Transactions on Engineering and Computing Sciences

Role of Cloud Computing & Artificial Intelligence in the Logistics & Supply Chain Industry

Natapong Sornprom — Sat, 09 Nov 2024 00:00:00 +0000

The logistics and supply chain sector finds itself at a critical inflexion point, with mounting pressures to enhance efficiency, increase cost competitiveness and serve the dynamic needs of consumers. To aid such goals, technologies of cloud computing combined with artificial intelligence (AI) come into play. Scalable resources, real-time data access (SaaS), and collaboration offer a superior environment for consolidation, better communication between departments and resource integration across business operations. Further, AI with sophisticated techniques of machine learning have ability to analyze large data sets which enables businesses to automate certain tasks while minimizing certain operations apart from providing predictions.

This paper dwells on how cloud computing combined with AI can help in transformation of logistics supply chain management. How businesses across the verticals are leveraging these technologies to improve operations and providing a beacon for technological advancements in their growth. Furthermore, how cloud and AI integration can help industry and gain competitive edge in a rapidly evolving market to foster a more agile, resilient customer centric ecosystem. By adopting such technologies, businesses can navigate through the complexities of modern logistics and supply chain challenges and stay relevant in this hyper competitive digital landscape.

A New Hypothesis Concerning the Big Bang

Vlad L. Negulescu — Fri, 06 Dec 2024 00:00:00 +0000

Using the fact that the power P^g is a tangent function, this paper develops a hypothesis concerning the beginning of the Universe and the Big Bang. Everything starts from an initial singularity which contains infinite power. Further the mass and the diameter of the whole Universe is also calculated.

Estimating Young Modulus of Elasticity of Terminalia catappa: A Machine Learning Approach

Thu, 12 Dec 2024 00:00:00 +0000

The purpose of this research was to evaluate the potential of Magnetic Resonance Spectroscopy (MRS) in estimating Young’s modulus of elasticity of wood species. To do so, Terminalia catappa, a wood species of common occurrence was chosen and its mechanical properties such as bending strength, compression parallel to the grain, and shear parallel to the grain properties were determined using testing methods for small and clear specimens of wood with the British (BS 373, 1957) and American Society of Testing Materials’ specifications (ASTM D143, 1983s. The results showed that at 18% moisture content the wood has a density of 520 kg/m³ with a mean modulus of rupture of 86.04 Mpa, compressive strength parallel to the grain of 42.02 Mpa, modulus of elasticity of 10,500 Mpa, and shear strength parallel to the grain of 16.42 N/mm². This dataset was used on machine learning approaches such as decision tree and random forest. The estimated value of Young’s modulus using the machine learning models varies between 1000 to 13000 MPa. The obtained results indicated that the use of Magnetic Resonance Spectroscopy (MRS) is an efficient tool for predicting Wood-Young’s modulus. This research paves the way for further investigations on the application of MRS and machine learning for predicting a wider range of wood properties. By employing machine learning techniques such as decision trees and random forests, researchers can develop robust models for estimating Young's modulus in other wood species. This approach allows for leveraging large datasets that encompass various influencing factors, ultimately leading to more accurate predictions compared to traditional methods.

Assessment of BIM Maturity in Benin: Analysis of Challenges and Opportunities According to Succar's Model

Edem Chabi, Ernesto Cabral Houéhanou, Marx Ferdinand Ahlinhan, Adriel Kpatindé — Thu, 12 Dec 2024 00:00:00 +0000

Building Information Modeling (BIM) has become an essential tool in the global construction industry, enhancing project efficiency, collaboration, and quality. However, its adoption varies considerably across national contexts. This study evaluates the level of BIM maturity in Benin through the prism of the eight development axes proposed by Succar, which include technology, processes, standards, education, culture, strategy, policy, and regulation. The methodology is based on an analysis of regulatory frameworks, technological infrastructures, and educational systems, combined with an evaluation of current practices in the construction sector. This approach allowed for the identification of the main obstacles and opportunities related to the adoption of BIM in the country. The results indicate that the absence of a national strategy, harmonized standards, and integration into educational curricula constitute major barriers to the development of BIM in Benin. However, sporadic initiatives and individual projects reveal potential for a progressive transition. These observations highlight the necessity of a structured approach to align local practices with international standards and fully leverage the benefits of BIM in the construction sector.

Thermal Aware Process Scheduling for Multicore Processors

Mahima Agrawal, D. A. Mehta — Mon, 23 Dec 2024 00:00:00 +0000

Multi-core processors seem to be an alternative way to higher frequencies for increasing microprocessor performance, by handling more work in parallel at lower frequencies. The addition of multiple cores on the same chip results in an increase in power density on the chip which in turn generates large amount of heat. The increased temperature increases leakage current; and negatively aﬀects chip’s performance, reliability and life expectancy. In addition, they release greenhouse gases in the atmosphere and have negative impact on the environment. The study done so far reveals that the issue of high temperature can be solved by assigning and migrating the processes to a cooler core; but this increases migration cost and temporal temperature gradients thereby decreasing the performance. So, in this work the issue of high temperature along with temporal temperature gradients of the cores is addressed at the Operating System level via scheduling of processes. The experiments are performed on an Intel i5-3470 Linux machine. The experimental results reveal reduction in the peak temperature of the processor up to 11.36%, thermal swings in the processor up to 80% and turnaround time of the processes up to 17.24%. The idea of thermal aware scheduler presented in this paper can be applied for scheduling of jobs in data centers and high-performance computers to achieve performance while making computing environmentally friendly.