bluuebunny/crossref_metadata_2025 huggingface dataset:
Surveillance of antimicrobial resistance in Streptococcus agalactiae isolated from pregnant women and newborns in 2010–2022
Authors: Khusnutdinova T.A. , Shalepo K.V. , Budilovskaya O.V. , Krysanova A.A. , Spasibova E.V. , Sinyakova A.A. , Tapilskaya N.I. , Savicheva A.M. , Kogan I.Yu.
Published: October 2024

Objective.
To study the dynamics and molecular mechanisms of resistance of Streptococcus agalactiae strains isolated from pregnant women and newborns in St. Petersburg in 2010–2022.

Materials and Methods.
The strains of S. agalactiae (n = 248) were obtained by analyzing the clinical material of pregnant women and newborns who were hospitalized at Research Institute of Obstetrics, Gynecology and Reproductology named after D.O. Ott in 2010–2011 and 2020–2022. Determination of susceptibility to antibacterial drugs was evaluated by disk diffusion method. The detection of resistance genes to macrolides and lincosamides (mef and ermB) was performed by real-time PCR using reagents «Resistom MLSB-Streptococcus» (Litech, Russia).

Results.
All isolates S. agalactiae (n = 248) were sensitive to penicillin and vancomycin. Compared to 2010–2011, the resistance of S. agalactiae to erythromycin increased from 18.4% to 42.1% and to clindamycin from 15.6% to 39.3%. Among S. agalactiae strains (n = 71) resistant to erythromycin and clindamycin, cMLSB phenotype was predominant (59.1%); iMLSB (31%) and M-phenotype (9.9%) were less common. The results of detection of genes encoding resistance to erythromycin and clindamycin showed that in 38 (53.5%) and 9 (12.7%) isolates of S. agalactiae resistance genes ermB and mefA were detected as one resistance determinant, respectively, association of two ermB/mefA genes was detected in 2 (2.8%) strains; resistance genes were not detected in 22 (31%) isolates.

Conclusions.
Penicillin and vancomycin are effective drugs for the treatment and prevention of infections caused by S. agalactiae. The increase in resistance to macrolides and lincosamides reflects the global trend of increasing resistance to these antibiotics. Regular monitoring of antibiotic resistance of the pathogen is necessary to update the clinical recommendations.
Dynamics of antimicrobial resistance of uropathogenic isolates of Escherichia coli
Authors: Otamuratova Nargiza Kh. , Abdukhalilova G.K.
Published: October 2024

Objective.
To evaluate the dynamics of antimicrobial resistance of Escherichia coli isolates from the urine of patients hospitalized in medical institutions of Tashkent city from 2018 to 2021.

Materials and Methods.
The study included 1020 E. coli isolates from the urine of patients with clinical symptoms of urinary tract infections admitted to the reference laboratory of the Antimicrobial Resistance Research Center (ARRC) from three medical institutions in Tashkent city for the period 2018– 2021. Determination of sensitivity to 15 antimicrobial agents was carried out by disc-diffusion method in accordance with the guidelines of the European Committee for the Determination of Sensitivity to Antimicrobial Agents (EUCAST v.8-v.12, depending on the year of isolates isolation and testing).

Results.
A high level of resistance to aminopenicillins, including inhibitor-protected ones (88.0–91.1%), 3–4 generation cephalosporins (84.5–87.7%) and fluoroquinolones (74.2–81.8%) was detected. Carbapenems showed the highest activity among the tested antimicrobials: resistance to imipenem, meropenem and ertapenem was 0%, 0.8% and 18%, respectively. Notable increase in resistance to ampicillin and ceftazidime was found, but a consistently high level of resistance to piperacillin-tazobactam. A statistically significant increase (p < 0.001) was found between 2018–2021 in resistance to the aminoglycoside antibiotics amikacin and gentamicin from 3.2% and 37.8% to 85.4% and 80.6%, respectively.

Conclusions.
In our study, for most antibiotic groups, the level of resistance remained relatively stable from 2018 to 2021, except for an explosive increase in the frequency of resistance to aminoglycosides, as well as a further increase in resistance to ampicillin and ceftazidime. The need for monitoring and tracking of antibiotic pathogens when choosing treatment regimens for various infections is obvious.
Optimizing Data Pipeline Performance in Modern GPU Architectures
Authors: Ashvini Byri  , Satish Vadlamani  , Ashish Kumar  , Om Goel  , Shalu Jain  , Raghav Agarwal
Published: October 2024

Optimizing data pipeline performance in modern GPU architectures is critical for achieving high computational throughput and efficient resource utilization in data-intensive applications. With the rise of deep learning, scientific simulations, and real-time analytics, GPUs have become integral in accelerating data processing tasks. However, ensuring optimal performance involves addressing several challenges, such as memory bandwidth limitations, data transfer bottlenecks between CPU and GPU, and efficient parallel execution of workloads.
This research explores techniques for improving data pipeline performance by focusing on memory management, load balancing, and task scheduling. One key strategy is optimizing data movement through techniques like memory coalescing, which minimizes access latency, and overlapping data transfers with computation. Furthermore, leveraging the architectural advances in modern GPUs, such as unified memory and NVLink, can significantly reduce data transfer overhead. Task parallelism and efficient workload distribution across multiple GPU cores also play a crucial role in enhancing pipeline throughput.
Additionally, the study highlights the importance of tuning GPU kernels and optimizing data preprocessing steps to ensure minimal latency and maximum throughput. By adopting advanced profiling tools and performance monitoring techniques, bottlenecks can be identified, and pipeline optimization strategies can be fine-tuned. The findings presented provide a comprehensive approach for designing and optimizing data pipelines, leading to significant performance improvements in GPU-based systems, ultimately driving the next generation of high-performance computing applications.
Explainable AI for Compliance and Regulatory Models
Authors: Indra Reddy Mallela  , Sneha Aravind  , Vishwasrao Salunkhe  , Ojaswin Tharan  , Prof.(Dr) Punit Goel  , Dr Satendra Pal Singh
Published: October 2024

The increasing complexity of compliance and regulatory frameworks across industries demands innovative solutions for managing and interpreting large volumes of data. Explainable Artificial Intelligence (XAI) offers a promising approach by providing transparent and interpretable AI models that can be utilized for compliance and regulatory decision-making. Traditional AI systems, often viewed as "black boxes," have been met with scepticism due to their opacity, especially in high-stakes domains like finance, healthcare, and legal sectors, where accountability and trust are paramount. XAI addresses these challenges by making the decision-making process more transparent, enabling stakeholders to understand the logic behind AI-driven recommendations and actions.
In regulatory environments, XAI can be used to explain the rationale behind risk assessments, fraud detection, or legal interpretations, thus ensuring compliance with laws and policies. Moreover, the integration of XAI into compliance models enhances auditability and traceability, providing regulators and auditors with the tools to validate and verify the adherence to standards. This transparency is crucial for building trust in AI systems and fostering collaboration between human decision-makers and AI tools.
Designing Resilient Multi-Tenant Architectures in Cloud Environments
Authors: Saurabh Ashwinikumar Dave  , Nanda Kishore Gannamneni  , Bipin Gajbhiye  , Raghav Agarwal  , Shalu Jain  , Pandi Kirupa Gopalakrishna
Published: October 2024

The increasing demand for scalable and cost-effective cloud solutions has led to the widespread adoption of multi-tenant architectures, where multiple customers share a single instance of software while maintaining data isolation. However, designing resilient multi-tenant architectures poses significant challenges, particularly in ensuring reliability, security, and performance across diverse tenant workloads. This paper explores the key design principles and strategies essential for building resilient multi-tenant systems in cloud environments. We discuss the importance of fault tolerance, emphasizing redundancy and data replication to ensure uninterrupted service delivery. Additionally, we address the critical need for robust security measures, including access control and encryption, to protect tenant data from breaches and unauthorized access. Performance optimization techniques, such as resource allocation and load balancing, are examined to enhance responsiveness and scalability while maintaining isolation among tenants. Furthermore, we highlight the role of monitoring and analytics in identifying potential failures and bottlenecks, enabling proactive management of system health. Through a comprehensive review of existing frameworks and best practices, this paper provides insights into the effective design of resilient multi-tenant architectures that meet the growing demands of cloud-based services while ensuring a secure and high-performance environment for all users. The findings aim to guide cloud architects and developers in creating robust systems that enhance customer satisfaction and operational efficiency.
Data-Driven Product Management: Strategies for Aligning Technology with Business Growth
Authors: Hemant Singh Sengar  , Nishit Agarwal  , Shanmukha Eeti  , Prof.(Dr) Punit Goel  , Om Goel  , Prof.(Dr) Arpit Jain
Published: October 2024

In today's rapidly evolving digital landscape, effective product management requires a strategic alignment between technology and business growth. This paper explores data-driven product management as a pivotal approach for organizations aiming to enhance their competitive edge. By integrating advanced analytics and data insights into the product development lifecycle, businesses can make informed decisions that directly influence product innovation and market responsiveness. The study highlights key strategies for leveraging data to understand customer needs, optimize product features, and drive user engagement. Furthermore, it examines the role of cross-functional collaboration and agile methodologies in fostering a data-centric culture within organizations. Through case studies and empirical evidence, this research underscores the importance of aligning product management strategies with organizational goals, ultimately facilitating sustainable growth and improved customer satisfaction. The findings provide a comprehensive framework for practitioners seeking to implement data-driven approaches in their product management processes, ensuring that technological advancements contribute effectively to overall business objectives.
КАЗАКИ И ГОРЦЫ СЕВЕРНОГО КАВКАЗА В УСЛОВИЯХ ГРАЖДАНСКОЙ ВОЙНЫ 1918 - 1921 гг.
Authors: Эльбуздукаева Т.У.
Published: October 2024

В статье рассматривается процесс установления советской власти на Северном Кавказе в условиях противостояния горско-казачьего населения. В Терской области социалистические преобразования затронули, прежде всего интересы казачьего населения. Антисоветские настроения среди казаков усилились после решений III съезда народов Терека в г. Грозном, ставшего рубежным в реализации программы социалистической революции. Вооруженная борьба красных чеченцев против добровольческой армии Деникина приняла широкий размах, поскольку они отстаивали свободу и суверенитет, обещанные большевиками. Решение национального вопроса в условиях гражданской войны неизбежно обострили политическое и гражданское противостояние между горцами и казаками, а учитывая специфику региона (наличие казачьего, иногороднего и горского населения), расширило границы социального конфликта.
НЕКОТОРЫЕ ШТРИХИ ОСНОВАНИЯ СЕЛЕНИЯ ДАДИ-ЮРТ (ПО ЭТНОГРАФИЧЕСКИМ МАТЕРИАЛАМ)
Authors: Хасбулатова З.И.
Published: October 2024

В представленной статье рассматривается проблема основания селения Дади-Юрт. Для раскрытия темы автор использовал собственные полевые материалы, собранные в разных районах Чечни и Ингушетии в течение длительного периода времени, архивные данные, све-дения журналистов, а также другие имеющиеся материалы.
ВЛИЯНИЕ ПОЛИТИЧЕСКОГО ИСЛАМА В АРАБСКИХ СТРАНАХ И ПОЛИТИКА САУДОВСКОЙ АРАВИИ
Authors: Хатуев И.З.
Published: October 2024

В статье авторы попытались раскрыть основные причины роста влияния вульгарного ис-лама на внутреннюю и внешнюю политику стран Арабского Востока, охарактеризовать новый этап политико-культурного и социально-экономического развития арабских стран примерно с конца 60-х годов ХХ века, раскрыть процесс исламизации различных сторон об-щественной жизни этих стран (при том, что во многих из них процессы секуляризации до-стигли значительного уровня), активизации религиозных организаций, повышению роли ис-лама в межгосударственных отношениях, выявить роль в этих процессах Саудовской Ара-вии, наиболее активно проводившую свой внешнеполитический курс, начиная с конца 60-х-начала 70-х годов ХХ века.
A DEEP LEARNING FACIAL EXPRESSION RECOGNITION BASED SCORING SYSTEM FOR RESTAURANTS
Authors: TEJA O.RAMYA , M.SOUMYA  , M.VANI  , PAVANI MANUPATI
Published: October 2024

This paper presents a novel approach to evaluating customer satisfaction in restaurants through a deep learning-based facial expression recognition system. Leveraging state-of-the-art convolutional neural networks (CNNs), our system analyzes facial expressions captured by in-house cameras to generate real-time satisfaction scores. This method aims to provide restaurant management with actionable insights into customer experiences, enhance service quality, and improve operational efficiency. We discuss the system's architecture, data collection, implementation, and evaluation results, demonstrating its effectiveness and potential applications. Recently, the popularity of automated and unmanned restaurants has increased. Due to the absence of staff, there is no direct perception of the customers' impressions in order to find out what their experiences with the restaurant concept are like. For this purpose, this paper presents a rating system based on facial expression recognition with pre-trained convolutional neural network (CNN) models. It is composed of an Android mobile application, a web server, and a pre-trained AIserver. Both the food and the environment are supposed to be rated. Currently, three expressions (satisfied, neutral and disappointed) are provided by the scoring system.
ACCESS CONTROL BY SIGNATURE-KEYS TO PROVIDE PRIVACY FOR CLOUD
Authors: DR.AR.SIVAKUMARAN  , SRI M.RAMYA , AKANKSHA JANYAVULA , SRI GANGULABHANU
Published: October 2024

Privacy of data in subjects of cloud computing or big data is one of the most principal issues. The privacy methods studied in previous research showed that privacy infringement for cloud computing or big data happened because multi risks on data by external or internal attackers. An important risk to take into consideration when speaking of the privacy of the stored transactions is represented by the transactions’ information which is not in the owner’s control. Such a case is represented by the cloud servers that are administered by cloud providers which cannot be wholly trusted by the users with sensitive, private data such as business plans or private information. A simple method for protecting data privacy is by applying certain privacy techniques onto transactions’ data, followed by the upload of the modified data into the cloud. In this paper, we are proposing a case study that is built on levels containing three models: cloud’s architecture, transaction’s manager and clients. Moreover, we consider that our case study is based on the premise of zero trust among the three models, therefore all the transactions take place with third-parties and the data movements are realized going through various levels of security.
ADVANCED SECURITY IN CLOUD COMPUTING OF MILITARY WEAPONS
Authors: DR.AR.SIVAKUMARAN  , K. HEMALATHA  , K.VARSHA  , N.NIHARIKA
Published: October 2024

Cloud storage systems are widely deployed in the world, and many people use them to download and upload their personal stuff like videos, text document, images, etc. Now a day many private firms, company’s, governments, military move their database on cloud storage. However, a significant question is, can users trust the media services provided by the media cloud service providers? Many traditional security approaches are proposed to secure the data exchange between users and the media cloud. However, the problem comes to military users if scientist develop a new weapon for military and he want to send a launching code to military admirals /chiefs through cloud, how he can trust cloud that he’s codes will be safely delivered to admirals. Now a day’s cloud storage can easily have cracked by hacker and gain information of military weapons and confidential secrets. It could be dangerous if they sold this information to terrorists or rival country, in this article, we propose to use steganography, watermarking, image encryption and visual cryptography schemes to protect military weapons data in clouds. steganography allows users to hide the weapons launch code in image captcha. Visual cryptography shares the image captcha in shares which is depend on number peoples in group in military. image encryption will apply on each share of captcha. After this watermarking is apply on each share for authentications between users and cloud. For receiving the launch code receivers have to from de-watermarking, image decryption then visual cryptography to get captcha and launch code. Our studies show that the proposed approach achieves good security performance and securing the future of country.
AGRICULTURAL LAND IMAGE CLASSIFICATION USING KNN AND COMPARE WITH RECURRENT NEURAL NETWORK
Authors: LIVINGSTON DAVID , GEETANJALI THUMU , NOMIKA ORUGANTI VENKATA , SANJANA PEGADA
Published: October 2024

Over the last few years, the research into agriculture has gained momentum, showing signs of rapid growth. The latest to appear on the scene is bringing convenience in how agriculture can be done by employing various computational technologies. To implement this project we have used LAND satellite images which contains images of FOREST, AGRICULTURE LAND, URBAN AREA and Range LAND. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RNN and kNN. In relation to the sample size, all three classifiers showed a similar and high OA when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.
DATA POISONING ATTACKS ON FEDERATED MACHINE LEARNING
Authors: DR.AR.SIVAKUMARAN  , ABHINAYA POLNENI , SWETHA PENDYALA , MAHITHA POKALA
Published: October 2024

Federated machine learning which enables resource constrained node devices (e.g., mobile phones and IoT devices) to learn a shared model while keeping the training data local, can provide privacy, security and economic benefits by designing an effective communication protocol. However, the communication protocol amongst different nodes could be exploited by attackers to launch data poisoning attacks, which has been demonstrated as a big threat to most machine learning models. In this paper, we attempt to explore the vulnerability of federated machine learning. More specifically, we focus on attacking a federated multi-task learning framework, which is a federated learning framework via adopting a general multi-task learning framework to handle statistical challenges. We formulate the problem of computing optimal poisoning attacks on federated multitask learning as a bilevel program that is adaptive to arbitrary choice of target nodes and source attacking nodes. Then we propose a novel systems-aware optimization method, ATTack on Federated Learning (AT2FL), which is efficiency to derive the implicit gradients for poisoned data, and further compute optimal attack strategies in the federated machine learning. Our work is an earlier study that considers issues of data poisoning attack for federated learning. To the end, experimental results on real-world datasets show that federated multi-task learning model is very sensitive to poisoning attacks, when the attackers either directly poison the target nodes or indirectly poison the related nodes by exploiting the communication protocol.
DEA-RNN: A HYBRID DEEP LEARNING APPROACH FOR CYBERBULLYING DETECTION IN TWITTER SOCIAL MEDIA PLATFORM
Authors: CH.RAGHUVARAN  , JAHNAVI YALABAKA , S POOJITHA  , SREEJA POKALA
Published: October 2024

Cyberbullying has emerged as a pervasive issue within social media platforms, posing significant risks to users' well-being. To address this growing concern and enhance platform safety, this paper proposes a novel hybrid deep learning model, termed DEA-RNN, designed specifically for detecting cyberbullying on Twitter. The DEA-RNN model integrates Elman-type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) to fine-tune RNN parameters and expedite training. Through comprehensive evaluation using a dataset comprising 10,000 tweets, we compared DEA-RNN's performance with state-of-theart algorithms such as Bi-directional Long Short Term Memory (Bi-LSTM), SVM, Multinomial Naive Bayes (MNB), and Random Forests (RF). Our experimental findings demonstrate the superiority of DEA-RNN across all scenarios, showcasing its effectiveness in cyberbullying detection on the Twitter platform. Particularly noteworthy is DEA-RNN's exceptional performance in scenario 3, achieving an average accuracy of 90.45%, precision of 89.52%, recall of 88.98%, F1-score of 89.25%, and specificity of 90.94%.
DEEP SIDE-A DEEP LEARNING FRAMEWORK FOR DRUG SIDE EFFECT PREDICTION
Authors: DR.K.JAYARAJAN  , GEETHIKA SAJJA , SAHITHI REGATTE , NAIDU VULAPU CHANDRASRUTHI
Published: October 2024

Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of cell line gene expression data perturbed by different drugs and creates a knowledge base for context specific features. The state-of-the-art approach that aims at using context specific information relies on only the high quality experiments in LINCS L1000 and discards a large portion of the experiments. In this study, our goal is to boost the prediction performance by utilizing this data to its full extent. We experiment with 5 deep learning architectures. We find that a multimodal architecture produces the best predictive performance among multi-layer perceptron-based architectures when drug chemical structure (CS), and the full set of drug perturbed gene expression profiles (GEX) are used as modalities. Overall, we observe that the CS is more informative than the GEX. A convolutional neural network-based model that uses only SMILES string representation of the drugs achieves the best results and provides 13:0% macro-AUC and 3:1% micro-AUC improvements over the state-of-the-art. We also show that the model is able to predict side effect-drug pairs that are reported in the literature but was missing in the ground truth side effect dataset.
DEEPFAKE DETECTION A SYSTEMATIC LITERATURE REVIEW
Authors: LAKSHMI B.HARITHA , RISHIKA VAIDYA , CHANDANA SANKEPALLI SRI , SREE NALLAMOTHU BHAVYA
Published: October 2024

Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people. The manipulated, high-quality and realistic videos have become known recently as Deepfake. Various approaches have since been described in the literature to deal with the problems raised by Deepfake. To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles from 2018 to 2020 that presented a variety of methodologies. We analyze them by grouping them into four different categories: deep learning-based techniques, classical machine learning-based methods, statistical techniques, and blockchain-based techniques. We also evaluate the performance of the detection capability of the various methods with respect to different datasets and conclude that the deep learning-based methods outperform other methods in Deepfake detection.
SCA: SYBIL-BASED COLLIONS ATTACKS OF IIOT DATA POISOING IN FEDERATED LEARNING
Authors: LIVINGSTON DAVID , G.SINDHU  , G.GEETHA  , K.SONIYA
Published: October 2024

The project focuses on enhancing security in Vehicular Ad Hoc Networks (VANETs) by proposing a novel approach for detecting Sybil attacks. Sybil attacks in VANETs involve a malicious vehicle creating multiple fake identities to manipulate network operations. This project employs a combination of Proofs of Work (PoW) and location-based techniques to detect and mitigate Sybil attacks effectively, ensuring the integrity and reliability of vehicular communication.
USE ARTIFICIAL NEURAL NETWORKS TO IDENTIFY FAKE PROFILES X_0005
Authors: SAI SREE M.SYMALA , K.TEJASWI  , K.SREEJA  , REDDY K.SNEHA
Published: October 2024

we use machine learning, namely an artificial neural network to determine what are the chances that Facebook friend request is authentic or not. We also outline the classes and libraries involved. Furthermore, we discuss the sigmoid function and how the weights are determined and used. Finally, we consider the parameters of the social network page which are utmost important in the provided solution. The other dangers of personal data being obtained for fraudulent purposes is the presence of bots and fake profiles. Bots are programs that can gather information about the user without the user even knowing. This process is known as web scraping. What is worse, is that this action is legal. Bots can be hidden or come in the form of a fake friend request on a social network site to gain access to private information. In 2017 Facebook reached a total population of 2.46 billion users making it the most popular choice of social media [1]. Social media networks make revenues from the data provided by users. The average user does not know that their rights are given up the moment they use the social media network's service. Social media companies have a lot to gain at the expense of the user. Every time a user shares a new location, new photos, likes, dislikes, and tag other users in content posted, Facebook makes revenue via advertisements and data. More specifically, the average American user generates about $26.76 per quarter [2]. That number adds up quickly when millions of users are involved. In today's digital age, the ever-increasing dependency on computer technology has left the average citizen vulnerable to crimes such as data breaches and possible identity theft. These attacks can occur without notice and often without notification to the victims of a data breach. At this time, there is little incentive for social networks to improve their data security. These breaches often target social media networks such as Facebook and Twitter. They can also target banks and other financial institutions.
WEB CLOUD WEB-BASED CLOUD STORAGE FOR SECURE DATA SHARING ACROSS PLATFORMS
Authors: M. MAHESHWARI  , PRANATHI JINNARAM , K. MOKSHAGNANJALI  , K. VARSHITHA
Published: October 2024

With more and more data moving to the cloud, privacy of user data have raised great concerns. Client-side encryption/decryption seems to be an attractive solution to protect data security, however, the existing solutions encountered three major challenges: low security due to encryption with low-entropy PIN, inconvenient data sharing with traditional encryption algorithms, and poor usability with dedicated software/plugins that require certain types of terminals. This work designs and implements WebCloud, a practical browser-side encryption solution, leveraging modern Web technologies. It solves all the above three problems while achieves several additional remarkable features: robust and immediate user revocation, fast data processing with offline encryption and outsourced decryption. Notably, our solution works on any device equipped with a Web user agent, including Web browsers, mobile and PC applications. We implement WebCloud based on ownCloud for basic file management utility, and utilize WebAssembly and Web Cryptography API for complex cryptographic operations integration. Finally, comprehensive experiments are conducted with many well-known browsers, Android and PC applications, which indicates that WebCloud is cross-platform and efficient. As an interesting by-product, the design of WebCloud naturally embodies a dedicated and practical ciphertext-policy attribute-based key encapsulation mechanism (CP-ABKEM) scheme, which can be useful in other applications.
LUNG CANCER DISEASE PREDICTION WITH CT SCAN AND HISTOPATHOLOGICAL IMAGES FEATURE ANALYSIS USING DEEP LEARNING TECHNIQUES
Authors: BHAVANI K.SHIVA , B.ASHRITHA  , P.SUSHMA  , J.THRIVARNA
Published: October 2024

Research and Development on cancer detection is more on imaging than textual data. With the help of documented symptoms in the form of text and Machine Learning (ML) techniques, it is possible to predict the lung cancerstages effectively. This paper conjectures the oeuvre modelwhich is efficient in predicting the stages of lung carcinoma by applying the concepts of ML algorithms. The proposed model is combination of K-Nearest Neighbours, Decision Tree and Neural Networks modelsalong with bagging ensemble method for enhancing the accuracy of the overall prediction. The predictedresults of the suggested model are showing better accuracy compared to individual algorithms.
GEFA: EARLY FUSION APPROACH IN DRUG-TARGET AFFINITY PREDICTION
Authors: B.VASANTHA  , BHANU SRI SINGI , DEDEEPYA SOMISHETTY , DEEPTHI POTHEPANGARI
Published: October 2024

Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug binding effects. In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel graph-ingraph neural network with attention mechanism to address the changes in target representation because of the binding effects. Specifically, a drug is modeled as a graph of atoms, which then serves as a node in a larger graph of residues-drug complex. The resulting model is an expressive deep nested graph neural network. We also use pre-trained protein representation powered by the recent effort of learning contextualized protein representation. The experiments are conducted under different settings to evaluate scenarios such as novel drugs or targets. The results demonstrate the effectiveness of the pre-trained protein embedding and the advantages our GEFA in modeling the nested graph for drug-target interaction.
NAVIGATING THE PATH TO ACADEMIC SUCCESS
Authors: M.PREMCHANDER  , SRUTHI VAIKUNTA , PRATIMA VARA , INDHU SRI VASAM
Published: October 2024

"Academic success is a multifaceted journey influenced by various factors, including personal motivation, effective study strategies, and supportive learning environments. This paper explores the pathways to academic success and identifies key strategies for students to navigate and thrive in their educational pursuits. Drawing on research from psychology, education, and personal development, the paper examines the importance of self-regulation, goal setting, time management, and resilience in achieving academic goals. Additionally, it explores the role of social support, mentorship, and academic resources in facilitating student success. By synthesizing evidence-based practices and practical insights, the paper offers actionable recommendations for students, educators, and institutions to foster a culture of academic excellence and empower individuals to reach their full potential in the academic realm."
3D bioprinting techniques and hydrogels for osteochondral integration regeneration
Authors: Tang Haiwei , Zhao Enze , Lai Yahao , Chen Anjin , Chen Xiaoting , Zeng Weinan , Hu Xulin , Zhou Kai , Zhou Zongke
Published: October 2024

Despite considerable advancements in regenerative medicine, restoring the osteochondral interface and facilitating the integration of osteochondral regeneration remain significant clinical conundrums. This challenge is predominantly attributed to the scarcity of appropriate tissue engineering materials for replacing osteochondral defects and facilitating tissue regeneration. 3D bioprinting constitutes a promising approach for bone fabrication, as it not only allows for the design of precise personalized scaffolds but also encapsulates cells and growth factors, with the potential to replicate the functions of native tissues. Many critical properties of hydrogels, such as their mechanical properties, elasticity, and bioactivity, make them the most prevalently utilized bioinks in tissue engineering. In addition, their structure can be easily adjusted to meet the needs of different situations. Therefore, 3D-bioprinted hydrogel scaffolds may have promising prospects for integrated osteochondral repair and are receiving increasing attention. In this review, we describe the current problems encountered in the field of osteochondral integration repair and review the latest advances in current 3D printing technology and 3D bioprinting hydrogel scaffolds. We propose prospects for the development of novel 3D-bioprinted hydrogel scaffolds, providing cues for future research directions.
Comparison of immune response parameters between homologous and heterologous COVID-19 vaccines: A scoping review
Authors: Khoo Samantha Si Mei , Tan Kang Wei , Mahendran Ashwini , Bhuvanendran Saatheeyavaane , Radhakrishnan Ammu Kutty
Published: October 2024

It has been over 4 years since the emergence of the coronavirus disease 2019 (COVID-19) pandemic. This highly contagious respiratory infection has endangered the health of millions and significantly impacted healthcare systems and economies. Vaccines are believed to confer immunity against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of COVID-19, reducing both the severity of infection and the spread of the virus. Within a short period, various COVID-19 vaccines were developed and extensively tested before being approved by the WHO for distribution and administration. Now, due to concerns about emerging new strains of the virus and limited vaccine availability, a heterologous vaccine strategy is being deployed. Therefore, this paper aims to conduct a scoping review of existing evidence to compare the immunogenicity of heterologous vaccines with homologous vaccines and determine which confers better immunity against COVID-19. A literature search was conducted across three electronic databases (Ovid MEDLINE, PubMed, and Scopus). The retrieved studies were screened for relevance and eligibility using the online platform Covidence. A total of 31 articles were shortlisted for data extraction and analysis. Among these, 21 were observational studies, and 10 were clinical trials. The analysis demonstrated that participants who received heterologous vaccination regimens generated higher levels of IgG antibodies against the spike protein of SARS-CoV-2, antibodies targeting the receptor-binding domain, and T-cell responses compared to those who received homologous vaccination regimens. Furthermore, heterologous vaccination produced higher titers of neutralizing antibodies against several variants of concern (VOC), including Alpha, Beta, Gamma, Delta, and Omicron. No severe vaccine-related adverse events were reported in these studies, and common local and systemic side effects were manageable. Overall, heterologous vaccination regimes induced strong humoral and cellular immunity, comparable to homologous vaccination regimes, with stronger neutralizing antibody activity against VOCs.
Adoption of Crop Insurance by Smallholder Farmers: Farm-Level Evidence from India
Authors: Acharya H Rajesh
Published: October 2024

The paper aims to analyse the extent and determinants of smallholder farmers’ adoption of crop insurance. The study conducted a primary survey and collected data from farmers in a drought-prone area of Karnataka state in India using a structured questionnaire. The study has applied a binary logistic regression model to identify the determinants of crop insurance adoption. Empirical results reveal that though most farmers experienced crop loss, only a small percentage subscribed to crop insurance regularly. Lack of money to pay premiums and lack of information are the most common reasons for not subscribing to crop insurance schemes. Further, farmers feel the premium is expensive and do not receive the promised compensation due to the stringent eligibility rules. Most farmers who received compensation think the money is inadequate to cover the cultivation cost. Farmers feel each farm should be treated as a unit against the area-based insurance concept, and more crops should be brought under insurance. They also highlighted the need to further subsidise the premium. Results of the logistic regression confirm that socially marginalised groups and farmers practising agriculture as an ancestral profession are less likely to insure their crops.
Rol socio económico y ambiental de la nutrición animal
Authors: Castro-Bedriñana Jorge Isaac , Chirinos-Peinado Doris Maritza
Published: October 2024

La nutrición animal no puede seguir teniendo una finalidad meramente productivista, debe cuidar el bienestar de los animales y su salud, garantizando la obtención de productos seguros y de calidad para el consumidor final, aplicando conceptos no solo bioquímicos y fisiológicos sino de salud preventiva, asegurando la sostenibilidad de los sistemas de producción y minimizando los impactos de la nutrición sobre el medio ambiente. La sociedad demanda el establecimiento de sistemas de producción que sean seguros, que garanticen el bienestar animal y que no dañen el medio ambiente, implementando procesos de economía circular. Para esto es importante que el profesional pecuario, especialmente el responsable de la nutrición y alimentación, conozca las interacciones nutrición-salud, nutrición-genética y nutrición-polución y las integre en la aplicación de estrategias y programas de alimentación que maximicen el rendimiento productivo y minimicen el impacto de la producción animal sobre el medio ambiente; y lo que es más importante, ofrecer al consumidor un producto de calidad, adecuadamente obtenido para la satisfacción de los mismos.
Humanising The 'Naire' in Money: A Scoping Review
Authors: Olutayo Lanre Akinpelu Olanrewaju , Oyenuga Adedeji Saheed
Published: October 2024

As the world progresses into the critical Year 2030, the magic Year for attaining the Sustainable Development Goals, doubts about the certainty of success are high. Amid COVID-19, the climate crisis, the Russia-Ukraine war, among many other wars, and the high cost of living pushing most of the people into poverty and hardship, it is time to reconsider what is being done to make the world a better place for most of the people. In doing this, this paper attempts to interrogate the influence of money in the consistent world economic crises. It argues that the 'inevitability of money', attendant on changing technology, pervading the World Capitalist System needs to be reoriented to homos sociologicus. In a way, it recommends the need to 'humanise' money and technology, such that humans construct it socially instead of vice versa.
Head Teachers’ Team Leadership Practices’ Influence on Financial Performance in Church of Uganda Founded Secondary Schools in Namirembe Diocese
Authors: Tumuhimbise Jasper M. , Muweesi Charles , Buluma Alfred , Mbabazi Faith , Ocheng Mary Kagoire , Eryenyu Charles , Kitayimbwa John M. , Wanyana Mercy
Published: October 2024

The influence of Head Teachers’ team leadership practices on the financial performance is highly acknowledged. The paper aims to highlight the challenges of head teachers’ team practices and advance insights on how they can be overcome to enhance financial performance. The methodology employed was a mixed-methods approach. Quantitative data was gathered through questionnaires assessing perceptions of the above metrics. Qualitative data was collected through documentary review of reports and interviews to explore the underlying mechanisms through which the variables above interrelate. The total number of respondents were 326 sampled from eight out of twenty-three schools. Quantitative analysis revealed that it has a coefficient of 0.52 and a p-value of 0.004 which indicates a strong correlation and statistically significant relationship between effective team leadership and financial health. This indicated that a team leader yielded better cash flow management, alignment with financial objectives, and overall sustainability. Qualitative insights supported the above crucial role that head teachers’ have in engaging teams of CFSs in shaping financial decision-making processes. The study concludes by emphasizing the need to foster team cohesion, positive financial culture and ensuring the long-term sustainability of CFSs. Recommendations include strengthening leadership development programs that are not available currently in Church of Uganda. Further, to enhance team dynamics and aligning leadership practices with organizational goals to optimize financial performance in these settings
The Role of Corporate Communication in Image Penetration through Podcast Media
Authors: Astaria Mia , Ruliana Poppy
Published: October 2024

In the era of society 5.0, Indonesia and the world are experiencing developments in all fields, especially technology, information, and global competition. It takes hard work from the corporate communication division so that the company has a clear identity that can make it different from similar companies in order to form a good image that stands out. The purpose of this research is to represent BRI's Corporate Communication strategy in shaping its image through podcast media. This research is a descriptive qualitative research with data collection techniques conducted through structured interviews, with guidelines for conducting research virtually through Google Meet, Zoom or Video call and WhatsApp text connection. In selecting informants using purposive sampling technique in determining research subjects. The research instrument used is a list of questions and data analysis techniques using open coding to selective coding which will draw conclusions from these results. In planning a Corporate Communication activity, the author also uses the RACE model from John Marston. The results showed that the penetration of BRI's new image through the BRI Office Break podcast program broadcast on Youtube was considered quite successful. The conclusion of this research: up to the 10th episode of the BRI Office Break Podcast has successfully portrayed BRI's new image, customers and non-customers view BRI as a sophisticated and modern bank, no longer old-fashioned or conventional.
Solidaritas Sosial Mahasiswa Asal Kabupaten Pegunungan Bintang Dalam Menempuh Studi
Authors: Deal Pison , Yuliana Yuliana , Idris Usman
Published: October 2024

The purpose of this study is to describe and analyse the form of social solidarity among fellow students living in the Pegunungan Bintang dormitory, and then to describe the efforts made to strengthen social solidarity among them. The research method used is descriptive qualitative with ethnographic approach. The data collection techniques used were in-depth interviews and observation. The research informants were purposively selected. The data analysis techniques include the stages of processing and preparing the data for analysis, reading the whole data, analysing the data in more detail by coding the data, applying the coding to describe the themes for analysis, re-presenting the themes and descriptions in the form of narratives, and interpreting and interpreting the data. The results showed that the form of social solidarity that exists among the students living in the Pegunungan Bintang dormitory is mechanical solidarity. The mechanism built to strengthen social solidarity among fellow students living in the Star Mountains dormitory by implementing mandatory rules for all members of the dormitory residents to help and support each other. In addition, the dormitories also often share meals, sports, religious activities by organising joint worship services, ceremonial celebrations and recreational activities.
Peran SAMPAN Kalimantan dalam Konservasi Mangrove di Kalimantan Barat
Authors: Suratiningsih Dewi , Hardilina Hardilina , Nugarahaningsih Nurfitri
Published: October 2024

This research aims to describe the role of SAMPAN Kalimantan as an NGO that contributes to the preservation of mangrove ecosystems as an effort to adapt to climate change. This research uses a qualitative descriptive research methodology with data processing using source triangulation. In addition, researchers used the theory of the role of non-governmental organizations (NGOs) to analyze the role of SAMPAN Kalimantan in mangrove conservation. The results of this research are that SAMPAN Kalimantan, which runs the program in Kubu Raya, has begun to fulfill its role as an NGO, namely as Executor/Implementer, Catalyst and Partner. As the Executor, it is known that there are several activities carried out by the community that can benefit the general public and provide employment opportunities and benefits for the local community by utilizing existing natural resources, one of which is the potential for fish, shrimp and crabs. As a catalyst, SAMPAN Kalimantan has encouraged areas that have not received permits to become social forestry since 2013.