Physical Sciences and Engineering

Self-medication with over-the-counter analgesics is a double-edged sword, with both advantages and disadvantages, and is common in developing countries. It helps patients, but it can also harm them. The aim of the current study was to estimate the prevalence of over-the-counter analgesics use among students in the department of environmental engineering and pollution technologies at the Technical College of Engineering / Kirkuk. A cross-sectional study was conducted among (50) participating students using a structured questionnaire.

This paper presents the design of a compact printed 5G monopole antenna for vehicular communication applications. The monopole antenna is placed directly on the laminated glass of vehicle for eliminating additional mounting space. The proposed antenna operates within the sub-6 GHz frequency range, covering the 5G bands from 26 GHz to 30 GHz. The antenna features a simple and compact design, making it suitable for integration into vehicles. The design utilizes a monopole resonator, magnetic line, and co- planar waveguide (CPW) to achieve improved performance.

Manual handling of procurement records remains a persistent challenge in many public institutions, often leading to inefficiencies, delays, and data inconsistencies. At Eastern Visayas State University-Ormoc Campus (EVSU-OC), the lack of an integrated system for managing Purchase Orders (PO) and Inspection and Acceptance Reports (IAR) has hindered operational efficiency and document retrieval.

Effective communication between healthcare providers and patients is vital for successful diabetes management, particularly in low-resource settings like Cameroon where the prevalence of diabetes is rising. This systematic review explored existing literature on communication practices in diabetes care, focusing on the types of communication used, the quality of provider–patient interactions, and barriers that hinder effective communication.

Applications of Artificial Intelligence (AI) across different fields have revolutionized industries, and law is no exception. In India, the judiciary has traditionally acted as the interpretation of the Constitution based on conventional legal axioms and human judgment. But with the advent of AI in the guise of Natural Language Processing (NLP), machine learning (ML), and big data analysis, the trend of interpretation of law will shift. The application of Artificial Intelligence (AI) in different areas has revolutionized the domain of governance, law, and policy-making.

Knowledge acquisition relies on the capacity of the students to read and grasp the meaning of the text. However, diverse factors are impeding students’ ability to comprehend the breadth and depth of the English subjects—reading material—consequently compelling misinterpretation and incompetence in understanding the projected lessons. This study aimed to identify the effectiveness of vocabulary enrichment in improving students’ reading comprehension among the students in Sto. Tomas College Danao City, Inc.

This work consisted of identifying the residues generated in the technological process of wood machining and the techniques of their recovery within the company SICOFOR in Pointe Noire, Republic of Congo. The ridges from the trunks of the trees were used as plant material. Data were collected through direct observation, interview and discussion, and then measurement of parameters. The results indicated that SICOFOR has a good production system because it generates little waste; i.e.

The purpose of this study was to investigate the influence of imagery, concentration, level of experience and playing position on penalty kick performance success among the players of the University of Cape Coast (UCC) Youngsters Football Club (FC). Thirty registered players of UCC Youngsters FC for the 2022/2023 season participated in the study. The players were subjected to the taking of kicks from the penalty spot after imagery and concentration intervention programs.

Ransomware attacks have emerged as a major cybersecurity threat in terms of the massive financial and data losses it has inflicted across the globe. Such attacks cannot easily be detected by traditional detection techniques, including signature-based and rule-based detection, because these are issues that rely heavily on predefined characteristics and static rules for their identification purposes.

Dermatological disorders, and especially skin cancer, are a worldwide health issue. Precise and early diagnosis is critical in order to pursue efficient treatment, and machine learning, in this case Convolutional Neural Networks (CNNs), has tremendous potential in computerizing the diagnostic process. This research suggests a Visual Geometry Group Network (VGGNet) model for dermatological disorder diagnosis, specifically for skin disorders like melanoma, psoriasis, and eczema.