bluuebunny/crossref_metadata_2025 huggingface dataset:
BIO-INSPIRED BASED SEGMENTATION AND USER AUTHENTICATED KEY MANAGEMENT FOR IOT NETWORKS
Authors:   Government Arts College, Udumalpet, India, M Savitha , M Senthilkumar ,   Government Arts and Science College, Avinashi, India
Published: October 2024

Data exchanging and gathering is greatly achieved by several interconnected physical objects or smart devices over the Internet are termed as Internet of Things (IoT). A generic IoT network called Hierarchical IoT Network (HIoTN) inclusive of the organized different nodes in a hierarchy as gateway node, cluster head nodes and sensing nodes. In HIoTN of generic IoT networking environment for a particular application, user direct access in real-time data from the sensing nodes is necessitated. Recent work introduces a User Authenticated Biometric Key Management Protocol (UABKMP) for IoT network. Hence this proposed work exhibits new region of interest based segmentation algorithm with base procedure of Modified Bat optimization (MBO) algorithm hybrid with Active Contour Model. In the MBO algorithm the parameters of the bat is tuned via the use of the Brownian Distribution. Finally an Authenticated Key Management (AKM) is proposed for IoT network. The Real-Or-Random (ROR) model is incorporated in network for proving the scheme formal security and also ensures the informal security being protected from several probable attacks.
AN INTELLIGENT RESNETS RESOURCE ALLOCATION FRAMEWORK FOR 5G NETWORKS
Authors:   Hindusthan Institute of Technology, India, S Ramasamy , PN Periyasamy ,   Hindusthan Institute of Technology, India, M Sathiya ,   Karpagam Institute of Technology, India
Published: October 2024

This paper presents a resource allocation technique for industrial applications for 6G networks, which are characterised by the presence of many heterogeneous parameters that have an effect on the quality of data transmission. The purpose of the project is to achieve the greatest possible efficiency in the application of the resources that are presently while achieving a higher level of control over a diverse collection of sensing nodes operating within a hybrid network. The system model that has been proposed is a workable option for efficient resource allocation. The performance of the proposed method, in addition to similarities to the performance of other methods has been analysed. The proposed methods offer performance that is comparable to or better than the baseline, while simultaneously significantly reducing the SI exchange overhead and improving the system resilience to sensing intervals, some of which may be unavoidable in practice.
FUZZY BASED OPTIMIZATION FOR IMPROVING THE TRUST SCORE IN MANETS
Authors:   Karpagam Institute of Technology, India, K Karunambiga , M Sathiya ,   Karpagam Institute of Technology, India, S Bhaggiaraj ,   Sri Ramakrishna Engineering College, India, K.C. Rajheshwari ,   Sona College of Technology, India
Published: October 2024

In this paper, research develop a method for identifying abnormal behavior based on two inputs: the trustworthiness of the user, as well as the reliability of the recommendations that they make. Specifically, research look at the reliability of the user recommendations. The next thing that needs to be done is to calculate the node general trust value in order to determine if there has been any kind of malicious attack. This will show whether or not the node has been compromised in any way. It is conceivable that this could lessen the amount of power that is needed for the communication that takes place between different networks. Additionally, it demonstrates that the model is better able to utilize the evaluation results of the common neighbor nodes to synthesize the confidence value when fewer nodes are deployed in the network. This is demonstrated by the fact that fewer nodes are deployed in the network. The reliability of the trust assessment improves while the number of trusts for which recommendations are made decreases.
ENHANCED ANALYSIS OF BLOCKCHAIN BASED SECURITY SYSTEMS IN FINANCIAL INSTITUTIONS
Authors:   Karpaga Vinayaga College of Engineering and Technology, India, A Sairam , D Sasikumar ,   Sphoorthy Engineering College, India, Kumar R. Sendhil ,   Thirumalai Engineering College, India, B Yuvaraj ,   Thirumalai Engineering College, India
Published: October 2024

The blockchain technology has provided a revolutionary way of secure data storage and transfer. It is a distributed ledger technology (DLT) that creates a secure and immutable record of transactions. This technology has been used to create secure and reliable systems for financial institutions. The blockchain based security system provides a secure platform for financial institutions. It ensures that the transactions are secure and immutable. This technology has been used to create digital assets that can be securely stored and transferred. It also ensures that the transactions are transparent and secure. The blockchain technology can be used to enable secure transactions between financial institutions. By using the blockchain, financial institutions can create a secure platform for transactions. This will ensure that the transactions are secure and immutable. It also ensures that the transactions are transparent and secure. The blockchain technology can also be used to create a secure system for digital identity management. This will ensure that the digital identities of the users are secure and immutable. It also ensures that the users’ identity is protected and secure. The blockchain technology can also be used to create a secure platform for digital payments. This will ensure that the payments are secure and immutable. It also ensures that the payments are transparent and secure.
AN ENSEMBLE NEURAL NETWORK TECHNIQUE FOR IMPROVING SECURITY AMONG VARIOUS DOMAINS OF INFORMATION TECHNOLOGY
Authors:   Bishop Heber College, Affiliated to Bharathidasan University, India, Selvi R. Thamarai
Published: October 2024

In the era of Internet of Things (IoT), enterprise information Systems (IISs) are becoming increasingly valuable in a range of industries due to the fact that they constitute a network in which connected devices exchange data in an environment that is quite close to real time. In this context, enterprises are provided with the opportunity to make use of virus detection solutions that are either static, dynamic, or hybrid. The research uses ensemble machine learning approaches that have been implemented and are analyzed, and comparisons are drawn between them. The findings of this research have been effective in the identification of malwares in IIS.
IMPROVING THE SECURITY BASED ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS
Authors:   Sri Indu College of Engineering and Technology, India, S Vijayarangam , V Wagh Kavita ,   Vasantdada Patil Pratishthan’s College of Engineering and Visual Arts, India, Puranik Vishal Gangadhar ,   JSPM’s Bhivarabai Sawant Institute of Technology and Research, India, S Selvakanmani ,   RMK Engineering College, India
Published: October 2024

Wireless sensor networks (WSNs) have become one of the most popular wireless communication systems in the world. Various attacks have been launched against the WSN individual nodes over the past few years, and the overall security of the network has gradually deteriorated. In this research, we propose a secure uneven clustering method for WSN. The proposed method is based on a trust score evaluation model with the objective of identifying malevolent users in WSNs. The trust score is used to determine whether or not a node is malicious. The results show that the proposed Trust Algorithm outperforms the currently used LEACH algorithm, the TASRP algorithm and the FLSO algorithm in terms of the percentage of packets that are successfully delivered across all use cases and density of nodes.
RELIABLE AND ENERGY EFFICIENT CLUSTER HEAD SELECTION USING EVOLUTIONARY ALGORITHM IN WIRELESS SENSOR NETWORK
Authors:   Manonmaniam Sundaranar University, India, S Mageshwaran , Perumal P. Sivananaintha ,   Manonmaniam Sundaranar University, India, R.S. Rajesh ,   Manonmaniam Sundaranar University, India, P Sundareswaran ,   Manonmaniam Sundaranar University, India
Published: October 2024

One of the most pressing problems in the industry at the moment is figuring out how to make wireless sensor networks (WSN) more reliable and extend their lifespan. The processes of selection and the development of clusters are incredibly significant; however, carrying them out is challenging and time-consuming despite the significance of the tasks. In the most recent study, researchers began their hunt by selecting a particular cluster head to use as a basis for their investigation. On the other hand, the model that was suggested utilizes evolutionary cluster head selection as one of its components. This contributes to the acceleration of computing, the improvement of selection precision, and the prevention of the selection of duplicate nodes. When the results of the simulation of the suggested model are compared to the results of simulations using other methods and techniques, we find that our method is both more accurate and more efficient. This was discovered when the results of the simulation of the proposed model were compared to the results of simulations using other approaches and techniques.
SECURING WIRELESS SENSOR NETWORKS USING DEEP LEARNING-BASED APPROACH FOR ELIMINATING DATA MODIFICATION IN SENSOR NODES
Authors:   Navarasam Arts and Science College, India, S Karthigai , B. Vantamuri Sushiladevi ,   S.G. Balekundri Institute of Technology, India, C Arunpriya ,   PSG College of Arts and Science, India, Desai Vinod ,   BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, India, Daniel A. Nicholas ,   Mukuba University, Zambia
Published: October 2024

Wireless Sensor Networks (WSNs) play a pivotal role in various domains, including environmental monitoring, surveillance, and industrial automation. However, the inherent vulnerabilities in WSNs make them susceptible to various security threats, such as data modification attacks, which can compromise the integrity and reliability of collected sensor data. To address this issue, we propose a novel approach called Residual Recurrent Transfer Learning (RRTL) to enhance the security of WSNs and eliminate data modification in sensor nodes. RRTL leverages the power of deep learning and transfer learning techniques to develop an intelligent and adaptable security framework. The proposed approach trains a deep residual recurrent neural network (RNN) model using a large dataset of normal sensor readings. This model learns the temporal patterns and dependencies in the data, enabling it to identify abnormal sensor readings that might indicate data modification attempts. To evaluate the effectiveness of RRTL, we conducted extensive experiments using a real-world WSN deployment. The results demonstrate that our approach significantly outperforms existing security mechanisms in terms of accuracy, detection rate, and false positive rate. Furthermore, RRTL exhibits robustness against adversarial attacks and dynamic environmental conditions, making it suitable for real-time applications in challenging WSN environments.
DESIGN OF EFFICIENT ROUTING PATHS USING SIMILARITY ESTIMATION BASED STOCHASTIC GRADIENT DESCENT IN WIRELESS SENSOR NETWORK
Authors:   Kings Engineering College, India, S Thumilvannan , B Yuvaraj ,   Kings Engineering College, India, C Srivenkateswaran ,   Kings Engineering College, India, V Balammal ,   Kings Engineering College, India
Published: October 2024

Wireless Sensor Networks (WSNs) offer versatile deployment options, particularly in battery-powered scenarios, addressing energy consumption concerns among sensor nodes. However, the data-intensive nature of WSNs poses challenges in routing, particularly in maintaining balanced paths while accommodating rapid data acquisition. This paper presents an innovative approach called Similarity Estimation-Based Stochastic Gradient Descent (SESGD) routing for WSNs, designed to establish stable routing paths that align with the speed of data acquisition. Sensor nodes play a crucial role in data collection and acquisition, while WSNs facilitate data routing through multiple hops from source to sink nodes. SESGD effectively manages data routing, synchronizing it with data acquisition rates, thereby ensuring network stability. Simulation results assess key performance metrics, including average delay, throughput, and network energy efficiency. The findings demonstrate that the proposed machine learning method outperforms existing algorithms, achieving superior network throughput.
ENHANCING COLLABORATIVE MITIGATION OF VOLUMETRIC DDOS ATTACKS AND FAILURE IN MULTI-SDN NETWORKS
Authors:   Institute of Technology and Management, Gwalior, India, Gupta Deepak , Sharma Ankita ,   Jodhpur Institute of Engineering and Technology, India, Pandey Rashmi ,   Institute of Technology and Management, Gwalior, India, Shrivastava Deshdeepak ,   Institute of Technology and Management, Gwalior, India, Acharya Archana ,   Institute of Technology and Management, Gwalior, India, Dubey Madhukar ,   Institute of Technology and Management, Gwalior, India
Published: October 2024

Multi-SDN (Software-Defined Networking) architectures offer enhanced flexibility and control over network traffic, which is essential in mitigating Distributed Denial of Service (DDoS) attacks. Volumetric DDoS attacks, characterized by overwhelming network traffic, pose significant threats to these networks, leading to severe service disruptions and failures. Despite the potential of SDN to manage network traffic effectively, the collaborative mitigation of volumetric DDoS attacks remains challenging. Existing solutions often lack coordination among multiple SDN controllers, resulting in inefficient resource utilization and delayed response times. This study proposes an enhanced collaborative mitigation strategy that leverages inter-controller communication and resource sharing in Multi-SDN environments. The method involves three key components: (1) a real-time detection algorithm using machine learning to identify DDoS traffic, (2) a dynamic resource allocation mechanism to distribute mitigation tasks among controllers, and (3) an inter-controller communication protocol to ensure coordinated responses. The detection algorithm was trained on a dataset of network traffic, achieving a 95% accuracy rate. The resource allocation mechanism optimizes the distribution of mitigation tasks, reducing response times by 30%. The proposed method was evaluated in a simulated Multi-SDN environment under various volumetric DDoS attack scenarios. The results demonstrated a significant improvement in mitigation effectiveness. The attack detection accuracy reached 94.5%, and the coordinated mitigation reduced average response times by 28%. Network throughput and latency were improved by 25% and 22%, respectively, compared to traditional non-collaborative methods.
OPTIMIZING WIRELESS SENSOR NETWORKS - ADVANCED ALGORITHMS FOR MULTI-CLUSTER ENVIRONMENTS
Authors:   KIT-Kalaignarkarunanidhi Institute of Technology, India, C Sivamani , Rao B. Srinivasa ,   Gokaraju Rangaraju Institute of Engineering and Technology, India, A Thangam ,   Pondicherry University Community College, India, S Mohanasundaram ,   Government College of Engineering, Erode, India
Published: October 2024

Wireless Sensor Networks (WSNs) are critical in various applications but face challenges in multi-cluster environments due to data aggregation and routing inefficiencies. This study addresses these issues by proposing an advanced approach leveraging the Deep K Nearest Neighbors (Deep KNN) algorithm for clustering. The method optimizes data routing by dynamically adjusting cluster heads based on deep learning insights, thereby enhancing energy efficiency and prolonging network lifespan. The experimental results, conducted on a simulated WSN platform, demonstrate significant improvements: a 30% reduction in energy consumption, a 20% increase in data transmission efficiency, and a 15% enhancement in network coverage compared to traditional methods. This approach not only improves network performance metrics but also ensures robustness and scalability in dynamic WSN environments.
OPTIMIZING MANET PERFORMANCE WITH IMPROVISED ALGORITHMIC INNOVATIONS FOR ENHANCED CONNECTIVITY AND SECURITY
Authors:   Government Engineering College, Jehanabad, India, Sah Raja Ram , Sahu Devendra Kumar ,   Raghuveer Singh Government Degree College, India, Kulkarni Nanda Satish ,   Siddhant College of Engineering, India, Ramana K. Venkata ,   Dr. J.J. Magdum College of Engineering, India, Maheshwari Shikha ,   Manipal University Jaipur, India, E Nagarjuna ,   Dr. J.J. Magdum College of Engineering, India
Published: October 2024

Mobile Ad-hoc Networks (MANETs) face significant challenges in maintaining connectivity and security due to their dynamic and decentralized nature. Passive attacks, such as eavesdropping and traffic analysis, pose a critical threat to MANET. This study proposes a novel algorithm, Radial ResNet, tailored for the classification of passive attacks in MANETs. The algorithm integrates radial basis function networks with residual networks (ResNet) to enhance classification accuracy and efficiency. Experimental results demonstrate the effectiveness of Radial ResNet, achieving an average classification accuracy of 95.2% across various passive attack scenarios. Moreover, the algorithm exhibits improved computational efficiency compared to traditional methods, reducing processing overhead by 30%. The proposed approach not only enhances security by accurately identifying passive attacks but also optimizes network performance by mitigating resource-intensive computations.
EFFECTIVE CLUSTERING AND CERTIFICATELESS ENCRYPTION TECHNIQUES IN THE MILITARY DOMAIN FOR ATTACK DETECTION
Authors:   Sri Ramakrishna Institute of Technology, India, S Rajesh , M.S. Premapriya ,   Anna University Regional Campus, Coimbatore, India, A Jayasmruthi ,   Sri Ramakrishna Institute of Technology, India, Lakshmi V. Muthu ,   Sri Ramakrishna Engineering College, India
Published: October 2024

The goal of a wireless sensor network (WSN) is to connect numerous nodes via a multi-hop self-organizing network. A variety of sensor nodes are arranged and positioned in relation to other nodes to provide safe data exchange. Sensor nodes watch as a sink node processes the data. The network includes a variety of assaults, including floods, wormhole, black hole, sinkhole, and so on. One of the most challenging tasks in WSN is secure routing because of the existence of attacks. The technology of attack detection is employed to identify attacks and enhances the security of data transmission. It provides a way to ignore network threats, enabling secure communication. Most recent efforts have been focused on implementing secure data communication in wireless sensor networks. However, the more accurate detection attack was not achieved. Numerous intrusion attackers are available to compromise network data packet communication. To provide better data distribution amongst soldiers in a combat environment, this wireless network invader node needs to be identified. Hence, in a WSN context, data transmission with a military scenario entails armed men engaged in combat, such as fighter aircraft, tankers, and shot ships. Finding intrusion attackers is a difficult task, especially in the highly dynamic military wireless sensor network. Therefore, different techniques are developed in WSN for achieving attack detection accuracy with minimal delay and maximum data delivery rate.
BLOCKCHAIN-BASED ROUTING PROTOCOLS FOR SECURE DATA TRANSMISSION
Authors:   R.M.K. Engineering College, India, Lakshmi K. R. Chairma
Published: October 2024

In the field of the Internet of Things (IoT) and wireless communication networks, secure data transmission remains a critical challenge due to the inherent vulnerabilities and dynamic nature of these networks. Traditional routing protocols often fail to provide adequate security and efficiency, leading to data breaches and communication delays. To address these issues, we propose a Blockchain-Based Greedy Routing Protocol (BBGRP) that integrates blockchain technology with a greedy forwarding strategy to enhance the security and reliability of data transmission in wireless networks. The BBGRP leverages blockchain''s decentralized and tamper-resistant ledger to validate the integrity and authenticity of data packets, while the greedy routing approach optimizes the selection of the most efficient transmission paths. Our method incorporates smart contracts to automate route verification, ensuring secure and real-time decision-making processes. Experimental results show that the proposed protocol significantly reduces the packet loss rate by 27.3% and improves data transmission speed by 35.6% compared to traditional routing protocols. Additionally, BBGRP demonstrates a 42.8% enhancement in overall network throughput and a 21.7% decrease in end-to-end latency. The security analysis confirms the robustness of BBGRP against common attacks, such as Sybil and man-in-the-middle attacks, by achieving a 98.4% attack detection rate. These results highlight the potential of blockchain-based routing protocols to revolutionize secure data transmission in IoT and other wireless communication environments.
MACHINE LEARNING ALGORITHMS FOR SPECTRUM MANAGEMENT IN MOBILE NETWORKS
Authors:   School of Management Sciences, India, Srivastava Shambhu Sharan , Bhattacharya Chaitali ,   Tecnia Institute of Advanced Studies, India, Kumar Ajay ,   Tecnia Institute of Advanced Studies, India, Bhendale Manisha Amol ,   Bharati Vidyapeeth College of Engineering, India
Published: October 2024

In the rapidly field of mobile networks, efficient spectrum management is critical to meet the growing demand for data services and optimize resource allocation. Traditional spectrum management techniques often face challenges in handling dynamic and complex network environments. This study addresses these challenges by proposing an ensemble machine learning algorithm combining Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) classifiers for effective spectrum management in mobile networks. The proposed ensemble model leverages the strengths of each individual algorithm, combining SVM’s robustness in high-dimensional spaces, RF’s capability in handling large datasets with higher accuracy, and DT’s efficiency in rule-based decision making. To evaluate the performance of the ensemble model, a dataset representing spectrum usage patterns in a dense urban mobile network environment was utilized. The model was trained to predict spectrum occupancy and allocate resources dynamically to minimize interference and maximize network throughput. The experimental results demonstrate a significant improvement in prediction accuracy and resource allocation efficiency. The ensemble model achieved an accuracy of 95.6%, surpassing individual classifiers—SVM at 92.3%, RF at 93.1%, and DT at 89.7%. Additionally, the ensemble approach reduced network interference by 18% and increased overall throughput by 23% compared to traditional spectrum management methods. The findings suggest that the proposed ensemble machine learning model provides a more accurate and efficient solution for spectrum management in mobile networks, potentially leading to enhanced service quality and network performance.
AI-ENHANCED ROUTING PROTOCOLS FOR EFFICIENT DATA TRANSMISSION IN WIRELESS NETWORKS
Authors:   Dhanalakshmi Srinivasan University, India, V Anitha , T.S. Umamaheswari ,   Jayagovind Harigopal Agarwal Agarsen College, India, B Saravanan ,   Sri Eshwar College of Engineering, India, Abhang Shilpa ,   Jyoti Nivas College, India, Ramagundam Shashishekhar ,   Comcast Corporation, Washington D.C., United States of America
Published: October 2024

The increasing demand for efficient data transmission in wireless networks, such as mobile ad-hoc networks (MANETs) and vehicular ad-hoc networks (VANETs), presents significant challenges in maintaining reliable communication and minimizing energy consumption. Traditional routing protocols often fail to adapt dynamically to varying network conditions, leading to suboptimal performance and increased latency. To address these limitations, this study introduces an AI-enhanced Position Assisted Routing Protocol (PARP) designed for efficient data transmission in wireless networks. The proposed protocol leverages machine learning algorithms, specifically deep reinforcement learning (DRL), to optimize routing decisions based on real-time network conditions and node positions. The PARP integrates position-based information with AI-driven prediction models to proactively determine the optimal routing paths, thus reducing packet loss and improving transmission efficiency. Extensive simulations were conducted using the NS-3 simulator to evaluate the performance of the AI-enhanced PARP against existing protocols such as AODV and DSR. The results demonstrate a significant improvement in key performance metrics: packet delivery ratio increased by 23%, average end-to-end delay reduced by 35%, and network throughput improved by 28% compared to conventional protocols. Additionally, the proposed protocol achieved a 15% reduction in energy consumption, highlighting its suitability for energy-constrained wireless networks. These findings indicate that the AI-enhanced PARP can dynamically adapt to network changes, providing a robust and efficient solution for data transmission in various wireless network scenarios. Future research will focus on incorporating additional environmental factors, such as interference and mobility patterns, to further enhance the protocol’s adaptability and performance.
Impact of Concurrent Media Exposure on Professional Identity: Cross-Sectional Study of 1087 Medical Students During Long COVID (Preprint)
Authors: Wu Manli , Yan Jun , Qiao Chongming , Yan Chu
Published: October 2024

BACKGROUND

Long COVID has widened the health gap across society and highlighted the vulnerabilities and risks faced by health care systems. For instance, the global trend of medical workers resigning has become a prominent topic on social media. In response to this severe social problem in global public health within the digital society, it is urgent to investigate how the professional identity of medical students, who are digital natives and the future workforce of medical practitioners, is affected by the media environment.

OBJECTIVE

This study aims to examine how media exposure relates to medical students’ perceptions of informational and emotional support, and how these perceptions further influence the development of their professional identity.

METHODS

Building on the Stimulus-Organism-Response (SOR) framework, this study develops a theoretical model to illustrate how media exposure affects medical students’ professional identity through the mediation of social support. Specifically, media exposure was assessed through online news media and social media exposure; social support was evaluated in terms of informational and emotional support; and professional identity was measured through medical students’ sense of belonging and professional commitment. A survey was conducted at a medical school in China, yielding 1087 valid responses that were analyzed using SmartPLS 4.0.

RESULTS

Consistent with our expectations, online news media exposure was positively associated with both informational support (β=.163; P<.001) and emotional support (β=.084; P=.007). Similarly, social media exposure showed positive associations with informational support (β=.122; P<.001) and emotional support (β=.235; P<.001). Thereafter, informational support (β=.228; P<.001) and emotional support (β=.344; P<.001) were positively associated with students’ sense of belonging. Meanwhile, both informational support (β=.245; P<.001) and emotional support (β=.412; P<.001) positively impacted medical students’ professional commitment. In addition, a mediation test was conducted. The results confirmed that informational support and emotional support partially mediated the effect of online news media, while fully mediating the effect of social media on medical students’ sense of belonging and professional commitment.

CONCLUSIONS

This study finds that exposure to online news media and social media can enhance medical students’ sense of belonging and professional commitment through the formation of informational and emotional support. It expands the discussion on the role of media in providing social support and facilitating the development of medical students’ professional identity. This is a valuable contribution to addressing complex public health crises through effective media governance in the network era.
Dynamics of Blood Lipids Before, During, and After Diurnal Fasting in Inactive Men: Quasi-Experimental Study (Preprint)
Authors: Aljaloud Khalid , Al-Barha Naif , Noman Abeer , Aldayel Abdulaziz , Alsharif Yahya , Alshuwaier Ghareeb
Published: October 2024

BACKGROUND

There is a lack of investigation into the dynamics of blood lipids before, during, and after diurnal fasting, especially in inactive men.

OBJECTIVE

This study determined dynamic changes in blood lipids in inactive men before, during, and after they underwent diurnal fasting.

METHODS

A total of 44 young men aged a mean 27.6 (SD 5.8) years were recruited to evaluate their habitual physical activity and diet using a questionnaire developed for this study. Body composition was evaluated using a bioelectrical impedance analysis machine (Tanita BC-980). An 8-ml blood sample was collected to evaluate blood lipids and glucose. All measurements were taken 2-3 days before Ramadan, during Ramadan (at week 2 and week 3), and 1 month after Ramadan. A 1-way repeated measures ANOVA was used to compare the measured variables before, during, and after the month of Ramadan. When a significant difference was found, post hoc testing was used. Differences were considered significant at P<.05.

RESULTS

There was a significant reduction in low-density lipoprotein during Ramadan compared to before and after Ramadan (83.49 mg/dl at week 3 vs 93.11 mg/dl before Ramadan [P=.02] and 101.59 mg/dl after Ramadan [P=.007]). There were significant elevations in fasting blood glucose (74.60 mmol/L before Ramadan vs 81.52 mmol/L at week 3 [P=.03] and 86.51 mmol/L after Ramadan [P=.01]) and blood pressure (109 mm Hg before Ramadan vs 114 mm Hg after Ramadan; P=.02) reported during and even after the month of Ramadan, although both fasting blood glucose and blood pressure were within normal levels.

CONCLUSIONS

Ramadan fasting could be an independent factor in reducing low-density lipoprotein. Further investigations are encouraged to clarify the impact of diurnal fasting on blood lipids in people with special conditions.
Detection of MCR-1 Gene in Colistin resistant Klebsiella Pneumoniae Isolated from Intensive Care Unit in Khartoum State Hospital, Sudan (Preprint)
Authors: Abdallah Mahadi H , Mohamed Misca  A , Ali Mawada A , Shogar Mai M
Published: October 2024

BACKGROUND

Colistin resistant Klebsiella species are increasingly being reported as causes of suppurative infections. Colistin resistance has emerged globally, threatening the efficacy of one of the last-resort antimicrobials used to treat MDR Gram-negative bacteria. However, data on the prevalence of mcr genes remain limited.

OBJECTIVE

This study was to determine the prevalence of the mcr-1 gene in colistin-resistant Klebsiella pneumoniae isolates from different intensive care units (ICUs) in Khartoum State hospitals.

METHODS

A total of 100 Klebsiella pneumoniae isolates from ICUs were re-identified based on colony morphology, Gram staining, and biochemical characterization. All confirmed Klebsiella pneumoniae isolates were subjected to antimicrobial susceptibility testing, and PCR was used to detect the plasmid-mediated mcr-1 colistin resistance gene.

RESULTS

Out of the 100 isolates, 8 (8%) were found to be colistin-resistant, with the mcr-1 gene detected in one sample (1%). The detection of colistin-resistant Klebsiella pneumoniae is a concerning development, signaling the emergence of resistance for health authorities and the community.

CONCLUSIONS

Notably, this research is the first to detect the mcr-1 gene in Klebsiella pneumoniae isolated from human samples in Sudan. Further studies on additional resistance genes and larger sample sizes, including cultivation of mcr-carrying bacteria, are needed to determine the spread of these genes in human pathogens and to elucidate the full epidemiology of colistin resistance in humans
Need Analysis of Clinician-Oriented Integrated Precision Oncology Decision Support Tools: A Mixed-Method Qualitative Descriptive Study (Preprint)
Authors: Zheng Sheng , Feng Yi , Long Jieran , Li Xiang , Zhang Meng , Chen Hui , Du Xue , Duan Huilong , Lu Xudong , Jia Shuqin , Wu Nan
Published: October 2024

BACKGROUND

With the advancement of next generation sequencing, healthcare professionals face challenges in keeping pace with increasingly extensive body of knowledge and information in prediction medicine. In oncology area, current functionalities of precision oncology decision support (PODS) tools only partially address clinicians' needs, leaving certain clinical requirements within PODS inadequately defined. Lack of need analysis may result in unaddressed clinician requirements.

OBJECTIVE

We aimed to explore clinicians’ needs and expectations of functions in integrated PODS tools.

METHODS

Qualitative investigation was held in Peking University Cancer Hospital (PUCH). Data were derived from structured participant observation records (n=143) from multidisciplinary team (MDT) and in-depth interviews (n=17). Participants included physicians, surgeons, molecular biologists, radiotherapists, radiologists, and pathologists.

RESULTS

Three main themes of functions arose from our data: better access to oncological knowledge; feasibility support; and support abilities demands in decision-making process. Oncological knowledge themes included support in therapies (guidelines, conferences, and consensuses; clinical trials; information of drugs and treatments; and knowledge of complex cases), diagnosis and prognosis. Feasibility support themes included accessibility of clinical trials, accessibility of drugs, and prediction models. Decision-making process support themes included flexible biological knowledge and phenotypes, automatic integration of patient information, better visualization of information, and optimization in retrieval, recommendation and question answering. Several example quotes describing these themes were provided. A functional framework of needs for integrated PODS tools was proposed.

CONCLUSIONS

PODS is complex, multi-level decision support. Clear elucidation of the actual clinical needs may impact the improvement of PODS. This study presents unique perspectives directly from clinicians in this new arena of precision oncology.
EFFECTIVENESS AND SAFETY OF USE OF BIGUANIDE AND SULFONYLUREA DRUGS WITH HISTOPATHOLOGICAL ANALYSIS AND PARAMETERS OF THE KIDNEY, LIVER AND PANCREAS IN ALLOXAN-INDUCED RATS
Authors: WAHYUNI ARIFAH S. , RAHMAWATI FIRDA AYU F. , WIJIANTO DISKI W. , UMNIYYAH RETNO A.
Published: October 2024

Objective: There are several drugs used as first-line in the treatment of type 2 diabetes with different mechanisms of action, such as metformin, glimepiride, and glibenclamide. The mechanism action of the drugs through stimulation and increased insulin sensitivity. This mechanism can have side effects on the organs excreting the drug. The aim of this study was to compare the effectiveness and safety of the three drugs in alloxan-induced rats.
Methods: A total of 15 rats were divided into five groups with each group consisting of three rats: Normal Control (NC), Induced Control (DC), Metformin 9 mg/kgBW, Glimepiride 1 mg/kgBW, and Glibenclamide 5 mg/KgBW. Fasting Blood Glucose (FBG) levels, Blood Urea Nitrogen (BUN), Creatinine Serum (SCr) and Serum Glutamate Pyruvate Transaminase (SGPT) were obtained. Data analysis using ANOVA and a post-hoc test.
Results: The results show that in comparison of the three anti-diabetic drugs, glibenclamide causes the highest damage to the pancreas and liver. Meanwhile, glimepiride provides the safest results for the kidneys, and metformin is safest for the liver.
Conclusion: The result showed that the drug glibenclamide caused the highest damage to pancreatic cells and glimepiride gave the greatest decrease in blood glucose levels, but the use of glimepiride for 14 d was safer for use than for glibenclamide for kidney parameters.
LITERATURE REVIEW: THE OMICS STUDY FOR DETERMINING BIOMARKERS IN HUMAN SERUM AND PLASMA WITH DIFFERENT COVID-19 SEVERITY
Authors: UTAMI WAHYU , FARRAS NAUFAL
Published: October 2024

The severity of COVID-19 provides information on various stages of changes in the body's normal state in various parameters called biological markers (biomarkers) as the initial identification that facilitates management, selection, and total outcomes in therapy. These biomarkers were selected from the selection of samples that are often used in the advanced diagnosis of COVID-19, serum and plasma. This study aims to determine what biomarkers are measured in serum and plasma samples of COVID-19 patients. This literature review is classified as non-experimental, qualitative, and descriptive research. The inclusion criteria are the full-text journals published within the last two years regarding biomarkers in the serum and plasma of COVID-19 patients. Based on these criteria, 49 relevant articles were obtained. The results show that changes occur in the protein, lipid, and metabolite in serum and plasma by the omics approach. These alterations can be in the form of increasing or decreasing levels of each parameter determined through various analytical methods. The biomarkers profile correlates with the severity of COVID-19 as well as with host cells.
ANTI-INFLAMMATORY ACTIVITY AND IRRITATION TEST OF TOPICAL DOSAGE FORMS OF CLOVE LEAF ESSENTIAL OIL (SYZYGIUM AROMATICUM) IN ANIMALS
Authors: JANUARTI IKA BUANA , LATIFAH FADZIL , FIRMANSYAH RICKY , PERTIWI AULYA D. , AGUSTYA NIKEN , WIDYAWATI HESTI
Published: October 2024

Objective: Clove leaves (Syzygium aromaticum) contain compounds, namely eugenol, alpha-humulene, beta-caryophyllene, and caryophyllene oxide with the main compound eugenol, which has anti-inflammatory activity. One of dosage form to treat inflammation on the skin is a topical dosage form because it is quickly absorbed into the skin. This study aimed to formulate Clove Leaf Essential Oil (CLEO) and evaluate the physical properties, anti-inflammatory activity, and irritation effects of various topical dosage forms (emulgel, balsam, massage oil, and stick balm).
Methods: Research design was a pre-posttest control group. Essential oils were obtained using the steam distillation method. The four different concentrations of CLEO topical dosage form (emulgel, balsam, massage oil, and stick balm) are 5% and 10%. Then, test are conducted on the physical qualities (organoleptic, pH, displaced volume, spreadability, sticking power, moisture content and viscosity). Anti-inflammatory activity using the carrageenan-induced paw edema method in Wistar rats was divided into four groups are dosage form CLEO concentrations of 5% and 10%, base preparation, and positive control. Anti-inflammatory activity results were analyzed with Post-hoc statistical analysis with a 95% confidence level. The irritation method was used to measure the Primary Irritation Index (PII) followed procedure by Indonesian Food and Drug Authority about Guidelines for in vivo Preclinical Toxicity Testing.
Results: All these dosage forms met the physical requirements. Anti-inflammatory activity will rise as result of increasing the CLEO content in the formulae. All these dosage forms did not cause irritation in animal.
Conclusion: Among the four formulations, balsam CLEO 10% showed maximum inhibitory oedema (94.05%), at the end of 360 min followed by balsam CLEO 5%, emulgel CLEO 10%, stick balm CLEO 10% and massage oil CLEO 10%.
A COMPARATIVE STUDY OF ANTIBACTERIAL, ANTIOXIDANT ACTIVITY AND TOTAL CONTENT OF PHENOLIC COMPOUNDS FROM A COMBINATION OF CLOVE AND CINNAMON ESSENTIAL OILS
Authors: SAFITRI META , SULISTYANI NANIK , WAHYUNINGSIH IIS , SYLVIA DIANA , APRILLIANI ARINI
Published: October 2024

Objective: This study aimed to identify the right combination of Clove Flower Essential Oil (CFEO) and Cinnamon Essential Oil (CEO) that has the potential to be used as a medicine for diabetic wounds.
Methods: Antibacterial activity against Staphylococcus aureus was observed using the paper disc method. Antioxidant activity was examined the DPPH (2.2-Diphenyl-1-Picrylhydrazyl acid) method. Phenol content was tested using the Folin Ciocalteau method.
Results: The results showed that the antibacterial activity of CFEO was lower than that of CEO in a single form. The combination of CFEO: CEO was made with varied concentrations of 1.25; 2.5; and 5%. Each concentration has the following ratios (1:1), (1:2), (1:3), (2:1), and (3:1). The highest antibacterial activity was found at a concentration of 5% combination (1:3) with an average inhibition zone diameter of 20.61±1.07. The minimum inhibitory concentrations of CFEO, CEO, and the combination of CFEO: CEO (1:3) against Staphylococcus aureus bacteria were 0.078%, 0.046%, and (0.0195:0.0935%). The antioxidant activity of the CFEO: CEO (3:1) combination showed the highest antioxidant activity with an IC50 of 42,706 ppm. Single CFEO showed had a higher phenol content of 548.065 mgGAE/g compared to single CEO. The combination CFEO: CEO (3:1) showed the highest phenol content of 262,473 mgGAE/g compared to combination (1:1) and (1:3).
Conclusion: Based on the results, the combination of CFEO and CEO (3:1) has the highest antibacterial activity, antioxidant and phenol content. Therefore, it can be proposed as an active ingredient for diabetic wound preparation.
MECHANISTIC INSIGHT INTO MEDICINAL PROPERTIES OF INDONESIAN DIVERSE MANGROVE SPECIES: A REVIEW
Authors: MUHTADI MUHTADI , PAMBUDI DWI B. , MARYATI MARYATI
Published: October 2024

Mangrove ecosystems in Indonesia harbor a rich diversity of plant species, some of which have been traditionally recognized for their medicinal properties. This study aims to provide mechanistic insights into the medicinal potential of various mangrove species found in Indonesian coastal regions. Through a comprehensive analysis of pharmacological activities and underlying mechanisms, our research seeks to elucidate the therapeutic properties of these diverse mangrove plants. The key terms "Mangrove", "Pharmacological", and "Indonesia" used for searching in three online databases: Science Direct, PubMed, and Google Scholar. The investigation into the pharmacological properties of mangrove species revealed versatile mechanisms of action. Notably, a convergence is observed in their antioxidant mechanisms, as exemplified by Aegiceras corniculatum, Avicennia marina, and Rhizophora mucronata, showcasing robust effects in DPPH, ABTS, and FRAP assays. Additionally, the study highlights significant findings in the realm of anti-inflammatory activities. Mangrove species like Aegialitis rotundifolia, Ceriops decandra, and Rhizophora apiculata demonstrate notable anti-inflammatory effects by inhibiting enzymes like LOX and responding positively to carrageenan induction. A commonality is unveiled in antibacterial effects, with species like Avicennia marina, Ceriops tagal, and Excoecaria agallocha exhibiting potent antibacterial properties in agar diffusion assays. These findings underscore the potential of mangrove species in combating microbial infections through distinct antibacterial mechanisms. Furthermore, understanding the mechanisms behind the medicinal properties of Indonesian mangrove species is crucial for both conservation efforts and the development of novel pharmaceuticals.
Survey and Effects of Weather Parameters on Powdery Mildew of Black Gram Caused by Erysiphe Polygoni DC
Authors: Meena Sakshi , Meena Mahendra Kumar , Bunker R.N.
Published: October 2024

Surveys were conducted during kharif 2018 in ten villages viz., Gandholi, Noorda, Joonawas, Odwariya, Banora, Malpur, Panarwa, Oda, Khandiovri and Sundara of four tehsil’s (Mavli, Salumbar, Jhadol and Kherwara) of Udaipur district. The maximum (54.67%) disease incidence was recorded in Joonawas village (Mavli tehsil) with 43.22% disease severity while, minimum incidence 25.40% with 19.54% severity from Malpur village (Salumber tehsil). Study of weather parameters in relation to powdery mildew development revealed that, the maximum disease severity (8.67-58%) was found during July 30- Sept 2, 2018. During this period the maximum temperature (29.1-30.5˚c), minimum temperature (22-22.9˚c), maximum relative humidity (82.6-85.1%), minimum relative humidity (58.6-78.3%), sunshine (1.2-3.6 hours) and rainfall (17.4-67.6 mm) was favoured for powdery mildew development.
Examining the Impact of Collaborative Teaching on Students’ Performance and Cultural Competence in Teaching Foreign Language
Authors: Walia Dr Sarabjit Singh
Published: October 2024

Among the works that consider political and social narratives, one can refer to the book "The Adventures of Two Captains," which also represents a kind of postmodern exploration. This work is intertwined with literary techniques, which is one of its prominent features, and serves as a bridge between science fiction stories, utopianism, and critical theories of international relations. However, it should be stated that the mentioned book aims at deconstructing modernist approaches to literature and the power structures that are used while defining global politics. The authors of this work, Elias Aghili and Paul John Amrod, while using epic storytelling intertwined with dystopian and utopian elements, have also used rich literary traditions from both Eastern and Western cultures in this direction. In this regard, this review analyzes the impact of the book on postmodernist discourse, exploring its noted literary techniques and broader thematic issues, especially its critique of power, identity, and narrative in terms of international relations.
Diferencias de desempeño académico con base en emociones en clases de matemáticas
Authors: Rigaud Téllez Nelly , Blanco Bautista Roberto , Flores Herrera Viviana , Sosa Rodríguez Mario
Published: October 2024

El uso de tecnologías para analizar expresiones faciales con el fin de identificar emociones y mejorar el rendimiento en educación es común. El objetivo de este trabajo es comprender mejor el aprendizaje matemático en este contexto, en especial de la Geometría Analítica, considerando emociones y desempeño académico. Para ello, se emplean la teoría del control-valor y el marco afectivo para el aprendizaje como cuerpo teórico. Se realizó un estudio con 95 estudiantes, durante el cual se grabaron sus expresiones faciales y se extrajeron emociones, junto con mediciones objetivas y subjetivas de aprovechamiento. Los resultados muestran que, en mediciones objetivas, los estudiantes de alto desempeño experimentan más cambios emocionales que los de bajo desempeño; mientras que los últimos reportan una sensación de seguridad en sus habilidades y conocimiento en mediciones subjetivas. El texto concluye con algunas recomendaciones para mejorar el aprendizaje matemático.
Vulneración de derechos fundamentales por la imposición de prisión preventiva oficiosa
Authors: Trejo Constantino Wendy Rubi
Published: October 2024

En el presente ensayo se analizan las características de la prisión preventiva oficiosa como la medida cautelar más lesiva vigente en el marco jurídico mexicano y se esgrimen argumentos tendientes a evidenciar determinadas circunstancias procesales y prácticas que implican vulneraciones a diversos derechos humanos de los procesados. Se subraya que el carácter oficioso de la prisión preventiva atenta incluso contra la naturaleza de las medidas cautelares y puede considerarse como una pena anticipada, por lo que es importante revisar la pertinencia respecto a la supresión de la prisión preventiva oficiosa de nuestro marco jurídico y conservar la medida cautelar únicamente en los casos en que esté plenamente justificada.