American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
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Application of Artificial Intelligence and Machine Learning in Seismological Studies
Full text linkSeismological studies have traditionally relied on classical statistical models and manual interpretation to detect, analyze, and predict earthquake events. However, the growing complexity and volume of seismic data have necessitated more efficient and adaptive approaches. This study explores the integration of artificial intelligence (AI) and machine learning (ML) techniques into seismology. This study highlighted the capacity of AI and ML to revolutionize seismic data processing and interpretation. Majorly, the study reviewed findings on algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), support vector machines (SVMs), and unsupervised clustering methods. Also, AI systems such as WaveCastNet, SCALODEEP, BNGCNN, Cycle-Jnet, SASMEX, and UREDAS were reviewed in areas that improved accuracy in earthquake detection, earthquake predictions, and earthquake analysis
Reduction of Material and Labor Costs in Construction Production: Optimization of Reinforcement Solutions for Monolithic Slabs
Full text linkThe objective of this research project is to optimize the reinforcement of monolithic reinforced concrete slabs to reduce material consumption and labor costs, which is a pressing issue in modern construction. To address this issue, advanced numerical modeling methods were applied using the LIRA-SAPR software suite. These methods include nonlinear finite element analysis (FEA), force redistribution principles, and the concept of variable reinforcement. A methodology for implementing variable reinforcement was developed, based on a detailed analysis of the slab\u27s stress-strain state. The results of numerical experiments demonstrated that the proposed optimization methods, particularly variable reinforcement, can reduce the consumption of reinforcing steel by 12-15% without significantly increasing labor intensity. The significance of this project lies in the development of practical recommendations for construction companies seeking to improve economic efficiency and resource conservation in monolithic construction. This contributes to reducing construction costs and promoting more rational resource utilization
Methodology for Managing High-Urgency Projects in Conditions of National Importance
Full text linkThe study is aimed at the theoretical substantiation of an adaptive hybrid methodology combining predictive principles and elements of iterative delivery to optimize the full lifecycle of national projects with high urgency. The relevance of the study is determined by the acute need for reliable tools for managing critically important projects and the existence of a theoretical gap between the capabilities of known methodologies and the specific requirements for national-scale projects. As a result of a systematic analysis of key scientific publications and analytical reports in recent years, a multi-level management architecture is proposed, integrating strategic planning and control (characteristic of Waterfall/PRINCE2) with iterative execution mechanisms and operational adjustments at the tactical level (borrowed from Agile/Scrum). The aim of the study is to substantiate an adaptive hybrid methodology focused on effective management of high-urgency and nationally important projects. The scientific novelty consists in integrating cascade and agile approaches into a single adaptive model aimed at improving the efficiency of implementing urgent projects of national importance. The obtained results will be of practical interest for leaders of governmental bodies, top managers of state-participated enterprises and researchers in the field of project management in the public sector
Application of Financial Time Series Techniques in Analysing the Volatility of Metical/dollar and Metical/rand Exchange Rates in Mozambique (2010-2020)
Full text linkExchange rates play an important role in the economic and financial outlook of any country, making it interesting to evaluate and predict their fluctuations. Based on the combination of ARMA (Autoregressive Moving Average) models with ARCH (Autoregressive Conditional Heteroscedasticity) class models, a study was carried out to analyse and predict the volatility of the metical/dollar and metical/rand exchange rates in Mozambique for the period from January 2010 to December 2020. The use of the ARMA-ARCH combination is justified by the fact that ARMA models are not capable of modelling the variation in the variance of financial series over time. During the empirical study, several common stylized facts of financial series were verified, such as the non-stationarity of financial time series, the existence of volatility clusters, among others. It was possible to find three (03) models with good adjustment to model the volatility of exchange rate returns, two (02) for metical/dollar namely: AR(1)-GARCH(1,1) and AR(1)- EGARCH(1,1) and ; one (01) for metical/rand designated AR(1)-ARCH(1). Based on the selection criteria, the results obtained show that for metical/dollar exchange returns the model with the best performance in terms of forecasting is AR(1)-EGARCH(1,1) and for metical/rand exchange returns the AR(1)-ARCH(1) model stands out, being this the only candidate model found for the series. The volatility forecasts made for the two series based on the two (02) best models point to slightly low values for 2021, meaning that there will not be major fluctuations in the short term
Reliability Models in Automated Release Cycles
Full text linkThis article presents an analysis of existing reliability assurance models within automated release cycles (CI/CD), covering a spectrum from classical rule-based and monitoring-oriented approaches to modern AI-accelerated AIOps solutions. Based on a comprehensive literature review, the study outlines a conceptual architecture for an AI orchestration layer that integrates data collection, predictive analytics, automated self-healing, and continuous retraining of ML modules. It is demonstrated that implementing the proposed model reduces mean time to detection (MTTD), decreases mean time to recovery (MTTR), and increases release frequency compared to traditional practices. The paper also discusses key aspects of ML model version management, Explainable AI, and potential directions for future research. The insights regarding reliability models in automated release cycles will be of interest to DevOps engineers and software reliability specialists applying stochastic methods and formal verification techniques to minimize risks during continuous deployment. The material will also be valuable for researchers and graduate students in the field of distributed microservice architecture resilience, particularly those working on integrating Bayesian predictive models with the formalization of service level agreements (SLA) within DevSecOps processes
A Smart Traffic Noise Prediction Model for Nairobi City, Kenya
Full text linkRoad traffic flow produces an undesirable externality since it distorts the ambient environmental noise, especially in cities. Such nuisance noise poses a risk to the health of the inhabitants. Globally, the combined concert of the forces of urbanization and road transport motorization has intensified the noise pollution challenge; yet, locally adapted predictive tools remain limited. In Nairobi, the capital city of Kenya, Road Traffic Noise (RTN) remains a less understood environmental nuisance. To date, no predictive RTN models have been developed, while established models such as CoRTN and RLS-90 lack applicability to Nairobi’s traffic and environmental conditions. This study aimed to develop an accurate smart model leveraging artificial neural networks (ANNs) to forecast RTN levels using traffic information data [22]. Traffic data, including audio recordings using a Samsung Galaxy A12 Model SM-A127F/DS Android Smartphone, equivalent noise levels (Leq) using a Lutron SL-4033SD Class 1 Sound Level Meter (SLM), vehicular volume using a manual tally form, and speed using a speed gun, was collected across 42 locations within Nairobi. Using this data, an Artificial Neural Network (ANN), Multi-Layer Perceptron (MLP) model, was developed with two hidden layers. Hyperparameter tuning via grid search was done to optimize model performance. The model achieved a Mean Absolute Error (MAE) of 0.97 dBA and an R2 value of 0.90, outperforming traditional statistical models like CoRTN with an MAE of 5.0 dBA and RLS-90 with an MAE of 11.0 dBA. These results highlight the model’s high accuracy in predicting Nairobi’s RTN. The model’s deployment on a web-based dashboard enables real-time noise monitoring and stakeholder engagement. This pioneering smart predictive model for Nairobi offers a scalable solution for urban noise management [25], with potential applications in traffic planning and policy implementation
Digital Sovereignty and the Reconfiguration of Comparative, Advantage: Toward a New Economics of Global Value
Full text linkThe global economy is moving into a stage where data and algorithms shape production as decisively as land, labor, or capital. Earlier theories of trade—beginning with Ricardo’s idea of comparative advantage and the Heckscher–Ohlin factor-endowment model—were built on the assumption that countries prosper by specializing according to natural resources and industrial skills. In the digital era, however, advantage increasingly depends on who manages the main channels of information: data ownership, cloud systems, and algorithmic design. This study proposes a framework for digital comparative advantage that links the unequal distribution of data and digital capacity to new global disparities. It presents digital sovereignty—the ability of a state to govern and gain value from its data networks—as a key influence on national performance. The paper concludes that a fairer global order will require redefining sovereignty not only as a political concept but also as an economic and informational one, extending classical trade thinking to the realities of artificial intelligence
Using AI and Machine Learning in QA Testing
Full text link The article will consider the possibilities of using artificial intelligence (AI) and machine learning (ML) technologies in the field of software quality control due to the fact that they are able to change the usual approaches to testing due to their abilities. Methods of using AI and ML to increase the effectiveness of quality assurance (QA) will be considered: automation of tests, detection of defects, prediction of anomalies. The methodology is based on the analysis of scientific papers, which will describe achievements in the application of these technologies during QA, including adaptive algorithms that automatically generate tests, clustering methods that systematize errors, and big data analysis that allows predicting defects. As part of the work, examples of organizations that demonstrate comparing user interface testing using a manual method and automated regression tests will also be considered. The data obtained show a decrease in the time spent on testing, a decrease in the probability of missing errors, and an improvement in the quality of processes. The information in the work will be useful to quality specialists, developers, and AI researchers working on optimizing testing. In conclusion, the article notes the success of applying such technological solutions in achieving QA goals
Optimization Plan for Agricultural Enterprises among the Military Personnel
Full text linkAgriculture is the only form of enterprise military personnel are officially allowed to embark on in addition to defending the Nation and ensuring its national security. While the military are engage in different agricultural enterprises, there is insufficient empirical information on the returns to agricultural enterprises among the personnel. The aim of the study was to investigate economic analysis of agricultural production enterprises among the Nigerian military personnel. The objectives were to: (i) assess the level of capacity for implementing optimal plan for agricultural enterprises; and (ii) determine the optimal plan for the enterprises.Based on survey as the research design, 275 military personnel from 10 out of all military formations across Nigeria used were selected through a two-stage sampling technique. A structured questionnaire with a reliability coefficient of 0.86 was used for the study. Descriptive statistics, linear programming, and logistic regression were used for data analysis. Tests of significance were carried out at 0.05 alpha level. The results shows that optimal plan for crop production was 3.25ha of yam/ maize enterprise with a total gross margin of N811,040.00 per season, that of livestock was 12.53 tropical livestock unit (tlu) of cattle and 2.728 tlu of layers, giving a total gross margin of N1,173,070.00 per annum; Commissioning status, marital status, and farming experience were the factors that affect the capacity to implement optimal plan for crop production while farming experience and farm income were the factors that affect the capacity to implement optimal plan for livestock production
Towards a Greener Türkiye: Renewable Energy Perspectives and Future Directions
Full text linkMany countries today rely heavily on fossil fuels such as coal and petroleum for electricity generation, and this dependence continues to grow as energy demands increase. However, transitioning to renewable energy sources like solar and wind is essential to mitigating the effects of climate change. This paper focuses on Türkiye, a country that remains dependent on fossil fuels but has been making significant strides in renewable energy development. Türkiye’s renewable energy sector has become a key pillar in its pursuit of energy security and sustainable growth. This review examines the current state of renewable energy utilization in Türkiye, analyzing the key drivers, policies, and challenges shaping the sector. Considerable progress has been made in harnessing solar, wind, hydropower, and geothermal energy, helping to meet the country’s rising energy demands while reducing fossil fuel dependence. However, challenges such as financial constraints, grid integration issues, and policy inconsistencies persist. Additionally, a survey conducted among Turkish high school students assessed their awareness of renewable energy. The results indicated that gender has no significant impact on their responses or their interest in pursuing careers in the renewable energy sector. By addressing these aspects, this paper provides a comprehensive overview of Türkiye’s renewable energy landscape, offering insights into innovative technologies, policy recommendations, and strategic approaches to overcoming barriers. Ultimately, it highlights Türkiye’s potential role in advancing global energy and climate goals