bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction Model of Ancient Village Pottery Building Microspace Design Style by Integrating Machine Learning
Authors: Jianqing Ye
Published: May 2024

Microspace design styles on pottery fragments offer a captivating window into the artistic heritage and cultural practices of past societies. These intricate details and recurring patterns hold valuable clues about rituals, beliefs, and artistic expressions. However, existing approaches struggle to capture the relationships between design elements within a single piece. This limitation hinders the ability to capture the holistic meaning conveyed by the microspace design style. To overcome this limitation, this research proposes a novel approach called MoANN-DSOA for predicting microspace design styles based on ancient village pottery. MoANN-DSOA utilizes a Mosaic Attention Neural Network (MoANN) to analyze both the visual image and the relationships between design elements. This allows for a more detailed understanding of the artistic message encoded within each fragment. Additionally, a Dove Swarm Optimization Algorithm (DSOA) optimizes the MoANN architecture, potentially enhancing the accuracy of capturing intricate details. The proposed MoANN-DSOA method attains 7.78%, 27.89% and 33.335% higher accuracy and 14%, 20.81% and 32.36% higher F-Score compared to the existing methods like Categorization and Retrieval of Painted Pottery with CNNs (CNN), Utilizing CNN-VGG16-VGG19 Approach for Distinguishing Surface Treatments in Wheel-Thrown Pottery (CNN-VGG16-VGG19) and Unsupervised Feature Extraction of Ceramic Profiles using a Deep Variational Convolutional Autoencoder (DVCA) respectively. By this, the proposed MoANN-DSOA methodology paves the way for efficient information extraction from pottery image, unlocking deeper understanding and facilitating the construction of more informed historical narratives.
Time Series Analysis and Neural Network Optimization of Tourist Destination Heat Prediction Model
Authors: Lei Wang
Published: May 2024

Accurate prediction of tourist destination heat is significant for effectively managing tourist flow in the tourism industry. However, forecasting the temperature of a tourist destination is challenging, and various factors, like weather conditions, influence it. In this study, we proposed a novel hybrid strategy using time series analysis and neural network optimization to predict tourist destination heat accurately. The study commences with collecting historical tourist visitation data, weather records, and other information. Then, the raw database was preprocessed to make the data effective and reliable for subsequent analysis. Further, the Autoregressive Integrated Moving Average (ARIMA) technique was employed as a time series analysis technique, capturing and identifying the database's underlying patterns and correlations. Consequently, a Fire Hawk Optimizer-based Dense Recurrent Neural Network (FHO-DRNN) was developed and trained using the ARIMA outcomes to predict the tourist destination heat. The proposed framework was implemented in the MATLAB software, and the results are examined in terms of accuracy, Mean Absolute Error (MAE), and computational time. Furthermore, we compared the existing predictive models to validate the proposed model's effectiveness in forecasting tourist destination heat.
Consumer Behavior Prediction and Market Application Exploration Based on Social Network Data Analysis
Authors: Pingfen Liu
Published: May 2024

It examines the junction of consumer behavior prediction and market application exploration using social network data analysis. Using the massive quantity of information available on social media sites, they use complex data mining tools to estimate consumer opinions, preferences, and behaviours. They discover useful insights that inform strategic decision-making processes for enterprises across several industry domains by combining approaches such as predictive modelling, natural language processing, and social network analysis. Results show that social network data analysis can effectively anticipate consumer actions and detect market trends. They employ sentiment analysis to categorize user sentiments regarding products, companies, and marketing campaigns, delivering actionable insights for marketing plan optimization. Furthermore, predictive modelling helps us to estimate purchase intent, segment clients, and spot new trends, allowing businesses to customize their products and improve customer engagement. Additionally, this study emphasizes the significance of addressing ethical concerns and privacy consequences in social network data analysis. Businesses that embrace transparent and responsible data policies can create consumer trust while also mitigating the dangers related to data misuse.
Enhancing Security and Sensors Emerging Internet of Things (IoT) Technology of Homophone-Based Encryption using MANET‐IoT Networks Technique
Authors: Adnan Yousif Dawod
Published: May 2024

The emerging Internet of Things (IoT) technology brings several benefits to the real-time applications of the smart city project. The IoT-enabled technologies are driven by the networks like Mobile Ad hoc Network (MANET), Vehicular Ad Hoc Network (VANET), Wireless Sensor Network (WSN), Underwater Wireless Sensor Network (UWSN), etc. The core functionality of IoT-enabled technologies mainly depends on routing functionality. The focus of this work is on the IoT-enabled MANET, i.e., IoT-MANET routing methods. There are several types of research conducted so far on the efficiency of MANET in different areas such as routing protocol, MAC protocol, queue, etc. Routing protocols are the major domain on which the performance of IoT-MANET has been analyzed. There are different types of routing protocols have introduced for the MANETs. The use and selection routing methods are done as per the real-time application needs. An average Ad hoc On-Demand Vector (AODV) routing protocol had shown good results under any type of network. The increase in internet usage for communication has led to a significant growth in the volume of data being transmitted over the internet, including computer data, text, video, audio, sound, and images. However, due to limited network capacities, efficient compression methods are needed to reduce the amount of data while preserving its quality and ensuring timely transmission. Data confidentiality is a crucial aspect of network security, and the homophone-based encryption with compression (HEC) technique offers a viable solution in this regard.
An Empirical Study of Adaptive Learning Algorithm Based on Intrinsic Motivation in English Online Teaching and Learning
Authors: Tan Juan
Published: May 2024

An empirical study of adaptive learning algorithms based on intrinsic motivation in English online teaching and learning delves into the intersection of technology and motivation in language education. By investigating how adaptive learning algorithms can leverage intrinsic motivation factors, such as personal interests and curiosity, researchers aim to optimize online English instruction for individual learners. This paper introduces Weighted Feature Clustering Adaptive Learning (WFCAL) as a novel approach to optimizing English teaching and learning processes. Traditional methods of instruction often fail to address the diverse needs and learning profiles of students, leading to suboptimal outcomes. WFCAL offers a solution by dynamically adapting instructional content based on the individual characteristics of learners, thereby creating personalized learning experiences. Through the application of WFCAL, significant improvements in language proficiency have been observed across various skill domains, including vocabulary, grammar, and reading comprehension. The algorithm clusters learners into groups based on their proficiency levels and adapts instructional delivery to cater to the specific needs of each cluster. This approach promotes engagement, motivation, and efficacy in language learning, leading to enhanced outcomes. learners in Cluster 1 showed an average increase in vocabulary knowledge from 65 to 80, while learners in Cluster 2 demonstrated an improvement from 70 to 85 in grammar understanding. Additionally, reading comprehension skills improved by an average of 10 points across all clusters. The algorithm clusters learners into groups based on their proficiency levels and adapts instructional delivery to cater to the specific needs of each cluster.
Design and Application of Basketball Tactical Drill System Based on Motion Trajectory Analysis Algorithm
Authors: Yan Xianyou
Published: May 2024

A basketball tactical drill system based on motion trajectory analysis algorithms employs advanced computational methods to enhance training effectiveness and strategy development. By utilizing these algorithms to analyze player movements and interactions during drills, coaches can gain valuable insights into team dynamics, spatial awareness, and decision-making processes. This analysis enables coaches to tailor drills to specific tactical objectives, such as improving defensive rotations, offensive spacing, or fast break execution. Additionally, the system can provide real-time feedback to players, helping them understand their positioning and movement patterns to refine their skills and decision-making abilities. This paper presents the design and implementation of a Basketball Tactical Drill System leveraging advanced object detection and motion trajectory analysis algorithms. The system aims to enhance basketball training by providing coaches and players with real-time insights into tactical drills and player performance. Two key algorithms, Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT), are utilized to accurately identify and track basketballs, players, and other relevant objects during training sessions. Experimental results demonstrate the effectiveness of the proposed system, showcasing high detection accuracy, minimal false positives, and competitive processing times across various scenarios and tactical drills. The system leverages Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT) algorithms to achieve exceptional detection accuracy, with an average of 93% across various scenarios and tactical drills. False positives are minimized, with an average of 12 per scenario. Processing times remain competitive, averaging 27 milliseconds per scenario. Integration of motion trajectory analysis provides coaches with invaluable real-time insights into player movements and interactions, facilitating data-driven coaching strategies. The system's versatility is demonstrated through successful application in a range of drills, resulting in performance improvements averaging 20%.
Intervention Algorithm for College Students' Psychological Problems Based on Decision Tree
Authors: Ai Shui
Published: May 2024

College students often face a variety of psychological problems due to the transition to higher education, academic pressure, social challenges, and lifestyle changes. Common psychological issues among college students include stress, anxiety, depression, loneliness, and adjustment difficulties. These problems can significantly impact students' academic performance, overall well-being, and quality of life. Factors such as academic workload, financial stress, relationship issues, and homesickness can exacerbate psychological distress among college students. This paper presents an innovative approach to addressing college student mental health challenges through the integration of a Recommender System with Psychometric Data Analytics (RC-PDA). With rising concerns about the psychological well-being of college students, there is a growing need for personalized and targeted interventions to support their mental health needs. RC-PDA offers a novel solution by harnessing psychometric data and decision tree algorithms to provide personalized assessments and interventions tailored to individual student profiles. Through the analysis of psychometric data, RC-PDA accurately predicts student well-being levels and guides the implementation of interventions aimed at alleviating stress and promoting mental health. This paper discusses the methodology and findings of a study that evaluates the effectiveness of RC-PDA in supporting college student mental health. Through the analysis of psychometric data, RC-PDA accurately predicts student well-being levels with an average accuracy of 85% and guides the implementation of interventions aimed at alleviating stress and promoting mental health. This paper discusses the methodology and findings of a study that evaluates the effectiveness of RC-PDA in supporting college student mental health. Results demonstrate the utility of RC-PDA in predicting student well-being levels and reducing stress levels by an average of 20% through targeted interventions.
Improved Decision Support System for College Sports Training Based on Id3 Algorithm
Authors: Chen Hui
Published: May 2024

A Decision Support System (DSS) is a computer-based tool designed to assist individuals and organizations in making informed decisions. It utilizes data analysis, modeling techniques, and interactive interfaces to provide users with relevant information and insights. DSSs help streamline decision-making processes by synthesizing large amounts of data from various sources, generating forecasts, and evaluating alternative courses of action. They are particularly valuable in complex and uncertain situations where decisions have significant implications. DSSs can be used in a wide range of domains, including business, healthcare, finance, and logistics, to support strategic, tactical, and operational decision-making processes. This paper introduces an Integrated Hybrid Feature Subset Decision Support System (HFS-DSS) designed to optimize decision-making processes in student performance evaluation within educational institutions. The HFS-DSS combines feature subset selection techniques with decision support algorithms to identify key attributes and make accurate predictions regarding student performance. Through experimental evaluation, the system demonstrates its effectiveness in accurately assessing student performance based on selected features, with predictions closely aligning with actual observations. The results underscore the system's reliability and effectiveness in supporting informed decision-making processes, offering valuable insights for educators and administrators to enhance academic outcomes. The adaptability and versatility of the HFS-DSS make it well-suited for various educational contexts, providing potential applications in curriculum planning, student support services, and performance monitoring. The present paper highlights the significant contributions of the Integrated HFS-DSS towards enhancing decision-making processes and improving academic outcomes in educational institutions.
Design of Badminton Technical Movement Recognition System Based on Improved Agnes Algorithm
Authors: Yulin Wang
Published: May 2024

The Badminton Technical Movement Recognition System is a technology-driven solution aimed at identifying and analyzing various technical movements performed by badminton players during gameplay. Leveraging advanced sensors, motion tracking devices, and machine learning algorithms, this system captures and interprets data related to player movements, racket swings, footwork, and other key actions on the court. By analyzing this data in real-time or post-match, coaches, players, and analysts can gain valuable insights into performance, technique, and areas for improvement. The system's ability to recognize and quantify specific movements allows for detailed performance assessment, personalized training programs, and strategic game planning. The Badminton Technical Movement Recognition System serves as a powerful tool for enhancing player development and optimizing performance in the sport of badminton. The paper presents a comprehensive study on the application of the Multi-Modal AGNES algorithm across diverse domains, encompassing movement pattern estimation, modal feature extraction, coordinate estimation, and badminton feature estimation. Through rigorous experimentation and analysis, the algorithm's efficacy in accurately identifying movement patterns, robustly extracting features from varied datasets, precisely localizing objects in three-dimensional space, and proficiently estimating badminton-specific metrics has been demonstrated. Through rigorous experimentation and analysis, the algorithm's efficacy in accurately identifying movement patterns, robustly extracting features from varied datasets, precisely localizing objects in three-dimensional space, and proficiently estimating badminton-specific metrics has been demonstrated. For instance, the algorithm achieved an average accuracy of 90% in classifying movement patterns in a dataset of 1000 observations. Additionally, it accurately estimated modal features such as swing speed, racket angle, and shuttlecock speed with a mean error rate of less than 5%.
Application of Depth Convolution Virtual Reality (VR) in Traditional Sculpture Design under Digital Modeling
Authors: Cheng Fang
Published: May 2024

Virtual reality technology continues to be used in other technical fields, art forms are becoming more and more abundant. Virtual reality technology provides convenience for art creation. The combination of art and technology can become a new art form, digital art, and has become a trend of art development. The earliest sculpture art in the art field is no exception, and has also blended with the virtual reality technology. Because the artistic presentation of traditional sculpture art is a relatively complex materialization process, the thinking presentation and creation process of sculpture art need to go through a series of processes to complete the final sculpture works of art; This makes sculpture art more complex and more difficult to operate than other arts when using virtual reality technology. If virtual reality technology wants to integrate perfectly with sculpture art, it needs to combine the process of sculpture art and provide different digital technology methods in different links. This document uses linear combination to solve two level problems, which is a very effective adaptive algorithm. The empirical results show that the improved fusion algorithm can re analyze the importance of the positive process in the rapid development of virtual reality technology, and the rapid development of virtual reality technology is crucial to shaping the value of this theme. Compared with traditional methods, the accuracy of this experiment is increased by 13.4%, and compared with the algorithm TIN, the time complexity and space complexity are 97% and 54% lower respectively.
Regulating the Rational Use of Antimicrobial Drugs and Improving the Quality of Healthcare Big Data: A Development and Usability Study as A Prelude to AI Assisted Healthcare
Authors: Juan Li
Published: May 2024

Background: Healthcare big data has become an important strategic resource, and the study of applying it to the rational application of antimicrobial drugs is of great significance in improving and optimizing the various tasks of medicine and health and promoting the development of social health. Based on its own characteristics, the issue of data quality has important research value in promoting data output and application.
Objective: To optimize the status quo of antimicrobial drug use in medical institutions, to explore the value of healthcare big data, and to explore the data governance methods in the era of digital intelligence in order to build a good ecosystem for the application of healthcare big data.
Methods: This paper constructs a set of intelligent full-closed-loop antimicrobial rational application management platform integrated with data quality, adopts PDCA cycle management mode, analyzes the problems in the process of applying healthcare big data and gives the corresponding solutions, authorizes according to the personnel's duties, and guarantees the data security.
Results: Since the platform went online in January 2021, the antimicrobial drug use intensity indicator of a hospital has decreased year by year from 38 to 34.4 as of June 2023, effectively standardizing the use of antimicrobial drugs. The time consumed for the statistics of antimicrobial use intensity related data has decreased from an average of 30min to 2min, which has significantly improved the work efficiency.Healthcare professionals are able to monitor indicator data in real time, with or without abnormal cases, and fully grasp the trend of indicators. At the same time, the patient's medication can be viewed at any time, and the condition can be grasped in a timely manner.
Conclusions: The platform not only effectively solves the problem of information barriers, but also accurately determines the data source and statistical logic of indicator collection, reflecting the actual clinical diagnosis and treatment, and at the same time greatly reduces labor costs and optimizes the workflow.The practical experience of platform construction has important reference significance for the governance and application of healthcare big data, which provides data support and technical guarantee for the scientific, standardized and refined management of hospitals.
Trust and Energy Effective Framework for Wireless Sensor Networks
Authors: Pavan Kumar M V N R
Published: May 2024

Sensor nodes have been manufactured from an enormous amount of very thick sensor systems. To a large extent, wireless sensor networks were powered by electricity. The sensor connection has to work under really uncertain conditions. The nodes are powerless to various types of dynamic attacks especially specific outbound and well attacks under such conditions. These attacks inject malicious packages by trading the knot. Topographical conventions of direction of wireless sensor connections were created by thinking about the safety and power angle aspects of these assaults. In this article, a directing convention named energy-efficient and confidence implanted greedy perimeter stateless routing algorithm (EETGPSR) suggested for wireless sensor connections to join confidence systems in power-aware greedy perimeter stateless routing algorithm (EGPSR). The results show that EETGPSR beats EGPSR by reducing maintenance and improving the connection delivery ratio.
Brain Tumor Detection and Segmentation using Improved Bat Algorithm with Improved Invasive Weed Optimization
Authors: Vimal Gupta
Published: May 2024

Brain tumours are serious diseases that grow in the brain and are made up of a collection of abnormal and unwanted cells. Therefore, the use of magnetic resonance imaging (MRI) for segmentation and early detection of such tumors is more important to save lives. When it comes to finding people with brain tumors, MRI is very effective and has a slightly higher detection rate than other imaging tests. Detection of brain tumors is an important complex issue in medical imaging systems due to their size, appearance, and irregular shape. Detection of brain tumors is a difficult task with medical imaging systems. In order to address the above-said issue and to develop an effective brain tumor detection technique, an improved Bat algorithm with improved Invasive Weed Optimization algorithm has been proposed in this paper. The improved Invasive Weed Optimization (IIWO) approach has been utilized in the proposed IBIIW algorithm along with the improved Bat algorithm (IBA). In the proposed work weight factor which is used for classification has been improved. Extensive experiments has been performed and it suggests that, the use of MR imaging to segment tumors has a significant influence on early detection of brain cancers. In addition to this, with MR images, the deep learning-based technique produced better detection results. The proposed method has outperformed against the baseline methods in terms of accuracy, sensitivity, and specificity with remarkable results giving values of 0.9394, 0.9281, and 0.9165 respectively.
Multi-Document Summarization using Discrete Bat Optimization
Authors: K. Adi Narayana Reddy
Published: May 2024

With the World Wide Web, we now have a wide range of data that was previously unavailable. Therefore, it has become a complex problem to find useful information in large datasets. In recent years, text summarization has emerged as a viable option for mining relevant data from massive collections of texts. We may classify summarizing as either "single document" or "multi document" depending on how many source documents we are working with. Finding an accurate summary from a collection of documents is more difficult for researchers than doing it from a single document. For this reason, this research proposes a Discrete Bat algorithm Optimization based multi document summarizer (DBAT-MDS) to tackle the issue of multi document summarizing. Comparisons are made between the proposed DBAT-MDS based model and three different summarization algorithms that take their inspiration from the natural world. All methods are evaluated in relation to the benchmark Document Understanding Conference (DUC) datasets using a variety of criteria, such as the ROUGE score and the F score. Compared to the other summarizers used in the experiment, the suggested method performs much better.
Assessing the Gap between Environmental Legislation and Effective Implementation: A Study and Guidelines for Enhancing the Implementation Performance of Environmental Laws
Authors: Ayoub F.R. Alqaisi
Published: May 2024

This article explores the gap between environmental legal regulations and their actual implementation in the Kingdom of Saudi Arabia. A comprehensive research approach was used, linked to a survey conducted among a randomly selected sample of 592 individuals in Saudi Arabia. Through a comparative assessment, various factors contributing to this gap were identified, including administrative, regulatory, financial, and cultural aspects specific to the region. Practical recommendations are provided based on the evaluation of research findings, aiming to enhance the implementation of environmental laws. The goal is to strengthen environmental safety and promote sustainable development in Saudi Arabia. The outcomes of this study hold significant importance for policymakers, aiding in the reinforcement of law enforcement and the encouragement of sustainable practices adaptable to local contexts, while also contributing to global environmental initiatives.
Enhanced Myocardial Infarction Prediction Using Machine Learning Algorithms and Gender-Specific Insights
Authors: Nudrat Fatima
Published: May 2024

A serious medical condition called myocardial infarction (MI), sometimes referred to as a "heart attack," is caused by disruptions in the blood supply to the myocardium. This research examines the efficacy of machine learning (ML) algorithms in forecasting myocardial infarction (MI) using a dataset of 350 records. The study identifies key risk factors for predicting myocardial infarction (MI), such as elevated cholesterol levels, diabetes, advanced age, overall health status, mental well-being, obesity, physical activity, smoking habits, hypertension, and depression. Significantly, gender does not manifest as a predictor of myocardial infarction (MI) when employing various classification methods. The research achieves high accuracy rates of 89.32%, 87.53%, 81.29%, and 76.59% using different machine learning algorithms, including Deep Belief Network (DBN), C4.5, Random Forest (RF), and Bayesian Network (BN), respectively. Algorithm-specific rule sets identify correlations, with the C4.5 algorithm revealing interesting connections between smoking habits and protection against myocardial infarction (MI). Performance metrics like accuracy, precision, sensitivity, and specificity attest to the effectiveness of the proposed technique. The results demonstrate the superior performance of the DBN algorithm, surpassing other algorithms in terms of accuracy (89.32%), precision (84.04%), sensitivity (86.63%), and specificity (82.45%). This paper provides crucial insights into predictive modeling for myocardial infarction (MI), highlighting the importance of various risk factors and advanced machine learning (ML) algorithms. The results offer clinicians and researchers a strong foundation for comprehending and potentially averting myocardial infarction, relying on personalized patient profiles. This paper has the potential to significantly contribute to the field by applying ensemble classifiers and machine learning models to forecast gender-specific myocardial infarctions. As a result, diagnostic precision and patient outcomes could be revolutionized.
BALANCING SARCASTIC HINGLISH SHORT TEXT DATA USING AUGMENTATION TECHNIQUES WITH HANDLING SPELLING VARIATIONS
Authors: Rajshree Singh
Published: May 2024

In the real world, there is a significant presence of imbalanced data due to the fact that the classes that make up the datasets are not evenly distributed. Even when using methods that are traditionally used to achieve class balance, such as re-sampling & re-weighting, current deep learning still faces a significant obstacle because of the class imbalance. This study’s major objective is proposing a data augmentation technique to balance the data to improve the sample sizes for the minority classes. Python, a well-known programming language, & multiple methods of machine learning are being employed in the execution of this study. Classification models like Logistic Regression, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, Extra Trees Classifier, AdaBoost classifier, Gradient Boost classifier was used to implement this study. Precision, recall, & F-score were used to determine which model would be the most effective. According to the findings of this study's analysis, the Naive Bayes approach, which has a F1-Score of 95.85% & has Wn = 3, Cn = 3, & CWn =3 as its parameters, is the technique that yields the most accurate results.
Securing the Industrial Backbone: Cybersecurity Threats, Vulnerabilities, and Mitigation Strategies in Control and Automation Systems
Authors: Jihane Ben Slimane
Published: May 2024

Industrial Control and Automation Systems are the invisible workhorses of modern industry, managing critical infrastructure and manufacturing processes. The increasing convert of IT and OT networks has exposed ICAS to a growing range of cybersecurity threats. This paper explores the current landscape of cybersecurity threats and vulnerabilities plaguing ICAS. It delves into the technical aspects of these vulnerabilities, ranging from network weaknesses to human error. Additionally, the paper proposes a comprehensive set of mitigation strategies that can be implemented to bolster the security posture of ICAS. These strategies encompass network segmentation, access control, vulnerability management, incident response planning, and ongoing security awareness training. Finally, the paper highlights the importance of adhering to industry security standards and fostering a culture of cybersecurity within organizations.
Cocinar y comer en clave decolonial
Authors: Osorio González Rebeca
Published: May 2024

Quisiera iniciar esta presentación partiendo del siguiente cuestionamiento ¿qué es lo colonial y por ende su contra mirada, lo decolonial, la decolonialidad? Osorio (2022) afirma, retomando a Quijano (1992), que la colonialidad del ser, el poder y el saber ha existido desde la época de la conquista y la colonia, misma que ha trascendido hasta nuestros días y que de hecho se ha fortalecido en el marco de una capitalismo recrudecido en su vertiente neoliberal. En este sentido, Quijano (1992) puntualiza que la colonialidad se manifiesta como la relación de dominación directa, económica, política, social y cultural de Occidente sobre los conquistados de todos los continentes.
Pelatihan Fotografi Praktis untuk Anak-Anak Panti Asuhan Yayasan ABAS Bogor
Authors: Alyatalatthaf Muhammad Dicka Ma'arief , Dyah Kusumawati  , Lobodally Altobeli , Widjaja Mardyan
Published: May 2024

Fotografi telah menjadi bagian penting dalam kehidupan bagi setiap kalangan baik tua maupun muda, profesional ataupun pehobi. Tak terkecuali bagi anak-anak panti asuhan Yayasan Abas Bogor, yang secara kreatif dan produktif memanfaatkan fotografi untuk mendorong kegiatan UMKM, aktivitas yayasan, dokumentasi harian, hingga foto formal untuk kebutuhan pas foto. Melihat kebutuhan tersebut, program studi Ilmu Komunikasi Institut Teknologi dan Bisnis Kalbis melalui kegiatan pengabdian kepada masyarakat (PKM) berupaya untuk memberikan pelatihan fotografi praktis kepada anak-anak panti asuhan Yayasan Abas Bogor agar mereka dapat menerapkan teknik fotografi praktis dalam kegiatan-kegiatan mereka sehari-hari.
Surgical management of the diabetic foot: The current evidence
Authors: Roberts Richard Henry Randall , Davies-Jones Gareth Rhys , Brock James , Satheesh Vaishnav , Robertson Greg AJ
Published: May 2024

The prevalence of diabetes mellitus and its associated complications, particularly diabetic foot pathologies, poses significant healthcare challenges and economic burdens globally. This review synthesises current evidence on the surgical management of the diabetic foot, focusing on the interplay between neuropathy, ischemia, and infection that commonly culminates in ulcers, infections, and, in severe cases, amputations. The escalating incidence of diabetes mellitus underscores the urgency for effective management strategies, as diabetic foot complications are a leading cause of hospital admissions among diabetic patients, significantly impacting morbidity and mortality rates. This review explores the pathophysiological mechanisms underlying diabetic foot complications and further examines diabetic foot ulcers, infections, and skeletal pathologies such as Charcot arthropathy, emphasising the critical role of early diagnosis, comprehensive management strategies, and interdisciplinary care in mitigating adverse outcomes. In addressing surgical interventions, this review evaluates conservative surgeries, amputations, and reconstructive procedures, highlighting the importance of tailored approaches based on individual patient profiles and the specific characteristics of foot pathologies. The integration of advanced diagnostic tools, novel surgical techniques, and postoperative care, including offloading and infection control, are discussed in the context of optimising healing and preserving limb function.
Intestinal parasitic infections in Polish soldiers deployed to Kosovo
Authors: Korzeniewski Krzysztof , Richert Wanesa
Published: May 2024

Introduction and objective

Soldiers of the Polish Military Contingents currently stationed in foreign countries serve under difficult environmental conditions. Military service abroad is associated with the risk of importing infectious pathogens back to soldiers’ home country. The aim of the study was to assess the current prevalence of infections caused by intestinal parasites in soldiers from the Polish Military Contingent Kosovo serving as part of the Kosovo Force operation in the Balkans.

Material and methods

Parasitological diagnostics was carried out in June 2023 in a group of 221 soldiers from the Polish Military Contingents Kosovo stationed in several military camps across the country. Each participant was asked to provide three stool samples collected at 2–3-day intervals. Faecal examinations were performed using three different light microscopy testing methods (direct smear, decantation, flotation) at the Department of Epidemiology and Tropical Medicine at the Military Institute of Medicine – National Research Institute.

Results

Intestinal parasites, all of protozoan aetiology, were found in 28 out of 221 Polish soldiers taking part in the Kosovo Force operation in Kosovo included in the study (0.9% participants were infected with Giardia intestinalis and 11.7% with potentially pathogenic parasites, Blastocystis spp. and Dientamoeba fragilis). There were no infections with nematodes, cestodes or trematodes among the study participants. No correlation was found between the detection of a parasitic infection and the presence of diarrhoea or other gastrointestinal symptoms within six months prior to the study. The analysis demonstrated that infections with protozoa were most often found in soldiers aged 35–45 years old. As for the military ranks, the rate of infections in each corps (privates, non-commissioned officers, and officers) was similar.

Conclusions

Cases of intestinal parasitic infections in soldiers from the Polish Military Contingent Kosovo could be associated with the effects of environmental conditions (poor standards of sanitation in the areas of deployment) and failure to comply with disease prevention principles (food and feed hygiene).
Organizational standards for medical teleconsultations and personal data protection
Authors: Nosarzewski Łukasz
Published: May 2024

Wprowadzenie i cel

W artykule omówiono standardy organizacyjne teleporad medycznych, obejmujące identyfikację pacjenta, zachowanie zasad poufności i stosowanie środków technicznych zabezpieczających dane pacjenta, uregulowane w rozporządzeniu Ministra Zdrowia z dnia 12 sierpnia 2020 r. w sprawie standardu organizacyjnego teleporady w ramach podstawowej opieki zdrowotnej. Analizie poddano relacje między tymi standardami a prawem ochrony danych osobowych, uregulowanym w Rozporządzeniu Parlamentu Europejskiego i Rady (UE) 2016/679 z dnia 27 kwietnia 2016 r. w sprawie ochrony osób fizycznych w związku z przetwarzaniem danych osobowych i w sprawie swobodnego przepływu takich danych oraz uchylenia dyrektywy 95/46/WE (ogólne rozporządzenie o ochronie danych, RODO).

Materiał i metody

Wykorzystano metody z obszaru nauk prawnych – dogmatyczną i teoretycznoprawną. Analizą objęto akty normatywne, zalecenia i wytyczne z obszaru ochrony danych osobowych oraz opracowania naukowe i piśmiennictwo.

Wyniki

W wyniku analizy zweryfikowano tezę, zgodnie z którą podstawowe wymagania organizacyjne dla teleporady uregulowane w rozporządzeniu Ministra Zdrowia z dnia 12 sierpnia 2020 r. w sprawie standardu organizacyjnego teleporady w ramach podstawowej opieki zdrowotnej nie są wystarczające, aby zachować wszystkie wymogi ochrony danych osobowych.

Wnioski

Skoro określony rozporządzeniem obligatoryjny standard organizacyjny dla teleporad zbyt nisko ustanowił wymogi minimalne i nie są one wystarczające, aby uczynić zadość wszystkim obowiązkom administratora danych osobowych wynikającym z RODO, to teleporady powinny być organizowane z założenia wedle wymogów ponadstandardowych względem tych, które przewidział prawodawca. Warto przy tym sięgać do zatwierdzonych przez Prezesa Urzędu Ochrony Danych Osobowych kodeksów postępowania.
Effects of Isotretinoin on Serum Vitamin-D Levels among Severe Acne Vulgaris patients
Authors: Asif Mohammad , Laique Talha
Published: May 2024

Background: Medical issue that causes inflammation of skin resulting in psychological and social distress is acne vulgaris. Vitamin D plays a critical role in its pathology due to its immune-modulatory effects. Aim: To evaluate the effects of isotretinoin on vitamin D levels among severe acne vulgaris patients. Study design: This was quasi experimental trial. Methodology: This experiment at the Lahore General Hospital involved 100 participants with severe acne vulgaris. Iso-tretinoin in a dose of 0.75 mg/kg was given daily for 4 months. Serum Serum vitamin-D levels were considered at two points: before and after treatment. The data was entered and analyzed in SPSS 25. A paired sample t-test was used to compare the mean Vitamin-D levels before and after treatment in acne vulgaris patients. A p-value ≤ 0.05 was considered significant. Results: The mean age of the patients was 28.5±6.2 years. There was significant difference in mean Vitamin D serum levels before and after treatment in acne vulgaris patients. Practical Implication: Acne vulgaris is a major problem in modern era. This investigation will highlight the role of oral iso-tretinoin on vitamin D levels among severe acne local patients. Due to lack of local literature, this study will add information to existing literature. Conclusion: It was concluded that isotretinoin treatment increased 25 OH vitamin D levels in patients suffering from severe AV. Although acne vulgaris impacts negatively to vitamin-D levels. Keywords: Acne Vulgaris, Isotretinoin, Effectiveness and Treatment Outcomes.
Rezension von: Schwedt, Herbert, Kulturstile kleiner Gemeinden
Authors: Hampele Walter , Schwedt Herbert
Published: May 2024

Herbert Schwedt: Kulturstile kleiner Gemeinden. (Volksleben Bd. 21.) Tübingen 1968. 174 S. DM 14,25
Rezension von: Hoffmann, Hermann (Bearb.), Die Urkunden des Reichsstiftes Kaisheim
Authors: Fik Karl , Hoffmann Hermann
Published: May 2024

Die Urkunden des Reichsstiftes Kaisheim 1135-1287. Bearbeitet von Hermann Hoffmann Verlag der Schwäbischen Forschungsgemeinschaft, Augsburg 1972. Reihe 2 a Urkunden und Regesten Band 11.
Rezension von: Mägerlein, Fritz (Bearb.), Die Archive des Landkreises Kitzingen
Authors: Ulshöfer Kuno , Mägerlein Fritz
Published: May 2024

Die Archive des Landkreises Kitzingen. Teil I: Die Gemeinde- und Marktarchive, bearb. von F. Mägerlein. Teil II: Die Stadtarchive, bearb. von F. Mägerlein und W. Scherzer. Bayerische Archivinventare 29 (1968) und 30 (1969).
Rezension von: Löhlein, Georg u.a. (Bearb.), Archiv Hl. Kreuz, Nürnberg
Authors: Ulshöfer Kuno , Löhlein Georg
Published: May 2024

Georg Löhlein - Horst Pohl (Bearb.): Archive der Freiherrn Haller von Hallerstein in Schloß Gründlach. Band I. München 1965 (= Bayerische Archivinventare Reihe Mittelfranken hrsg. vom Bayerischen Staatsarchiv Nürnberg, Heft 7).
Rezension von: Lühmann, Werner, Sankt Urban
Authors: Wunder Gerd , Lühmann Werner
Published: May 2024

Werner Lühmann: St. Urban. Beiträge zur Vita und Legende, zum Brauchtum und zur Ikonographie. (Quellen und Forschungen zur Geschichte des Bistums und Hochstifts Würzburg 19). Würzburg: Schöningh 1968 - 160 S. Ill.
Rezension von: Schäfer, Gerhard, Um eine deutsche Reichskirche 1933
Authors: Wunder Gerd , Schäfer Gerhard
Published: May 2024

Gerhard Schäfer: Die evangelische Landeskirche in Württemberg und der Nationalsozialismus. Eine Dokumentation zum Kirchenkampf. Band 2. Um eine deutsche Reichskirche 1933. Stuttgart: Calwer Verlag 1972. 1120 S. DM 48,-
Rezension von: Kottmann, Albrecht, Das Geheimnis romanischer Bauten
Authors: Grünenwald Elisabeth , Kottmann Albrecht
Published: May 2024

Albrecht Kottmann : Das Geheimnis romanischer Bauten. Stuttgart: Hoffmann 1971. 228 Zeichnungen im Text, 86 Fotos
Rezension von: Bosl, Karl, München
Authors: Wunder Gerd , Bosl Karl
Published: May 2024

Karl Bosl: München. Bürgerstadt - Residenz - heimliche Hauptstadt Deutschlands. Stuttgart: K. Theis 1971. 144 S. Ill.
Rezension von: Munz, Eugen; Kleinknecht, Otto, Geschichte der Stadt Marbach am Neckar
Authors: Wunder Gerd , Munz Eugen , Kleinknecht Otto
Published: May 2024

Eugen Münz, Otto Kleinknecht: Geschichte der Stadt Marbach am Neckar. Stuttgart: Kohlhammer 1972. 328 S. Ill. DM
Rezension von: Woltering, Herbert, Die Reichsstadt Rothenburg ob der Tauber und ihre Herrschaft über die Landwehr Bd. II
Authors: Ulshöfer Kuno , Woltering Herbert
Published: May 2024

Hubert Woitering : Die Reichsstadt Rothenburg ob der Tauber und ihre Herrschaft über die Landwehr. II. (Jahrbuch 1971/72 des Vereins Alt-Rothenburg.) Rothenburg 1971. 129 S.
Rezension von: Jahrbuch für fränkische Landesforschung Bd. 28, 29, 30, 31
Authors: Wunder Gerd
Published: May 2024

Jahrbuch für fränkische Landesforschung. 28 (1968) 346 S. Ill. - 29 (1969) 310 S. - 30 (1970) 356 S. - 31 (1971) 365 S. DM 48,-
Rezension von: Ruperto-Carola Bd. 47 + 48
Authors: Wunder Gerd
Published: May 2024

Ruperto-Carola Bd. 47 (1969). 432 S. - Bd. 48 (1970) 256 S. DM 21,-
COVID-19 Pandemisinde Gebelik İzlemlerinde Müdahale Çarkının Kullanımı
Authors: Öztürk Dönmez Renginar , Fidan Gürcühan
Published: May 2024

Tüm dünyada pandemi olarak ilan edilen Covid-19 önemli ve yeni bir halk sağlığı sorunun ortaya çıkmasına neden olmuştur. Dünyada ve ülkemizde pandemi ile birlikte sağlık hizmetlerinin sunumunda birtakım değişiklikler olmuş ve bu durum birinci basmak sağlık hizmetlerinin önemini arttırmıştır. Halk sağlığı hemşireliği risk gruplarından birisi olan gebeler immün sistemdeki değişiklikler nedeni ile enfeksiyona daha yatkın hale gelmektedirler.
Bu derlemede halk sağlığı hemşirelerinin öncelikli yaptıkları uygulamaların kapsamını açıklamak, hedefler oluşturmak, girişimler belirlemek amacı ile kullanılan bir model olan müdahale çarkı çerçevesinde covid-19 pandemisinde gebelik dönemi izlemlerinin ile ele alınması amaçlanmıştır.
Nursing Care of the Patient Diagnosed with Multiple Myeloma as a Result of Trauma-Induced Fracture Development: A Case Report
Authors: Akduran Funda , Kenar Nebiha
Published: May 2024

Multiple myeloma is a neoplasm of plasma cells. Observed usually in patients over 65 years of age. This case report 41 year old has been prepared in order to determine the nursing diagnoses grouped according to the "Functional Health Patterns" model and to evaluate them in the nursing care plan of a patient who was diagnosed with multiple myeloma as a result of the examination which is that were noticed during a different examination. In the case for the problems; diagnoses of acute pain, sleep disturbance, undernutrition, deterioration of oral mucous membrane, constipation, fatigue, activity intolerance, risk of deterioration in individual identity, risk of deterioration in individual resistance, and nursing interventions for these diagnoses were planned and implemented. Before the study, verbal and written consent was obtained from the patient, and the data were collected by face-to-face interview method. As a result; the patient care process, which was planned and applied in line with the functional health patterns model, provided a positive improvement in the patient's behaviors that would negatively affect the disease process such as facilitated discharge, and guided nurses about care, effectiveness and follow-up of treatment. This model is recommended for the effective implementation of nursing care in clinics such as hematology, oncology, palliative care, where patients need to be followed actively.
Tuvalet Eğitimi Olmayan Çocuklarda İdrar Toplama Yöntemleri
Authors: Bakırlıoğlu Burcu , Çetinkaya Bengü
Published: May 2024

Tuvalet eğitimi olmayan çocuklarda sfinkter gelişimi tamamlanmamıştır. Bu yüzden küçük çocuklardan idrar örneği toplamak sağlık profesyonelleri ve ebeveynler açısından zorlayıcı bir hal almaktadır. Bunun yanında çocuklar için idrar toplama işlemi acı ve ağrı verici olabilir. İdrar toplama yöntemleriyle ilgili profesyonel kuruluşların önerileri olmasına rağmen güncel uygulamalar da bu tanılama sürecinin karmaşıklığını ortadan kaldırmaya yardımcı olacaktır. Pediatri hemşiresinin uygun tekniğin seçilmesinde ve idrar örneğinin toplanması sürecinde avantaj ve dezavantajları göz önünde bulundurmalı rol ve sorumluluklarını yerine getirmelidir. Bu derlemede geleneksel ve güncel idrar toplama yöntemleri literatürde bulunan çalışmalarla desteklenerek özetlenmiştir.