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Journal for Technology and Science
Published by Antis Publisher
ISSN : -     EISSN : 30474337     DOI : https://doi.org/10.61796/ipteks.v1i1
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering
The Journal for Technology and Science published by Antis Publisher eISSN 3047-4337 is a scholarly journal that focuses on original research articles in natural science and technology relevant to industries and communities in developing countries. Released annually in March, August, and November, it is inclusive of scientists, researchers, educators, and scholars. The journals scope encompasses various topics addressing current challenges encountered by industries, governments, and communities in developing nations. With a commitment to advancing knowledge and fostering innovation, The Journal for Technology and Science welcomes submissions that contribute to the advancement of science, technology, and their applications in addressing societal needs and promoting sustainable development in emerging economies
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 75 Documents
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PREPARATION AND PHYSICOCHEMICAL ANALYSIS OF POLYMER COMPOSITES FROM RECYCLED PET WASTE Norbutayev, S. Q.; Tavashov, Sh. Kh.
Journal for Technology and Science Vol. 3 No. 2 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i2.476

Abstract

Objective: This study investigates the physicochemical properties of polymer composite materials based on recycled PET and CaCO3. Method: The thickness of the samples was selected in compliance with the standard requirements: 3,49 mm for sample 1, 3,43 mm for sample 2, and 3,91 mm for sample 3.The experiment was initiated at an ambient temperature of 22,5°C, with a controlled heating rate of 50°C per hour. Under an applied load of 10 N, the penetration of the needle to a depth of 1 mm for the first sample was achieved after 118,68 minutes, corresponding to a temperature of 152,4°C. Results: The results indicate that optimal performance is achieved at 5–15% CaCO3, demonstrating the potential of recycled PET composites for cost-effective industrial applications. Novelty: The results indicate that optimal performance is achieved at 5–15% CaCO3, demonstrating the potential of recycled PET composites for cost-effective industrial applications.
INVESTIGATION OF ZINC HYDROXIDE CARBONATE FORMATION FROM ZINC NITRATE SOLUTION BY SODIUM CARBONATE PRECIPITATION Hamdamov, A. A.; Ochilov , J.M.; Tavashov , Sh.Kh.
Journal for Technology and Science Vol. 3 No. 2 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i2.477

Abstract

Objective: This study presents the results of zinc hydroxide carbonate production through precipitation from a zinc nitrate solution using an 18% sodium carbonate solution. Method: The effects of key process parameters on the precipitation efficiency were systematically examined, and optimal operating conditions were determined. Results: The initial zinc nitrate solution contained 13,16% ZnO. The highest precipitation efficiency of zinc hydroxide carbonate was achieved within a pH range of 7,9–8,3 and at a temperature of 65–70°C. Additionally, extending the process duration from 40 to 45 minutes led to an increase in the precipitation degree from 98,79% to 99,96%. Novelty: The effects of key process parameters on the precipitation efficiency were systematically examined, and optimal operating conditions were determined.
ARTIFICIAL INTELLIGENCE DRIVEN INFRASTRUCTURE SECURITY ENHANCING CYBERSECURITY AND PROTECTING NATIONAL SECURITY SYSTEMS Chowdhury Amin Abdullah; Md Jahidul Islam Ridoy
Journal for Technology and Science Vol. 1 No. 3 (2024): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v1i3.498

Abstract

Objective: Artificial Intelligence (AI) functions as a fundamental technological solution which defends national security systems through improved cybersecurity infrastructure against sophisticated cyber threats. The fast growth of digital technology has made vital infrastructure systems vulnerable to cyber threats which include phishing attacks and ransomware and malware infections and data breach incidents. Method: The research used a quantitative survey-based design which collected data from 175 participants worked as cybersecurity professionals and IT experts and government officials and academic researchers. Descriptive statistics to analyze data through frequency and percentage and mean and standard deviation and ranking analysis and Pearson correlation which studied the connection between AI adoption and cybersecurity performance indicators. Results: The study found that 81.1% of participants selected phishing attacks as their primary security risk while ransomware attacks received 77.7% and data breaches obtained 73.7% of the votes. Correlation data revealed that countries which adopt AI technology tend to experience better national security results with r = 0.77 and improved threat detection with r = 0.74 and stronger data protection with r = 0.69. The system provides multiple benefits to users but users encounter three major obstacles which include privacy concerns at 74.3% and insufficient qualified staff at 71.4% and costly setup expenses at 69.1%. Novelty: AI functions as a fundamental element which enhances both cybersecurity systems and protects national security interests.
ADOPTION OF DIGITAL TECHNOLOGIES IN THE HIGHER EDUCATION Abdukadirova Xalida Abduxamedovna; Mukumova Nargis Nuriddinovna
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.502

Abstract

Objective: This article examines the digital transformation of higher education institutions and their impact on the development of distance education. It analyzes modern approaches to defining the concepts of "university digitalization" and "distance learning technologies," identifying key drivers and barriers to the implementation of digital solutions in the educational process. Method: Based on an analysis of domestic and international experience, the author identifies key areas of digitalization: the implementation of electronic educational platforms, automation of educational process management, and the use of artificial intelligence and big data technologies. Results: Particular attention is paid to the changing roles of teachers and students in the context of the transition to hybrid and fully online learning formats. Novelty: The article identifies key areas of digitalization: the implementation of electronic educational platforms, automation of educational process management, and the use of artificial intelligence and big data technologies.
REVOLUTIONIZING CARDIOVASCULAR CARE: AN AI-DRIVEN APPROACH TO EARLY INTERVENTION Hussein Ali Al-jashamy; Hashim Adnan; Hussein Majid
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.505

Abstract

Objective: Cardiovascular diseases (CVDs) continue to be a primary cause of early death globally, with both their prevalence and the costs associated with healthcare consistently increasing. Epidemiological Researches has pinpointed a range of risk factors, including high cholesterol levels, elevated blood pressure, diabetes, obesity, smoking, and lack of physical activity, which together account for more than 90% of the risk linked to CVDs. The integration of artificial intelligence (AI) into healthcare has revolutionized medical diagnosis and treatment, particularly in the field of cardiology. Natural Language Processing (NLP) algorithms further enhance this by converting unstructured clinical notes into structured data, thus supporting clinical decision-making processes. This study explores the implementation of both traditional machine learning methods—such as Decision Trees (DT), Multilayer Perceptron (MLP)and advanced deep learning techniques in conjunction with NLP to diagnose heart conditions requiring catheter intervention. Method: This study explores the implementation of both traditional machine learning methods—such as Decision Trees (DT), Multilayer Perceptron (MLP)and advanced deep learning techniques in conjunction with NLP to diagnose heart conditions requiring catheter intervention. Results: Our findings suggest that the hybrid model employing deep learning methods outperforms traditional models, demonstrating the potential of AI in advancing cardiovascular healthcare. Novelty: Our findings suggest that the hybrid model employing deep learning methods outperforms traditional models, demonstrating the potential of AI in advancing cardiovascular healthcare.

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