Agriculture 4.0 is a field that has spread widely around the world in this century, as it has undergone an exceptionally rapid evolution, especially when it comes to fruit recognition.Decisions about their quality are crucial to maximize profits and meet customer expectations. In the past, apples or even other fruits were based solely on visual assessments by experts, which led to errors. These old methods no longer consider the genetic evolution of apples, as they only consider their size, color, and skin imperfections. Digitizing this process saves energy and reduces costs and human error as well. Recent technological advances, which combine AI and CAO at the same time for fruit sorting, make it possible to achieve high levels of quality and meet the growing challenges of food safety on a global scale. This study proposes a machine learning-based multiclass model to improve the accuracy and efficiency of apple quality assessment. The model is trained on a large image dataset of three apple varieties: Gala, Fuji, and Golden Delicious (G.D). The model automatically classifies apples based on attributes such as color, shape, and imperfections, and evaluates their conformity. Experimental results demonstrate the effectiveness of this model, which achieves 97% accuracy in identifying apple varieties and assessing their quality. This approach significantly reduces inspection time and errors, optimizing operations in the production chain.

The object of this study is to integrate Hazard and Operability (HAZOP) analysis and Fuzzy Logic to improve risk assessment in the Liquefied Petroleum Gas (LPG) filling industry. the research introduces a new approach to resolve the uncertainties and imprecise information inherent in traditional risk evaluation methods. Using HAZOP, a qualitative analysis conducted by the experts of industry identifies potential process deviations and hazards, categorized by impact type such as fire, explosion, and environmental damage. Fuzzy Logic, quantifies these risks by evaluating the likelihood and consequences of accidents using linguistic variables like "high," "medium," and "low." The simulation of different parameters considers various scenarios, including the interplay of consequence severity, event frequency, and detection capability on overall risk using model fuzzy-Hazop. The results provide more precision and actionable risk assessments, showing the potential of the combination of Hazop and fuzzy logic for safety measurement in the LPG filling process. The study concludes HAZOP with fuzzy logic evaluates risks in uncertain conditions, offering an alternative for clearer, decision-making in risk-based design processes

The finishing process in the textile sector is recognized as one of the most complex. This complexity arises from the diversity of structures, the multiple steps involved, the use of complex machinery, the variety of materials, chemicals and dyes, and the need to combine creativity and precision. Therefore, it is crucial to have tools that can improve efficiency, flexibility, and decision-making in this complex area. This literature review aims to provide relevant information on the use of digital engineering in the field of textile finishing. In this review, we used a systematic literature review methodology to examine how digital engineering is applied in the dyeing and finishing process. The data for this study was collected from reputed databases such as Science Direct, IEEE Xplore, Textile Research Journal and Google Scholar. We used the Prisma framework to select relevant articles, which led to the exclusive inclusion of journal articles in our literature review. A comprehensive framework has been developed to understand the impacts of using digital engineering. The approach presented in this framework provides a comprehensive and highly effective approach to addressing the complex challenges associated with ambiguity, modifications and subtleties frequently observed in the ennobling process. The results of various studies explored different aspects, such as properties of textile materials, chemicals and dyes, performance of finishing machines, organizational performance of finishing companies, as well as health concerns and safety at work. Although these studies have provided valuable solutions, they unfortunately remain insufficient to meet the requirements of the finishing process, which remains a complex area characterized by uncertainties, variations, and subtleties inherent to the practice. This particularity of each dyed and finished product promotes an environment conducive to experimentation and continued research

This paper presents a solid methodology for improving the efficiency and productivity of assembly lines using Lean Manufacturing tools, in particular the Define, Measure, Analyze, Improve, and Control approach (DMAIC) and line balancing techniques, followed by a concrete application in a case...

This paper presented the architecture and construction of a novel smart building system that could monitor and control buildings’ use in a safe and optimal way. The system operates on a Raspberry local server, which could be connected via the cloud technology to a central platform. The local system includes nine modules that inter-communicate. The system detects sensor faults, and provides a friendly interface to occupants. The paper presented the software architecture IoT used for the building monitoring and the use of this system for the management of fifteen social housing units during a year. The system allowed the investigation of indoor comfort and both energy and hot water consumptions. Data analysis resulted in the detection of abnormal energy consumptions. The system could be easily used in buildings’ management. It works in a plug-and-play mode.

In the context of Industry 4.0, Total Productive Maintenance (TPM) is undergoing a major shift driven by digital technologies such as the IoT, AI, cloud computing, and Cyber–Physical systems. This study explores how these technologies reshape traditional TPM pillars and practices through a two-phase methodology: bibliometric analysis, which reveals global research trends, key contributors, and emerging themes, and a systematic review, which discusses how core TPM practices are being transformed by advanced technologies. It also identifies key challenges of this transition, including data aggregation, a lack of skills, and resistance. However, despite the growing body of research on digital TPM, a major gap persists: the lack of a standardized model applicable across industries. Existing approaches are often fragmented or too context-specific, limiting scalability. Addressing this gap requires a structured approach that aligns technological advancements with TPM’s foundational principles. Taking a cue from these findings, this article formulates a systematic and scalable framework for TPM 4.0 deployment. The framework is based on four pillars: modular technological architecture, phased deployment, workforce integration, and standardized performance indicators. The ultimate goal is to provide a basis for a universal digital TPM standard that enhances the efficiency, resilience, and efficacy of smart maintenance systems.