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The increasing adoption of Internet of Things (IoT) technologies has facilitated the creation of advanced applications in various industries, notably in complex workplaces where safety and efficiency are paramount. This paper addresses the challenge of monitoring worker presence in vast workplaces such as shipyards, large factories, warehouses, and other construction sites due to a lack of traditional network infrastructure. In this context, we developed a novel system integrating Bluetooth Low Energy (BLE) beacons with multi-hop IoT networks by using the ESP-NOW communications protocol, first introduced by Espressif Systems in 2017 as part of its ESP8266 and ESP32 platforms. ESP-NOW is designed for peer-to-peer communication between devices without the need for a WiFi router, making it ideal for environments where traditional network infrastructure is limited or nonexistent. By leveraging the BLE beacons, the system provides real-time presence data of workers to enhance safety protocols. ESP-NOW, a low-power communications protocol, enables efficient, low-latency communication across extended ranges, making it suitable for complex environments. Utilizing ESP-NOW, the multi-hop IoT network architecture ensures extensive coverage by deploying multiple relay nodes to transmit data across large areas without Internet connectivity, effectively overcoming the spatial challenges of complex workplaces. In addition, the Message Queuing Telemetry Transport (MQTT) protocol is used for robust and efficient data transmission, connecting edge devices to a central Node-RED server for real-time remote monitoring. Moreover, experimental results demonstrate the system's ability to maintain robust communication with minimal latency and zero packet loss, enhancing worker safety and operational efficiency in large, complex environments. Furthermore, the developed system enhances worker safety by enabling immediate identification during emergencies and by proactively identifying hazardous situations to prevent accidents. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has radically changed the industrial world, enabling the integration of numerous systems and devices into the industrial ecosystem. There are many areas of the manufacturing industry in which IoT has contributed, including plants' remote monitoring and control, energy efficiency, more efficient resources management, and cost reduction, paving the way for smart manufacturing in the framework of Industry 4.0. This review article provides an up-to-date overview of IoT systems and machine learning (ML) algorithms applied to smart manufacturing (SM), analyzing four main application fields: security, predictive maintenance, process control, and additive manufacturing. In addition, the paper presents a descriptive and comparative overview of ML algorithms mainly used in smart manufacturing. Furthermore, for each discussed topic, a deep comparative analysis of the recent IoT solutions reported in the scientific literature is introduced, dwelling on the architectural aspects, sensing solutions, implemented data analysis strategies, communication tools, performance, and other characteristic parameters. This comparison highlights the strengths and weaknesses of each discussed solution. Finally, the presented work outlines the features and functionalities of future IoT-based systems for smart industry applications. [ABSTRACT FROM AUTHOR]

Management of hard-to-heal wounds often requires specialized care that surpasses the capabilities of conventional treatments. Even the most advanced commercial products lack the functionality to meet the needs of hard-to-heal wounds, especially those complicated by active infection, extreme bleeding, and chronic inflammation. The review explores how supramolecular nanovesicles and nanoparticles-such as dendrimers, micelles, polymersomes, and lipid-based nanocarriers-can be key to introducing advanced wound healing and monitoring properties to address the complex needs of hard-to-heal wounds. Their potential to enable advanced functions essential for next-generation wound healing products-such as hemostatic functions, transdermal penetration, macrophage polarization, targeted delivery, and controlled release of active pharmaceutical ingredients (antibiotics, gaseous products, anti-inflammatory drugs, growth factors)-is discussed via an extensive overview of the recent reports. These studies highlight that the integration of supramolecular systems in wound care is crucial for advancing toward a new generation of wound healing products and addressing significant gaps in current wound management practices. Current strategies and potential improvements regarding personalized therapies, transdermal delivery, and the promising critically evaluated but underexplored polymer-based nanovesicles, including polymersomes and proteinosomes, for wound healing., (© 2024 Wiley‐VCH GmbH.)

In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid COOT-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid COOT-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques. [ABSTRACT FROM AUTHOR]

In the 21st century, the biggest issue is produced in terms of storage, processing, and sharing of computing resources with the enhancement of techniques. This issue is resolved with the amalgamation of big data and Cloud Computing techniques. Big data techniques help in handling new datasets efficiently Whereas Cloud Computing techniques provide an online storage model where the concept of virtualization is used. The combination of cloud computing and big data has set a new standard for future educational institutions and corporations. Users are capable to work according to their convenience by using Cloud Computing environments, while big data environments provide useful insights and information to them. This paper introduces big data along with various machine learning methods and Cloud Computing concepts. It also discusses the relationship between big data and cloud computing platforms, as well as the issues they pose. [ABSTRACT FROM AUTHOR]