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Robots are perfect substitutes for skilled workforce on some repeatable, general, and strategically important tasks, but this substitution is not always feasible. Despite the evolution of robotics, some industries have been traditionally robot-reluctant because their processes involve large or specific parts and non-serialized products; thus, standard robotic solutions are not cost-effective. This work presents a novel approach for advanced manufacturing applied to the aerospace industry, combining the power and the repeatability of the robots with the flexibility of humans. The proposed approach is based on immersive and symbiotic collaboration between human workers and robots, presenting a safe, dynamic, and cost-effective solution for this traditionally manual and robot-reluctant industry. The proposed system architecture includes control, safety, and interface components for the new collaborative manufacturing process. It has been validated in a real-life case study that provides a solution for the manufacturing of aircraft ribs. The results show that humans and robots can share the working area simultaneously without physical separation safely, providing beneficial symbiotic collaboration and reducing times, risks, and costs significantly compared with manual operations.

Hazard detection in industrial and public environments is critical for ensuring safety and regulatory compliance. This paper presents an energy-efficient, cost-effective IoT-based hazard detection system utilizing an ESP32-CAM microcontroller integrated with temperature (DHT22) and motion (PIR) sensors. A custom-built convolutional neural network (CNN) deployed on a Flask server enabled real-time classification of hazard signs, including “high voltage”, “radioactive”, “corrosive”, “flammable”, “no hazard”, “no smoking”, and “wear gloves”. The CNN model, optimized for embedded applications, achieves high classification accuracy with an F1 score of 85.9%, ensuring reliable detection in diverse environmental conditions. A key feature of the system is its adaptive monitoring mechanism, which dynamically adjusts image capture frequency based on detected activity, leading to 31–37% energy savings compared to continuous monitoring approaches. This mechanism ensures efficient power usage by minimizing redundant image captures while maintaining real-time responsiveness in high-activity scenarios. Unlike traditional surveillance systems, which rely on high-cost infrastructure, centralized monitoring, and subscription-based alerting mechanisms, the proposed system operates at a total cost of SGD 38.60 (~USD 28.50) per unit and leverages free Telegram notifications for real-time alerts. The system was validated through experimental testing, demonstrating high classification accuracy, energy efficiency, and cost-effectiveness. In this study, a hazard refers to any environmental condition or object that poses a potential safety risk, including electrical hazards, chemical spills, fire outbreaks, and industrial dangers. The proposed system provides a scalable and adaptable solution for hazard detection in resource-constrained environments, such as construction sites, industrial facilities, and remote locations. The proposed approach effectively balances accuracy, real-time responsiveness, and low-power operation, making it suitable for large-scale deployment.

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Rental accommodations near European university campuses keep rising in price, while listings remain scattered and opaque. This paper proposes a solution that overcomes these issues by integrating real-time open listing ingestion, zone-level geospatial enrichment, and a reinforcement-learning recommender into one streamlined analysis pipeline. On demand, the system updates price statistics for most districts in Timișoara and returns five budget-safe offers in a short amount of time. By combining adaptive ranking with new spatial metrics, it significantly cuts search time and removes irrelevant offers in pilot trials. Moreover, this implementation is fully open-data, open-source, and free, designed specifically for students to ensure accessibility, transparency, and cost efficiency.

Cloud-Radio Access Network (C-RAN) is a promising network architecture to reduce energy consumption and the increasing number of base station deployment costs in mobile networks. However, the necessity of enormous fronthaul bandwidth between a remote radio head and a baseband unit (BBU) calls for novel solutions. One of the solutions introduces the edge-cloud layer in addition to the centralized cloud (CC) to keep resources closer to the radio units (RUs). Then, split the BBU functions between the center cloud (CC) and edge clouds (ECs) to reduce the fronthaul bandwidth requirement and to relax the stringent end-to-end delay requirements. This paper expands this architecture by combining it with renewable energy sources in CC and ECs. We explain this novel system and formulate a mixed-integer linear programming (MILP) problem, which aims to reduce the operational expenditure of this system. Due to the NP-Hard property of this problem, we solve the smaller instances by using a MILP Solver and provide the results in this paper. Moreover, we propose a faster online heuristic to find solutions for high user densities. The results show that make splitting decisions by considering renewable energy provides more cost-effective solutions to mobile network operators (MNOs). Lastly, we provide an economic feasibility study for renewable energy sources in a CRAN architecture, which will encourage the MNOs to use these sources in this architecture.

This paper explores cost-effective solutions for automated guided vehicle (AGV) through the design and implementation of a low-cost, hoverboard-based line-following AGV tailored for textile manufacturing environments, specifically within sewing plants. The designed AGV leverages the capability of a commercial hoverboard as its mobility platform, significantly reducing development costs while maintaining effective operational performance. Utilizing affordable sensors such as infrared line detectors and ultrasonic sensors, the AGV autonomously navigates pre-defined pathways marked on the factory floor. Its primary function is transporting materials such as fabric bundles and partially or finished products between workstations, addressing common logistical challenges in dynamic and labor-intensive textile production settings. The system is designed for easy integration with both existing plant layouts and information and communication environment, requiring minimal infrastructural changes. Field testing demonstrated the AGV’s reliability, maneuverability, and responsiveness in real-world sewing plant conditions. The proposed solution underscores the potential of retrofitting existing consumer electronics for industrial automation, offering a scalable and economically viable alternative for small- to medium-sized textile enterprises seeking to enhance productivity and workflow efficiency.

The development of society and the economy depends on the wise use of renewable energy sources and reduced reliance on fossil fuels. In both industrialized and developing nations, solar energy, which is a significant source of renewable energy, is emerging as a dependable and cost‐effective replacement for fossil fuels. In several industries, including residential, commercial, and agricultural, there is an increasing demand for solar photovoltaic (PV) modules. However, dust buildup on solar panels can limit energy transmission and result in power loss in regions with significant amounts of sunlight. The solar cells are shaded by dirt, debris, and soil carried by the wind, which reduces the energy they receive and raises cell temperature, which further reduces solar cell efficiency. The natural accumulation of dust and heat significantly lowers the panel’s ability to produce electricity. This article is intended to develop an automatic self‐cleaning mechanism to solve this problem, which seeks to increase panel efficiency, monitor and control cell temperature, and provide a more affordable solution than the current alternatives. The experimental evaluation of cleaning system performance shows a 14.81% increase in output efficiency, demonstrating its effectiveness in preventing solar degradation. For PV modules, the suggested technique provides an accessible and low‐cost automatic self‐cleaning alternative.

Precise and efficient air quality monitoring is a pivotal step in combating the harmful effects of pollution. Our research addresses the challenges in obtaining accurate air quality data and identifying pollution sources. It introduces an algorithm to optimize the deployment of air quality monitoring devices, enhancing distribution efficiency. The algorithm considers spatial distribution to minimize capital and operational costs. Utilizing an extensive 300 days dataset covering Durgapur (80 km 2 ), it achieves an above 90% accuracy rate in predicting air quality. By strategically selecting monitoring locations, it maximizes data coverage while minimizing costs. This advancement supports cost-effective pollution control and resource allocation decisions in affected regions.

Gas leakage detection is a critical concern in both industrial and residential settings, where real-time systems are essential for quickly identifying potential hazards and preventing dangerous incidents. Traditional detection systems often rely on centralized data processing, which can lead to delays and scalability issues. To overcome these limitations, in this study, we present a solution based on tiny machine learning (TinyML) to process data directly on devices. TinyML has the potential to execute machine learning algorithms locally, in real time, and using tiny devices, such as microcontrollers, ensuring faster and more efficient responses to potential dangers. Our approach combines an MLX90640 thermal camera with two optimized convolutional neural networks (CNNs), MobileNetV1 and EfficientNet-B0, deployed on the Arduino Nano 33 BLE Sense. The results show that our system not only provides real-time analytics but does so with high accuracy—88.92% for MobileNetV1 and 91.73% for EfficientNet-B0—while achieving inference times of 1414 milliseconds and using just 124.8 KB of memory. Compared to existing solutions, our edge-based system overcomes common challenges related to latency and scalability, making it a reliable, fast, and efficient option. This work demonstrates the potential for low-cost, scalable gas detection systems that can be deployed widely to enhance safety in various environments. By integrating cutting-edge machine learning models with affordable IoT devices, we aim to make safety more accessible, regardless of financial limitations, and pave the way for further innovation in environmental monitoring solutions.

The construction industry is a major contributor to global carbon emissions, primarily due to the energy-intensive production of traditional fired bricks. This study presents a comparative analysis between conventional and geopolymer bricks enhanced with phase change materials (PCMs), focusing on environmental, thermal, mechanical, and economic performance. Geopolymer bricks, synthesized from industrial by-products such as fly ash and PET waste, eliminate the need for high-temperature kiln firing, resulting in up to 80% lower CO₂ emissions[1]. The integration of PCMs further enhances their thermal regulation capabilities by absorbing and releasing heat, stabilizing indoor temperatures, and reducing energy consumption in buildings[2]. Life Cycle Assessment (LCA) confirms these traditional bricks offer a significantly smaller environmental footprint. Mechanically, geopolymer bricks maintain high compressive strength and durability, even with PCM inclusion[3]. Economically, they are cost-effective due to reduced energy use and the availability of low-cost raw materials[4]. Overall, geopolymer bricks with PCMs represent a sustainable, high-performance alternative to traditional bricks, aligning with global climate goals and offering a viable solution for energy-efficient and environmentally responsible construction.