bluuebunny/crossref_metadata_2025 huggingface dataset:
A STUDENT PERFORMANCE PREDICTION APPROACH USING MACHINE LEARNING
Authors: Mourya Geetanjali
Published: May 2024

Machine learning is used in a variety of fields, including education, pattern identification, gaming, business, social media services, online customer care, and product recommendations. The future of the children is a major factor in the importance of the educational system. All of today's kids want to go to college, which raises the need for M.L. operations in the educational system and leads to higher education producing a lot of data. For the objective of assessing student performance, many tools are available. Reviewing student data will benefit from data mining, which is a technique for discovering hidden information. The amount of information available in the subject of education is very helpful to both instructors and pupils. As the institute grows, it is becoming increasingly crucial to integrate M.L. technology in the classroom. Clustering is one of the core techniques widely used in data analysis. Modified K-means is one of the most well-liked and successful clustering techniques, while there are others as well. There are several methods for classifying data, with decision trees being the most popular. Decision trees are commonly used in analyses of student performance even though they are less stable than modified K-means. the topic of unsupervised algorithms is raised They use cluster analysis to classify the students into groups based on characteristics. The cluster size may be calculated using the elbow technique, which will help in determining the optimal solution. There is an elbow method that scans the length of the arm and the elbow point. It is simple to improve children's performance and future using the M.L. technique. Together with students, institutions and teachers may boost performance
IOT BASED SOIL MOISTURE MONITORING AND CONTROL SYSTEM
Authors: Mudgil Mrs.Shuchita , Rajoriya Shri.Rohan
Published: May 2024

Crop development and yield are influenced by variables such as temperature, humidity, and soil moisture. Regarding agriculture.In a field, soil moisture is a crucial factor in the plant's growth. Agriculture productivity rises as a result of ongoing soil moisture monitoring and maintenance at the required level in the field. Modern technology known as the Internet of Things (IoT) is used commonly used in a variety of industries, household settings, and the disciplines of agriculture, medicine, and the military. IoT technology is employed in this suggested system to continuously monitor soil moisture levels in agricultural fields. Real-time data can be easily accessed by the user via their mobile phones. The schedule for the irrigation system can be planned using the data collected. The suggested solution is small, simple to use, portable, robust, available anywhere to retrieve data, and advantageous economically
REVIEW OF VARIOUS APPLICATION OF IOT USING MACHINE LEARNING
Authors: Lala Dr. Ajay , Kalsi Prof. Kamaljeet Singh Kalsi
Published: May 2024

Now that the Internet of Things (IoT) is growing swiftly, a wide range of applications are being created by both academia and business. Machine learning allows millions of machines to collaborate in order to extract customer preferences from individual user-generated data. Machine learning is also essential in the IoT sector to manage the massive volume of data generated by those machines. Automated systems using actions deduced statistically can supplement or replace manual processes in critical activities. These applications of intelligent machine learning will be the main emphasis of this paper. Machine learning for IOT can be used to forecast trends of future, spot anomalies, and improve intelligence by ingesting picture, video, and voice data. By examining vast amounts of IoT data, machine learning can assist explain the underlying patterns in that data
IMPORTANT SHRIMP VIRAL DISEASES IN AQUACULTURE AND THEIR CONTROL
Authors: Teja Samanthula Surya , Chovatia Ravi , Singh Arya , Sheikh Samad , Mahendrajit Amom
Published: May 2024

Aquaculture, particularly shrimp production, has experienced rapid growth, surpassing the capture fishery, and has become a vital global industry meeting the growing demand for seafood. However, the practices involved in shrimp farming, such as displacement from natural environments, high-density stocking, exposure to stress, and transportation, create conditions conducive to pathogenic infections, leading to viral pandemics. The sustainability of shrimp aquaculture hinges on the establishment of more efficient and bio secure facilities that cultivate specific pathogen-free (SPF) shrimp, genetically enhanced for growth and disease resilience. Key requirements for SPF stock development include robust pathogen surveillance and effective disease prevention methods. Ongoing research explores shrimp-pathogen interactions, yielding promising results at the laboratory level. Potential applications involve the use of immunostimulants for "immune priming" or "trained immunity," RNA interference, and endogenous viral elements, representing innovative approaches for enhancing disease resistance in shrimp aquaculture. Utilizing a comprehensive approach that integrates various control strategies would offer more enduring and effective solutions for reducing viral infections in marine aquaculture compared to relying solely on a single disease control method, such as vaccination alone
BIOSECURITY PRACTICES FOR HEALTH MANAGEMENT IN AQUACULTURE
Authors: Shrivastava Yashwee
Published: May 2024

Global production of aquaculture has doubled over the past decade, making it one of the world's fastest-growing food production sectors. It is fulfilling the growing demand for seafood around the world, serving as a substantial source of protein and sustaining the livelihoods of millions of people. Fisheries heavily impact many countries‘ Gross Domestic Product (GDP). Countries generate revenue through the sale of farmed fish and seafood products both domestically and internationally. The current trend in fish farming is intensification and commercialisation. However, as this industry is expanding and adapting intensive farming, concerns about disease occurrence are also growing. Over the years, many attempts have been made to avoid and control disease outbreaks in aquaculture. It has been observed that the best method to manage fish health is to adopt biosecurity practices. A biosecurity program is a set of procedures, protocols, and measures designed to prevent the entry and transmission of diseases and pathogens within the facility. This chapter describes the role of biosecurity and different health management measures to minimise disease risk in aquaculture
ECOLOGICAL CONSEQUENCES OF HARMFUL ALGAL BLOOMS
Authors: Swamy Jayashri Mahadev , Yadav Somnath R.
Published: May 2024

This book chapter delves into the complex dynamics of harmful algal blooms (HABs) and their far-reaching implications in marine ecosystems. Highlighting the critical role of microscopic planktonic algae as a primary food source for crustaceans, fish, and shellfish, the narrative explores the dual nature of algal blooms, oscillating between advantageous and detrimental impacts on aquaculture and wild fisheries. The chapter underscores the global prevalence of harmful algal blooms, defined by the prolific expansion of specific microalgae, macroalgae or cyanobacteria. Recognizing the escalation of HABs as intricately linked to climate change, nutrient enrichment, and habitat disturbance, the authors elucidate the diverse causes behind their occurrence. Focusing on categories of bloom-forming algae, the chapter details their distinctive characteristics, impacts on aquatic ecosystems, and associated health risks, including various toxins such as paralytic shellfish poisons and neurotoxic shellfish poisons. The discussion further delves into the environmental forces driving HABs, including eutrophication, climate change, and ocean acidification. The consequences of HABs span phycotoxin accumulation, toxin transfer, high biomass blooms, ecosystem disturbance, aerosolized toxins, and localized hazardous blooms. In advocating for a nuanced understanding of the multifaceted contributors to HABs, the chapter emphasizes the need for effective mitigation and management strategies. A holistic approach that considers both anthropogenic influences and broader ecological contexts is crucial to curtail the expanding impact of HABs and restore balance to coastal ecosystems globally
Data Analysis with DevOps: DataOps
Authors: Pandit Amita , Kumar Rakesh , Maheswary Priti
Published: May 2024

Understanding data can be tricky and sometimes overwhelming. Using automation helps a data analysis team improve their methods more consistently. Specifically, automating software tasks is the best way to manage the many repetitions and repetitive jobs that come with thorough data analysis. When starting a project that involves software, automation, and working together, DevOps is the best way to go. DevOps improves all parts of the data analysis process, allowing teams to automatically handle software-related tasks in data analysis and work well with everyone involved in the project. The combination of DevOps, Data Analytics, and Data Engineering introduced the new DataOps which is used for data processing and analytics. DataOps improves communication, collaboration, and faster data pipeline
Performance Analysis and Evaluation of Crop Yield Prediction from Soil using Supervised Machine Learning Techniques
Authors: Kushwah Dr. Jitendra Singh , Shrivastava Saurabh , Singh Manali , Gaur Neeraj
Published: May 2024

This study emphasizes how important soil is in forecasting crop performance. Nutrient analysis can help farmers and soil scientists increase crop output by utilizing cutting-edge techniques. The research focuses on predicting mustard crop production using Kaggle's soil dataset utilizing multiple data prediction techniques. The study evaluates accuracy, recall, precision, and F-score using Decision Trees and SVM, two supervised machine learning techniques. The study finds that the best methods for precisely predicting mustard crop productivity are Logistic Regression, Decision Trees, and SVM
Suspicious Object Detection Using Images Using YoloV8 Model
Authors: Dubey Prati , Kumar Rakesh
Published: May 2024

The rapid advancement of computer vision technologies has necessitated the development of efficient and accurate suspicious object detection models. In this paper, we propose a yolov8 model with integrated digital filter. The proposed model enhances the input image quality by distinguishing objects through effective noise suppression, adaptability, and interpretability. Following this, YOLOv8 is deployed for feature extraction and suspicious object identification. The model achieved a mean average precision (mAP) of 88% and an execution time of 5.3 seconds, thereby outperforming existing state-of-the-art methods
Hybrid Seed Production
Authors: Thakur Anubhav , Sharma Pratibha , Jyoti Aparna , Nissa Kamrul , Kanwar Rajesh
Published: May 2024

Food is the basic necessity of human being and good quality seed is most critical input for enhancing the food productivity. Use of good quality seed can enhance the yield by 15-20% and this can be raised up to 45% by effective management of other inputs. Hybrid seed is a type of seed which have potential of enhancing global food and nutritional security. Hybrid seed are produced by crossing of two homozygous parental lines of two different gene pools. These are also known as F1 seeds and have more yield, better uniformity and resistance to insect, pests as compared to their parents due to heterosis. Hybrid seeds are produced by the effective manipulation of cross-pollination by a number of methods such as manual emasculation and pollination, use of genetic male sterility (GMS), use of Cytoplasmic male sterility, use of self-incompatibility and use of cytoplasmic- genetic male sterility. Although hybrid seeds provide numerous advantages to the growers, but they also have some shortcomings too. The production of hybrid seeds is a labour intensive and costly process and requires the assistance of skilled personnel. Hybrid breeding of crops for one desired character may often lead to degeneration of other traits in the seed. Hybrid seeds cannot be saved and have to be produced every year due to segregation of generations. This makes it expensive for small and marginal farmers to afford hybrid seeds
SURVEYING SOCIAL DISTANCING MONITORING WITH WEARABLE AND IOT SENSORS: TECHNIQUES AND TRENDS
Authors: Poornima Dr. D. , Ramprasad S. R. , Vijayabharathi R. , Manivannan Dr. R.
Published: May 2024

The global COVID-19 pandemic has resulted in a significant number of individuals becoming infected with the coronavirus, with the total number of cases continuing to rise. The transmission of the virus occurs through direct, indirect, or intimate contact with persons who are infected. This study survey presents a novel smart social distance system that enables individuals to effectively uphold appropriate social distances from others in various settings, including both indoor and outdoor environments. By doing so, this system effectively mitigates the risk of COVID-19 exposure and curtails its transmission at both local and national levels. It is challenging to ascertain the behaviours of individuals infected with COVID-19, including predicting symptoms, identifying cases, and monitoring isolated individuals. The use of the Internet of Things (IoT) platform is suggested as an effective means to accomplish this objective, owing to its well-established sensing capabilities and seamless connectivity. The integration of Internet of Things (IoT) technology is being employed in several domains such as healthcare, residences, and cities to establish a more efficient and sophisticated community. The present study aimed to collect data on the potential integration of the Internet of Things (IoT) inside an epidemic prevention and control framework
ADVANCEMENTS IN LEUKEMIA DIAGNOSIS: A COMPREHENSIVE REVIEW OF DEEP LEARNING APPROACHES FOR WHITE BLOOD CELL IMAGE ANALYSIS
Authors: Dhiravidaselvi S. , Deepan Dr. P. , Santhoshkumar Dr. R. , Rajalingam Dr. B. , Vidhya R.
Published: May 2024

Cancer of the blood is one of the most prevalent and perilous forms of the disease. The aberrant and unregulated synthesis of white blood cells (WBC) in the bone marrow is the root cause of leukaemia, a kind of blood cancer. Getting an early diagnosis of leukaemia increases the likelihood that cancer can be cured with the appropriate therapy. One method that can be used to identify leukaemia is the counting of the quantity of white and red blood cells (RBC). In the conventional approach, the counting of white blood cells and red blood cells is performed with the assistance of a piece of machinery called a haemocytometer. These examinations take a lot of time and are quite difficult to understand, which might lead to errors in categorization. Image processing of microscopic pictures, which is very affordable due to the uncomplicated nature of the circuitry, is another method that may be used to improve the identification of cancer cells. Researchers have found cancer cells, but because they didn't look at all of the criteria at the same time, such as picture enhancement, noise reduction, image recognition, and so on, their findings aren't as accurate as they might be. The goal and purpose of this research is to create a new system that is capable of taking into account all of the aspects that have been discussed thus far. Image acquisition, image pre-processing, picture segmentation, edge detection, and feature extraction are some of the steps that are included in the process. These phases are used to identify the presence of cancer as well as the stages at which cancer progresses. The paper contributes to the process of measuring the amount of white blood cells and red blood cells, as well as their average cell sizes and whether or not they are regular or irregular. This information may be used to determine whether or not a person has cancer
HEALTH PULSE PROGNOSIS: PREDICTING HEART DISEASE THROUGH INTELLIGENT DATA MINING STRATEGIES
Authors: Prabhu D. , Anand T. , Poornima Dr. D. , Bobby Ms. M. P.
Published: May 2024

In the modern day, fatalities that are caused by heart disease have become a significant problem. It is estimated that nearly one person passes away due to heart disease every minute. In the majority of situations, the diagnosis of cardiac disease is dependent on a complicated mix of clinical and pathological data. This complexity results in exorbitant medical expenses, which in turn affects the quality of medical care that is provided. The objective of the research is to evaluate medical information in order to make a prediction regarding heart disease. In order to discover information contained within a dataset pertaining to heart illness, which was retrieved from the UCI Machine Learning repository, the suggested technique makes use of data mining classification algorithms such as KNN, SVM, and Decision Tree. In all, there are 282 observations and 75 characteristics that make up the input dataset. The feature selection of the input dataset is accomplished through the utilisation auto-correlation in order to locate the most optimised collection of features and enhance the overall performance of the classifiers. When it comes to accuracy, the results are achieved through the use of classification techniques, both with and without the utilization of feature selection. The final step is to make a prediction about the class labels by employing the Majority Voting method
AUTOMATED ABNORMAL ECG DETECTION FOR EARLY HEART DISEASE DIAGNOSIS
Authors: Kodeboina Sai Rahul , Chitteti Kusela
Published: May 2024

Our research aims to develop an intelligent system that utilizes pre-recorded Electrocardiogram (ECG) data to provide a comprehensive cardiac health assessment, extending beyond arrhythmia classification. We integrate advanced predictive capabilities, including Heart Rate Variability (HRV), ischemia detection, conduction abnormalities, cardiac hypertrophy, arrhythmia triggers, electrolyte imbalances, Long QT syndrome, drug effects, risk stratification for sudden cardiac death, ventricular repolarization dynamics, detection of atrial fibrillation, and autonomic nervous system assessment, to offer an all-encompassing insight into cardiac health status. By combining sophisticated signal processing techniques and deep learning models, we evaluate various cardiac parameters, contributing to improved patient care and holistic cardiac well-being evaluation
LEUKEMIA DISEASE CLASSIFICATION AND DETECTION IN BLOOD MICROSCOPIC IMAGES USING DEEP LEARNING TECHNIQUES
Authors: Vinnarasi Vimala , Manivannan Dr. R. , Karthi S. , Vaishnnave Dr. M. P
Published: May 2024

The early identification and diagnosis of leukaemia is a key challenge in the field of sickness diagnostics. This challenge entails making an accurate differentiation between healthy and cancerous leukocytes in the early stages of the disease while keeping expenses to a minimum. In spite of the widespread occurrence of leukaemia, there are only a limited number of flow cytometers, and the diagnostic procedures followed in laboratories are time-consuming. Cancer of the bloodforming tissues, sometimes known as leukaemia, can affect the lymphatic system as well as the bone marrow. If it is discovered at an earlier stage, treatment may be more successful. As a result of this research, a new classification model for blood microscopic images was established. This model makes a distinction between images unaffected by leukaemia and those that are afflicted by leukaemia. The detection times and accuracy of machine learning and deep learning methods are far higher than those of human analysts. In order to address this scenario, we have used deep learning based Leukemia disease classification and detection in blood microscopic images. This is by comparing two models, namely traditional CNN and deep CNN model (Alex Net and VGG-16 Net model). According to the C-NMC 2019 dataset, a total of 11,154 blood microscopic images were collected for the purpose of evaluating our proposed approach. Based on our study findings, it has been noted that the performance of a VGG-16 Net model, surpasses that of other two models such as the Traditional CNN and Alex Net model. The optimal performance of the model is achieved by the utilization of VGG-16 Net model as a feature extractor, and Soft-max as the classifier. This configuration yields an accuracy of 97.44, precision of 97.5, recall of 97.5, and F1-score of 97.5
ENHANCED BRAIN TUMOR CLASSIFICATION: A TRANSFER LEARNING APPROACH WITH PRE-TRAINED MODELS
Authors: Sudha Mrs. R. , Venkateswaran Mr. S. , Poornima Dr. , Kalaiselvi Mrs. R.
Published: May 2024

The categorization and prediction of brain tumors are crucial in facilitating the timely identification and management of brain-related disorders. The significance of brain tumor detection and analysis for any indication system is supported by a substantial body of research and ongoing methodological advancements. The investigation necessitates precise identification of tumors, thereby necessitating the implementation of an effective automated methodology. A variety of segmentation algorithms have been developed to facilitate and enhance the categorization of brain tumors. Brain imaging segmentation is well recognized as a challenging topic within the domain of medical image processing. The study proposes a novel automatic detection and categorization method based on the findings. The proposed methodology is comprised of many sequential phases, encompassing the preprocessing of magnetic resonance imaging (MRI) scans, the segmentation of these scans, the extraction of pertinent characteristics, and the subsequent categorization of these features. This work introduces a transfer learning methodology that aims to achieve the precise classification of brain tumors and accurate prediction of their malignancy through the utilization of sophisticated deep learning techniques. The MRI scans underwent feature extraction, followed by the utilization of a deep learning model to categorize the pictures into several classes, including gliomas, meningiomas, nontumors, and pituitary tumors. To facilitate categorization, the researchers employed pre-trained VGG-16 Net models. To improve the performance of the VGG-16 Net model, data augmentation approaches are employed to mitigate the issue of overfitting and boost generalization capabilities. The model that has undergone training demonstrates exceptional accuracy in the categorization of brain tumors, surpassing previous benchmarks. It exhibits notable sensitivity and specificity in identifying the presence of tumors and properly predicting their grades. The VGG16 Net, when utilized for the analysis of MRI images of brain tumors, had a commendable accuracy rate of 97.21 percent
ANOMALY HUNTER: YOLOV3 ADVANCED DEEP LEARNING MODEL FOR HUMAN ABNORMAL DETECTION
Authors: Deepan Dr. P.
Published: May 2024

The identification of aberrant human behaviour is an important component in the process of protecting the health and safety of persons as well as the preservation of the safe environment in public areas. Advanced algorithms such as YOLO (You Only Look Once) and Convolutional Neural Networks (CNN) are utilised by emerging technologies such as human behaviour and anomaly detection. These algorithms are utilised to extract characteristics, manage temporal relationships, and enhance the accuracy and efficiency of human behaviour detection systems. Examples of such algorithms are the YOLO and CNN algorithms. This approach was designed with the purpose of being utilised in real-world contexts, and more specifically for recognising suspicious behaviours in surveillance video collected by closed circuit television (CCTV) cameras. The technique was developed with the intention of helping to identify suspicious actions. This model takes use of the object identification approach known as YOLOv3 in order to recognise human actions and abnormalities contained in the video data. Additionally, a Convolutional Neural Network (CNN) is utilised in order to extract action characteristics from each tracked trajectory. This is done after the previous step has been completed. In the end, but certainly not least, a You Only Look Once (YOLOv3) is utilised in order to construct a model for the purpose of recognising abnormal behaviours. This model enables the prediction of abnormal actions that are carried out by individuals. To summarise, the functioning of this technology entails getting video information as input from security cameras and then employing a variety of complicated algorithms in order to identify and categorise anomalous actions. This is done in order to prevent and detect any potential threats. By utilising YOLO and CNN for the purpose of feature extraction and anomaly identification, the system is able to accurately recognise and anticipate abnormal types of human activity. This is made possible by the usage of these two techniques
ARTIFICIAL INTELLIGENCE ROLE, TECHNIQUES, AND OPTIMIZATION ALGORITHMS IN INDUSTRIAL-LEVEL APPLICATIONS
Authors: Arora Dr. Amit , Kacker Dr. Ruchin , Singh Dr. Sanjay Kumar
Published: May 2024

Artificial Intelligence Role, Techniques, and Optimization Algorithms in Industrial-Level Applications" delves into the transformative impact of artificial intelligence (AI) in various industrial domains. This comprehensive exploration unravels the pivotal role that AI plays in optimizing processes, enhancing decision[1]making, and automating operations across sectors such as manufacturing, logistics, healthcare, and more. The chapter offers an insightful overview of the diverse AI techniques employed in these industrial settings, including machine learning, deep learning, reinforcement learning, and natural language processing. It highlights their practical applications and showcases how these techniques are revolutionizing the industrial landscape. Moreover, the chapter introduces readers to optimization algorithms, such as genetic algorithms, particle swarm optimization, simulated annealing, and ant colony optimization, which play a vital role in finding optimal solutions to complex industrial problems. Real-world case studies are presented, demonstrating the remarkable successes achieved through the synergy of AI techniques and optimization algorithms. This chapter provides a window into the present and future of AI in industry, revealing how these technologies are reshaping productivity, quality, and efficiency while opening up new avenues for innovation
AN INTEGRATIVE APPROACH TO DIAGNOSING PARKINSON’S DISEASE USING ENSEMBLED MACHINE LEARNING MODELS
Authors: Deepa Dr. A , Kamatchi Dr. S. Bangaru , Vaishnnave Dr. M. P.
Published: May 2024

In the early stages of Parkinson's disease (PD), a neurodegenerative disorder that affects the brain's neurological, physiological, and behavioural systems, a correct diagnosis can be challenging due to the presence of subtle variances. Slow movement, or bradykinesia, is a common sign of this condition. The onset of symptoms occurs around midlife, and the condition progresses steadily worse with age. Changes in speech are one of the early indicators of Parkinson's disease. This study proposed the use of supervised classification algorithms like support vector machine (SVM), naive Bayes (NB), k-nearest neighbour (K-NN), and artificial neural network (ANN) for diagnosing subjective diseases. To enhance resilience beyond the capabilities of a single model, a method known as ensembling has been implemented. Ensembling involves training numerous models instead of relying on a single model, then subsequently combining the predictions generated by these models. This research evaluates four distinct ensemble algorithms, including Adaboost, bagging, majority voting and weighted voting. To assess the efficacy of the proposed methodology, a dataset comprising speech signal collection includes 195 biomedical sounds, of which there are 147 phonetics for persons with Parkinson's disease (PD) and 48 for healthy people. Through analysis, it was determined that the implementation of an ensemble approach utilising the majority voting technique, in conjunction with MLP, SVM and decision tree classifiers, yielded superior results. Specifically, the ensemble approach achieved an accuracy rate of 95.7%, surpassing the performance of the individual classifiers
Problem Penggunaan AI (Artificial Intelligence) dalam Bidang Pendidikan
Authors: Budiyono Slamet , Azhari Pebri , Pamungkas Maulana Al Bana
Published: May 2024

Artificial intelligence (AI) has become an increasingly important research subject in various fields, including education. In the educational context, the use of AI promises a fundamental transformation in the way we teach and learn. However, despite AI's enormous potential to improve the efficiency and effectiveness of education, there are a number of challenges that need to be overcome. The purpose of this research is to provide readers with the view that apart from the many positive sides of AI, there are also negative sides. This research focuses on the negative side of using AI in the education sector. The method used is library research. However, if managed wisely, the use of AI in education can provide significant benefits. For example, AI can be used to personalize learning, enabling teaching tailored to each student's individual needs. Additionally, AI can help in automated grading, freeing up teachers' time to focus on direct interactions with students. By considering existing challenges and exploiting the opportunities offered by AI, education can make significant progress in improving its quality and accessibility. Therefore, further research and development is needed to overcome technical and ethical obstacles to the application of AI in education, while ensuring that its benefits are widespread and non-discriminatory.
Peranan Relationship Marketing pada Keputusan Nasabah dalam Menggunakan Tabungan IB Haji di PT Bank KB Bukopin Syariah Cabang Bukittinggi
Authors: Mulyani Devi Sri , Martilova Novera
Published: May 2024

This research is motivated by fluctuations in the percentage increase in the number of savings customers.  IB Hajj at PT Bank Kb Bukopin Syariah Bukittinggi Branch, because relationship marketing is an approach taken by Relationship Officers to prospective customers. Current research aims to find out what or how the Role of Relationship Marketing on Customer Decisions in Using IB Hajj Savings at PT Bank Kb Bukopin Syariah Bukittinggi Branch. The research conducted by the author is using a method with a qualitative approach, where the data sources taken by the author are primary data and secondary data. As for the data collection techniques, namely observation techniques, interviews and field documentation techniques. And the data analysis technique carried out by the author is obtained through data analysis, namely reduction, display, conclusions and data verification. Through the results of research and analysis that has been done by the author, it can be concluded that relationship marketing is very instrumental in making customer decisions in using IB Hajj savings at PT Bank KB Bukopin Syariah Bukittinggi Branch. This can be seen from the increasing number of customers, increasing the trust of prospective customers in making decisions to use IB Hajj savings, establishing a good relationship between prospective customers and the bank and can also establish a good relationship between prospective customers and PT Bank KB Bukopin.
Implementasi Good Corporate Governance (GCG) dalam Pengelolaan Manajemen Risiko pada PT. BPRS Jam Gadang (Perseroda)
Authors: Ramadhani Fadhila Rahadatul , Martilova Novera
Published: May 2024

This research is motivated by the occurrence of fluctuations in the increase in the number of customers and the increase in the number of problematic financing customers of PT. BPRS Jam Gadang Perseroda, because the number of problematic financing customers is closely related to the implementation of good corporate governance (GCG) in risk management. This study aims to determine how the Implementation of Good Corporate Governance (GCG) in Risk Management at PT. BPRS Jam Gadang (PERSERODA). The research that the author did was with a qualitative approach method, the data sources that the author took were from primary data and secondary data. And data collection techniques with observation, interviews, and documentation. While the data analysis technique obtained using data analysis, namely data reduction, data display, conclusions and verification. Based on the results of research and analysis that the authors conducted, it can be concluded that the Implementation of Good Corporate Governance (GCG) in Risk Management at PT BPRS Jam Gadang (PERSERODA) shows that the company has run it well, adhering to principles such as transparency, accountability, responsibility, openness, and independence. The bank develops risk management strategies to effectively manage its activities and maintain good operational performance. And there are two obstacles faced, namely laws and regulations made by the bank, and the quality of human resource investment.
Ce este inteligența?
Authors:   Romanian Academy, Sfetcu Nicolae
Published: May 2024

Inteligența, adesea considerată apogeul cunoașterii umane, este un construct care i-a fascinat pe savanți, oameni de știință și filozofi de secole. Originile inteligenței pot fi urmărite până la călătoria evolutivă a vieții însăși. În lumea naturală, inteligența nu se limitează la oameni, ci este un produs al strategiilor de adaptare și supraviețuire a diferitelor specii. Inteligența exercită implicații profunde asupra indivizilor, influențând rezultatele academice, succesul în carieră, interacțiunile sociale și bunăstarea generală.
About Us: Guided Pathways and the Recontextualization of Teaching English
Authors: Penner Donald
Published: May 2024

This essay conducts critical discourse analysis of website landing pages for community college English departments that have explicitly adopted Guided Pathways reforms. The analysis examines how the social practice of teaching English is recontextualized through discourse.
Egészségügyi Hatásvizsgálat és Egészséges Közpolitika: Narratív áttekintés a meghatározásokról és megközelítésekről
Authors: Atmaca Leman , Dózsa Csaba
Published: May 2024

BACKGROUND: Despite advancements in various fields due to globalization and technology, societal health and quality of life have not proportionally improved. Health inequalities, and unjust disparities in health within and between communities, have garnered attention as preventable challenges. The influence of non-health-related policies and decisions on public health has been overlooked for an extended period. The concept of Health Impact Assessment (HIA) has emerged to address this issue. HIA aims to inform decision-making processes, promote health, and reduce health disparities. Healthy Public Policy (HPP), which integrates health and equality considerations across policy sectors, further contributes to improved overall health outcomes. This study investigates the interplay between HIA and HPP. The focus is on elucidating the evolving dynamics between these two concepts and scrutinizing the increasing efficacy of the popular HIA.
METHODS: Peer-reviewed, English-language articles reporting on health impact assessment that were published between 1990 and 2021 were looked for on PubMed, Science Direct, and JSTOR. Health impact assessment with either public policy or health promotion was used for the search. To find further studies, reference lists of papers were examined. A subset of studies was chosen for analysis.
RESULTS: Studies support that the interplay between HIA and HPP is a potent force that shapes the landscape of public health. Their collaboration amplifies the impact of each and transforms policies into vehicles of positive change. Recommendations include aiming for healthier communities, reducing health inequalities, and a sustainable future by synergistically evaluating health impacts and creating policies that prioritize well-being. However, concerns have been raised about the potential for HIA to expand into non-health areas, leading to discussions about renaming it to a more inclusive term such as "general policy assessment", although no concrete steps have been taken. It has been mentioned that the complexity of HIA can lead to difficulties in certain contexts and makes it difficult for it to become widespread and that it needs to be improved. Making it simpler, more flexible, and applicable to different conditions is among the suggestions.
CONCLUSION: HIA emerges as an indispensable and potent tool for cultivating HPP. Its application should extend to diverse domains, necessitating the identification and resolution of obstacles that may impede its effectiveness.
Cikkismertetés: A gyermek- és serdülőkori elhízás kezelése
Authors: Ádám Judit
Published: May 2024

Az elhízás komoly népegészségügyi probléma, valamint biológiai, fejlődési, környezeti, viselkedési és genetikai összetevőkből álló, komplex betegség. Leggyakoribb oka gyermek- és serdülőkorban az energiaegyensúly felborulása. Terjedése összefüggésbe hozható a felnőtteknél már azonosított társbetegségek számának növekedésével. Az obezitás elleni egyszerű gyógymód hiányában az ellátás elsősorban a táplálkozási tanácsokra és a testmozgásra fókuszál. A serdülők ezzel összefüggésben azonban egészségtelen étkezési szokásokat vehetnek fel, melyeknek káros következményei lehetnek. A csupán az életmódot célzó intervenciók mérsékelt hatékonyságot mutattak a fogyásra nézve. Korlátozott mennyiségű adat áll rendelkezésre a súlycsökkentő gyógyszerek hatásosságáról és biztonságosságáról gyermekkorban. Jelen áttekintés összefoglalja a gyermekkori obezitás kórélettanát, klinikai és pszichológiai vonatkozásait, valamint a rendelkezésre álló kezelési lehetőségeket.
Cloninger bio-pszicho-szociális személyiség- modelljének dimenziói, mint közvetítők a szülői nevelési bánásmód és szerhasználat között
Authors: Madarász Csilla , Reisz Ákos Márton , Teleki Szidalisz Ágnes
Published: May 2024

BEVEZETÉS: Kutatásunkban a Cloninger pszicho-biológiai személyiségmodelljében (TCM) szereplő faktorok közvetítő hatását vizsgáltuk a szülői bánásmód és szerhasználat között. További kérdésfeltevésünk az volt, hogy maga a szülői bánásmód milyen hatással lehet közvetlenül a szerhasználatra.
MÓDSZERTAN: Vizsgálatunkban a Temperamentum és Karakter Kérdőív Rövid változatát (TCI-55), a Parker-féle Parental Bonding Instrument (H-PBI) magyar változatát, illetve egy általunk összeállított szerhasználati kérdőívet alkalmaztunk, mely magába foglalta az AUDIT első három kérdését tartalmazó rövidített változatát (AUDIT-C).
EREDMÉNYEK: A kérdőívcsomagot 165 fő (többségében egyetemi hallgató) töltötte ki. Az eredményekből kiderült, hogy a szülői bánásmód egyes dimenziói az Önirányítottságra, az Újdonságkeresésre, és az Ártalomkerülésre vannak hatással. A temperamentumfaktorok az alkohol, illetve a marihuána fogyasztásának megkezdésére és a dohányzási tendenciákra, az Önirányítottság a dohányzási szokásokra és az alkoholfogyasztásra van befolyással. A szülői bánásmód a szerabúzusra nincs közvetlen hatással.
KÖVETKEZTETÉSEK: A szerhasználat tekintetében a személyiség szintjén eredményeink az öröklött viselkedés közvetlen hatására, illetve a környezet közvetett hatására engednek következtetni, ami prevenció szempontjából igen releváns.
Cikkismertetés: A fizikai aktivitási intervenciók objektíven mért eredményekre gyakorolt hosszú távú hatásai – áttekintés
Authors: Devosa Iván
Published: May 2024

Jóllehet a fizikai aktivitással kapcsolatos intervenciók hatékonyságáról gyakran születnek beszámolók, az is ismert tény immár, hogy hosszú távú változtatásokra van szükség ahhoz, hogy jelentős egészségügyi előnyök szülessenek. A kritikák alapja, hogy a fizikai aktivitással kapcsolatos intervenciók kiértékelései többnyire rövid távú eredményekből születnek, és ezeket gyakran önmagukban, összehasonlítás nélkül értékelik. Ennek a tanulmánynak ezért az a célja, hogy felmérje a viselkedési intervenciók hosszú távú (legalább 24 hónapos) hatékonyságát objektíven mért fizikai aktivitásra.
Cikkismertetés: Mi kell a közösségek szerint ahhoz, hogy a gyermekek és fiatalok boldogok és egészségesek legyenek?
Authors: Devosa Iván
Published: May 2024

A közösségek bevonása a kutatási prioritások meghatározásába növelheti a kutatás relevanciáját és hatékonyságát, ami jobb kimenetekhez vezethet az egészség területén. Ezek a gyakorlatok azonban gyakran nem egyértelműek például a közösségek bevonásának módját illetően, illetve nem világos, hogy milyen mértékben veszik figyelembe a prioritásokat a döntések során. A ritkán megfigyelt csoportok, például az etnikai kisebbségek, akadályokba ütközhetnek a részvétel során.
Cikkismertetés: Egy újszerű iskolai táplálkozási intervenció, a COOKKIT (Cooking Kit for Kids) kifejlesztése és kezdeti minőségértékelése
Authors: Devosa Iván
Published: May 2024

A túlsúly és az egészségtelen táplálkozás ismert kockázati tényezői a rákos megbetegedéseknek. A magas jövedelmű országokban mindkettő előfordulása gyakoribb az alacsony jövedelmű családok körében. Az általános iskolás korú (11 év alatti) gyermekeket célzó táplálkozási intervenciók javíthatják az egészséges táplálkozási magatartást, de a legtöbb programot nem úgy tervezték, hogy támogassák az osztályteremben tanult készségek otthoni átültetését.
Vizuális kivonat: Magyarország - Egészségügyi országprofil 2023
Authors: Vitrai József
Published: May 2024

Az “Egészségügyi helyzet az EU-ban” című országprofilok tömör és szakpolitikai szempontból releváns áttekintést nyújtanak az EU/Európai Gazdasági Térség egészségéről és egészségügyi rendszereiről. (OECD/European Observatory on Health Systems and Policies, 2023) Az egyes országok sajátos jellemzőit és kihívásait hangsúlyozzák az országok közötti összehasonlítások tükrében. A cél az, hogy a politikai döntéshozók és a befolyásoló szereplők számára a kölcsö-nös tanulás és az önkéntes csere eszköze legyen.
Szakdokumentum: Halálozás Magyarországon 2023
Authors: Vitrai József
Published: May 2024

Jelen közlemény annak a tanulmánynak a vezetői összefoglalója, ami a 2021-ben megjelent jelentéshez (Vitrai és Bakacs, 2021) hasonlóan a 2023-ban elérhető magyar halálozási adatok átfogó elemzésével az egészségveszteségek csökkentéséhez kínál információt a magyar népesség egészségével foglalkozó szakemberek számára.
Szakdokumentum: Már születéskor eldől a magyar gyerekek sorsa
Authors: Vitrai József
Published: May 2024

Nagy biztonsággal állítható, hogy nincs olyan, aki ne tartaná igazságtalannak a gyermekkorban elszenvedett egyenlőtlenségeket. Nem csak amiatt, hogy a gyermekek az ilyen különbségekért nem hibáztathatók, hanem azért is, mert a korai hátrány erősen korlátozza a gyermek jövőjét, életkilátásait. Ezért is elismerésre méltó a szerzők azon törekvése, hogy az elmúlt évtizedre vonatkozó rendszeresen gyűjtött, ún. adminisztratív, illetve a felmérésekben gyűjtött adatokat felhasználva kimutassák, milyen egyenlőtlenségek voltak tapasztalhatók a magyar gyermekek egészségében és az azt meghatározó életkörülményeikben.
Comprehensive Assessment of Pharmaceutical Waste Disposal Practices in Ishaka-Bushenyi Municipality: Implications for Public Health and Environmental Sustainability
Authors:   School of Pharmacy Kampala International University, Uganda., Edwin Tibagaruka
Published: May 2024

Proper management of pharmaceutical waste is crucial for safeguarding public health and the environment. This study aimed to comprehensively assess pharmaceutical waste management practices in hospitals, pharmacies, and households within Bushenyi-Ishaka Municipality, Uganda. The study employed a qualitative case study design, gathering data through observations, interviews, and questionnaires administered to five pharmacies, two hospitals, and 100 households. The results revealed that the mean amount of pharmaceutical waste generated by pharmacies and hospitals in Ishaka was 40.5 kg ± 32.39 kg per month, of which 26.83 kg ± 29.10 kg were disposed of safely. However, 60% of the waste was categorized as hazardous. The study found that while most facilities used labeled or color-coded containers for waste segregation, the practice of segregation was inconsistent, with 71.43% of the facilities not segregating their waste. Incineration (42.86%) and dilution followed by flushing in protected soak pits (42.86%) were the primary disposal methods employed by the facilities. Concerningly, only 4% of households returned unused or expired medications to pharmacies, with the majority disposing of them in dustbins (46%) or flushing them down toilets (41%). The findings also highlighted gaps in awareness and training, with 50% of pharmacy staff not receiving any training on proper pharmaceutical waste disposal, and only 14.29% of the facilities having established waste management guidelines. These deficiencies in knowledge and practices contribute to the suboptimal management of pharmaceutical waste, posing significant risks to public health and the environment. The study underscores the urgent need for comprehensive policies, targeted training programs, and community-based initiatives to enhance pharmaceutical waste management practices in Bushenyi-Ishaka Municipality and similar settings. Addressing these challenges is crucial for mitigating the adverse impacts of improper pharmaceutical waste disposal and promoting sustainable healthcare waste management. Keywords: Pharmaceutical waste, Waste management, Disposal practices, Awareness, Hazardous waste.
The role of serum magnesium in the prediction of acute kidney injury after total aortic arch replacement: A prospective observational study
Authors: Jiang Xinyi , Li Ziyun , Pan Chixing , Fang Heng , Xu Wang , Chen Zeling , Zhu Junjiang , He Linling , Fang Miaoxian , Chen Chunbo
Published: May 2024

Background: Considerable morbidity and death are associated with acute kidney damage (AKI) following total aortic arch replacement (TAAR). The relationship between AKI following TAAR and serum magnesium levels remains unknown. The intention of this research was to access the predictive value of serum magnesium levels on admission to the Cardiovascular Surgical Intensive Care Unit (CSICU) for AKI in patients receiving TAAR. Methods: From May 2018 to January 2020, a prospective, observational study was performed in the Guangdong Provincial People's Hospital CSICU. Patients accepting TAAR admitted to the CSICU were studied. The Kidney Disease: Improving Global Outcomes (KDIGO) definition of serum creatinine was used to define AKI, and KDIGO stages two or three were used to characterize severe AKI. Multivariable logistic regression and area under the curve receiver-operator characteristic curve (AUC-ROC) analysis were conducted to assess the predictive capability of the serum magnesium for AKI detection. Finally, the prediction model for AKI was established and internally validated. Results: Of the 396 enrolled patients, AKI occurred in 315 (79.5%) patients, including 154 (38.8%) patients with severe AKI. Serum magnesium levels were independently related to the postoperative AKI and severe AKI (both, P < 0.001), and AUC-ROCs for predicting AKI and severe AKI were 0.707 and 0.695, respectively. Across increasing quartiles of serum magnesium, the multivariable-adjusted odds ratios (95% confidence intervals) of postoperative AKI were 1.00 (reference), 1.04 (0.50-2.82), 1.20 (0.56-2.56), and 6.19 (2.02-23.91) (P for Trend < 0.001). When serum magnesium was included to a baseline model with established risk factors, AUC-ROC (0.833 vs 0.808, P = 0.050), reclassification (P < 0.001), and discrimination (P = 0.002) were further improved. Conclusions: Serum magnesium levels on admission are an independent predictor of AKI. In TAAR patients, elevated serum magnesium levels were linked to an increased risk of AKI. In addition, the established risk factor model for AKI can be considerably improved by the addition of serum magnesium in TAAR patients hospitalized in the CSICU.