Earthquakes are one of the most serious natural disasters affecting the stability and the durability of buildings, threatening the life of its occupants. These buildings should be withstanding earthquakes by both architectural and structural engineers. The Integration between structural and envelope system is negatively affected due to; the lack of architectural knowledge in earthquake resistance, and the absence of cooperation between architectural and structural engineers in earthquake resistant design. In this research the lack in the nature of the integrative relationship between the structural and envelope system of earthquake-resistant buildings design is presented. Also, he relationship between these systems, their patterns, and levels in the building to resist earthquakes are highlighted. Where the concept of integration, patterns and levels are verified, using inductive methodology (descriptive, and analytical) through election, analyzing of two different case studies. major result show that the performance pattern is the most common type of three other integration patterns. Also the envelope ,structural system response achieves an equal degree of response as both of them are integrated with each other without revoking one the role of other or affecting the optimal seismic resistance of buildings, and conclusion are presented further.
The convergence of cloud and edge computing in smart manufacturing offers significant potential for improving efficiency in Industry 4.0. However, task scheduling in this context remains a complex, multi-objective challenge. This study introduces a novel Cloud-Edge Smart Manufacturing Architecture (CESMA), leveraging a hybrid approach that integrates NSGA-II and the Improved Monarch Butterfly Optimization (IMBO) algorithms. The combination utilizes NSGA-II's global search and non-dominated solution capabilities with IMBO's fine-tuning and local optimization strengths to enhance task scheduling performance. Where CESMA combines the scalability and analytics power of cloud computing with edge-based real-time decision-making to address the dynamic demands of smart manufacturing. Through extensive simulations and experiments, the feasibility and effectiveness of CESMA are validated, showing improved task scheduling quality, resource utilization, and adaptability to changing conditions. This research establishes a robust platform for managing the complexities of task scheduling in cloud-edge environments, advancing intelligent manufacturing processes, and contributing to the integration of evolutionary algorithms for real-time industrial decision-making
Responding quickly and economically to the diversification of customer needs has forced manufacturing companies adopting approaches to delivering low cost, high quality sustainable products based on finding a link between the design or the manufacturing processes and other key elements of sustainability; economic, environmental, and social. However, these approaches had limited success. The most likely reason for the lack of integration between the design and manufacturing stages of the product and complexity of addressing the above mentioned three key elements of sustainability due to existing of many variables in relation to design, manufacturing, locations, logistic operations and so on. Taking into account the required integration as well as the associated complexity of considering sustainability elements can lead to large space alternative solutions and it is more difficult to use only exact methods to the optimization of such problem. This paper presents a genetic algorithm (GA) approach aiming to optimize a high sustainability performance by designing a product and the corresponding manufacturing processes for that product. Process optimization is carried out in terms of the highest fitness function achieved where different objectives are to be optimized simultaneously. The proposed GA approach is applied to the industrial case example. The proposed approach can assist decision makers to help explain when justifying their decision on what are the best product design and its manufacturing processes to obtain high sustainability performance.
Advanced prosthetics are a crucial aspect of rehabilitation technology and are receiving increased attention globally. Approximately 2 million people require prosthetic limbs, presenting opportunities for enhancing their quality of life. State-of-the-art technologies such as realistic arms and myoelectric prostheses are gaining popularity. Progress in sensor technology, artificial intelligence, and materials has driven the field forward. Various types of controllers, including direct, pattern recognition, and proportional-derivative, have been developed. Integration of material science, computer science, artificial intelligence, and neurology has facilitated controller advancements. Techniques like targeted muscle reinnervation and Osseo integrated prostheses offer improved surgical options. Gesture recognition technologies and intelligent sensors are enhancing hand control. Future advancements will involve machine learning, artificial intelligence, and sensing techniques, while ethical concerns must be addressed. Advanced myoelectric prostheses, also known as myocontrolled or lower-limb micromod investigative prostheses, have a patient acceptance rate of 75% to 80%. However, while these methods offer advantages, there are also drawbacks. Integrating different types of controllers for these smart prostheses and enhancing the overall device's strength and robustness will have a significant impact. This discussion focuses on various types of smart prosthetic controllers, dividing muscle activity into extracellular myoelectric potential and EEG signals
minimizing the surface area of a reservoir during maximum evaporation losses period. A mathe-matical relationship linking the depth of water with the surface area of the reservoir has been formulated, and its integration can be used to estimate conservable water quantities. Reducing the water level in the reservoir to the minimum permissible level before the dry months has re-duced the evaporation losses by 65% and 51% for the two scenarios. These two scenarios have been conducted by assuming that the dry months start with the presence of the water level in the reservoir at a height of 14 and 12 m, respectively. On the other hand, evaporation losses during drought months have been decreased by 24%. By this technique, it can be possible to obtain wide areas suitable for agriculture, contributing to the economic and social development of the region. Also, Depth index(DI) suggested in this study and defined as the ratio of volume of the water in the reservoir to corresponding surface area, to compare the location of the best dam among the 13 proposed dams in Wadi Houran by reducing evaporation losses. The results of this index showed the best location was at DI=10.901 in DM 7, and the worst is at DI=2.425 in DM 8.
The extensive global competition between companies and the development of new industrial technologies have greatly contributed to the current competitive conditions Like industrial companies, customers demand high quality products, low prices and better performance. This fierce competition has led to concerns about improved product design. This development is based on GQFD. Model of this developed Water pump is employed by CAD solid model (version 7). In order to achieve competition and high quality and high performance in the Iraqi market. GQFD demonstrates the balance between product development and environmental protection. Used a water pump for a home air cooler as a case study. Data is collected and distributed using personal interview methods and questionnaire forms to indicate customer requirements. The data is then analyzed using Pareto chart and AHP to prioritize customer needs. These priorities are then placed in house of quality and matrix of relationships between customer requirements and technical characteristics is established. The product has been developed from electrical to mechanical, in addition to using accumulated, stored and recycled materials; it also saves 20% of energy, thereby combining energy reduction with the use of damaged materials and their re-entry into work. As a result, the cost of pump manufacturing will decrease