Pshtiwan Shakor

Advanced solutions for detecting defects within products have lately been an important area of study in manufacturing quality control. Even with great attempts, the problems concerning accuracy, speed, and flexibility on various types of products still happened with the previous methods. Our research fills this obvious gap with the implementation of a unique deep learning-based method that uses a YOLO model in identifying faults in real-time. The main objective is to reduce the rate of human intervention and error rates, increase precision in detection, and enhance operational efficiency. They also adopt an application from the well-known YOLO model for high performance in object identification applications. For the model to do comprehensive learning and to have good detection accuracy, it was trained on a large dataset of different product images labeled with the type of defects. Those are then improved with data augmentation techniques to build artificial versions that reproduce the variability of scenes in a real-world situation, which boosts generality. The proposed defect detection system, based on YOLO, is doing great compared to other classic methods, maintaining real-time processing capability with much higher recall and accuracy rates. Moreover, such a system possesses huge flexibility towards many categories of products and types of defects and thus shows adaptability and applicability in regard to numerous productions. The application of the YOLO model in the automated identification of product defects in industrial quality control is a huge novelty. However, displayed an opening for new horizons for the processes of smart manufacturing by an effective, scalable, and exact defect detection method.

While blockchain technology (BT) has gained attention in the construction industry, limited research has focused on its application in sustainable construction procurement management (SCPM). Addressing this gap, the present study investigates the key drivers influencing BT adoption in SCPM using a hybrid methodological approach. This study includes a systematic review of academic and grey literature, expert consultations, and quantitative analysis using advanced fuzzy-based algorithms, k-means clustering, and social network analysis (SNA). Data were collected through an online survey distributed to professionals experienced in SCPM and blockchain implementation. The Fuzzy DEMATEL results identify “high quality”, “decentralization and data security”, and “cost of the overall project” as the most critical drivers. Meanwhile, SNA highlights “stability of the system”, “overall performance of the project”, and “customer satisfaction” as the most influential nodes within the network. These insights provide actionable guidance for industry stakeholders aiming to advance SCPM through blockchain integration and contribute to theoretical advancements by proposing novel analytical frameworks.

Construction safety is critical, and unmanned aerial vehicles (UAVs) have emerged as a transformative tool to enhance safety management in the sector. While UAVs are widely recognized for their efficacy, limited research has specifically addressed the barriers to their integration into construction safety management systems. This study aims to identify, prioritize, and analyze the interrelationships among these barriers to aid in their effective resolution. Using a mixed-methods approach, this research combines a systematic literature review (SLR) to identify barriers and a questionnaire survey to prioritize and examine their interconnections. The findings reveal significant barriers, including restricted airspace, inadequate safety regulations, limited flight durations, collision risks, insufficient piloting skills, lack of UAV awareness, resistance to new technologies, human errors, training needs, and legal constraints. Restricted airspace emerged as the most critical barrier, strongly linked to flight duration limitations and piloting proficiency. This study also highlights regional disparities: respondents from developed nations emphasized collision risks, legal restrictions, and resistance to new technologies, while those from developing countries focused on restricted areas, limited flight time, and piloting expertise. These findings emphasize the importance of addressing region-specific challenges and tailoring strategies to facilitate UAV integration, paving the way for safer and more efficient construction practices.

Significant weather-induced delays often plague construction projects in India’s extremely cold regions, yet comprehensive studies addressing this issue remain scarce. This study aims to fill this gap by identifying key delay factors and proposing mitigation strategies for the construction industry. Through an extensive literature review, 42 delay factors were identified and categorized into four groups. A survey of 83 experts from cold regions was conducted to evaluate these factors’ significance to contractors and subcontractors. Employing exploratory factor analysis (EFA), structural equation modeling (SEM), and artificial neural networks (ANN), the study analyzed the relationships between these factors and ranked their impact. The findings reveal that snowfall, rainfall, and low temperatures are the most significant contributors to delays, with snowfall being the most influential (significance: 1.000), followed by rainfall (0.890) and low temperatures (0.790). This research establishes a risk hierarchy and develops a predictive model to facilitate the proactive scheduling of challenging tasks during favorable seasons. This study advances the understanding of weather-induced delays in India’s cold regions and offers valuable insights for project management in such climates. However, it underscores the importance of clearly articulating its novel contributions to differentiate it within the existing literature on weather-related construction delays.

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as mean absolute error (MAE), root mean square error (RMSE), and R-squared. The future forecast is compared with an outcome of a previous physical model that integrates wells and reservoir properties to simulate gas production using regressions and forecasts based on empirical and theoretical relationships. Regression analysis ensures alignment between historical data and model predictions, forming a baseline for hybrid model performance evaluation. The results reveal the complementary attributes of these methodologies, providing insights into integrating data-driven and physics-based approaches for optimal reservoir management. The hybrid model captured the production rate conservatively with an extra margin of three years in favor of the physical model.

Разработан логический тестер на базе микроконтроллера, специально адаптированный для большинства интегральных схем (ИС) логических элементов серии 74xx и использующий ATmega328. Воспроизведены атрибуты логического элемента ИС с использованием таблицы истинности, что позволяет определить состояние элементов ИС. Исследовано несколько методов проектирования, выбран единый подход к реализации. Модули разрабатывались независимо. После обширных испытаний и успешного моделирования эти компоненты были объединены для завершения проекта. Разработанный тестер микросхем является экономичным и удобным в использовании; он быстро исследует микросхемы и представляет результаты для оценки их функционирования. Тестер сравнивает выходные данные каждого вентиля с таблицей истинности. Результаты как для рабочих, так и для неисправных микросхем отображаются на ЖК-дисплее. Результатом проекта является создание цифрового тестера микросхем, который упрощает процесс проверки микросхем и функционирует как надежный инструмент в промышленных условиях.

Background: In the electronics lab, testing equipment, especially those concentrating on integrated circuits (ICs), is critical. The ubiquitous usage of one or more integrated circuits (ICs) in electronic systems needs thorough testing of their functioning. Objective: This research aims to develop a Microcontroller-based Logic Tester specially tailored for most Integrated Circuits in the 74xx Series Logic Gates and uses the ATmega328. The primary purpose is to reproduce the attributes of a logic gate IC using the truth table, enabling the state of the IC's gates to be determined. Methods: Several design techniques were investigated. Following careful consideration, a single approach for implementation was selected. Modules were developed independently. After extensive testing and successful simulations, these components were combined to complete the project. Results: The designed IC tester is cost-effective and user-friendly; it examines ICs quickly and presents findings to assess their functioning. The tester compares the output of each gate to a truth table. The results for both working and faulty ICs are shown on an LCD. Conclusion: In today's fast-changing electronics scene, an efficient digital IC tester that is both economical and dependable is critical. The project's conclusion provides a digital IC tester that simplifies the IC verification process for students and functions as a reliable tool in industrial settings.

Использовался смешанный подход для оценки эффективности и осуществимости внедрения 5G-RAN (fifth Generation-Radio Access Network). Это достигается за счет интеграции количественных данных пилотных исследований с качественным анализом литературы. Результаты показывают, что использование технологии 5G приводит к существенному улучшению скорости передачи данных, уменьшению задержек, повышению надежности сети, масштабируемости и снижению задержек. Выявлено несколько препятствий, в том числе значительные затраты, связанные с инфраструктурой, проблемы совместимости и проблемы безопасности. Интеграция 5G и RAN обеспечивает реальный подход к обеспечению бесперебойной связи, что приводит к значительным преимуществам во многих отраслях.