American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
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2107 research outputs found
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Disputes as Risks: A Transformative Approach
Full text linkTraditional approaches to dispute resolution, such as arbitration and litigation, are often reactive, costly, and untimely. These methods often focus on contract variations, rather than addressing the root causes of disputes. In contrast, this study proactively addresses underlying issues. The purpose of this study is to analyse the effectiveness of treating potential disputes as risks to prevent conflicts in construction projects from evolving into disputes. This study highlights the negative impacts of disputes in construction projects, presents case studies in which potential disputes are treated as risks, and compares the outcomes with those of previous similar projects performed under the same scope and circumstances. By identifying potential disputes in the Risk Register and addressing them as risks or opportunities, disputes are integrated into a project’s risk management framework. By identifying potential conflicts early in the project lifecycle, this proactive approach allows teams to manage conflicts before they escalate into formal disputes, thereby avoiding costly delays and budget overruns. Case studies from the Gulf Cooperation Council (GCC) region, including those recognised as ‘Building Project of the Year’ and ‘Small Project of the Year’ at the MEED Projects Awards 2024, illustrate the practical application and effectiveness of the proposed approach
Performance Analysis of Fan Configurations for Greenhouse Cooling under Abu Dhabi Climatic Conditions,A Case Study from Al Kuwaitat Research Station, Al Ain, UAE
Full text linkThis short communication presents an evaluation of cooling efficiency in an agricultural greenhouse located at Al Kuwaitat Research Station, Al Ain, UAE, under the climatic conditions of Abu Dhabi. The study focused on comparing fan configurations, with particular attention to the performance of upper fans. Among the tested scenarios, running only the upper fans (Scenario 2) demonstrated the most effective cooling performance, as indicated by significant temperature differences recorded across multiple sensors. This enhanced cooling effect is likely due to improved airflow patterns and heat dissipation facilitated by the strategic placement of the upper fans. To ensure the accuracy and continuity of temperature data, sensors were regularly maintained and calibrated, and a redundant system was implemented to prevent data loss. These measures helped avoid gaps in the dataset, which are critical for reliable analysis. The findings highlight the importance of fan configuration and data integrity in optimizing greenhouse climate control under arid conditions
Applications of Artificial Intelligence Models in Teletherapy: A Review of Efficacy, and Ethical Implications
Full text linkThis review synthesizes contemporary evidence on the emerging applications of artificial intelligence (AI) models in teletherapy, based on their efficacy and ethical implications. Drawing on 27 peer-reviewed studies published between 2014 and 2025, it integrates diverse perspectives on how AI-enabled systems are increasingly incorporated into mental health care through digital cognitive behavioral therapy (CBT), virtual human (VH) agents, speech recognition platforms, and personalized e-therapy modules. Evidence from clinical studies indicates promising outcomes in patient engagement, symptom reduction, satisfaction, and scalability, particularly in resource-limited settings. Evidence underscores that while AI-assisted teletherapy enhances accessibility, efficiency, and real-time monitoring compared to traditional models, it remains limited in replicating the therapeutic rapport, empathy, and contextual sensitivity that are essential for effective treatment. Key ethical challenges include data privacy concerns, bias, transparency, accountability, and the irreplaceable role of human therapists. The review concludes that the future of teletherapy lies in hybrid care models, substantial data governance, and clinician oversight to ensure safe, effective, and ethically sound integration of AI in mental health practice
Detection and Interpretation of X-Ray Scans for the Presence of Pneumonia Using Convolutional Neural Network
Full text linkConvolutional neural network’s application is essentially an impactful technology to proffering solutions in medical diagnostics. This research carried out a design and implementation of a medical imaging analysis and classification of X-ray scans of pneumonia images using a convolutional neural network. The CNN system was designed using an algorithm of a convolutional neural network. The designed CNN system was processed by uploading 5,216 data which comprised normal and pneumonic image scans. The CNN system was trained with 5,000 datasets and tested. The findings from the study established that the implemented system based on a convolutional neural network algorithm is 76% accurate. This study is subjected to further studies
Spectrum of Lipid Profile among Cancer Patients: Pre and Post Treatment Analysis, a Prognostic Factor Determining Quality of Life
Full text linkThe objective of the study was to analyze the lipid profile among cancer patients and their pre and post treatment evaluation to monitor the effect of Lipids in cancer progression.It was a retrospective observational study conducted at Shaukat Khanum Memorial Cancer Hospital from January 1st 2023 to January 1st 2025. Serum samples of 392 patients were included in the study that were already analysed on automated chemistry analyser Atellica for serum lipid profile along with two levels of Quality Control by BioRad. The lipid profile included serum total cholesterol, triglyceride (TGs), low-density lipoprotein (LDL), and high-density lipoproteins (HDL). Kolmogrov’s Smirnov test was applied for normality. For pre-post treatment comparison, Wilcoxon rank sum test was used. The data was analysed using SPSS version 29. The results showed that about 63% females participated compared to 37% males. Participants were put in four age groups and lipid parameters were highest in the age group of 26-40. Breast carcinoma was the most frequent (41%) followed by CA Colon (7.4%). There was a significant difference (P value< 0.005) in lipid parameters pre and post-treatment except HDL-C (P value=0.538). We also found a significant difference in serum lipids with highest and the lowest value of LDL-C in CA Breast and CA kidney (116 ± 38.9 vs 89 ± 33.1). It was concluded that for early monitoring of the disease, patients can be grouped as low and high risk depending on their LDL levels. Furthermore, LDL-C proved to be a target for treatment in cancer patients for restricting the growth and progression of the disease
AI-Augmented Data Modeling: Enhancing Star Schema Design for Modern Analytics
Full text link The star schema remains a foundational dimensional modeling approach in business intelligence, valued for its simplicity, performance, and compatibility with OLAP queries. However, manual schema design is labor-intensive and error-prone in large-scale or rapidly evolving data environments. This study investigates the application of Artificial Intelligence (AI), particularly large language models (LLMs), in automating and optimizing star schema generation. Models such as OpenAI’s GPT-4, Google Gemini, and Meta’s LLaMA 3 were evaluated for their ability to infer schema structures, enforce relational integrity, and enhance semantic alignment. Experimental results demonstrated that AI-assisted modeling can reduce development time by over 80%, while increasing accuracy and consistency. These findings highlight the growing potential of AI in streamlining enterprise data modeling processes
Conceptual Foundations of Comprehensive Cybersecurity for Critical Medical Services
Full text linkThe study is aimed at the formation of a methodological basis for a comprehensive system protecting critically important medical infrastructure. As research tools, systemic analysis and synthesis of the current scientific corpus of 2021–2025 devoted to cyber-protection models, risk management and regulatory-legal aspects of security in healthcare were employed. A multi-level conceptual model unifying technological, organizational and process components, thereby ensuring the resilience of the medical-information ecosystem, is described. Primary attention is given to the protection of EHR: mechanisms for the application of AES-256 encryption, role-based access control (RBAC), intrusion detection and prevention systems (IDPS) and regulated incident response plans (IRP) are proposed. The results obtained demonstrate that the reliability of cyber-protection is determined not by isolated measures but by their synergistic combination, which includes proactive risk management, continuous threat monitoring and the development of an institutional culture of cybersecurity. The scientific novelty of the work lies in the systematization of disparate approaches and the development of unified foundations for constructing adaptive, scalable protection of medical data and services. The conclusions presented are addressed to managers of medical organizations, information security specialists, developers of medical IT systems and regulators involved in ensuring the resilience of the national healthcare system
Methodological Foundations of Risk Register Development for Large-Scale Projects
Full text linkThe 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
The Evolution of English Language Teaching Methods in the Information Age
Full text linkThis 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
Smart Farm Animal Intrusion Detection Using Multi-Modal IoT, Edge AI and Kafka Streaming
Full text linkWild animals such as deer or rabbits cause significant crop losses worldwide and create major problems for farmers. Traditional methods, such as fences, are often too expensive, difficult to maintain and not consistently effective. This paper provides details about an animal intrusion avoiding system that uses a combination of sensors, including thermal cameras, microphones, and motion detectors. The sensors work in conjunction with artificial intelligence running on edge devices, sending alerts through a Kafka streaming system. Unlike existing systems that rely on only one type of sensor or fixed models, our method combines several signals and can also learn to recognize new animals with only a few examples. In simulation tests, the system achieved about ninety-four percent accuracy, reduced false alarms by more than a third, and responded in less than two hundred milliseconds. When compared with systems that used only motion sensors or only cameras, our approach proved to be more reliable. The work is still limited because it is based on simulations rather than real-world farm testing, but plans include real-world trials, adding long-range communication such as LoRaWAN, and utilizing advanced techniques like federated learning to make the system even stronger