bluuebunny/crossref_metadata_2025 huggingface dataset:
Pseudomonas aeruginosa producing beta metalloenzyme (VIM, NDM): A case report
Authors: Sara Elmalhi  , Errim Ahl cheikh  , Fayçal Idam  , Loubna Ait said  , Loubna Ait said  , Kawtar Zahlane
Published: September 2024

Pseudomonas aeruginosa is an opportunistic bacterium with a high rate of acquisition of new resistance. The production of metallo-ß-enzyme is a resistance mechanism that is still relevant today. We report the case of a diabetic patient presenting with a trauma to the lower limb; the patient underwent immediate surgery, a discharge of pus was marked in the evolution; the bacteriological profile was in favour of a strain of Pseudomonas aeruginosa producing type VIM metallo carbapenemase, with a positive EDTA synergy test and a positive carba test. Despite repeat surgery, the introduction of antibiotic therapy, treatment and control of the glycaemic cycle, the evolution was unfavourable, threatening the vitality of the limb.
Cultural dynamics and consumer behavior: An in-depth analysis of Chinese preferences for western imported products
Authors: Sazib Hossain  , Md Sab Bir Hossen  , Sazzad Kadir Zim  , Imane El Hebabi
Published: September 2024

In the ever-evolving society of global commerce, understanding consumer behavior is paramount, particularly demand for western products is growing around the world especially in China as Chinese consuming behaviors are rapidly changing, and recognizing the pivotal role it plays in the contemporary global market. With a focus on brand perception, product quality, and the impact of cultural factors, by dissecting these dynamics, businesses can glean valuable insights to strategically navigate the complexities of the Chinese market, the research aims to unravel the complexities that influence purchasing decisions by using a survey which was analyzed to identify patterns and trends in consumer behavior. As businesses strive to expand their presence in the dynamic Chinese market, a nuanced understanding of these consumer dynamics becomes important setting the stage for an exploration that not only seeks to decode the preferences of Chinese consumers but also offers insights crucial for businesses aiming to navigate and thrive in this unique economic society.
Climate change impacts on the hydrological behaviour of watershed in northeastern Tunisia (Oued El Abid watershed)
Authors: Amina Zoubida Madani  , Taoufik Hermassi  , Sabrina Taibi  , Khaoula Boudabbous  , Mohamed Mechergui
Published: September 2024

Climate change significantly impacts watershed hydrology, altering water supplies, natural disasters, and eco-hydrologic processes. These effects vary geographically, with some regions facing severe droughts while others experience increased precipitation and flooding. Climate change influences atmospheric evaporation demand, precipitation patterns, vegetation composition, streamflow characteristics, and groundwater dynamics. The temperature increases and rainfall changes in some watersheds can lead to increased potential evapotranspiration and decreased streamflow and soil water. Flood frequency analyses indicate an overall increasing trend in the latter half of the 21st century. The Oued El Abid catchment area, as like all catchments in northern Tunisia, is likely to be subject to major climatic and hydrological variations as a result of climate change. Advanced hydrological modelling using the HBV-Light model was set up to simulate current conditions and future scenarios based on Afric-Cordex climate models. The model demonstrates robust performance, achieving Nash-Sutcliffe Efficiency (NSE) values of 0.71 in calibration (1972-1992), and 0.66 in validation from 1993-2001. This climate change has a direct effect on the hydrological projections, which reveal significant changes, with annual flow reductions ranging from 35% to 43%, with more severe impacts under the RCP8.5 scenario for 2069-2099. Spring and summer seasons are particularly vulnerable, showing flow decreases of up to 68% and over 40%, respectively, extreme monthly variability is projected, reaching 88% reduction in July under RCP8.5. This research provides important results for water resource managers, highlighting the necessity of long-term planning and sustainable water management practices in the face of climate change.
The Impact of Microorganisms on our Environment and our life
Authors: Shler Ali Khorsheed
Published: September 2024

Microorganisms, often overlooked due to their microscopic size, play an indispensable role in the functioning of Earth's ecosystems and the health of all living organisms. From the depths of the ocean to the human gut, these tiny entities drive critical processes such as nutrient cycling, disease regulation, and the maintenance of environmental balance. This article delves into the profound influence of microorganisms on our world, exploring their contributions to biogeochemical cycles, their symbiotic relationships with larger organisms, and their impact on global health and disease. Through a closer examination of their diverse roles, we uncover how these unseen powerhouses are not only essential to sustaining life but also offer untapped potential for innovations in medicine, agriculture, and environmental management. The intricate interplay between microorganisms and their environments underscores the importance of preserving microbial diversity in the face of global challenges, highlighting the necessity of a deeper understanding of these life forms that, though small in size, have a monumental impact on our world This abstract encapsulates the core ideas of your article, focusing on the significance of microorganisms in various aspects of life and their potential for future advancements.
Preparation and evaluation of sustained release microspheres for the treatment of diabetic neuropathy
Authors: Pathloth Nandini  , M. Sunitha Reddy  , K. Anie vijetha
Published: September 2024

This study aimed to prepare and evaluate sustained release microspheres for the treatment of diabetic neuropathy using Various polymers including sodium alginate, ethyl cellulose, HPMC K100, Carbopol 934, and Eudragit L-100-55. Polymers were employed to assess their impact on several crucial parameter’s entrapment efficiency, production yield Particle size analysis, swelling index, in vitro dissolution, and release kinetics. The production yield of microspheres varied depending on the polymer used, with some polymers performing in advanced yields. Entrapment effectiveness was set up to be optimal with certain polymer combinations. Particle size analysis indicated that the microspheres were within the asked range for controlled release operations. The swelling index showed polymer-dependent lump actions. In vitro dissolution studies demonstrated sustained medicine release over time, with different polymers impacting both the release rate and profile. medicine release kinetics analysis revealed that the release followed specific models, reflecting the commerce between the microsphere matrix and the medicine.
Antioxidant and antimalarial properties of hot aqueous leaf extracts of Setaria megaphylla, Ageratum conyzoides and Chromolaena odorata
Authors: Emmanuel Emeka Ezeokeke  , Aloysius Chinedu Ene  , Chidi Uzoma Igwe  , Chinyere Henrietha Onuoha  , Doris Ifeyinwa Ukairo
Published: September 2024

This study investigated the antioxidant and antimalarial properties of leaf extracts of Setaria megaphylla, Ageratum conyzoides Linn, and Chromolaena odorata Linn, plants used in folkloric treatment of malaria. The plants were assessed for phenolic profile, antioxidant capacity and radical scavenging activities using standard methods. Single, binary and ternary combinations of the processed leaves were subjected to hot aqueous extraction, and evaluated for in vivo antiplasmodial efficacy against Plasmodium berghei NK 65 using Swiss albino mice. The plants’ total phenolic profile varied as S. megaphylla (Sm; 118.06 µg/ml) > C. odorata (Co; 116.58 µg/ml) > A. conyzoides (Ac; 73.01 µg/ml), with catechin, dihydrocytisine and tannin being the most abundant phenolics. Total antioxidant capacities (TAC) and reducing power potentials (RPP) of the plants increased with increase in extract concentrations with Sm showing highest TAC (1.49 ± 0.02 mgAAE/g) and RPP (0.47 ± 0.01 mgAAE/g) values. A similar trend was observed for the hydroxyl, nitric oxide and DPPH radicals scavenging potentials, showing dose-dependent increases in scavenging potentials but with no observed significant (p<0.05) differences in activities at the highest extract dose of 400 µg/ml. The in vivo antimalarial study demonstrated that the combination of A. conyzoides and S. megaphylla was the most effective, significantly reducing parasitemia without causing mortality in the mice. This research highlights the potential of S. megaphylla, A. conyzoides, and C. odorata, especially the combination of their extracts as source of effective anti-malarial agents, and further confirms the folkloric use of hot aqueous extracts of the plants in malaria treatment.
Toxicological evaluation of a herbal tea made from the leaves of Setaria megaphylla, Ageratum conyzoides and Chromolaena odorata
Authors: Emmanuel Emeka Ezeokeke  , Aloysius Chinedu Ene  , Chidi Uzoma Igwe  , Chinyere Henrietha Onuoha  , Doris Ifeyinwa Ukairo
Published: September 2024

The toxicological effect of an herbal tea made from leaf extracts of Ageratum conyzoides, Chromolaena odorata, and Setaria megaphylla, plants traditionally used in Nigeria for malaria treatment was studied. Hot aqueous extracts of the ternary combination (1:1:1 ratio) of the plants were administered in varying doses (0, 50, 100, and 200 mg/kg) to assess impact on hepatic, renal and haematologic function parameters, and liver and kidney tissue histology of forty male albino rats divided into four groups of ten animals each. The tea extracts were administered orally daily for 60 days. Biochemical analyses revealed that the extract significantly (p<0.05) reduced ALT (25.91 ± 2.35 to 6.84 ± 0.31 U/L), AST (36.39 ± 1.96 to 19.24 ± 1.19 U/L), and ALP (21.94 ± 3.51 to 11.06 ± 2.76 IU/L) activities, with no significant (p>0.05) changes in bilirubin, protein and albumin concentrations, suggesting non-hepatotoxic property. Among the kidney function, significant (p<0.05) dose-dependent increases was observed only in serum urea ranging from 23.09 ± 0.79 mg/dl (Control group) to 34.90 ± 0.52 mg/dl (200 mg/kg treated group). Increase in WBC count was observed indicative of a possible immune response, while other blood parameters remained stable. Histopathological examination confirmed that lower extract doses were relatively safe, whereas higher doses presented early signs of organ stress. These findings suggest that this herbal tea extract has a favourable safety profile at moderate doses, though higher doses may pose risk of organ toxicity, warranting further studies on dose optimization and elucidation of its folkloric therapeutic potential.
Diagnosis of bacteria isolated from patients with cutaneous eczema by PCR technique at Azadi Hospital, Kirkuk, Iraq
Authors: Shler Ali Khorsheed
Published: September 2024

Eczema is a common skin condition that can be worsened or caused by a bacterial infection. This study aimed to investigate the bacterial pathogens associated with skin eczema in patients admitted to Azadi Hospital in Kirkuk using polymerase chain reaction (PCR) technology. The goal was to enhance the accuracy and efficiency of bacteria identification compared to traditional methods. A total of [300] patients suffering from symptoms of skin eczema between the ages of 1-40 years, females and males, were included. Bacterial isolates were obtained from skin samples, and conventional microbiological techniques were initially used for identification. Subsequently, molecular analysis using PCR was performed to confirm and characterize the bacterial species. The study included 165 male patients represented (55%), 135 female (45%), of participants who had symptoms of skin eczema. The PCR test targeted specific genes associated with common bacteria implicated in skin infections, such as Staphylococcus. aureus (33 %), Staphylococcus epidermidis (28%), Klebsiella pneumoniae (20 %) , pseudomonas aeruginosa (13 %), respectively, and the remaining( 6 %). of the samples were natural flora. The genetic fingerprints of these bacterial species were identified, an image of them was taken on an agarose gel electrophoreses , and the results were compared with those obtained through traditional methods to evaluate the sensitivity and specificity of PCR in diagnosing bacterial infections in cases of skin eczema. Preliminary results indicated that PCR significantly improved the accuracy and speed of bacterial identification compared to traditional methods. It has allowed the detection of specific bacterial species that might be missed using conventional methods. The prevalence and diversity of bacterial species causing skin eczema in Azadi Hospital in Kirkuk were also explored. This study contributes valuable insights into the microbial etiology of cutaneous eczema, emphasizing the importance of molecular techniques in accurate and rapid diagnosis. The findings may guide more targeted and effective treatment strategies for patients with bacteria-associated skin eczema, ultimately improving patient outcomes and reducing the risk of complications.
The Critical Role of Role-Based Access Control (RBAC) in securing backup, recovery, and storage systems
Authors: Taresh Mehra
Published: September 2024

In the contemporary digital landscape, safeguarding data through robust access control mechanisms is crucial. Role-Based Access Control (RBAC) is an effective strategy for managing access to backup and recovery systems, as well as storage devices. RBAC assigns permissions based on user roles, thereby enforcing the principle of least privilege and enhancing data security. This blog explores the significance of RBAC in these contexts and extends its relevance to ransomware and malware threats, as well as disaster recovery. Supported by scientific research and industry case studies, it underscores how RBAC can mitigate risks, ensure regulatory compliance, and improve data management practices.
XRD and SEM analysis of fly ash obtained from TPPs of Raipur district of Chhattisgarh
Authors: Rakesh Kumar Kurre  , Umakant Shrivastava
Published: September 2024

This paper deals with the study of fly ash samples with respect to its physical, chemical and structural properties collected from distinct sites of Raipur district of Chhattisgarh state in India. XRD diffractogram for the original fly ash confirms the fly ash is mainly an amorphous material with the presents of crystalline phase of quartz. Fly ashes samples consist of mostly glassy, hollow, spherical particles, which are cenospheres (thin-walled hollow spheres); the micro structure appearance of the original fly ash is well agreed. Equal amounts of Al2O3, Fe2O3, MnO, TiO2 were found in both the samples which are good fit for construction application. SEM images revealed the amorphous nature of the fly ash.
The Relationship between Nomophobia and Phubbing among IT Employees
Authors: Siva B  , Jaimathisa A
Published: September 2024

The interplay between nomophobia and phubbing is particularly relevant in the IT field due to the high dependence on mobile phones for work (Kuss et al., 2018). This study examined the relationship between nomophobia and phubbing among IT employees. In the present study, 200 participants working in different IT companies were selected using convenience sampling. The Nomophobia Questionnaire (Yildirim and Correia, 2015) and Phubbing scale (Karadağ et al., 2015) were used to collect the data. The results revealed that nomophobia was significantly and positively related to phubbing (r = .487 p < .001). This finding suggests that an increase in nomophobia was associated with an increase in phubbing among IT employees.
A comprehensive biochemical and epidemiological review of avian influenza: Mechanisms, pharmacology and emerging trends in treatment
Authors: Umer Ahmed  , Hafiza Amna Farooq  , Faiza Bashir  , Muhammad Haseeb  , Farzeen Yaseen  , Nawal Amin
Published: September 2024

Avian influenza viruses, particularly H5N1, H7N9, and H9N2, pose significant threats to avian and human populations through zoonotic transmission, as they have potential to change their genetic material through mutations. Symptoms of Avian Influenza ranges from mild to severe, mainly respiratory problems in both and muscle aches, fatigue in humans. This review highlights the molecular mechanisms by which the virus infects host cells, emphasizing the roles of hemagglutinin (HA) and neuraminidase (NA) in viral entry and release. Biochemical processes involved in viral replication, immune responses, and cytokine production are discussed, with a detailed examination of how antiviral drugs like neuraminidase and polymerase inhibitors disrupt these processes. Outbreak of AIV can cause mass culling results in massive economic loss, trade disruptions, and consumer reductions. The review also addresses the challenges posed by antiviral resistance and outlines novel therapeutic strategies, including combination therapies, vaccine advancements, and host-directed treatments. With an interdisciplinary “One Health” approach, this paper underscores the need for enhanced biosecurity, international cooperation, and continued research to mitigate the global impact of avian influenza.
Review on submandibular gland stone surgical techniques and new advances
Authors: Turki Nasser Alotaibi  , Tarek Mohammed Alsheshani  , kais Mohammed Malallah
Published: September 2024

This review delves into the complexities and advancements in the surgical management of submandibular gland stones, a prevalent condition within sialolithiasis, with a particular focus on the submandibular gland which is most frequently affected. Traditional surgical interventions, such as submandibular sialoadenectomy, though effective, come with significant risks and postoperative complications including gland deficiency, scarring, and digestive issues. The emergence of minimally invasive endoscopic techniques marks a significant advancement, offering a less traumatic alternative for stone removal without sacrificing gland functionality. Despite these advances, challenges remain with endoscopic techniques, particularly in the complete removal of calcified stones and in cases involving large stones, leading to extended surgery times and increased costs. The review highlights a novel transoral endoscopic technique aimed at improving the removal of deep lobe stones, showing promising results in terms of efficacy, reduced trauma, and lower complication rates. This study underscores the ongoing evolution in the management of submandibular gland stones, emphasizing the importance of innovation and the adoption of new technologies to enhance patient outcomes and reduce healthcare burdens.
Impact of data science and cybersecurity in e-commerce using machine learning techniques
Authors: TEMITOPE OLUWATOSIN FATUNMBI
Published: September 2024

Due to the growth of e-commerce, new opportunities and threats have appeared in the electronic environment. This paper identifies the works that relate to the e-commerce transformation through data science and cybersecurity and highlights the importance of the machine learning algorithms. Data science can be used to make significant customer-related revelations, organize stocking, and form flexible pricing strategies in e-commerce businesses. At the same time, information security, backed up by machine learning, contributes to combating fraud, safe payment transactions, and the protection of information. From the current practices and the examples involving Amazon, Alibaba, and others, this paper demonstrates how the application of the most popular machine learning algorithms, like supervised and unsupervised learning, reinforcement learning, as well as natural language processing, improves operations and security at once. In supervised learning, methods are trained to anticipate customers’ behavior and, at the same time, identify cases of fraud, whereas in the case of unsupervised learning, algorithms are designed to find patterns in the customer’s data that have not been identified earlier. RL enhances dynamic price determination as well as the recommendation system, while NLP enhances customer service and feedback analysis. The paper also elaborates on the problem areas that are related to the machine learning application in e-commerce, such as data security, transparency of the machine learning model, and learning in real-life conditions. Understanding these dimensions within this paper will give a comprehensive analysis of how data science and cybersecurity, using machine learning approaches, are transforming e-commerce. Thus, the need for continuous development in these fields is underlined in order to maintain growth and protect e-commerce environments in the context of a world that progressively becomes digital.
Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review
Authors: Toochukwu Juliet Mgbole  , Adedoyin Zainab Olayanju
Published: September 2024

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.
The prevention of infectious diseases in prisoners in Cipinang Prison, Jakarta, Indonesia
Authors: Durotul Yatimah  , Elsa Fitri Ana  , Adman  , Chaidar Malisi
Published: September 2024

This study explores the stages of conducting educational outreach on preventing infectious diseases among inmates at prison institutions in Cipinang, Jakarta. The research method was a qualitative descriptive approach in November 2024 Data collection techniques include interviews and documentation. The findings indicate that the prevention of infectious diseases by outreach method among prisoners is carried out through several stages: assessing the health needs of the inmates, planning the educational sessions, selecting appropriate methods and media for the outreach, and evaluating the effectiveness of the sessions. This initiative has successfully reduced behaviors that could lead to the transmission of diseases through various means, including airborne, food and drink, vector-borne, and other methods. The conclusion is that the outreach is conducted systematically. As a result, participants not only understand the content of the sessions but also take action to prevent the spread of diseases.
Theoretical perspectives on digital literacy programs: A comparative study of initiatives in Africa and the United States
Authors: Chinasa Iroabughichichi Evurulobi  , Adebukola Olufunke Dagunduro  , Olanike Abiola Ajuwon
Published: September 2024

This review paper analyses digital literacy programs in Africa and the United States, highlighting the theoretical underpinnings and practical implementations across diverse socio-economic, cultural, and technological contexts. Examining key theoretical frameworks—Technological Literacy, Media Literacy, and Information Literacy—explores how these theories inform the design, execution, and evaluation of digital literacy initiatives. The analysis reveals both region-specific challenges and universal opportunities for enhancing digital literacy, emphasizing the need for contextually adapted, inclusive, and sustainable programs. Insights into policy implications and practical recommendations for practitioners are discussed alongside future research directions to deepen understanding and improve the effectiveness of digital literacy programs globally. This paper underscores the pivotal role of digital literacy in empowering individuals and fostering societal development in the digital age.
AI powered privacy protection: A survey of current state and future directions
Authors: Elijah Oluwatoyosi Abolaji  , Oladayo Tosin Akinwande
Published: September 2024

The research is conducted to investigate how AI transforms the notion of protection of privacy through discussing the status quo technologies, challenges, and future directions. All due to the sudden rise of digital data, protection of personal information has evolved as a major concern for which AI-enabled solutions are being introduced. Advanced concepts of AI are therefore marking the paradigm shift in how organizations handle sensitive data, letting more secure and privacy-oriented practices lead the way with notions such as differential privacy, federated learning, and anomaly detection. However, despite these advances, formidable challenges remain regarding the opacity of AI models, possible algorithmic biases, and regulatory compliance. The paper further discusses the future of AI for privacy protection, with new developments: XAI, integration of AI with blockchain, and quantum-resistant cryptography. These advances offer great transparency, security, and responsibility in privacy management. It further underlines that the collaboration of governments, industrial leaders, and researchers is required in providing appropriate frameworks for the usage of AI, given the ethical and regulatory concerns around privacy protection as the influence of AI grows. While AI is indeed very promising in improving privacy protections, the degree to which it can actually function depends on the surmounting of present limitations and harmonization of technological development with shifting data privacy criteria. The AI in this research paper will continue to play a leading role in shaping the future of privacy preservation, with answers continuing to emanate from innovation, security, and ethical concerns. It is through continuous improvement and collaboration that AI can ensure effective privacy measures in an increasingly data-driven world.
Sap S/4hana applications on data security and protections for sap cloud services
Authors: Vedaprada Raghunath
Published: September 2024

This study focuses on how to secure enterprise resource planning (ERP) systems that are hosted in the cloud. We utilized SAP S/4HANA cloud (Public edition) for this research. Here we take a look at the difficulties and concerns around cloud data security. The essay will provide a comprehensive analysis of the research findings. We implement global protection strategies and policies to mitigate risks and threats, guaranteeing the highest level of data safety in Cloud ERP. While many apps would benefit from access to ERP data in the cloud, doing so would also make that data available to apps that may already have security flaws.We chose to investigate cloud-based ERP since many organizations are moving away from on-premises ERP systems in favor of them. Concerns regarding the safety of data while it is in transit or at rest are also covered in the article. The study makes use of platforms as a service (PaaS) and software as a service (SaaS) at all levels. As part of our investigation, we look at potential fixes for several Cloud ERP security vulnerabilities.
Security issues analysis based on big data in cloud computing
Authors: Vedaprada Raghunath
Published: September 2024

This study will investigate the security problems that are linked with the Hadoop ecosystem, Big Data, Map Reduce, and cloud computing. The objective of this research is to analyze these concerns. Specifically, the primary emphasis is placed on the security concerns that are linked with big data in cloud computing. Big data applications have a lot to offer organizations, companies, and a broad range of industries, no matter how big or little. There have been issues with Hadoop and cloud computing security, therefore we also look at several possible solutions to these difficulties. The field of cloud computing security is experiencing fast growth in several areas, including computer security, network security, information security, and data privacy. Data, apps, and the infrastructure related to them are greatly helped by cloud computing's use of rules, technologies, controls, and big data tools to keep everything safe. Researchers also anticipate that cloud computing, big data, and their associated benefits will represent the most exciting new areas of study.
Long-Term Clinical Evolution of McCune-Albright syndrome: 15 years of follow-up
Authors: Loubna LOUDAY  , Mustapha ASFOUR  , Mamadou TOGO  , Abderahmane TADLAOUI  , Jad ISSOUANI  , Ahmed Anas GUEBOUB
Published: September 2024

Mc Cune Albright syndrome is a rare, sporadic disorder manifested by the triad of fibrous bone dysplasia (FD), café-au-lait skin spots and precocious puberty (PP), which may be associated with other hormonal hyperfunctions. The broad spectrum of manifestations reflects the mosaicism of the activating somatic mutation of the GNAS gene in different tissues. Follow-up of patients with Mc Cune Albright syndrome in adulthood remains essential, and requires multidisciplinary management, as this population remains susceptible to sometimes serious complications related to the hyper-secretory nature of all possible endocrine conditions. We report the case of a 15-year-old female patient with complete Mc Cune Albright syndrome since the age of 9 months.
Part two: “thirty-seven years later - the UK migrant crisis” Deconstructing the erosion of British national sovereignty
Authors: Antony D. Miller
Published: September 2024

This part two body of work 37 years after this researcher’s first delving into immigration entitled “The Worst is Yet to Come. The Challenges of Multiculturalism in Britain: An Examination of Over-Migration, Assimilation, and A Warning for the Future.” (1987). This investigation will seek to dissect the administrative practices that have allowed for unshackled policies on immigration to be sustained in the United Kingdom for six decades and undermine the tenets for the checks and balances in a democracy. Why has the British public become so subjugated that it accepted over-migration without any initial protest in the past and had waited for a tragedy of three little girls to occur, and to finally revolt against the state - notably the recent summer of rioting. This study investigates how a once influential nation, became acquiescent to a dangerous level of devaluation. A comparison was made with the repressive attitude of Poland towards immigration of dissimilar faith, specifically resulting, in zero terrorist activity that has been observed in their country. The research explores the extent to which ethnic stability can be expected with over-immigration - addressing factors such as tolerance, the state of the economy (Nash’s Equilibrium) and alternative models of social integration. It is also recorded from the results that the United Kingdom has failed in its policies regarding immigration which has caused social problems, cultural disquiet, and economic pressures that have put at risk the societal structure or the fabric of Britain itself. Concerning the theoretical contributions, the study squarely cites the lack of responsibility or poor governance from all political parties. Their neglect of nation sovereignty even though, governments have critical role that they play in ensuring the protection and welfare of its native citizenry. Finally, this research will serve as a warning to other neoliberal oriented nations of which ignore their fellow citizenry’s well-being.
Comparative analysis of machine learning algorithms for ECG-based heart attack prediction: A study using Bangladeshi patient data
Authors: Md Alif Sheakh  , Mst. Sazia Tahosin  , Lima Akter  , Israt Jahan  , Md Nakibul Islam  , Md Rafiuddin Siddiky  , Md Mahadi Hasan  , Sakibul Hasan
Published: September 2024

This study aims to identify the most accurate machine learning algorithm for predicting heart attacks using demographic data, physiological measurements, and electrocardiogram (ECG) results. We utilized a dataset of 4,000 patient records, combining data from DMCH and Kaggle. Our methodology involved comprehensive data preprocessing, including ECG noise removal and feature selection using the Brouta algorithm. We implemented and compared six machine learning algorithms: Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, XGBoost, and K-Nearest Neighbors. The results demonstrate that our proposed method can accurately predict heart attacks with high sensitivity and specificity. Among the tested algorithms, Random Forest achieved the highest accuracy of 87%, with well-balanced precision (0.86), recall (0.85), and F1-score (0.87). K-Nearest Neighbors and XGBoost also showed strong performance, with accuracies of 81% and 80% respectively. This study contributes to the field by utilizing a large, diverse dataset and providing a comprehensive comparison of multiple algorithms. Our findings suggest the potential for integrating machine learning, particularly Random Forest models, into clinical practice for early heart attack risk assessment, representing a significant step towards improving cardiovascular care through advanced data analysis techniques.
OI url: https://doi.org/10.30574/wjarr.2024.23.3.2934
Authors: Chisom Assumpta Nnajiofor  , Daniel Edet Eyo  , Adedolapo Olujuwon Adegbite  , Isyaku Abdullahi Odoguje  , Elizabeth W. Salako  , Femi Emmanuel Folorunsho  , Abdulafees Adeolu Adeyeye
Published: September 2024

Artificial intelligence (AI) has emerged as a key enabler in optimizing renewable energy systems, significantly contributing to global efforts toward environmental sustainability. This review explores the application of AI technologies in enhancing the efficiency, reliability, and integration of renewable energy sources such as solar, wind, and hydropower. It focuses on how machine learning (ML), deep learning (DL), and other AI-driven algorithms improve energy forecasting, grid management, and storage optimization. Survey data and case studies demonstrate the potential of AI to minimize energy waste, reduce costs, and lower greenhouse gas emissions, reinforcing its role in transitioning to a sustainable energy future. The review concludes with a discussion of challenges and future research directions.
Explainable AI (XAI) in healthcare: Enhancing trust and transparency in critical decision-making
Authors: Adewale Abayomi Adeniran  , Amaka Peace Onebunne  , Paul William
Published: September 2024

The integration of artificial intelligence (AI) in healthcare is revolutionizing diagnostic and treatment procedures, offering unprecedented accuracy and efficiency. However, the opacity of many advanced AI models, often described as "black boxes," creates challenges in adoption due to concerns around trust, transparency, and interpretability, particularly in high-stakes environments like healthcare. Explainable AI (XAI) addresses these concerns by providing a framework that not only achieves high performance but also offers insight into how decisions are made. This research explores the application of XAI techniques in healthcare, focusing on critical areas such as disease diagnostics, predictive analytics, and personalized treatment recommendations. The study will analyse various XAI methods, including model-agnostic approaches (LIME, SHAP), interpretable deep learning models, and domain-specific applications of XAI. It also evaluates the ethical implications, such as accountability and bias mitigation, and how XAI can foster collaboration between clinicians and AI systems. Ultimately, the goal is to create AI systems that are both powerful and trustworthy, promoting broader adoption in the healthcare sector while ensuring ethical and safe outcomes for patients.
Fortifying multi-cloud Kubernetes: Security strategies for the modern enterprise
Authors: Diana Kutsa
Published: September 2024

Kubernetes-based multi-cloud architecture is becoming an integral part of modern enterprises, which makes ensuring its security a top priority. The main threats in such a system are related to configuration management, network security, access control and compliance with regulatory requirements. For effective security management, it is proposed to use CNAPP platforms that integrate several protection mechanisms, including cloud security Management (CSPM) and Kubernetes Security Management (KSPM). Automation of monitoring processes and authentication in Kubernetes, including centralized identity and access management, play an important role. The use of encryption technologies, software-defined networks (SDN) and regular audits contribute to reducing risks. The implementation of advanced security strategies and their constant adaptation to new threats ensures the sustainability of the cyber environment of multi-cloud deployments and the maintenance of data integrity.
Is the occurrence of Gestational Diabetes Mellitus in pregnancies higher following In Vitro Fertilization treatment? Why? A retrospective cohort study
Authors: Aypar Akbağ Nuran Nur , Aluş Tokat Merlinda , Özöztürk Sevcan , Ünsal Avdal Elif , Okyay Recep Emre , Doğan Ömer Erbil
Published: September 2024

Purpose: This study aim to determine the occurrence of gestational diabetes mellitus (GDM) in pregnancies after IVF treatment and to evaluate the factors that reduce this risk.
Material and Methods: This retrospective cohort study was conducted using the medical records of pregnant women who conceived following IVF at the in-vitro fertilization center between 2002–2019. The data were obtained from medical records and phone interviews. Univariate and multivariate logistic regression analyses were performed.
Results: The incidence of GDM was found to be 16.7%. The regression model indicated that the risk of GDM was 4.57 times higher in the age group 36–40 at conception during the IVF cycle than the age group 31–35 (95% CI = 1.18–17.73, p = .028). Furthermore, women who conceived after the second IVF trial had a risk of GDM 3.464 times higher than those that conceived after their first IVF trial (95% CI = 1.07–11.23, p= .038).
Conclusion: As age and number of IVF trials increase in infertile women, the risk of GDM increases after IVF treatment.
AI-Assisted Survival Prediction in Colorectal Cancer: A Clinical Decision Support Tool
Authors: Mısırlıoğlu Hüseyin Koray , Leblebici Asım , Çalıbaşı Koçal Gizem , Ellidokuz Hülya , Başbınar Yasemin
Published: September 2024

Purpose: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Accurate survival prediction is crucial for advanced-stage patients to optimize treatment strategies and improve clinical outcomes. This study aimed to develop an artificial intelligence-assisted clinical decision support system (CDSS) for survival prediction in CRC patients using clinical and genomic data from the Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD) dataset.
Methods: Machine learning algorithms, including C4.5 Decision Tree, Support Vector Machines (SVM), Random Forest, and Naive Bayes, were employed to create survival prediction models. Clinical parameters and genomic data from key pathways, such as glycolysis/gluconeogenesis and mTORC1, were integrated into the models. The models were evaluated based on accuracy and performance.
Results: The Random Forest algorithm achieved the highest accuracy (82.3%) when only clinical parameters were used. When clinical data were combined with gene expression data, the model’s accuracy increased further. The resulting models were incorporated into a user-friendly web interface, SurvCOCA, for clinical use.
Conclusions: This study demonstrates the potential of AI-based tools to improve prognosis predictions in CRC patients. Further research is needed, with larger datasets and additional machine learning algorithms, to enhance clinical decision-making and optimize treatment strategies.
Psychological and Educational Approach in Teaching and Learning Foreign Languages
Authors: Amira Hamel
Published: September 2024

Schools play a central role in the holistic development of children, providing a structured environment that fosters their intellectual, social, emotional, and physical growth, and equipping them with essential skills and knowledge for their future through the experiences each child will have at school. These contribute to the cognitive and academic development of children in several ways. First and foremost, the primary objective of school is to provide a pedagogical education to children. In this regard, the school promotes intellectual development through a structured and organized learning environment where children can acquire knowledge, develop cognitive skills, and build their academic journey. Teachers play a crucial role in delivering effective instruction, facilitating learning experiences, and providing individualized support to meet the diverse needs of students. Furthermore, schools provide resources, programs, and assessments that help children progress academically and prepare them for their future studies. Moreover, children are also exposed to several languages from a very young age, which promotes the acquisition and mastery of languages. The learning of multiple languages in a multilingual school will lead children to develop cognitive skills such as attention, memory, and executive functions. These cognitive skills are transferable and can support academic abilities in areas such as reading, comprehension, mathematics, and problem-solving. As the basis of all educational activities, whether they involve listening, speaking, reading or writing, language plays a crucial role in the fulfilment of many important tasks and goals of a school. Language in school is not only a subject to be taught, but a source of study for other subjects as well, as it enables students to gather data and gain knowledge in a variety of subject areas. As a result, primary education provides all students with a basic education as well as access to other languages, such as French or English. This initiative aims to give students an appropriate linguistic approach that enables them to communicate, express themselves and use their language skills in their academic, personal and social activities, as well as direct access to information and knowledge. Foreign language education aims as well as to give learners the ability to communicate, express themselves, and use their language skills in their school work, personal lives, and social interactions. It also helps them have direct access to information and knowledge. From this point of view, we seek through the following scientific paper to discuss the purpose of the need for children to acquire foreign languages and the most appropriate age to start learning them. We will also try to present the scientific opinions of the most important thinkers and researchers on how languages are learnt and taught and the proposed teaching methods in this field.
Bir Simülakr ya da Eğitim Çıktısı Olarak Özgürlük: Jean Baudrillard’ın Simülasyon Kuramı Bağlamında Bir Okuma
Authors: Çamcı Hikmet , Atay Rıfat
Published: September 2024

Simülasyon çağının özelliği kelime, kavram, durum gibi göndergeye atfedilenlerin kendinden başka referansının olmamasıdır. Bu bağlamda birçok kavramın da anlam kaymasına veya bozulmasına uğradığı söylenebilir. Bu incelemede, insanı tanımlarken kullanılan temel kavramlardan biri olan özgürlük mefhumu ele alınmıştır. Genelde serbestiyet olarak anlaşılan bu kavram, Baudrillard’ın durum tespitleri ve Türkçenin imkanlarından faydalanılarak, “gürleştirilmiş bir özün çıktısı olarak özgürlük” anlayışı doğrultusunda tartışılmıştır. İnsanın maddi ve manevi bir bütün olduğu ön kabulüne dayanan araştırmada, simülasyonun ortaya çıkardığı metastazların eğitim yoluyla giderilme ihtimali dikkate alınarak bazı eğitim ilkelerine ulaşılması hedeflenmiştir. Alanyazın taramasından elde edilen verilerin nitel tahliline dayanan çalışmada, öz eğitimiyle kişinin doğa ve Aşkın arasında kendini fark etmesi; kavramlara sahte anlamlar yüklenmesini azaltmaya yönelik etkinlikler; bilgi işlemsel düşünme öğretiminin geliştirilmesi gibi bazı ilkeler tespit edilmiştir. Bu ilkeler, simülasyon düzeninin metastazlarına ve sibernetik çağa uygun yeni bir eğitim anlayışı oluşturma ve geliştirme süreçlerinde faydalı olabileceği için, benzer tetkiklerin gerekliliği araştırmanın çıktıları arasındadır.