье рассматриваются некоторые вопросы процессуальных полномочий начальника органа дознания и начальника подразделения дознания в досудебном уголовном производстве. Исследуется, в частности, вопрос о расширении круга процессуальных полномочий начальника органа дознания на стадии возбуждения уголовного дела, а также с учётом реформы УПК РФ 2007 года в части разграничения процессуальных статусов начальника подразделения дознания, руководителя следственного органа и прокурора при реализации уголовно-процессуального института процессуального контроля за деятельностью дознавателя при расследовании уголовного дела.

The article discusses some issues about the procedural powers of the head of the inquiry body and the head of the inquiry unit in pre-trial criminal proceedings. In particular, the issue of expanding the range of procedural powers of the head of the inquiry agency at the stage of initiating a criminal case is being explored, as well as taking into account the reform of the Code of Criminal Procedure of the Russian Federation in 2007 in terms of delimiting the procedural statuses of the head of the inquiry unit, the head of the investigative agency and the prosecutor in the implementation of the criminal procedural institute of procedural control over the activities of the investigator during the investigation of a criminal case.
A Study on the Impact of College Students' Media Literacy on Learning--Effectiveness in the Context of New Media
Authors: Zhang Yunxi
Published: July 2024

In the context of new media, the impact of media literacy on college students' learning effectiveness has been widely noticed, but there are still many shortcomings. This paper analyzes the current situation of college students' media literacy. It is concluded that they have a high degree of mediatization and a strong ability to use media, and there is a certain degree of critical thinking. However, at the same time, there are problems such as low ability to think rationally about media information and irrational behavior caused by new media cognition. Given this, this paper puts forward the following suggestions: establish an effective education model, introduce policies to regulate media behavior, and strengthen media literacy education for teacher education students. In this way, the quality and ability of college students in media cognition and use can be enhanced. This is not only related to the development of individuals but also to the construction of a harmonious society.
Litecoin price prediction based on random forest regression, LightGBM and LSTM
Authors: Lang Shaohui
Published: July 2024

Cryptocurrency has caught huge amounts of investment attentions and interests ever since its first introduction. However, due to the highly instable nature of its price, it is crucial for investors to avoid risks when investing in cryptocurrency. Studies has been conducted to predict the price of cryptocurrency with different price influencing factors using various machine learning models. On the other hand, most of them only focused on major types of cryptocurrencies, e.g., Bitcoin and overlooked minor ones. This study focuses on one type of minor cryptocurrency, Litecoin. Three machine learning algorithms, random forest regression, light gradient boosting machine (LightGBM), and long short-term memory (LSTM) are used to predict long-term Litecoin price. The effects of 19 price influencing factors are considered, including Litecoin price variables, other popular cryptocurrency prices, major foreign exchange prices, market indices, and major commodities prices. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared score are used to evaluate model performances. The results suggest that, among the 3 models, random forest model shows the best prediction with the least error, while LSTM model has the most error. Such result can provide insights for investors to avoid risks in Litecoin investments. Future studies are still necessary to take more types of cryptocurrency and price influencing factors into consideration.
The investigation of symptom to disease chatbot based on LLAMA-2
Authors: Xu Naijie
Published: July 2024

In the swiftly advancing realm of Artificial Intelligence (AI), there has been a remarkable evolution in Natural Language Processing (NLP). Language models like ChatGPT have ushered in a new era of human-computer interaction, bestowing upon people an unparalleled capacity to comprehend and generate text that mimics human language. When these models are fine-tuned for specific domains, such as medicine, they become endowed with domain-specific knowledge, bridging the chasm between language comprehension and specialized expertise. However, a noteworthy research gap exists when it comes to harnessing AI-powered NLP for medical diagnosis and symptom analysis, particularly in the development of a Symptom to Disease Chatbot. This innovative approach aims to provide accurate and accessible healthcare information, enhancing the initial steps of medical consultation. This research proposes and develops a pioneering Symptom to Disease Chatbot, powered by the sophisticated Llama 2 13b pre-trained language model. By integrating extensive medical knowledge and AI capabilities, this chatbot seeks to empower individuals to make informed decisions about their health, potentially transforming personal health management. The research includes data preparation involving curated medical datasets, transforming them into a conversational format suitable for training. The base model, Llama 2, is fine-tuned for this medical context. While preliminary results are promising, further exploration with larger datasets is essential to enhance performance. Additionally, an open-source Gradio interface enhances user interaction. This research addresses the critical need for accessible healthcare information and demonstrates the potential of AI-powered language models in the healthcare sector.
Self-driving car implementation guided by computer vision for detection
Authors: Yu Pinyi
Published: July 2024

This conference paper discusses the influence of computer vision in self-driving car implementation. The automotive industry has experienced a significant transformation by integrating artificial intelligence, such as computer vision. The global market for AI in automotive has grown remarkably since many organizations in the electric vehicle industry have engaged in computer vision due to its benefits. Object detection in computer vision contributes to autonomous driving systems, assisting self-driving vehicles in classification of the road objects accurately. This paper has provided an overview of the importance of computer vision to self-driving cars, such as user experience, reduction in cost and vehicle safety. However, some challenges, such as adverse weather conditions, have also been experienced. A case study analysis has been done to have practical implications of the self-driving car implementation guided by computer vision for detection.
Image authentication and tamper localization based on coupling between adjacent pixels
Authors: Jiang Qingyi
Published: July 2024

Digital image information has the advantages of easy storage and communication, especially with the continuous emergence of powerful image processing software, editing and modifying digital images has become extremely convenient. Subsequently, issues such as low security and easy tampering of digital images have emerged, and the integrity and authenticity of images have been questioned. Some important applications, such as news images, court evidence, medical diagnoses, etc., are not allowed to have their content modified. Passive authentication methods are often only suitable for specific images or situations, dont have the ability to locate the tamper areas. Active methods based on fragile watermarks often embed external information, making it inconvenient to perform blind authentication on the receiving end and resist malicious attacks that aim at bypassing tamper detection. In this paper, we propose to combine the advantages of passive authentication and active authentication. Firstly, an image is first divided into non-overlayed blocks, then generate check code for each pair of strongly coupled pixels within the same block. Fragile watermark technology is exploited to embed the check codes randomly based on a private key in the pixels of the image itself to achieve blind authentication for the receiver. Finally, we conduct the experiment in which a large number of images have been simulated for tampering and detected for authentication. The results show that compared with other similar methods, this paper not only has high detection accuracy, but also has high accuracy in locating the tampering location. In addition, the method proposed in this paper has other advantages in terms of computational cost and security.
Machine learning application: A kind of prostate disease early warning model
Authors: Zhang Rener
Published: July 2024

Prostate Diseases pose significant health risks, and the author has developed an integrated machine learning model using a medical indicator dataset of prostate patients. The article introduces seven different machine learning algorithms for classification tasks. The approach involved detailed exploratory data analysis, descriptive statistics, feature engineering, and data visualization. Additionally, data preprocessing was performed by addressing missing values and eliminating non-numeric characters. During the model training process, cross-validation techniques are employed to determine the optimal model parameters, ensuring the accuracy of the training. Furthermore, the training performance of the seven models is assessed through histograms and ROC curves. Based on their performance, three models are selected for ensemble modeling, aiming to further enhance training accuracy and improve precision. Conclusively, the findings indicate that the likelihood of prostate diseases correlates significantly with the medical indicator generated through feature engineering, specifically PSA (free)/PSA (Total), aligning with clinical guidelines for diagnosing prostate diseases. Furthermore, individual baseline data indicators such as body weight have a crucial impact on the likelihood of prostate disease, with obesity serving as a significant risk factor. Among the individual models, the k-Nearest Neighbors (KNN) model achieved the highest accuracy, while the ensemble model further improved accuracy. In summary, the work effectively alerts individuals to the potential occurrence of prostate cancer and hyperplasia by evaluating medical indicators. Ultimately, this initiative aims to raise awareness of maintaining good health and reducing the risk of prostate diseases.
Prediction of maternal health risk levels based on multiple machine learning algorithms
Authors: Yang Xu
Published: July 2024

With the development and popularisation of the Internet of Things (IoT) technology, more and more medical devices have begun to connect with the Internet, forming IoT medical devices. Among them, for pregnant women's health risk monitoring, IoT devices can achieve remote monitoring, data transmission, intelligent analysis and other functions to provide more comprehensive health management services for pregnant women. To evaluate the model's effectiveness in predicting the health risk level of pregnant women, we use four metrics: precision, accuracy, recall, and F1 score. Where precision indicates the proportion of correctly predicted samples to the total number of samples; accuracy indicates the proportion of true cases to the number of all samples predicted to be positive; recall indicates the proportion of true cases to the number of all samples that are actually positive; and F1 score is the reconciled average of precision and recall. By using these metrics, we can more fully assess the predictive effectiveness of the model. In this paper, we use three models, decision tree, random forest and logistic regression, for training and testing. All three models are common machine learning algorithms, but they each have different strengths and weaknesses. By training and testing the three models, we found that the random forest model performed the best, with a prediction accuracy of 80%. In conclusion, this paper describes an IoT-based health risk monitoring system for pregnant women, trained and tested using three models: decision tree, random forest and logistic regression, it can provide more comprehensive health management services for pregnant women.
Research on sales strategies for target customers of electric vehicles based on gradient boosting ensemble trees
Authors: Yang Kaiwen
Published: July 2024

This paper focuses on the automotive industry, especially the new energy vehicle sector, which is vital for the economy and needs careful market sales decisions. It analyzes data from 1,964 potential customers, including their satisfaction and personal information. The study uses machine learning to create a math model and give sales advice. The data is first cleaned without changing its original structure, and then normalized using the Z-Score method. For any outlier values in the data, the mean of similar items is used to fill in gaps. Specifically, for 500 missing entries about the number of children (B7), the paper groups customers by age, calculates the frequency of children in each group, and uses these frequencies to fill in missing values.Using the cleaned data, the XGBoost algorithm ranks the factors influencing customer decisions. For Brand 1, the top factors are economy (a3), work experience (B10), and safety performance (a4). For Brand 2, comfort (a2), disposable income (B15), and mortgage expenses (B16) are key. For Brand 3, car loan expenses (B17), battery technology (a1), and again mortgage expenses (B16) are most important.The paper also addresses imbalanced data by using an XGBoost binary classification model. This model predicts the likelihood of buying an electric vehicle using the Sigmoid function on tree node weights. It evaluates the model using Precision, Recall, and Area Under the Receiver Operating Characteristic Curve (AUC), which works well even with imbalanced data. The Precision and Recall rates for all three brands are above 90%, with AUC over 85% for Brands 1 and 2, and over 75% for Brand 3. This shows that the model is effective and can be used in real-world situations.
Prediction of building settlement amount based on the ARIMA-LSTM-GXBoost combined model
Authors: Chen Qiaojun , Qin Jiale , Zhang Tianyu , Guang Jiahe , Gao Jiahao
Published: July 2024

Accurate prediction of building settlement amounts is crucial for ensuring the safety of building structures and human lives. Addressing issues such as the scarcity of building settlement measurement data and the lack of weighting in combined prediction model results, which have not yet led to further improvements in prediction accuracy, this study first selected building CJ06 in Yizheng City, Yangzhou, as the research subject. Polynomial fitting was applied to the obtained non-equidistant time series data, and the fitting data with a preferable polynomial order was chosen to replace the original numerical data. Then, using the first 140 days of data as the training set and the subsequent 63 days of data as the test set, an ARIMA (AutoRegressive Integrated Moving Average) model was initially established for the building settlement, to address the linear features in the fitted sequence. This was followed by using LSTM (Long Short-Term Memory) to correct the ARIMA model's prediction residuals, and combining the results with the XGBoost (Extreme Gradient Boosting) prediction model. Weights were determined using the reciprocal error method for combined prediction, and the results of the combined prediction model were compared with those of the ARIMA-LSTM, ARIMA, LSTM, and GXBoost models. The comparison periods were chosen as follows: 143d, 158d, 173d, 188d, and 203d; ultimately, the maximum absolute relative error and mean square error (MSE) were used as the evaluation metrics for model prediction accuracy. The results showed that the ARIMA-LSTM-GXBoost prediction model's results were more closely aligned with actual values, with a maximum absolute relative error and mean square error of 1.68% and 0.03, respectively. The prediction accuracy was higher than the other four comparison models, achieving high-precision prediction of building settlement amounts.
Segmentation of subcutaneous fat CT images based on the TransUNet model
Authors: Chen Ruiying
Published: July 2024

With the improvement of living standards, people pay more and more attention to the aesthetics and health of the body, and subcutaneous fat is one of the most important fat storage areas in the human body, so its segmentation and quantitative analysis are of great clinical significance. Subcutaneous fat CT image segmentation is an important research direction in the field of medical image processing. In this paper, image segmentation of the fat part of CT images is performed based on the TransUNet model, and the accuracy and IoU of segmentation are calculated.The results show that the prediction error of the model decreases gradually, and the accuracy reaches 96.2% and the IoU reaches 89.62, which indicates that the model has learnt the patterns and features in the dataset, and is able to accurately predict the new data. This paper provides an effective method for subcutaneous fat CT image segmentation, which is expected to play an important role in clinical applications. The subcutaneous fat CT image segmentation method proposed in this study has high accuracy and reliability, and is of great significance in both clinical applications and medical research.
Feature-preserving adaptive 3D scene multi-model simplification algorithm
Authors: Li Xin , Huang Wenpei
Published: July 2024

In a complex 3D scene, which typically contains multiple models with distinct feature differences, determining how to set appropriate simplification rates for each model so that the 3D scene maintains its characteristics well while achieving the desired simplification rate is a challenging task. To address this, this paper proposes a 3D scene simplification algorithm that integrates neural networks to accomplish this task. The algorithm is divided into two stages: the first stage extracts local and global features of each model in the 3D scene to form feature vectors, and the second stage combines neural networks to construct a multi-layer perceptron that predicts the simplification labels for each model in the 3D scene and calculates the simplification weights for each model based on these labels. Experiments have proven that this algorithm not only maintains a good appearance of the 3D scene but also has a smaller overall simplification error compared to the traditional QEM algorithm.
Comparison of machine learning models in credit risk assessment
Authors: Chn Yuchn , Wang Hongling , Han Yuxuan , Fng Yuxuan , Lu Hongchi
Published: July 2024

Crdit risk plays an important rol in financ. In a sns, crdit risk rflcts th stability of financial institutions and th protction of invstors. Du to th impact of th pidmic on th world conomy, w nd to rassss th crdit risk. For a long tim, machin larning and dp larning in statistics hav bn vry ffctiv in prdicting crdit risk. But in machin larning and dp larning, whn prdicting crdit risk, w citd four modls, namly XGBoost, Dcision Tr, Random Forst and Convolutional Nural Ntwork (CNN) modls. Analyz th advantags and disadvantags of ths modls: XGBoost can optimiz th tr modl and prvnt ovrfitting through rgularization, but XGBoost has poor intrprtability. Dcision Tr has strong xplanatory powr, but it is prone to overfitting. Compared with th dcision tr, Random Forsts incras accuracy and rduc th probability of overfitting, but Random Forsts consum mor tim and computing rsourcs. Although Convolutional Nural Ntwork has a high accuracy rat, it abandons intrprtability. Thrfor, in our xprimnts, w found that ths modls ar not prfct and hav thir own dfcts. So, in futur rsarch, w will mak an intgratd modl to includ th advantags of th modl and discard som dfcts, so that th intgratd modl will hav bttr gnralization and accuracy.
Marine management and monitoring system based on AHP cost-benefit analysis with integrated evaluation and genetic algorithms for recurrent searches
Authors: Yuan Zhuangzhuang
Published: July 2024

Marine management and monitoring systems need to develop a safety procedure for submersibles that predicts changes in the position of the submersible over time. This would effectively prevent the submersible from losing communication with the host and would allow it to react to possible deficiencies. In this paper, a random walk model is used to predict the position of the submersible at each moment on the seafloor. The probability of the highest point on each plane is 0.87-0.92. The error in the position of the submersible caused by the uncertainty on the seafloor is corrected by the Kalman filter model. In this paper, we use AHP cost-benefit analysis to comprehensively evaluate and select the best additional search equipment. In this paper, the genetic algorithm based on cyclic search is used to determine the optimal initial deployment point and search pattern. After a large number of iterations, the shortest search path length of 817.273 is obtained, and the shortest search path is taken as the optimal path for search and rescue. In this paper, the model is extended using particle swarm algorithm and environment modelling. The predictive model of the submersible is combined with the environmental model of different sea areas. Therefore, the actual motion model of the submersible is approximated. After simulation, it was found that the accuracy of the submersible probability increased by 23.72%.
Estresse percebido e sofrimento psíquico em gestores de saúde na pandemia da Covid-19
Authors: Neves Fabiana Marin das , Silva Lorena Aparecida da , Leachi Helenize Ferreira Lima , Perfeito Ribeiro Renata , Dadalt Patricia Aroni
Published: July 2024

Objetivo: analisar o nível de estresse percebido e sofrimento psíquico em gestores de saúde na pandemia da Covid-19. Método: estudo descritivo, transversal com abordagem quantitativa. A coleta ocorreu de abril a setembro de 2021, com 40 gestores de serviço de saúde. Utilizou-se para a coleta de dados um instrumento para caracterização sociodemográfica e ocupacional e as escalas “Perceived Stress Scale-14” e “Self Reporting Questionnaire” para avaliação do estresse percebido e sofrimento psíquico. Os dados coletados foram analisados no Statistical Package for the Social Sciences versão 22.0. O presente estudo faz parte de um projeto intitulado “Trabalhadores dos Serviços de Saúde Frente à Pandemia de Covid-19”, aprovado pelo Comitê de Ética em Pesquisa sob CAAE número 35260620.9.0000.5231. Resultados: a maioria dos profissionais eram do sexo feminino (90%, N=36), casados (70%, N=28), com filhos (80%, N=32), com média de idade de 45 anos e com pós--graduação (47,5%, N=19). A média dos escores relacionados ao estresse percebido foi 31,13 pontos (DP=3,77) sendo o mínimo 24 e máximo de 42 pontos. Com relação ao sofrimento psíquico, (40%, N=16) os gestores apresentaram prováveis casos de transtornos. A prática de atividades físicas e de lazer (p<0,05) tem papel importante na diminuição do estresse percebido e do sofrimento psíquico. Conclusão: os gestores em saúde apresentaram, durante a pandemia, estresse e sofrimento psíquico, resultados esses que devem ser considerados para promoção de autocuidado aos gestores de saúde, enfatizando a necessidade da realização de atividades físicas e de lazer
Pengaruh Keterlibatan Orang Tua dan Teknologi terhadap Nilai IPK Mahasiswa Fakultas Ekonomi Universitas Negeri Medan
Authors: Siburian Marshanda , Perangin-Angin Emia , Lizu Sitanggang Twin , Lumban Batu Siska , Taufiq Umar Andi
Published: July 2024

This research aims to identify the influence of parental involvement and technology on student GPA scores at the Faculty of Economics, Universitas Negeri Medan. This research uses quantitative methods with a descriptive approach and hypothesis testing. Data collection was carried out by distributing questionnaires or Likert scales via Google Form. Data analysis uses multiple linear regression. The research results show that (1) parental involvement has a partial and significant influence on increasing student GPA with a tcount value of 4,859 which is greater than ttable 2,024, (2) technology involvement hasnt influence on increasing student GPA with a tcount value of 0.388 which greater than ttable 2,024, (3) parental involvement and technology together have a significant positive influence on increasing student GPA with a calculated Fvalue of 19,232 which is greater than Ftable 3,25. Based on these results, it can be concluded that parental involvement and technology influence the increase in GPA of students at the Faculty of Economics, Universitas Negeri Medan
Restoranlarda Kişilik Özellikleri ile Müşteri Vatandaşlık Davranışı İlişkisinde Algılanan Hizmet Kalitesinin Aracılık Rolü
Authors: Aslantürk Elif , Kınıklı Muhammet Abdulmecit , Unur Kamil , Aytemiz Tevfik
Published: July 2024

Araştırmanın amacı restoranlarda kişilik özellikleri ile müşteri vatandaşlık davranışı ilişkisinde algılanan hizmet kalitesinin aracılık rolünü incelemektir. Bu kapsamda Mersin ilinde bulunan fine dining restoranlarda yemek yiyen 325 müşteriden yüz yüze anket yöntemi kullanılarak veri toplanmıştır. Verilere uygulanan regresyon analizlerde algılanan hizmet kalitesinin aracılık etkisinin varlığı incelenmiş, inceleme sonucunda algılanan hizmet kalitesi değişkeninin, kişilik özellikleri değişkenine aracılık ettiği ve bu aracılığında tam aracılık olduğu sonucuna ulaşılmıştır. Elde edilen sonuçların restoran yöneticilerine, çalışanlarına ve bu alana yönelik çalışma yapan akademisyenlere önemli bilgiler sunacağı ve algılanan hizmet kalitesinin müşteri vatandaşlık davranışı üzerindeki etkisi konusunda önemli bilgiler verebileceği düşünülmektedir.
Studi Kualitatif Deskriptif tentang Pemahaman Mahasiswa Sekolah Tinggi Teologi Soteria Purwokerto mengenai Dosa
Authors: Santus Santoso Yudi
Published: July 2024

Penelitian ini untuk mengetahui pemahaman Mahasiswa STT Soteria Purwokerto mengenai dosa. Untuk mengetahui penegasan Alkitab tentang fakta dan gejala serta penyelesaian dosa dalam kehidupan manusia. Penelitian ini menggunakan rancangan penelitian kualitatif deskriptif dalam kajian teologi biblika, dengan penelitian lapangan melalui pertanyaan angket atau kuesioner. Pertanyaan kuesioner digunakan sebagai sumber informasi untuk mendapatkan data secara langsung dari sumbernya. Data yang terkumpul dianalisis untuk menemukan pemecahan masalah dan menemukan kesimpulan untuk menjawab masalah penelitian. Hasil dan penemuan menyatakan bahwa dosa merupakan pelanggaran terhadap hukum Allah. Putus hubungan relasi dengan Allah.  Dosa bersifat merusak dan mengakibatkan kematian. Solusi dan penyelesaian dosa adalah karya Allah melalui Yesus Kristus yang diterima melalui anugerah kelahiran baru, yang diikuti dengan pertobatan dan iman sebagai respon dari orang yang sudah dilahirbarukan untuk menikmati hidup kekal dan bertumbuh dewasa sebagai anak-anak Allah yang semakin menyerupai Kristus.
Screening dan Edukasi Penyakit Tidak Menular (PTM) Warga Desa Je’netaesa Maros
Authors: Komariah Euis Dedeh , Situngkir Rosmina , Novia Kristia , Sili Beda Nikodemus
Published: July 2024

Penyakit tidak menular (PTM) merupakan masalah kesehatan yang menjadi global burden. Aktifitas yang kurang, merokok, konsumsi alkohol, dan pola makan tidak sehat merupakan faktor penyebab kasus penyakit tidak menular (PTM). Tujuan dari kegiatan pengabdian kepada masyarakat ini adalah untuk melakukan screening dan edukasi kesehatan terkait PTM pada masyarakat di Desa Je’netaesa Kecamatan Simbang Kabupaten Maros yang dihadiri oleh 50 peserta. Sementara itu, kegiatan PKM didahului dengan screening kesehatan dilanjutkan dengan ceramah serta tanya jawab dengan peserta. Media yang digunakan dalam penyuluhan yaitu leaflet. Hasil kegiatan ini adalah Pendidikan Kesehatan dapat terlaksana dengan baik atas keterlibatan dan kerjasama tim dosen dan mahasiswa STIK Stella Maris Makassar. Dari hasil kegiatan menunjukkan jika para peserta dapat mengalami peningkatan pengetahuan dibuktikan dengan peserta dapat menjawab secara sederhana dengan bahasa yang mudah dipahami tentang pengertian dari setiap masalah kesehatan, penyebab, dan dampak dari masalah kesehatan.
Satış Stratejilerinin Oluşturulması ve Uygulanmasında Gerilla Pazarlamanın Önemi
Authors: Bağcı Selda , Kılbaş Ayşe Nur
Published: July 2024

Teknolojik ilerlemeler ve dönüşen küresel dinamikler, hem ürün hem de hizmet sunan işletmeler için yeni zorluklar ve fırsatlar yaratmaktadır. Bu bağlamda, işlevsellik ve somut özellikler artık temel bir farklılaşma stratejisi olmaktan uzaklaşmış, pazarlama faaliyetleri ise başarıya ulaşmanın kritik bir parçası haline gelmiştir. Günümüzde, geleneksel pazarlama yöntemlerinin etkinliğini yitirdiği, tüketici davranışlarının değiştiği bir dönemde, sınırlı bir bütçeye sahip olan ancak büyük hayalleri olan girişimciler için gerilla pazarlama, ideal bir pazarlama stratejisi olarak değerlendirilmektedir. Özellikle küçük ve orta ölçekli işletmelerde kullanılan gerilla pazarlama, küçük ancak etkileyici ataklarla diğer rakiplerin moralini bozmayı ve piyasada kendine bir yer edinmeyi hedefler. Bu strateji, sıra dışı fikirlerle dikkat çekmeyi, rakiplerden farklılaşmayı ve tüketiciye unutulmaz bir deneyim sunmayı amaçlar. Gerilla pazarlama, yaratıcılığı ve esnekliği bir araya getirerek, sınırlı kaynaklarla büyük etki yaratma potansiyeline sahiptir. Böylece küçük işletmeler, daha az maliyetle ve yaratıcı yaklaşımlarla büyük başarılar elde edebilirler. Bu çalışmada ilgili literatür taranarak, günümüz rekabetçi iş dünyasında firmaların ayakta kalabilmesi, rekabet üstünlüğü elde edebilmesi ve marka başarısını artırabilmesi için gerçekleştirmeleri gereken stratejilere odaklanmaktadır. Derleme makale türünde hazırlanan bu çalışmada, ürün veya hizmet sunumu sürecindeki çeşitli evrelerin - üretim, ürün geliştirme, satış ve pazarlama - her birinin önemi vurgulanmakta ve özellikle pazarlama stratejisinin, ürünün piyasaya sürülmesi ve sonrasındaki başarısı üzerindeki etkisi incelenmektedir. Geleneksel yaklaşımlardan farklı olarak, gerilla pazarlama anlayışının yaratıcı ve yenilikçi pazarlama stratejileri sunma potansiyeli tartışılmaktadır. Özetle, bu derleme, rekabetçi iş dünyasında sürdürülebilir başarıya ulaşmanın ve pazarda elde edilecek başarıyı pekiştirmenin, özgün ve yenilikçi pazarlama yöntemlerini benimseme yeteneğine bağlı olduğunu ortaya koymaktadır
Battery Health Monitoring System Lithium-Ion Based on Fuzzy Logic
Authors: Firmanto Yogo Bekti , Ahmadi Ahmadi , Purnama Rangsang , Herulambang Wiwiet
Published: July 2024

Batteries that can store electrical energy and are easy to carry make them the most practical technology choice as an electricity source. Even so, lithium-ion batteries are not free from the risk of damage when used. Therefore, a Lithium-ion battery health monitoring system was created. This system uses the INA219 sensor as a current and voltage detector and the DS18B20 sensor as a temperature detector. Arduino as a data process. The test results show that all components function well. In battery capacity testing, the highest error was 1.8%. For the DS18B20 sensor as a temperature sensor, an error of 2.4% was obtained. Testing capacity against temperature on the battery when the temperature was 25 C, the current was 485mAh; when the temperature was 44.8 C, the current was 550mAh, there was a difference of 65mAh or 11%. This difference corresponds to the difference in battery capacity. Testing using the Fuzzy Logic method was carried out on 3 batteries with different capacities to obtain the State of Health (SOH) value for each battery. Testing is carried out in real-time, as well as Matlab simulation. In battery test 1, with a capacity of 2200mAh and the highest temperature of 32.1 oC, the device's State of Health (SoH) was 90%, and Fuzzy Matlab was 87.6%. Battery 2, 1500mAh capacity with the highest temperature of 33.4oC obtained State of Health (SOH) of 60%, Fuzzy Matlab 60%. Battery 3 Capacity 2200mAh, Highest temperature 32.2 oC, State of Health (SOH) of 90%, Fuzzy Matlab 87.6%. The test results show that the overall error is still below 5%. A properly functioning Internet of Things (IoT) system can display information on lithium-ion batteries' State of Health (SoH) on devices and smartphones.
The Anthropomorphic God of James Sterba
Authors: Hrmo Patrik ,   Katolícka univerzita v Ružomberku, Filozofická fakulta, Katedra filozofie
Published: July 2024

In this paper, the author tries to give his second and more precise response to the argument of James Sterba. According to Sterba’s words, if a good God existed, he would prevent all the horrendous evil occurrences. This is because a good God, if existed, would be a rational agent and subject to moral requirements. Regarding the Thomistic notion of eternal law, Sterba says that God is not an abstract norm. Moreover, he says that, based on Thomism, God is exonerated from the responsibility for evil. As a result, wrongdoers are exonerated from such responsibility as well. The author of this paper argues that God, while being the eternal law, is still rational. It follows that God is the perfect moral agent. Finally, the actions of God and wrongdoers are logically incomparable. The author improves the contemporary interpretation of Thomas Aquinas on some points and, by that, responds to Sterba’s objections.
Enhancing Photovoltaic Array Performance through Optimizing Power during Mismatch Conditions under Series-parallel (SP) and Total Cross-Tied (TCT) Configurations
Authors: Abad Lahlou , Tamalouzt Salah , Djermouni Kamel , Sayeh Karim Fathi
Published: July 2024

Photovoltaic (PV) systems, harnessed from the sun's energy, serve as a vital component in the global shift towards sustainable energy sources. This paper presents a comprehensive investigation into the performance optimization of PV arrays operating under mismatched conditions, examining both Series-Parallel (SP) and Total Cross-Tied (TCT) configurations. The study explores the influence of variations in series and parallel resistances within PV modules on power generation. Two Maximum Power Point Tracking (MPPT) techniques are employed to enhance system efficiency. Our research unequivocally demonstrates the superiority of the TCT configuration, yielding a remarkable 28-watt advantage over the SP configuration when subjected to internal resistance changes. Additionally, the application of fuzzy logic-based MPPT exhibits exceptional responsiveness, surpassing the conventional Perturb and Observe (P&O) approach. These findings emphasize the pivotal role of system configuration and control strategies in optimizing PV array performance under varying operational conditions. This study contributes valuable insights to advance the harnessing of solar energy and underscores the significance of configuration and control methodologies in maximizing power output from PV systems
Gastrointestinal problems in a valproic acid-induced rat model of autism: From maternal intestinal health to offspring intestinal function
Authors: Li Sha , Zhang Nan , Li Wang , Zhang Han-Lai , Wang Xiao-Xi
Published: July 2024

BACKGROUND

Autism spectrum disorder (ASD) is a developmental disorder characterized by social deficits and repetitive behavior. Gastrointestinal (GI) problems, such as constipation, diarrhea, and inflammatory bowel disease, commonly occur in patients with ASD. Previously, GI problems of ASD patients were attributed to intestinal inflammation and vertical mother-to-infant microbiome transmission.

AIM

To explore whether GI problems in ASD are related to maternal intestinal inflammation and gut microbiota abnormalities.

METHODS

An ASD rat model was developed using valproic acid (VPA). Enzyme-linked immunosorbent assay and fecal 16S rRNA sequencing were used to test GI changes.

RESULTS

VPA exposure during pregnancy led to pathological maternal intestinal changes, resulting in alterations in maternal gut microbiota. Additionally, the levels of inflammatory factors also increased. Moreover, prenatal exposure to VPA resulted in impaired duodenal motility in the offspring as well as increased levels of inflammatory factors.

CONCLUSION

GI problems in ASD may be associated with maternal intestinal inflammation and microbiota abnormality. Future research is required to find more evidence on the etiology and treatment of GI problems in ASD.
Characterization of the Growth of Pseudomonas sp. strain DrY135 on Acetamide
Authors: Halmi Mohd Izuan Effendi , Rahim Mohd Badrin Hanizam Abdul , Uba Garba , Yaka Hafeez Muhammad
Published: July 2024

Acetamide is heavily used as a plasticizer and stabilizer and in the methylamine manufacturing process. There has been a modest but steady rise in worldwide interest in microbe-mediated acetamide decomposition or degradation as a bioremediation method. A previously isolated molybdenum-reducing bacterium with amide-degrading capability is characterized for its growth on acetamide in this study. The bacterium growth optimally in between 500 and 1000 mg/L of acetamide, an optimal pH of between 6.5 and 8.0, and optimal temperatures supporting growth of between 30 and 35 °C. Toxic heavy metals, such as mercury, silver and copper slowed down the growth of this bacterium on acetamide. Growth of acetamide exhibits potent inhibition on growth as evidenced from the considerable lag phase that increases as the concentrations of acetamide was increased. As the tolerance level to acetamide for this bacterium is relatively higher than other acetamide-degrading bacteria in the literature, this bacterium can potentially be an excellent acetamide bioremediation agent.
Thailand’s Maize Prices Forecasting using Ensemble Technique
Authors: Mao Sararith , Soonthornphisaj Nuanwan
Published: July 2024

Crop prices forecasting is crucial for farmers, policymakers, and investors. This paper aims to propose suitable machine learning models for forecasting Thailand’s maize prices by implementing and comparing various machine learning algorithms, including regression trees (RT), support vector regression (SVR), ensemble bagging with RT and SVR as the base learner (Bag-RT and Bag-SVR), and random forest (RF). The dataset used in this study is collected from two main sources: the Office of Agricultural Economics in Thailand (OAE) and the investing.com website for the period from January 2002 to August 2023 consist of 260 records and 53 features. Given the dataset numerous independent variables, we applied the recursive feature elimination combined with Pearson correlation feature selection method to reduce feature dimensions by focusing on the most relevant features. The prediction models were trained using 10-folds cross validation and evaluated using three metrics: R-squared (R2), mean absolute error (MAE), and root mean square error (RMSE). The top-performing model, Bag-SVR, achieved the best R2 value of 0.961, MAE of 0.234, and RMSE of 0.315 follow by SVR model with R2 value of 0.959, MAE of 0.251, and RMSE of 0.333. In contrast, the RT model demonstrated the lowest performance scores with R2 value of 0.846, MAE of 0.44, and RMSE of 0.617. In conclusion, our study emphasizes the influence of feature selection on model performance and showcases the potential of machine learning models for accurate maize prices forecasting in Thailand.