In SoCs, static random-access memory (SRAM) occupies 60% of its area. Under nanoscale CMOS technology at lower supply voltages and technology nodes, the MOSFET undergoes various short channel effects and the design of the SRAM cell becomes progressively challenging due to increased leakage power consumption and degraded data stability. Therefore, it is very important to overcome those limitations and improve its performance. This paper presents the design of a 6T SRAM cell using new technologies like FinFET, CNTFET and GNRFET at different nodes to improve the performance in terms of leakage power and stability. All the design simulations are carried out using the SYNOPSIS HSPICE tool at different technology nodes with appropriate power supplies. Performance characteristics of the CMOS 6T SRAM cell is compared with FinFET, CNTFET, and GNRFET technologies based 6T SRAM cells. Results analysis shows that CNTFET based 6T SRAM cells achieve greater stability than other designs whereas GNRFET based 6T SRAM cells dissipate less power than other designs. Therefore, this paper concludes that the new technology-based 6T SRAM cell design offers greater stability and lower leakage power, which is suitable for low power applications.

A huge increase in the number of spam emails has led to the requirement for the evolution of more reliable and robust anti-spam techniques or filters that are utilized for preventing these emails (spam) from getting into inboxes. Machine Learning-based methods have been predominant and efficient in classifying emails as spam. This paper presents a broad review of successful and current machine learning-based methods that have been employed in email spam filtering. It also compares the strengths and limitations of current machine learning approaches that will guide researchers in efficiently dealing with the threat of spam in the future.

The probability of failure on demand or per hour (PFD or PFH) of safety instrumented systems (SIS) depends on the configuration of its subsystems due to different devices' characteristics. Minimizing the probability of failure results in a high level of optimum safety integrity. This paper presents an approach whose objective is to determine an SIS architecture using the Simplex method based on PFH, failure rate, required safety integrity level (SIL) and the cost associated to the various prevention devices. A case study (tandem Arcelor Mittal in Algeria) is proposed to validate the model.

Traditional approach of software development needed efficiency enhancement to meet the market expectations of a product with enhanced features at regular iterations. This made way for Agile software development strategy. Agile software development companies required continual testing of the software to evolve and debug the product for quality purposes. The complex development process needed to remain focused and cost effective through the right software development strategy. Many organizations utilize MCDM (multi-criteria decision-making) approach to decide the most optimum testing strategy for efficient agile development of a project. A framework was also required for selecting the most appropriate testing approach quantitatively to drive the testing efforts towards the right path to meet market requirements effectively. For this, a combined approach of Multi-Objective optimization (Fuzzy-based) on ratio analysis base (MOORA) with AHP (Analytic Hierarchy Process) is proposed to quantitatively select and rank the most appropriate agile testing strategies. Five widely used agile testing strategies were considered along with six criteria i.e. reliability, agile testing team, lead time, customer feedback, iterative development and cost. Reliability and XP ranked as the most suitable criteria and strategy of suitable agile testing in an enterprise.

Currently, inventory and logistics performance play a significant role for the efficiency augmentation and competitiveness of the manufacturing industry. The objective of this research study focuses on inventory management and measurement of logistics performance metrics of on-time in full and vehicle capacity utilization. The problem of maintaining inventory due to the dynamic fluctuating schedule of the customer results in stock-outs due to fluctuating demand and poor performance of on-time in full. Primarily, annual sales data were collected and classified based on ABC-FSN analysis. Then, the outcome of the analysis was used to classify about 123 parts into three classes such as fast, slow, and non-moving categories. Demand forecasting was carried out using various forecasting techniques, followed by ordering policy by economic order quantity. Reorder point model and safety stock were also analyzed. Logistics performance metrics, on-time in full, and vehicle capacity utilization were studied and measured. From the final observed results, it was found that vehicle capacity utilization was poor and all vehicles had an average utilization of 52.61%. Further suggestions were made to demand and adopt the maintaining inventory safety stock and proper packaging of goods to improve the overall performance.

Carbide cutting tools form an essential part of the manufacturing industry. A cutting tool, as the name suggests, is a cutting aid, generally harder than the workpiece material which is used for cutting the workpiece material and removing excess material in the form of chips. Any deviation in its condition affects the complete material removal process with respect to quality, accuracy, and durability. Thus, a condition supervision system for fault identification has turned out to be a key priority. The current era of Machine Learning (ML) stimulates the induction of classifier training for tool condition. In this paper, a study on carbide cutting tools is presented during a turning operation carried out on a simple lathe machine. The signature analysis of vibration generated due to the change in the carbide tool condition is carried out. Finally, a Boosted Trees Ensemble is deployed for training of various tool conditions.

All devices run on software and enterprises trust in some invisible big infrastructure software to make decisions for us every day. For that, trustworthiness becomes more and more important for users and developers. In this paper, we focus largely on the trustworthiness aspect in the software development process. We propose a trustworthiness framework that has a great design and implements trustworthiness aspects. The framework is a platform that determines if a project is compatible with the required software development, ensuring reliability, trustworthiness, and security fundamentals. Furthermore, the proposed framework is considered like a plateforme that examines the big infrastructure source code and compares two different conceptions for the same project idea. The proposed framework is based on two layers, which are PetriNets Simulator Modelling and Master dependency, to ensures a retro-propagation design and verification tool between the side of requirement specification, analysis, and design system with the side of the architecture of the source code, which ensures the trustworthiness of the development process and software production. The framework makes software more reliable and trustworthy for users and allows enterprises and developers to control and test the trustworthiness of internal self-production software and integrated external source code.