American Journal of Engineering , Mechanics and Architecture (2993-2637)
American Journal of Engineering, Mechanics and Architecture (2993-2637) is an international peer-reviewed journal published to reach excellence on Research and Scientific Development. The journal is not limited to a specific aspect of engineering and architecture but is instead devoted to a wide range of subfields in the engineering sciences, electrical, mechanical and architecture. Articles of interdisciplinary nature are particularly welcome. The journal strives to maintain high-quality of publications. There will be a commitment to expediting the time taken for the publication of the papers. The editorial board reserves the right to reject papers without sending them out for review. The journal also publishes innovative contributions on every aspect of the architectural endeavor.
Solution of Problems in Distribution of Oil Products
The article discusses the existence of many problems in the distribution of oil (fuel) products in any condition, including extreme conditions. For example, It is recognized that in what tanks fuel products are stored, the vertical and horizontal location of the tank depends on the size of the slot through which the liquid flows and its geometric shape (circle, cone, etc.) Based on Bernoulli's laws, the flow of liquid and the dependence of the liquid level on the height of the liquid were scientifically discussed. Most importantly, the relationship between temperature range of [-10 ... +40] °C and density of oil (gasoline) products was calculated and graphed using the Excel program. In addition, a mathematical differential formula was developed for the time distribution or reception of gasoline used for military and agricultural vehicles, large quantities of products in the field and extreme conditions. Based on the laws of molecular dynamics, the flow times of fuel (gasoline) in vertical and horizontal containers under the same conditions were analyzed.
Reserves Estimation Using Production Rate Decline Trend Analyses
A constant production rate decline was simulated for reserves estimation, using flow regimes in oil and gas reservoirs. The problem of estimating oil reserves in place or cumulative recovery factor in a dynamic state calls for research. The problem has been so because no researcher has estimated accurate decline constant which can predict an accurate recovery factor or estimate accurate reserves initially in place. Flow regimes in reservoirs were established as a field of study using equations of fluid flow in porous media. The fluid flow equations were used to establish the decline trend, and the estimated production rate decline constant was also used to model the decline. The decline rate constants were subsequently used to build models to predict yearly production data for any given period. These were used to generate models for projecting oil and gas cumulative production and reserves in place. The results obtained were validated using actual yearly production field data from the original data source. The evaluation models were subsequently built using the predicted yearly data. The models were then used to estimate cumulative reserves recovery in any given period and reserves initially in place. The values obtained compared favourably with the respective tank values. The percentage accuracy for gas fields ranged from 99.86% and above, while the percentage accuracy for oil ranged from 98.64% to 99.98%. The models reduce the complexity and time used in reservoir simulation. They are very flexible and can be applied with high accuracy from the reservoir decline stage to abandonment. They are equally used for the establishment of production and for making economic decisions.
Methods for Restoring the Working Surfaces of Various Types of Parts by Surfacing with Wear-Resistant Materials
One of the perspective methods of the reconstruction worker to surfaces of the details of the different type is on melting wear-out steadfast material. For наплавке of the details of the type gross layer s on surfaces, possessing high wear-out by stability. In article is considered recovering the details of the type gross on melting wear-out steadfast metal on damaged worker of the surfaces and influence change the thickness to hardness of the gross after on swimming trunks.
Copyright (c) 2026 American Journal of Engineering , Mechanics and Architecture (2993-2637)
A Comparative Policy Analysis of Plastic Waste Governance and The Construction-Sector Recognition Gap in Five Sub-Saharan African Countries
Sub-Saharan Africa generates a rapidly growing share of global plastic waste, and over the past three years several of its largest economies have introduced ambitious bans, levies, and extended producer responsibility (EPR) regimes to address it. Yet a critical question remains under-examined: do these regulatory frameworks formally recognise plastic waste as a feedstock for construction materials, or do they remain focused on packaging and single-use items at the point of disposal? This paper presents a comparative, desk-based policy analysis of plastic waste governance in five Sub-Saharan African countries — Nigeria, Ghana, Kenya, South Africa, and Rwanda, selected for their advanced but divergent regulatory trajectories. Using a structured five-dimension framework (single-use restrictions, EPR architecture, construction-sector recognition, financing instruments, and informal-sector integration), the study synthesises national policy documents, regulatory gazettes, and recent peer-reviewed and grey literature (2017–2026) to map each country's governance architecture. Findings show that all five countries have adopted upstream interventions — bans, levies, or EPR — but none has a codified building-code provision, national standard, or certification pathway for recycled-plastic construction products, even where pilot-scale evidence, notably Rwanda's sisal-reinforced plastic tiles, demonstrates technical compliance with international structural and durability standards. Financing instruments such as Ghana's SME grant scheme and South Africa's EPR fees are similarly disconnected from materials-standards development. The paper argues that this construction-sector recognition gap, rather than technical infeasibility, is the principal regulatory bottleneck constraining the transition from technically viable recycled-plastic building materials to commercially deployable ones. It proposes a phased standards-development pathway for Nigeria that draws on lessons from Rwanda's cooperative-research model and Kenya's labelling regime.
Issues of Adaptive Neuro-Fuzzy Modeling of Operational and Predictive Control Systems for Information Flows in Telecommunication Networks
This article discusses the issues of improving the efficiency of information flow management in telecommunication networks. In particular, scientific approaches to the development of operational and predictive control systems based on adaptive neuro-fuzzy modeling are analyzed. The proposed models make it possible to reduce computational load in traffic flow distribution, enhance real-time decision-making capabilities, and ensure stable network operation under various uncertainty conditions. Furthermore, the performance of neuro-fuzzy models of different orders is comparatively evaluated, and the mechanisms by which their application in military communication networks reduces information transmission losses and increases network throughput are revealed. The research findings are of significant scientific and practical importance for identifying priority directions in the development of communication systems of the Armed Forces.
Smart Recruitment System Using Artificial Intelligence and Natural Language Processing
This paper is an intelligent and innovative application to automate and optimise the recruitment process. Today’s job market can see companies receiving hundreds, or even thousands, of resumes for one position, thus making manual screening an inefficient, time consuming and human bias sensitive process. This project proposes an AI-driven approach to overcome these limitations by leveraging Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) techniques to automatically analyse and rank resumes for their suitability to specific job roles. The system allows candidates to upload their resumes in common document formats like PDF or DOCX. Uses state-of-the-art NLP algorithms to extract key information such as the candidate's name, contact details, educational background, technical & soft skills, and professional experience. At the same time, recruiters can upload a job description (JD) that details the qualifications, skills and experience required for the position. The AI engine then compares the extracted features from the resume with the features from the job description using similarity metrics such as TF-IDF vectorisation and cosine similarity. The system can generate a match score for each candidate by doing this comparison.
Development Of Modern Pavement Materials For Highways Under Climate Change Conditions (Challenges And Solutions)
This paper analyzes the impact of climate change on road pavements and the current scientific and technical challenges in the development and application of modern pavement materials. A comparative approach was used to evaluate the operational, economic, and environmental advantages of polymer-modified bitumen, recycled asphalt pavement (RAP), and geosynthetic materials. The study also examines pavement deterioration and reduced service life under the influence of high temperatures, moisture, and traffic loads in the sharply continental climate of Uzbekistan. The comparative analysis revealed that polymer-modified bitumen and basalt fiber-reinforced pavements exhibit higher resistance to deformation under high-temperature conditions.
Artificial Intelligence Allowed Structural Health Monitoring for Resilient Infrastructure Systems
Background: The development of Artificial Intelligence (AI)-based Structural Health Monitoring (SHM) systems brings a new advanced system which protects infrastructure sustainability and safety through established performance standards. Methods: Implemented a quantitative cross-sectional survey study through Google Forms, they used to distribute their online questionnaire. The study gathered 175 complete answers from people at worked in engineering and AI and infrastructure-related fields. Descriptive statistical methods which combined frequency distribution with percentage analysis to study people understood things and how they perceived things and their readiness to adopt and their major obstacles. Results: AI-based SHM systems well because damage detection systems received the highest recognition at 76.8%. The survey results demonstrated a strong positive perception because 82.1% of respondents agreed that AI technology boosts structural safety and 79.6% of participants reported better monitoring performance. The survey results showed that 78.9% of participants supported AI technology for smart infrastructure development. The survey showed that 68.6% of participants wanted to use AI-powered SHM systems but 19.4% of them stayed uncertain about their choice. Conclusion: AI-powered SHM systems as effective tools which improve both structural security and operational efficiency and environmental sustainability. The moderate adoption rate exists because financial obstacles combine with technological barriers and infrastructure problems. The worldwide adoption of AI-based resilient infrastructure systems needs barrier elimination because this process enables faster implementation and full system potential achievement.
Copyright (c) 2023 American Journal of Engineering , Mechanics and Architecture https://creativecommons.org/licenses/by/4.0
Investigation of Waterflooding in an Oil Reservoir by Streamline Simulation
Perhaps Waterflooding is the most popular method of secondary recovery to pressure stability and support a higher withdrawal rate but the large of produced water in waterflooding of offshore oil reservoir is one of the most important oil industry problems. So determining of optimized injection rate for injection wells is one of the important factors in a successful waterflooding. Like using conventional finite difference method, developments of streamline methods lets streamline based reservoir simulation has a lot of effects on solving complex geologically large and heterogeneous reservoir and also streamline simulation is explained in the text of producer and injector usage, relationship between injectors and producers and well allocation factors.So using of streamline simulators is a suitable method to manage waterflooding in oil reservoir. The purpose of this paper is to explain the usage of streamline method for a waterflooding simulation in an Iranian offshore oil reservoir that leads to optimization of water injection rate for decreasing the produced water from reservoir.
The Significance of Vibration Motion in Reducing the Traction Resistance of Technical Means for Soil Processing
This study examines the significance of vibrational motion in reducing the traction resistance and energy consumption of technical means used for soil processing. A comprehensive review of scientific literature, patent resources, and existing vibration-based tillage technologies was conducted to evaluate the effectiveness of forced vibration in improving soil cultivation performance. The study analyzes the influence of vibration parameters, including direction, frequency, and amplitude, on the interaction between tillage tools and soil. The findings indicate that the application of controlled vibrational motion decreases soil adhesion and draft resistance, enhances soil fragmentation, improves moisture retention, and reduces the wear of working components. Experimental evidence reported in previous studies demonstrates that vibration-assisted tillage can reduce traction resistance by up to 35–60%, depending on soil conditions and machine design. The results also highlight that the optimal selection of vibration parameters and oscillation-generating mechanisms contributes to improved energy efficiency, increased operational productivity, and sustainable agricultural practices. The study concludes that the implementation of forced vibration technology is particularly suitable for the soil and climatic conditions of Uzbekistan, where it can significantly enhance the efficiency of tillage operations while reducing fuel consumption and overall operating costs.
Resilient and Scalable Photovoltaic Supply Chains: Artificial Intelligence–Enabled Manufacturing Optimization Strategies
The rapid expansion of photovoltaic (PV) technologies has increased the need for manufacturing systems and supply chains that are resilient, scalable, efficient, and sustainable. Traditional PV supply chain operations often face challenges related to demand uncertainty, quality control, equipment failures, inventory imbalances, and global supply disruptions, limiting overall operational performance. This study examines the transformative role of artificial intelligence (AI) in optimizing photovoltaic manufacturing and supply chain management through data-driven decision-making and intelligent automation. AI applications, including predictive analytics, machine learning–based demand forecasting, predictive maintenance, quality assurance, process optimization, and real-time supply chain monitoring, are analyzed for their ability to improve operational efficiency, reduce costs, minimize downtime, and strengthen supply chain resilience. The study also explores the integration of Internet of Things (IoT) technologies and circular economy principles to support sustainable manufacturing and resource optimization in PV systems. The challenges associated with AI adoption, such as implementation complexity, cybersecurity risks, data dependency, and high initial investment, are critically discussed. The findings demonstrate that AI-enabled optimization strategies can significantly enhance the adaptability, reliability, and scalability of photovoltaic supply chains while supporting sustainability goals and renewable energy security. This work provides a comprehensive perspective on AI-driven manufacturing innovation as a pathway toward more resilient and sustainable photovoltaic industry development in the global clean energy transition.
Historical Foundations of Flower Garden Formation and Landscape Design In Uzbekistan
This article analyzes the historical stages of garden and park culture that has long developed in the territory of Uzbekistan. The study focuses on the formation of horticulture, landscape organization, and irrigation systems during the periods of the Bactrian, Sogdian, and Khorezmian civilizations, highlighting the social, aesthetic, and economic significance of the gardens created in these regions. The article also provides a scholarly explanation of the symbolic meaning of plants in Zoroastrian teachings, including the interpretation of trees and flowers as sacred symbols. The findings of the study demonstrate that the ancient garden culture of Uzbekistan serves as an important historical and methodological foundation for modern landscape design.
Public Eating Spaces as Catalysts for Cultural and Educational Life: A Historical-Architectural Analysis of Uzbekistan's Public Catering Infrastructure
This paper analyzes the historical-architectural evolution of public catering buildings in Uzbekistan, tracing their transformation from traditional spatial forms into the Soviet network of "Red Teahouses" (qizil choyxona), alongside their socio-cultural roles. Focusing on the southern regions of Uzbekistan (Kashkadarya and Surxandarya), the study investigates the socio-spatial transformation of catering infrastructure. The methodological framework combines historical-typological analysis of archival and statistical data with mathematical spatial density modeling. The results demonstrate that the Soviet authorities leveraged the high social communication potential of traditional teahouses as part of an "ideological appropriation of space" strategy. While the architectural shells of the buildings were preserved, their internal functional substance was systematically replaced with socialist propaganda and anti-religious hubs (the "League of Militant Atheists"). Furthermore, the research proves that the conversion of sacred architectural sites, such as the Charmgar Mosque in Karshi, into public catering spaces served to reprogram the collective spatial memory of the local population. By the late 1930s, the network transitioned from static built structures to seasonal field-based service models integrated into the agrarian sector, marking a new evolutionary phase. The scientific contribution of this study lies in conceptualizing public catering infrastructure not merely as a commercial asset, but as an active spatial catalyst shaping societal dynamics.
A Dual-Method EES Framework for Temperature-Dependent Elastic Modulus Simulation and Material Selection in Five Metallic Alloys
The paper describes a dual-method Engineering Equation Solver (EES) based simulation tool to calculate the temperature-dependent elastic moduli E(T) for five engineering metallic alloy-type materials: Copper C110, Ti-6Al-4V, Inconel 718, Hastelloy C276, and SS 316L from 20 °C to 600 °C. Two complementary computational techniques were developed and validated: Method A is based on using linear interpolation from experimental lookup tables published in the ASM Handbook, MMPDS-14, Haynes International, and MatWeb; Method B implements quadratic polynomial regression E(T)=aT²+bT+c. Cross-validation of the two methods indicated a maximum deviation of 2.70% for all alloys and the entire thermal range, demonstrating good agreement between the two methods and high predictive accuracy of the polynomial equation model. The five alloys tested exhibited a monotonic decrease in elastic modulus with increasing temperature, with the average elastic modulus decreasing 21.3% at 600 °C when compared to the equivalent elastic moduli at ambient temperature. The alloy Inconel 718 exhibited the greatest relative elastic modulus retention, decreasing only 18.59% at 600 °C from ambient. The maximum thermal stress developed in alloy SS316L (1415 MPa at 600 °C) is primarily due to its relatively high coefficient of thermal expansion (16x10⁻⁶/°C). Results of the thermal sensitivity assessment resulted in the identification of two groups of metal alloys: one group having low sensitivity to temperature fluctuations (Cu C110 and Ti-6Al-4V, |dE/dT| < 0.048 GPa/°C) and a second group having high sensitivity to changes in temperature (Inconel 718, Hastelloy C276, and SS 316L, |dE/dT| > 0.057 GPa/°C). The development of an integrated multi-criteria methodology for evaluating metal alloys based on stiffness, retention loss, thermal sensitivity, and thermal stress produced a ranking of Inconel 718 > Hastelloy C276 > Ti-6Al-4V > Cu C110 > SS 316L, which has provided quantitative criteria that will allow for the selection of alloys that can be utilized in thermally demanding structural applications. The establishment of the dual-method EES process has simplified the entire thermal elastic response of each alloy into three scalar coefficients, which allows for the direct analytical incorporation of those three coefficients into finite element analysis equation solvers and digital twin platforms.
Landscape Design of the Mazlumkhan Sulu Mausoleum Territory: Principles of Cultural Heritage Preservation and Sustainable Development
This article examines the landscape design project developed for the territory surrounding the Mazlumkhan Sulu Mausoleum, one of the significant historical and architectural monuments of Karakalpakstan. The study focuses on the integration of cultural heritage preservation principles with modern landscape architecture approaches. Special attention is paid to spatial organization, green infrastructure, pedestrian circulation systems, environmental sustainability, and visual harmony between the historical monument and its surrounding environment. The proposed landscape design aims to enhance the aesthetic, ecological, and functional value of the site while preserving its historical authenticity. The findings indicate that sustainable landscape planning can significantly contribute to the conservation and promotion of cultural heritage sites.
Copyright (c) 2026 American Journal of Engineering , Mechanics and Architecture https://creativecommons.org/licenses/by/4.0
Eco-Reurbanization and Regenerative Design in Arid Climate Conditions: Transformation of the Architectural Environment of Uzbekistan
This article explores the concept of eco-reurbanization as a tool for adapting the urban environment of Uzbekistan to the challenges of climate change and rapid urbanization. Against the backdrop of increasing building density in megacities such as Tashkent, a decline in air quality and a reduction of green areas are being observed. The study substantiates a transition from harm-minimization strategies to regenerative design, in which architecture functions as a “living ecosystem” that replenishes natural resources.
Sustainable Construction Practices in USA
The construction industry is a critical sector in promoting sustainability objectives because constructions form a major portion of energy use and greenhouse emission levels in the world. Sustainable construction practices have gained importance in the United States in the quest to deal with the environmental issues and achieve long-term climate goals. This study investigates the success of sustainability construction and energy efficiency programs in New York City with particular emphasis to the enactment and implementation of Local Law 33 (LL33), requiring buildings to publicly report their energy performance yearly. The research design adopted in the study is a quantitative research design based on secondary data of the NYC Department of Finance (2021), which comprises more than 18,000 large buildings and such variables as Energy Efficiency Grades (A-D), Energy Star Scores (1-100), and Gross Square Footage. Python, Excel and Tableau were used to calculate descriptive statistics, correlation analysis, and visualization of data to measure the performance trends and spatial variation. Its findings show that most buildings have a Grade A and B, which are very energy-performing, but a significant fraction of buildings are in Grade D and indicate inefficiencies majorly on older or small buildings. Weak correlation between building size and energy efficiency is also found in the analysis indicating that operational practices and adoption of technology are considered to have more influence than scale. The 8th Avenue and Exchange Place are high-performing zones, which point to the areas of successful sustainability implementation. The conclusion of the study is that the use of data transparency policies such as LL33 has made a huge increase in the accountability and performance awareness of building owners. Further policy development and retrofitting incentives or technological advancement is however necessary to realize fair, city-wide development toward sustainable construction and carbon neutrality.
Urban Sustainability and Green Building Policy
Machine Learning-Based Career Recommendation System Using User Interests and Behavioural Analysis
Career choice is an important but often confusing decision for students and people who are laying the path for their future in today’s fast-paced and competitive world. There are so many professional options out there that many find it difficult to identify a career that aligns with their true interests, skills, and long-term goals. The project aims to provide a smart, data-driven solution to make this important decision much easier through a machine learning-based career recommendation system. The system works by analyzing users' hobbies and interests, as well as their responses to a well-designed set of binary (yes/no) questions. Then, the answers are processed by a tree-based classification model to determine the suitable career fields for each person. It provides customized career suggestions in a user-friendly manner, considering each user's preferences and behavioral patterns. This approach demonstrates the increasing potential of artificial intelligence in educational guidance and counseling. This system provides a faster, more flexible, and scalable path to career exploration than traditional aptitude tests or manual evaluations. The results of this project demonstrate how interests and passions, combined with smart algorithms, can provide useful insights into possible future professional opportunities. In the end, this tool helps people make more informed, confident, and meaningful career decisions.
Temporal Evolution of Dominant Periodicity in All India and Kerala Monsoon Rainfall (1901-2024): A Comparative Spectral Analysis
Understanding the temporal organization of monsoon rainfall variability is essential for characterizing long-term climatic behavior over the Indian subcontinent. This study investigates the dominant periodicities in All-India and Kerala annual monsoon rainfall using long-term observational records spanning 1901–2024. Fast Fourier Transform (FFT) analysis is applied to the complete datasets to identify primary variability scales. To assess potential non-stationarity, the 124-year record is divided into three equal epochs and examined through epoch-wise spectral analysis. The results reveal that All-India rainfall exhibits pronounced temporal shifts in dominant periodicities, transitioning from interannual (~2–3 years) variability in the early twentieth century to decadal (~8–14 years) dominance in subsequent epochs. In contrast, Kerala rainfall demonstrates persistent decadal-scale variability throughout the record, with a gradual increase in dominant periodicity from approximately 10 years to around 14 years across successive epochs. The complete Kerala dataset further indicates a prominent quasi-decadal to multi-decadal peak (~17–18 years), underscoring the importance of longer-term oscillatory components at the regional scale. The comparative spectral analysis demonstrates clear regional contrasts in the temporal organization of monsoon rainfall variability. While the aggregated All-India rainfall series reflects evolving and non-stationary dominant periodicities, the Kerala record maintains relatively stable decadal-scale dominance. This contrast suggests that national-scale rainfall integrates multiple time-varying variability modes, whereas regional rainfall may preserve more persistent oscillatory characteristics. Overall, the findings provide statistical evidence of structural evolution in dominant variability scales and contribute to improved understanding of the evolving multi-scale behavior of Indian monsoon rainfall.
The Significance of Measures for the Protection of Mountain and Foothill Roads from Tectonic Movements, Washing, and Erosion
This article examines the significance of protective measures for highways located in mountainous and foothill regions against the impacts of tectonic movements, soil erosion, washing, and related natural processes. The study highlights that intensive precipitation, surface runoff, wind activity, landslides, and geological deformations significantly deteriorate road stability and reduce transportation safety. The research analyzes different types of erosion processes, including water, wind, and irrigation erosion, and their influence on roadbed degradation. Special attention is given to engineering and agrotechnical solutions such as drainage systems, slope reinforcement, geosynthetic materials, and vegetation cover. The findings indicate that integrated protection strategies substantially reduce soil loss, prevent road deformation, and improve long-term durability of road infrastructure. The study concludes that sustainable road management in complex mountainous conditions requires a combination of hydraulic, geotechnical, and ecological approaches to minimize the adverse effects of natural and anthropogenic factors and ensure safe transportation networks across regions worldwide.
Deep Learning-Based Prediction and Analysis of the Influence of Lubrication on Gear Efficiency
This paper involves an analysis and prediction of the effects of spur gears on efficiency based on the use of deep learning. The efficiency of the gear transmission is highly influenced by friction of the two meshing tooth faces, the formation of films of lubricant, power loss, and temperature of operation. Five lubrication conditions were studied in this work: unlubricated reference condition, grease, low-viscosity oil, medium-viscosity oil, and high-viscosity oil. Calculations of torque and speed were used to calculate input power, output power, power loss, and gear efficiency using the experimental data. Alongside, three deep learning algorithms, such as Deep Neural Network, 1D-Convolutional Neural Network, and Long Short-Term Memory network, were implemented to forecast gear efficiency and power loss based on various lubrication circumstances. The experimental data revealed that gear performance was greatly enhanced by the presence of lubrication as opposed to the absence of lubrication. The lowest efficiency of 87.9, highest power loss of 75.9 W and highest operating temperature of 58o C was observed with the unlubricated gear pair, whereas the lowest power loss of 37.2 W and the highest efficiency of 95.4 and 95.7 was observed with the low and high viscosity oil respectively. Medium-viscosity oil produced the best performance with the highest efficiency of 97.7 percent, the lowest power loss of 14.7 W, and the lowest operating temperature of 27 C compared to the unlubricated condition which had higher efficiency, lower power loss, and operating temperature. Deep learning outcome indicated that Deep Neural Network model had the most optimal prediction performance with an efficiency prediction RMSE of 0.29, power loss RMSE of 0.78 W and an R. 2 value of 0.98. The 1D-CNN and LSTM models made acceptable predictions, although, their errors were larger compared to the DNN model. In general, the findings support the view that adequate lubrication enhances the efficiency of gears by preventing the occurrence of frictional losses and heat. The article also shows that deep learning can become an efficient method to predict the gear performance and find the most appropriate lubrication condition.
Optimization of Series and Shunt Capacitor Compensation for Power Factor Improvement in Electrical Power Systems
Background: Power factor improvement stands as a vital requirement because low power factor generates multiple challenges which include rising power loss during transmission and unstable voltage levels and decreased system operational capacity. The solution to these problems arrives through capacitor compensation which uses shunt capacitors as the main solution for actual operational systems. Methods: This study conducted a quantitative data gathering first-hand information from 185 electrical experts completed a standardized survey. The study selected participants demonstrated specific expertise through its purposive sampling technique. Descriptive statistics together with Pearson correlation analysis to study power factor improvement relates to shunt capacitors and series capacitors and load levels and harmonic distortion and voltage levels and power loss reduction. Results: Shunt capacitors serve as the primary solution which users select at 68.1% frequency while hybrid systems follow at 57.8% and users select series capacitors at 51.9%. The primary advantage exists in power factor enhancement which accounts for 40.0% of the benefits followed by efficiency improvement at 28.1%. The correlation analysis demonstrates that shunt capacitor usage produces the most substantial positive correlation with power factor improvement at r = 0.72 and series capacitors create a substantial effect at r = 0.65. Power loss reduction (r =0.60) and voltage level (r =0.54) demonstrate moderate positive relationships. The system performance shows negative effects from harmonic distortion because of the correlation coefficient which reached -0.51. Conclusion: The study establishes that shunt capacitor compensation serves as the main power factor enhancement method which delivers the most effective results.
Copyright (c) 2024 American Journal of Engineering , Mechanics and Architecture (2993-2637)
Advanced Low-Carbon Concrete for Sustainable Infrastructure Development in the United States
Background: The construction industry stands as a major source of worldwide carbon emissions because standard concrete manufacturing generates about seven to eight percent of all global CO2 emissions. The construction industry now receives advanced low-carbon concrete (LCC) technology which functions as an environmentally friendly building material solution. The United States has not accepted these technologies because they face multiple obstacles which include technical problems and economic barriers and regulatory restrictions. Methods: This study employs a quantitative cross-sectional design based on a structured survey of 175 construction industry professionals. A Likert-scale questionnaire was used to assess four key dimensions: environmental awareness, technical performance, economic feasibility, and policy support. The research team used descriptive statistics and Pearson correlation and multiple regression analysis to study the connections between variables which affect LCC adoption. Results: The research data shows that people have strong environmental knowledge with a score of 4.18 and they trust system performance at a level of 4.05 which indicates positive views about LCC technologies. The economic viability assessment shows a score of 3.72 which creates a moderate obstacle for project implementation. The regression model shows that environmental awareness and technical performance and policy support have major effects on adoption rates through R² = 0.64 and p = 0.001 but cost-related elements create adverse effects on decision-making processes. Conclusion: The technical and environmental capabilities of LCC technologies show great potential but their wide adoption faces two major obstacles which include financial restrictions and government policy barriers.
Copyright (c) 2024 American Journal of Engineering , Mechanics and Architectur https://creativecommons.org/licenses/by/4.0
Recommendations for Improving The Training System for Passenger Specialists
This article presents recommendations for improving the system of training passenger and road sector specialists in higher education institutions. The study focuses on strengthening the role of universities in the labor market, enhancing cooperation between educational institutions and road construction enterprises, and improving the quality and practical orientation of engineering education. Special attention is given to the integration of theoretical training with real production conditions, as well as the involvement of industry professionals in the educational process. The analysis reveals several challenges in the current training system, including insufficient interaction between universities and enterprises, limited participation of industry specialists in curriculum development, and the dominance of traditional teaching methods. These factors negatively affect the competitiveness of graduates in the labor market. The article proposes a set of measures aimed at modernizing the educational process, including the development of joint curricula with enterprises, expansion of internship programs, introduction of innovative teaching technologies, and improvement of assessment systems. The implementation of these recommendations is expected to enhance the quality of training, strengthen university-industry collaboration, and ensure the preparation of highly qualified and competitive specialists for the road and transport sector.
Research of Physical-Chemical and Mechanical Properties and Development of Effective Composite Materials Compositions for the Production of Asphalt Concrete Highway Coverings
This article presents the results of studies of the compositions and physical-chemical properties of selected bitumens, rubber powders and other organic-mineral ingredients based on local raw materials, the possibility of improving the physical-chemical properties of bitumens by modifying fine rubber powders and other organic-mineral ingredients.
Improving the Physical and Mechanical Properties of Asphalt Concrete Using Nanoparticles
This article investigates the improvement of the physical and mechanical properties of asphalt concrete pavements using nanoparticles. During the research, the effects of adding nanomaterials to asphalt concrete mixtures on the pavement’s strength, elasticity, water resistance, and deformation resistance were analyzed. The influence of nano-silica, carbon compounds, and nanoclay materials on the properties of asphalt concrete was evaluated based on laboratory experiments. The obtained results showed that nanomodified asphalt concrete has higher operational efficiency compared to conventional asphalt concrete.
Principles of Architectural and Spatial Organization of Commercial and Service Facilities in Highway Roadside Areas
This article analyzes the issue of the architectural and planning organization of trade and service facilities in highway roadside areas from the perspectives of contemporary urban planning theory, transport safety, functional zoning, landscape organization, and territorial identity. The main idea of the research is that highway roadside areas should not be regarded merely as vacant spaces adjacent to transport infrastructure, but rather as complex urban spaces that guide urban and suburban development, shape economic activity, and define the visual image of city entrance areas. The Urban Planning Code of the Republic of Uzbekistan is considered the principal legal document aimed at regulating relations in the field of urban planning activities. The Law “On Highways” interprets highways as a complex of engineering structures ensuring the safe movement of vehicles. Based on this approach, the article develops six principal guidelines for organizing roadside trade and service areas: functional zoning, transport safety, pedestrian convenience, architectural and compositional integrity, ecological and landscape sustainability, and territorial identity principles.
Python-Based Slot Machine Game Simulation using Randomization and Control Structures
The Python Slot Machine project is a simple and interactive console based application that simulates the working of a classic casino slot machine using python programming. The main purpose of this project is to show and use fundamental programming concepts in a fun and practical way. This reinforces the concepts of loops, conditional statements, functions, random number generation, and user input/output handling in a game-like environment, which makes learning fun through interaction. In this game the user has a predefined balance and can make virtual bets before spinning the slot machine. Each spin will create a random combination of symbols through the use of randomisation features in Python. The outcome of each spin is evaluated according to pre-determined rules, where combinations of symbols result in payouts, and other combinations result in the loss of the bet placed. The game is on a loop so the user can play several rounds until he decides to stop or runs out of balance. The system has important features such as input validation to only accept valid bet amounts, a balance tracking mechanism to keep track of the player’s credits, and a clear win/lose logic to determine the outcome of each spin. These features make the game play smoothly and prevent invalid inputs from breaking the program flow. Overall, the Python Slot Machine project is a simple entertainment application and an educational tool. It provides hands on experience with basic python concepts and also introduces you to basic game development logic which is a nice stepping stone towards more advanced programming and interactive game development projects.
Turbulent Flow Dynamics in A Curved 2D Channel
This paper investigates the dynamics of turbulent flow in a curved 2D channel, focusing on the changes in flow characteristics due to curvature. Understanding the turbulent flow behavior is crucial for various engineering applications, including pipe design, aerodynamics, and environmental fluid mechanics. The study highlights the effects of centrifugal forces, flow separation, and secondary vortices that develop due to the curvature. Numerical simulations are performed using Reynolds-Averaged Navier-Stokes (RANS) equations with appropriate turbulence models. Results show that flow separation occurs on the inner wall of the curved channel, while strong secondary flows arise, significantly influencing the velocity distribution and pressure gradients.
Accessibility of Cultural Heritage Sites for People with Reduced Mobility: Architectural Approaches and Practical Solutions
This article addresses the pressing challenge of creating an architecturally accessible environment at cultural heritage sites. It examines the theoretical foundations of the universal design concept, the principles of international heritage protection legislation, and the rights of people with reduced mobility. Special attention is given to the tension between accessibility requirements and the principles governing the preservation of historic buildings, and methodological tools for resolving this tension are proposed. Using the Gur-Emir architectural ensemble in Samarkand, Uzbekistan — a fifteenth-century monument inscribed on the UNESCO World Heritage List — as a case study, the article analyses accessibility barriers and proposes a set of concrete architectural adaptations that comply with the principles of minimum intervention and reversibility. The guiding principles of an architectural concept for integrating accessibility elements into the historic setting are formulated. The findings are applicable to the development of methodological guidelines for adapting other historic heritage sites.