Model Selection and Inference in Competing Risk Regression Model to Determine the Potential factors of under-5 Child Mortality in Bangladeshin Bangladesh

Under-5 child mortality is always a critical term for the developing countries like Bangladesh. The primary objective of this study is to investigate under-5 child mortality using Fine and Gray (1999) competing risk regression. The secondary objective is to decide among many other covariates which covariates should be included in Fine and Gray model and which should not. For this purpose, data are extracted from Bangladesh Demographic and Health Survey (BDHS) 2011, where the event of interest, under-5 child mortality may occur due to any of the three causes: Disease, Non-disease and Other. It is found that for “Disease Cause” 4 covariates (wealth index, size of child at birth, gender of child, availability of maternal and child welfare center) are selected. For “Non-disease Cause” 6 covariates (mother\u27s education, place of delivery, size of child at birth, NGO  membership of mother, gender of child, birth order number) are selected and for “Other Cause” 6 covariates(mother\u27s education, availability of MCWC, NGO membership of mother, father\u27s education, birth order number, main access road to village)  are selected. Finally, for the selected covariates, the Fine and Gary competing risk regression models are fitted to identify the potential factors of under-5 child mortality due to the three different causes of deaths. The identified factors may help to take decision by the health policy makers to increase under-5 child survival in Bangladesh

Reference Intervals for Haematological and Morphometric Parameters in Balkan Donkey in Serbia

The Balkan donkey is an indigenous primitive breed that is bred in the hilly and mountainous regions of Serbia and Montenegro. It represents an important resource in terms of preserving the genotype of our country. Today, the majority of donkey population in Serbia is situated in Special Nature Reserve “Zasavica”, Nature park of “Stara Planina and Kovilj vilage, near Novi sad. They are mainly used for the production of donkey milk and as a tourist attraction on farms. There are no many literature data  related to of the values of basic hematological and morphometric parameters in donkeys. This led to the development of a this study, especially on donkeys from Stara Planina, whose end result were the definition of basic physiological values of triassic parameters, hematological and biochemical values of blood parameters and precise morphometric measurements. Knowing the values of these parameters is of great importance for veterinarians for the correct interpretation of clinical findings and diagnosis of diseases in donkeys

Cloud Carbon Footprint Tracker for Sustainable Practices

In today’s digital era, businesses heavily rely on cloud-based solutions to support their operations, leading to substantial carbon emissions due to the extensive use of cloud resources. As organizations increasingly adopt cloud computing, the environmental impact of these infrastructures has become a growing concern. To address this issue, this paper presents a Carbon Footprint Tracker for Cloud Resources, a comprehensive solution that leverages cloud analytics and artificial intelligence (AI) to measure, analyze, and minimize carbon emissions. Our proposed system integrates seamlessly with Azure Cost Management APIs, Azure Monitor, and advanced Machine Learning models to provide organizations with real-time insights into their cloud consumption and its associated carbon footprint. By analyzing usage patterns and optimizing resource allocation, the system offers data-driven recommendations to enhance sustainability. Additionally, it generates detailed sustainability reports, enabling businesses to track their environmental impact and make informed decisions toward greener cloud strategies. Through this innovative approach, enterprises can effectively reduce their carbon footprint, improve operational efficiency, and align with global sustainability goals. By embracing eco-friendly cloud practices, organizations can contribute to a more sustainable future, ensuring responsible and energy-efficient cloud usage

Methodological Foundations of Risk Register Development for Large-Scale Projects

The article examines the methodological foundation for developing a risk register for large-scale projects, including the key principles of its formation, integration, and application in managing uncertainty. The urgency of the topic is justified by the scale of systemic deviations in megaprojects: the average budget overrun reaches 62?percent, while in 91.5?percent of cases projects experience cost overruns, delays or both simultaneously, which underscores the need to embed a risk?management framework at the investment justification stage, when the work breakdown structure and preliminary budget are refined. The study aims to conduct a systematic analysis and comparison of methodologies for developing a risk register, drawing on PMI, USACE and ISO standards and practical regulator recommendations, to identify novel approaches to consolidating information in a single source of truth, linking risks to WBS elements and explicitly declaring residual risk; the novelty of the research lies in combining empirical data from more than sixteen thousand megaprojects, the results of a survey of four hundred companies and software?market forecasts to produce cohesive methodological recommendations. Key findings demonstrate that integrating the risk register with the project baseline during the investment justification phase transforms it from a passive catalogue into an active driver of schedule and cost forecasts. Additionally, four methodological principles form a dynamic system capable of supporting decision-making and enhancing the adaptability of project teams. This article will be valuable to project managers, risk managers, and researchers in the field of large?scale initiative management

Review of Cable Fault Locating Methods and Usage of VLF for Real Cases of High Resistance Fault Location First

We are in the cable age, and cable circuits are the primary carriers for electrical transmission and distribution networks. Cable and its associated accessories faults are always predictable and quite common. Failure of cable circuits can occur at the commissioning stage or while they are in service. Utilities and service providers consider such failures unacceptable as they impact their reliability and business continuity, necessitating the implementation of specific measures to mitigate them. This paper examines the history of cable fault-finding methods and the most effective techniques for locating high-voltage cable and accessory faults. It identifies the most effective methods based on their ease of use, speed, accuracy, and minimal impact on the cable circuit\u27s life cycle. It also talks about how useful it was to use the very low-frequency (VLF) method to convert high-resistance faults into low-resistance. It also talks about the real failure scenarios for high-resistance faults in high-voltage cable circuits at the commissioning stage

Assessment of Timelines in the Management and Human Papilloma Virus (HPV) Status of Head and Neck Cancer Patients

Background: In Nigeria, many of the patients with Head and Neck cancers (HNCs) usually present late, with advanced disease, thereby reducing the chances of cure and survival. The commonest sites of HNCs among Nigerians are nasopharynx, sinonasal, larynx, oropharynx and oral cavity, with a total prevalence of 3-5%. Human Papilloma Virus (HPV) have been known to be associated with malignant tumours arising from these sites, but with little documentation in literature in Nigeria.  Objectives/Aim: This study is to assess the socio-demographic factors responsible for delay in hospital presentation, diagnosis and treatment and also to determine the presence of HPV which is a risk factor in head and neck cancer, among patients that presented in the Lagos University Teaching Hospital (LUTH), South West Nigeria, during the study period. Patients/Method: This is a prospective study conducted to determine the timelines, socio-demographic factors responsible for delays in presentation, diagnosis, treatment and HPV status, among patients presenting with Head and Neck cancers in the Ears, Nose and Throat (ENT) and Oral and Maxillofacial Surgery (OMFS) clinics in the Lagos University Teaching Hospital (LUTH), South West Nigeria during the study period.  Consecutive patients with HNC who presented to ENT and OMFS clinics were selected until the required sample size was met. Informed consent was taken from the patient. Semi-structured questionnaires were used to collect data on history, physical examination, investigation and treatment. History entailed details about the presenting complaints, duration, site of malignancy, socio-demographic, socioeconomic, time taken for symptom recognition, fear associated with treatment, hospital related delays and health related behaviour data were documented. The tumour type was confirmed by histopathological diagnosis and Immunohistochemistry was used to detect HPV viral particles in pathological specimen of patients. The tumour was staged according to TNM classification of the American Joint Committee on Cancer (AJCC), Head & Neck cancer sites was coded and classified according to ICD-10 (International statistical classification of diagnosis).  Results: Ninety-eight patients were recruited for this study while ninety-one (91) completed the study. The median timeline for presentation to a health facility on account of HNC symptoms was 34 weeks, timeline from hospital presentation to review by an ENT/OMF specialist was 5 weeks, from specialist review to diagnosis was 5 weeks and treatment was 6 weeks. The distribution of the Primary sites of Head and Neck Cancers includes Larynx (29.7%), Nasopharynx (28.6%), Oral cavity (16.5%), Nose and Paranasal sinuses (13.2%), Oropharynx (7.8%), Parotid (2.2%). The other sites were from the External Auditory Canal and the Mastoid. Only 14.3% of the HNCs were positive for HPV. Conclusion: There was delay in presentation and management of patients with HNCs, the economic factor being the major cause. HPV-associated HNCs is higher in this study, compared to other studies in the sub-Saharan Africa

The Evolution of English Language Teaching Methods in the Information Age

This study traces and analyzes the evolution of English language teaching methods with the emergence of information technologies. The research describes modern teaching methodologies and the opportunities that have become available to educators. It also examines the relationship between the mechanics of learning new information and contemporary methods of its delivery. The findings suggest that technology-driven methods are more effective than traditional approaches. The relevance of the study is driven by the rapid development of information technologies and their integration into educational practices. Special attention is given to the interaction between teachers and students with artificial intelligence. The study outlines AI capabilities and their applications in pedagogy, highlighting the advantages of AI-assisted foreign language learning. The research concludes that the advent of the digital era does not necessarily lead to the adoption of all its benefits by every educator. Additionally, it is argued that while teaching methodology as an algorithm remains largely unchanged due to digitalization, the development of information technologies has significantly influenced the ways material is delivered and reinforced. This article will be useful for both novice and experienced educators seeking to automate certain processes, enhance the efficiency of their lessons, and introduce greater diversity into their teaching methods

Technologies and Methods for Optimizing Web Application Performance

The article reviews modern technologies and methods applied to optimize web application performance, emphasizing the direct impact of site speed on user experience, business performance, and competitive positioning. The authors analyze contemporary approaches for enhancing both front-end and back-end performance. Front-end strategies discussed include code splitting, lazy loading, server-side rendering (SSR), image optimization, and minimizing render-blocking resources. Back-end methods encompass various caching strategies, database query optimization, effective API design—particularly comparing GraphQL and REST—and deployment of Content Delivery Networks (CDNs) alongside edge computing solutions. A structured review methodology was applied, synthesizing recent peer-reviewed literature, expert reports, and empirical case studies from industry settings published within the past five years. Quantitative data are provided, illustrating significant performance improvements, including latency reduction, increased throughput, and enhanced user interaction metrics. The authors highlight practical implementation considerations and trade-offs inherent to each technique. Presented findings contribute valuable insights for developers, system architects, and researchers aiming to deliver faster, more reliable, and user-friendly web applications

Novel Machine Learning Approach for Defect Detection in DFT Processes

Recent advances in semiconductor technology have highlighted significant challenges in effectively testing modern integrated circuits (ICs). As device densities increase and defect mechanisms become more diverse, conventional Design for Testability (DFT) methodologies – while indispensable – must contend with exponential growth in test complexity. This paper reviews the essential DFT practices, including scan-based structures, boundary scan, and built-in self-test (BIST), and examines how these practices address a variety of logical fault models. It further explores machine learning (ML) techniques as valuable tools for enhancing defect detection and diagnosis. By leveraging classification algorithms such as support vector machines and neural networks, ML-driven approaches can reduce test pattern generation time, improve bridging-fault coverage, and streamline board- or wafer-level screening. Collectively, this paper underscores how strategic synergy between DFT and ML can raise fault coverage, improve diagnostic precision, and contain testing costs in the face of ongoing technology scaling

Automated Customer Support Systems in Service Companies: Analysis of AI Implementation

This study analyzes the potential of integrating artificial intelligence (AI) into customer support systems within service companies. The research is based on a review of the technological foundations of AI solutions, including natural language processing (NLP, NLU), interactive systems (chatbots, virtual and voice assistants), business process automation, and analytical tools for predictive modeling. Through statistical analysis and case studies from various industries—such as the banking sector, retail, and the implementation of the Pega platform—the study identifies key performance changes, including reduced response times, an increase in first-contact resolution rates, improved customer satisfaction, and lower service costs. The study presents an integrated model for evaluating the effectiveness of AI implementation, offering recommendations for optimizing customer support automation. By addressing a scientific gap, this research combines technical, economic, and ethical aspects of AI applications in customer service. The findings will be of interest to service company executives, customer support specialists, and IT directors seeking to enhance customer interactions through AI-driven automation. Additionally, the study provides valuable insights for analysts and researchers in digital transformation, examining the impact of artificial intelligence on business processes and user experience