Environmental Efficiency Research Articles

A distributed algorithm for the multi-robot minimum time task allocation problem

Purpose The purpose of this study is to address the challenge of task allocation in multi-robot systems by getting the minimum overall task completion time and task allocation scheme while also minimizing robot energy consumption. This study aims to move away from traditional centralized methods and validate a more scalable distributed approach. Design/methodology/approach This paper proposes a distributed algorithm for the multi-robot task allocation problem, aimed at getting the minimum task completion time along with the task allocation scheme. The algorithm operates based on local interaction information rather than global information. By using the Consensus-Based Auction Algorithm (CBAA), it seeks to effectively minimize energy consumption without affecting the minimum completion time required for overall task allocation. Findings The proposed distributed algorithm successfully reduces robot energy consumption while effectively obtaining the shortest overall task completion time and corresponding task allocation scheme. Numerical simulations conducted using MATLAB software demonstrated its superior performance, and empirical testing on the Turtlebot3-Burger robot platform further substantiated these findings. Originality/value The original contribution of this study lies in the development of an enhanced distributed task allocation strategy using CBAA to improve efficiency in multi-robot environments. Its value extends to applications that require rapid and resource-aware coordination, such as automated logistics or search-and-rescue operations.

Autonomous Underwater Vehicle Design and Development: Methodology and Performance Evaluation

This study focused on the development of an autonomous Underwater Vehicle (AUV) across four key domains: Mechanical Design, Software, Electronics, and Security. The Mechanical Design phase involved utilizing computer-aided drawing programs to create the AUV model. Important design considerations encompassed manufacturability, cost, power, weight, and durability. Comparisons with nominal values of existing market products validated the precision of the produced designs. Particular emphasis was placed on optimizing the weight of the models, ensuring the AUV's efficiency, lightness, and exceptional maneuverability in underwater environments. The Software stage entailed the development of AUV software to enable sensitive and effective vehicle operations. Seamless functioning, devoid of errors or complications, was imperative to ensure optimal autonomous driving, running, and operational capabilities. Image processing algorithms were incorporated into the software to maintain high accuracy and provide dimensional and geometric information of targeted areas. Furthermore, the software phase involved the development of an image processing algorithm based on color analysis, further augmenting accuracy. The selection of electronic components for the AUV was also a vital consideration, alongside ensuring safety measures at every stage of the UAV's development.

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

A Study on Cyclical Learning Rates in Reinforcement Learning and Its Application to Temperature and Power Consumption Control of Refrigeration System

In recent years, with the advancement of computer hardware technology, an increasing number of complex control systems have begun employing reinforcement learning over traditional PID controls to address the challenge of managing multiple outputs simultaneously. In this study, we have for the first time adopted the cyclical learning rate method, which is widely used in deep learning, and applied it to deep reinforcement learning. Utilizing MATLAB Simulink, a detailed simulation model was developed with the RSD-4TFK5J model refrigeration storage as the reference object. We precisely evaluated the effects of various cyclical learning rate strategies on the training process of the model. The simulation results demonstrate the effectiveness of the cyclical learning rate method during the training phase, showcasing its potential to enhance learning efficiency and system performance in complex control environments.

Improving Iron Content in Sustainable Mycoprotein Production through Seawater Fermentation

The growing global population and rising protein demand are straining freshwater resources. Fusarium venenatum (Fv) mycoprotein offers a sustainable protein alternative, with environmental efficiency and potential health benefits. However, its low iron content remains a concern, especially for vegetarians and vegans. This study introduces a sustainable approach, employing seawater as a fermentation medium for Fv production. Our analysis reveals that mycoprotein derived from SEA Fv exhibits elevated levels of sodium and calcium, with a notably high iron content (2.2 mg/100g wet weight). The sodium content, while 3.31 times higher than in non-seawater fermentation, remains within recommended daily intake parameters. No plasticizers or heavy metals were detected in the SEA Fv cell body, minimizing long-term toxicity risks from seawater use. A unique metabolite, dihydroorotic acid, was identified from an in-house library of 774 metabolites, serving as an internal biomarker for seawater-based production methods. An acute safety study condensing 600g of SEA Fv to simulate high mycoprotein digestion showed no effects on key physical behaviors or major organs, including the heart and lungs. This positions the product as a viable protein alternative with enhanced iron content, highlighting seawater-based fermentation as a sustainable method for future food production and industry progress.

Synergistic CO2 capture using PANI-polymerized UiO-66 embedded in PEBAX mixed matrix membranes

In this study, mixed-matrix membranes (MMMs) were fabricated using a composite of UiO-66 and polyaniline (PANI) integrated into a polyether-block-amide (PEBAX) matrix. The successful synthesis of the UiO-66 and PANI@UiO-66 composites and their incorporation into the PEBAX matrix were validated through X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR) Spectroscopy, Brunauer–Emmett–Teller (BET) analysis, and Thermogravimetric Analysis (TGA). Quantitative permeation tests revealed that the CO2 permeability in UiO-66 based MMMs increased by 90 % at 30 % filler loading (from 82 to 156 Barrer), and by 45 % (from 82 to 119 Barrer) in PANI@UiO-66 based MMMs, alongside substantial improvements in selectivity. For UiO-66 membranes we observed a selectivity drop for both gas pairs (CO2/CH4 and CO2/N2) that led to our motivation to modify the MOF. The CO2/CH4 selectivity of the PANI@UiO-66 based MMMs enhanced from 22 to 29 (34%) and the CO2/N2 selectivity from 48 to 57 (18%). Mixed-gas permeation tests further confirmed the efficacy of the membranes in real-world separation scenarios. The diffusivity and solubility results provide insights into the gas transport mechanisms, revealing the synergistic effects of filler incorporation on membrane performance. The integration of UiO-66 and PANI with PEBAX offers a promising pathway for developing efficient and effective gas separation technologies, aligning with the industrial requirements for environmental sustainability and energy efficiency.

A novel acid resource: waste hydrochlorofluorocarbons refrigerants selective leaching Cu-In-Ga-Se (CIGS) and catalyzed mineralization under the hydrothermal atmosphere.

With the upgrading and obsoleting of electric refrigeration equipment, significant amounts of waste hydrochlorofluorocarbon (HCFCs) refrigerants are being generated, bringing serious ozone-depleting and global warming effects. HCFCs, containing chlorine and fluorine, have the potential to be converted into acids by mineralization. Hydrothermal technology possesses a tightly sealed environment and high thermal efficiency, providing significant advantages in treating volatile HCFCs. However, the stable C-F bonds and the by-product CClF=CH2 greatly impede the mineralization of HCFCs. Herein, an indium compound from waste CIGS (CuInxGa1-xSe2) was introduced to promote the process, and this strategy successfully increased the mineralization rates of organic chlorine and fluorine to 99.2% and 95.7%, respectively. The acid produced from HCFCs degradation was utilized for the selective leaching of metals in CIGS. Meanwhile, the leaching product InOOH catalyzed further degradation of HCFCs. We revealed that InOOH can adsorb reactants and provide interfaces, resulting in (i) the elongation of C-X (F, Cl) bonds; (ii) a reduction in the energy barrier of the CClF=CH2 degradation reaction. This study provides a promising approach and theoretical basis for the harmless treatment and resourceful utilization of waste refrigerants containing Cl and F.

Enhancing high-performance concrete with local andesite and calcined marl: Insights from heat treatment and untreated conditions

This study explores the incorporation of local Algerian andesite and calcined marl as supplementary cementitious materials in high-performance concrete (HPC), with a special focus on the effects of heat treatment. Advanced analytical techniques, including X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA), were used for material characterization. The Sherbrooke method was utilized for mix design optimization, and thermal curing effects were critically evaluated. Key results demonstrated that both andesite and calcined marl significantly enhance HPC performance. Fresh mixtures showed excellent workability and consistent density. In the hardened state, these admixtures reduced porosity and water absorption capacity, improving durability. Heat-treated samples exhibited substantial early strength gains, while non-heat-treated samples showed superior long-term strength due to ongoing pozzolanic activity. Ultrasonic pulse velocity (UPV) measurements confirmed the high quality of the concrete. Tomography revealed a dense pore structure and strong interfacial transition zones, contributing to overall performance. Statistical modeling using the Design of Experiments (DOE) methodology validated the experimental findings and identified optimal conditions for maximizing compressive strength. Sulfuric acid exposure tests indicated good chemical resistance, with balanced performance in maintaining residual strengths and managing weight losses. This research underscores the potential of using local andesite and calcined marl to enhance HPC, promoting sustainable construction practices in Algeria by reducing reliance on imports and utilizing regional resources. In summary, this study contributes to sustainable HPC development by demonstrating the effectiveness of local mineral admixtures. The dual benefits of heat treatment for early strength gain and non-heat-treated conditions for long-term durability offer valuable insights for optimizing HPC formulations, advancing our understanding of pozzolanic materials, and promoting environmental stewardship and economic efficiency in the construction industry.

Sequentially amplified integration of catalytic DNA circuits for high-performance intracellular imaging of miRNA and interpretation of mRNA-miRNA signalling pathway

The cascaded catalytic circuits are viable tools for improving the signal gain of biosensors, yet their sensing performance is still limited by the signal leakage from complex biological environment and unsatisfying reaction efficiency from inter-reactants steric hindrance. Herein, we proposed a catalytically localized DNA (CLD) circuit for the accurate and high-efficiency imaging of microRNA (miRNA) in living cells by virtue of the sequentially and successively amplified integration of catalytic DNA circuits. The compact CLD circuit was constructed by integrating two elemental catalytic circuits, cell-responsive EDR module and analyte-sensing CHA module, where CHA module was initially caged in EDR module for eliminating the unwanted off-site and off-target signal leakage. Only by cell-specific messenger RNA (mRNA)-activated EDR operation then the elemental CHA circuit could be successively connected to facilitate the highly efficient intramolecular reaction with low steric hindrance, thus leading to accelerated reaction efficiency for miRNA analyte. The multiple molecular recognition and the spatial self-confinement of the smart CLD circuit enable the accurate and high-efficiency imaging of intracellular miRNA. The interaction network of mRNA and miRNA was then investigated in situ through our CLD circuit, which provides a powerful tool for discovering the underlying signal pathways between these different RNAs in living cells.

Federated learning in edge computing: Enhancing data privacy and efficiency in resource-constrained environments

Federated learning (FL) has become one of the most promising machine learning techniques that solve important data confidentiality and security challenges by training a model across decentralized devices without having raw data. Similarly, edge computing allows data analysis near the source, reducing time and using less bandwidth. This work examines the applications of federated learning in edge computing but focuses on scenarios with resource limitations, as seen with IoT devices and mobile networks. Reviewing the currently used approaches, issues, and trends, the features related to the future development involving opportunities and risks are considered. The emerging studies show that federated learning in the context of edge computing improves processing capacity in the federated model by preserving privacy concerns and is applicable in smart cities, healthcare smart grids, and self-driving systems. Some research directions for future works are suggested to address the scale-up and use of resources.

Optimization of Motion Planning for Quadrotor-Equipped Robotic Manipulators

The focus of the work is on optimizing motion planning for quadcopter-equipped robotic manipulators by integrating quadcopter design, modeling, and path planning for a robotic arm mounted on a quadcopter through MATLAB. Recently, quadcopters have also been used in many application fields with increased viability. This paper will attempt at bringing a complete framework that enhances the operational capabilities of quadcopters through optimized path planning algorithms. The approach will rely on obstacle avoidance techniques for safe operation with satisfactory efficiency in unstructured environments. Advanced algorithms such as Rapidly-exploring Random Trees and dynamic window approaches provide solutions to this challenge of redundancy in the path followed and adaptability in its surroundings. Simulations prove that the proposed optimization methods strongly improve the accuracy and efficiency of the path planned by the robotic arm as it adheres to the dynamic constraints imposed. This work can apply practical insight into many real-world fields such as logistics, surveillance, and search-and-rescue applications aside from contributing to the theoretical understanding of quadcopter dynamics.

Multimodal Human–Robot Interaction Using Gestures and Speech: A Case Study for Printed Circuit Board Manufacturing

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform’s flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system’s potential to improve operational efficiency and adaptability in smart manufacturing environments.

Numerical Investigation of Underwater Vehicle Maneuvering Under Static Drift Conditions

The hydrodynamic behavior of underwater vehicles is crucial for achieving optimal maneuverability and energy efficiency in various underwater environments, thereby ensuring effective underwater operations. This research addresses the drift characteristics of an underwater vehicle by conducting Computational Fluid Dynamics (CFD) simulations. DARPA Suboff model was used to analyze its maneuvering characteristics under static drift conditions at a velocity of 3.34 m/s and drift angles ranging from 0 to 18 degrees with 2-degree intervals. The simulations replicate actual sea conditions using the Reynolds-Averaged Navier-Stokes (RANS) equations combined with the k-ω Shear Stress Transport (SST) turbulence model. The computational domain and boundary conditions are carefully defined to optimize the computational cost. The results revealed a significant decrease in longitudinal force when the drift angle increased, while the lateral force and yaw moment showed substantial increases, indicating the complex interactions between drift angles and hydrodynamic performance. This research provides valuable insights into the hydrodynamic forces and moments acting on underwater vehicles, contributing to their design optimization for improved stability and efficiency.

Application of circular economy principles to midstream infrastructural sector: A review of strategies for waste minimization and recycling

Circular economy in midstream operations promotes resource reuse, waste reduction, and sustainable pipeline life cycle management. The transition toward a sustainable oil and gas industry needs to be enabled through the adoption of this concept, especially in the midstream sector, where environmental impact is high. This review focuses on practices in the waste minimization and recycling strategy of midstream infrastructure, including material recycling, reuse of processing debris and the conversion of waste into valuables. It underlines potential lifecycle environmental savings and resource efficiency, besides emphasizing on how innovative approaches are important in mitigating the ecological footprint of midstream activities. It also points out the knowledge gaps on practical integration of the circular economy principles within this very industry and came up with actionable recommendations for industry stakeholders. Through the analysis of the most recent published literatures and case studies of successful implementation, it has been indicated that many companies that use these techniques have succeeded in reducing waste output by over 30%. Conclusively, full scrutiny by this review has helped to attain a holistic approach towards sustainability in the oil and gas industry through wider adoption of recycling and sustainable practices in midstream operations.

Recent Advances in Metal Halide Perovskites for CO2 Photocatalytic Reduction: An Overview and Future Prospects.

The photocatalytic reduction of CO2 into valuable chemicals and fuels has become a significant research focus in recent years due to its environmental sustainability and energy efficiency. Metal halide perovskites (MHPs), renowned for their remarkable optoelectronic properties and tunable structures, are regarded as promising photocatalysts. Yet, their practical uses are constrained by inherent instability, severe electron-hole recombination, and a scarcity of active sites, prompting substantial research efforts to optimize MHP-based photocatalysts. This review summarizes the latest advancements in MHP-based photocatalysis. First the fundamental principles of photocatalysis are outlined and the structural and optical characteristics of MHPs are evaluated. Then key strategies for enhancing MHP photocatalysts, including morphology and surface modification, encapsulation, metal cation doping, heterojunction engineering, and molecular immobilization are highlighted. Finally, considering recent research progress and the needs for industrial application, challenges and future prospects are explored. This review aims to support researchers in the development of more efficient and stable MHP-based photocatalysts.

Enhancement of calcium alginate and urea in gel-modified dry water inhibition of methane-air explosion

Methane has a wide range of applications in several industries. However, it also exhibits flammable and explosive properties, which pose a potential hazard. This study used calcium alginate to make ordinary dry water powder have a stable network gel structure, and urea was added to enhance further dry water powder’s chemical inhibition. A visual test system verified the explosion suppression effect of gel-modified dry water powder on the methane-air explosion. The results show that the optimal parameters for Ordinary dry water preparation were as follows: mass ratio of SiO2 to H2O = 9:100, stirring speed = 5000 r/min, stirring time = 240 s. The powder concentration for the best explosion suppression effect is 0.52 g/L. 10 mass% Urea-modified dry water, which achieves high explosion suppression efficiency in the methane-air explosion system by cooling and endothermic suppressing the explosion reaction. The spatial distribution of Calcium alginate gel urea modified dry water hinders the propagation of the flame. Urea continuously produces nitride at different temperatures, and nitride competes for O2 and OH· Free radicals, thus destroying the methane-air explosion chain reaction. Calcium alginate gel urea (10 mass%) modified dry water of 0.52 g/L has the best explosion suppression efficiency, which reduces the flame propagation velocity of methane-air explosion system by 42.48 %, the suppression efficiency of flame temperature reaches 40 %, and the suppression effect on explosion relief pressure reaches 16 %. Calcium alginate gel urea modified dry water, a new explosion suppression material, significantly improve the explosion suppression effect of methane-air explosions, which can not only improve the industrial safety of methane-containing processes, extend equipment life, and reduce production costs but also help improve environmental protection and resource utilization efficiency by inhibiting methane explosions. This study helps to promote the development of gas explosion suppression materials and has practical significance for safe energy utilization and industrial methane applications.

Innovative Solutions for Sustainable Development: The Role of Social Entrepreneurship in Alleviating Poverty

In Iraq, social entrepreneurship is increasingly recognised as a promising path towards alleviating poverty and fostering holistic societal development. Poverty eradication is crucial for sustainable progress, and social entrepreneurship offers avenues for job creation, accessible goods and services, and overall economic growth. This study aims to enhance the effectiveness of social entrepreneurship in Iraq by improving economic, social, and environmental efficiencies. The research developed three hypotheses, drawing on theories such as Opportunity-Based Entrepreneurship, Social Network Theory, and the Schumpeterian Innovation Model. Through purposive sampling and 220 structured questionnaires, 217 responses were analysed. The findings indicate that social impact strongly predicts poverty alleviation, followed by economic empowerment and social value, though social innovation showed mixed results. Government policies were found to have insignificant result on the relationship between social entrepreneurship and poverty alleviation, attributed to perceived inadequate support for the private sector. Recommendations include regular evaluation of social innovation strategies, brainstorming sessions, empowerment programs, and fostering social impact through community and individual empowerment. By fostering an environment conducive to social entrepreneurship, Iraq can harness local innovations and community strengths to combat poverty effectively, ensuring sustainable development and societal well-being.

Implementation of projects for using RDF as a tool for increasing energy and resource efficiency of enterprises

Increased attention to energy and resource efficiency at the country, regional and corporate levels brings to the forefront the issues of using alternative energy sources. In this regard, the attention of scientists and practitioners has turned to solid waste as potential sources of material and energy value, in particular to those components that have low potential to be reused or recycled, but can become a source of alternative fuel. On the other hand, the unused portions of solid waste are threat to the environment. The purpose of this study is to evaluate the process of the industrial energy supply chain including the RDF as a part of transition to circular economy. Traditionally, RDF has been used in the cement industry, but, as recent studies show, the transition to alternative fuels brings benefits to enterprises in other industries, as well as to the territories at which they are located. A large array of articles on the use of RDF in various countries and in various industries were studied. To obtain primary data, a pool of experts was formed and their opinions were collected and processed regarding the impact of RDF usage projects on various aspects of enterprise activities. The results show predetermined conditions for implementation of projects for the production and use of RDF. The scientific novelty of the article lies in justification of factors allowing increasing economic, financial, scientific and technological, marketing, social, resource and environmental efficiency of enterprises by including RDF in their energy supply chains.

Cooperative SLAM Algorithm for Multi-AUV Underwater Exploration and Mapping

SLAM (Simultaneous Localisation and Mapping) is very important in the task of mapping unknown deep-sea environments. This paper proposes an AUV cluster SLAM algorithm to improve the efficiency of SLAM mapping and navigation. The algorithm includes three main parts: (1) multi-beam sonar image processing algorithm, which detects and eliminates dynamic points while removing redundant information. (2) Combining DVL (Doppler Velocity Log), IMU (Inertial Measurement Unit) and DM (Depth Meter) data, SLAM is performed based on a Rao-Blackwellised particle filter (RBPF ). (3) The innovative iUSBL (inverted ultra-short baseline) system is used to realise the cooperative positioning between the master and slave AUVs. The multi-AUV underwater detection and mapping collaborative SLAM algorithm proposed in this paper not only significantly improves the mapping efficiency in unknown deep-sea environments but also effectively suppresses the errors introduced by dynamic points and ensures stable SLAM performance. Compared with a single AUV, the efficiency of mapping is significantly improved.

Numerical simulation and experimental study on electrochemical recovery of copper using cylindrical and conical flow reactors

This study investigates the electrochemical recovery of copper from wastewater using cylindrical and conical flow reactors. Electrochemical approaches offer advantages such as environmental compatibility, operational versatility, and energy efficiency for heavy metal remediation. However, effective recovery from dilute effluents remains challenging due to mass transfer limitations. In this work, numerical simulations were conducted to analyze the flow field and current distribution in the reactors, with a focus on the copper reduction process. Experimental results were compared with transient simulations to assess the influence of geometry and operating parameters on the performance of the reactors. The study identified that conical reactors, particularly those with a narrowing electrode gap, enhanced mass transfer rates, leading to improved copper removal efficiency. Key findings include the relationship between electrolyte flow rates, Coulombic efficiency, and copper recovery efficiency. This work contributes to the investigation of electrochemical processes for sustainable heavy metal remediation.