The agricultural industry has undergone several significant changes over the past few centuries, influenced by the industrial revolutions that have occurred. These changes have progressed from Indigenous agriculture to mechanized farming and the current precision agriculture. While the industrial farming model has increased output, it has also faced various challenges in recent years. Industry 5.0 is expected to have a significant impact on the agriculture sector and potentially lead to a fifth agricultural revolution. In this paper, we examine the motivation behind the industrial and agricultural revolutions 4.0 and 5.0, review the phases of these revolutions that have occurred so far, and offer suggestions for the future. We also provide an overview of the concepts of Industry 4.0 and 5.0, as well as Agriculture 4.0 and 5.0, and discuss the smart strategies that are being implemented in different countries to advance these sectors. Additionally, we focus on the potential applications of Industry 5.0 technologies in the agriculture industry and the research challenges associated with them. Our goal is to provide industrial and agricultural professionals with new research opportunities.

Radio Frequency Identification (RFID) is a contactless technology that has developed over the 90s and 20th centuries. It employs electromagnetic or electrostatic coupling in the radio frequency part of the electromagnetic spectrum to uniquely identify traceable objects, and is widely used in various sectors (e.g., medical, Supply Chain Management, transportation, and IoT applications.). Through the supply of real-world monitoring and context information about things, the integration of this technology in such areas delivers various benefits in the future of ubiquitous computing. However, one of the primary challenges will be the capacity to manage data since RFID events have specific characteristics and requires special treatment, such as the large volume of data flow, inaccuracy, temporal and spatial data, are typical examples of RFID event data. The goal of this research is to first highlight the concerns and limitations of existing middleware architectures before introducing and implementing a new Middleware architecture to address the identified issues, specifically real-time processing of massive volumes of data coming from physical RFID infrastructure. This middleware combines role-based access control with an encryption algorithm to increase security, a NoSQL database for storing large amounts of data, complex event processing (CEP) to provide high-volume data stream processing, and improved interoperability via the Data Transformation Module. Finally, our architecture is evaluated and compared to several middleware architectures based on standard ISO/IEC 9126 metrics.

Radio frequency identification (RFID) is a contemporary technology that enables the identification of objects and facilitates the transmission of additional information, making it possible to achieve real-time object tracking in a mobile object network and to report information on the object's current state at each step. RFID devices continuously generate large amounts of data, and collecting, filtering, and consolidating these data are therefore crucial tasks, which characterize RFID data management by a set of challenges. However, one of the greatest challenges in this field is managing large volumes of data in complex applications, where real-time operation is vital, given that the volume and speed of RFID data often exceed the capacity of the existing technological infrastructure. The aim of this study is to propose an RFID middleware that manages both the RFID hardware network and the large amounts of data that are captured, in order to process and transmit the collected data under the right conditions for ultimate use by an information system. This new RFID middleware architecture, named BTMiddleware combines complex event processing (CEP) with a MongoDB database to offer large-volume data streaming, processing, and storage in real time, as well as better interoperability thanks to the use of the JavaScript object notation (JSON) format for data presentation.

The global freight transportation industry has experienced exponential growth, significantly contributing to economic development. However, this expansion has also led to considerable environmental challenges, particularly due to the sector’s dependence on fossil fuels and inefficient logistical practices, resulting in high carbon emissions, air pollution, noise pollution, and resource depletion. The complex problems facing the freight transportation sector are directly impacting several United Nations Sustainable Development Goals (SDGs), particularly SDG 2, SDG 3, SDG 7, SDG 9, SDG 11, SDG 12, and SDG 13. This study addresses these challenges by first examining the direct contribution of sustainable freight transportation to the United Nations Sustainable Development Goals (SDGs). Building on this foundation, the paper explores the transformative potential of artificial intelligence (AI) to enhance sustainability in freight transportation. Focusing on advanced analytics, predictive modeling, and real-time optimization, AI provides opportunities to improve route planning, energy efficiency, and emission reduction, while supporting more resilient and sustainable logistics systems. The paper introduces a holistic framework, integrating AI seamlessly throughout the entire freight logistics process. To contextualize these insights, an empirical survey was conducted among Moroccan freight transportation companies, highlighting current practices, the perceived effectiveness of AI adoption, and the level of confidence in achieving long-term carbon neutrality targets. Finally, the paper introduces a practical framework for integrating AI into freight transportation systems, aligning technological innovation with sustainability goals, and offering actionable guidance for both industry stakeholders and policymakers.

RFID technology is a contemporary method of identifying objects and transmitting other associated information. It enables the real-time tracking of mobile objects through a network while enabling the communication of the object's status at each stage. Due to the volume of data that RFID devices produce, significant data collection, filtration, and consolidation are necessary for their intended purpose. A software layer that serves as a connection point between business applications and RFID reader known as RFID middleware is necessary to control this data and transfer it effectively to an information system. The "BTMiddleware1.0" described in this article is a new RFID middleware architecture. The "BTMiddleware1.0 architecture" described in this article is a brand-new RFID middleware architecture. This design enables real-time processing, efficient storage and management of large data streams by combining complex event processing (CEP) with a MongoDB database. Moreover, it presents data in JSON format, which improves interoperability. Additional Keywords and Phrases: RFID, Middleware, IoT, Data-processing, Tracking-systems.

Nowadays, with the urbanization and globalization of trade, companies are facing fundamental challenges,This is why companies seek to stabilize and increase their market share while minimizing costs and maximizing profits. distribution problem as a structure constructed by the participants, represents the most important part of logistic expenses. Vehicle routing problem has been a growing interest as a distribution problem, it concerns to create the routes of a set of vehicles in order to deliver a list of customers, In the goal to minimize the cost of delivering goods. Open vehicle routing problem (OVRP) is an extension of vehicle routing problem where Each route could be a Hamiltonian path over the route's subset of customers. In this paper we provide an extension of the open vehicle routing problem, called Open vehicle routing problem with fuzzy time windows, we adopt a mathematical model for this type of problem, and by using alpha cut method through a decomposition of this original problem in their original fuzzy form into a set of crisp models (OVRPFTW) we propose a solution of this problem. Computational results indicate that this model can fulfill a significant cost-saving while securing a high level of service.