Search Results (34)

Problem: Existing Human–Robot Collaboration (HRC) allocators cannot react at a sub-second scale while accounting for worker fatigue. Objective: We designed a fatigue-aware combinatorial auction executed every 100 ms. Method: A human and a FANUC robot submit bids combining execution time, predicted energy, and real-time fatigue; a greedy algorithm (≤1 ms) with a $1-1/e$ approximation guarantee and O (|Bids| log |Bids|) complexity maximizes utility. Results: In 1000 RoboDK episodes, the framework increases active cycles·min⁻¹ by 20%, improves robot utilization by +10.2 percentage points, reduces per cycle fatigue by 4%, and raises the collision-free rate to 99.85% versus a static baseline (p < 0.001). Contribution: We provide the first transparent, sub-second, fatigue-aware allocation mechanism for Industry 5.0, with quantified privacy safeguards and a roadmap for physical deployment. Unlike prior auction-based or reinforcement learning approaches, our model uniquely integrates a sub-second ergonomic adaptation with a mathematically interpretable utility structure, ensuring both human-centered responsiveness and system-level transparency. Full article

One of the most commonly performed environmental explorations is soil sampling to identify soil properties of agricultural fields, which can inform the farmer about the variable rate treatment of fertilisers in precision agriculture. However, traditional manual methods are slow, costly, and yield low spatial resolution. Deploying multiple robots with proximal sensors can address this challenge by parallelising the sampling process. Yet, multi-robot soil sampling is under-explored in the literature. This paper proposes an auction-based multi-robot task allocation that efficiently coordinates the sampling, coupled with a dynamic sampling strategy informed by Kriging variance from interpolation. This strategy aims to reduce the number of samples needed for accurate mapping by exploring and sampling areas that maximise information gained per sample. The key innovative contributions include (1) a novel Distance Over Variance (DOV) bid calculation for auction-based multi-robot task allocation, which incentivises sampling in high-uncertainty, nearby areas; (2) integration of the DOV bid calculation into the cheapest insertion heuristic for task queuing; and (3) thresholding of newly created tasks at locations with low Kriging variance to drop those unlikely to offer significant information gain. The proposed methods were evaluated through comparative simulated experiments using historical soil compaction data. Evaluation trials demonstrate the suitability of the DOV bid calculation combined with task dropping, resulting in substantial improvements in key performance metrics, including mapping accuracy. While the experiments were conducted in simulation, the system is compatible with ROS and the ‘move_base’ action client to allow real-world deployment. The results from these simulations indicate that the Kriging-variance-informed approach can be applied to the exploration and mapping of other soil properties (e.g., pH, soil organic carbon, etc.) and environmental data. Full article

Electricity market reforms and decreasing technology costs have propelled residential solar PV growth leading distribution network operators to face operational challenges including reverse power flows and voltage regulation during peak solar generation. Traditional mono-facial south-facing PV systems concentrate production at midday when demand may be low, leading to high curtailment, especially for downstream households. This study proposes vertically installed east–west-facing bifacial PV systems (BiE and BiW), characterized by two energy peaks (morning and evening), which are better aligned with residential demand and alleviate grid constraints. Using load flow simulations, the performance of vertical bifacial configurations was compared against mono-facial systems across PV capacities from 1 to 20 kW. Fairness in curtailment was evaluated at 10 kW using Jain’s fairness index, the Gini index, and the Curtailment index. Simulation results show that BiE and BiW installations, especially at higher capacities, not only generate more energy but also are better at managing curtailment. At 10 kW, BiE and BiW increased bid energies by 815 kWh and 787 kWh, and reduced curtailed energy by 1566 kWh and 1499 kWh, respectively. These findings highlight the potential of bifacial PV installations in mitigating curtailment and improving fairness in energy distribution, supporting the demand for residential PV systems. Full article

This paper highlights the transition from traditional procurement systems to the newly introduced eProcurement system in Saudi Arabia, emphasizing the differences and improvements and their implications for sustainable development. The new system aims to enhance transparency, clarify purchasing methodologies, and build trust with the government through effective governance of government purchases and tender management. Guided by Royal Decree, this system aligns with the eProcurement Program to transition into digital processes for proficient bids and government purchases, contributing to more efficient and sustainable procurement practices. While some public agencies have attempted to adopt the new model contract for executing construction projects, it has faced challenges due to its lack of alignment with the best practices and sustainability considerations. The authors argue that many large projects remain exempt from this system, which poses obstacles to achieving the goals of sustainable economic development. The objective of this paper is to explore the newly revised Saudi procurement contracts in comparison with traditional public works contracts, with a focus on how they address socio-economic and environmental sustainability. The research provides an overview of various aspects related to public works contracts (PWCs) in Saudi Arabia, including framework agreements, online reverse auctions, industry localization, knowledge transfer, traditional lump sum contracts, two-phase tenders, and construction project competitions, analyzing their alignment with sustainable development goals. There is limited literature on recent models introduced by the Saudi government, but there are extensive resources on general contract law principles and international public policy. This foundation helps with understanding the legal aspects of public works contracts in Saudi Arabia, their alignment with international standards, and their implications for fostering sustainable development. By examining the literature, researchers can gain insights into the legal and policy framework governing public works contracts in Saudi Arabia and their role in promoting sustainability. The importance of this research lies in its comparative analysis, offering valuable insights into the evolution of procurement practices in Saudi Arabia and their contribution to sustainable socio-economic growth. Full article

The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU’s redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework. Full article

Multiplex sampling, so far mainly used as a tool for S/N ratio improvement in spectroscopic applications and separation techniques, has been investigated here for its potential suitability for time-resolved monitoring where chromatograms of transient signals are recorded at intervals much shorter than the chromatographic runtime. Different designs of multiplex sample introduction were developed and utilized to analyze lithium-ion battery degradation products under normal or abuse conditions to achieve fast and efficient sample introduction. After comprehensive optimization, measurements were performed on two different GC systems, with either barrier discharge ionization detection (BID) or mass spectrometric detection (MS). Three different injector designs were examined, and modifications in the pertinent hardware components and operational conditions used. The shortest achievable sample introduction time was 50 ms with an interval of 6 s. Relative standard deviations were lower than 4% and 10% for the intra- and inter-day repeatability, respectively. The sample introduction system and column head pressure had to be carefully controlled, as this parameter most critically affects the amount of sample introduced and, thus, detector response. The newly developed sample introduction system was successfully used to monitor volatile degradation products of lithium-ion batteries and demonstrated concentration changes over the course of time of the degradation products (e.g., fluoroethane, acetaldehyde and ethane), as well as for solvents from the battery electrolyte like ethyl carbonate. Full article

Farming is a major sector required for any nation to become self-sustainable. Quality seeds heavily influence the effectiveness of farming. Seeds cultivated by breeders pass through several entities in order to reach farmers. The existing seed supply chain is opaque and intractable, which not only hinders the growth of crops but also makes the life of a farmer miserable. Blockchain has been widely employed to enable fair and secure transactions between farmers and buyers, but concerns related to transparency and traceability in the seed supply chain, counterfeit seeds, middlemen involvement, and inefficient processes in the agricultural ecosystem have not received enough attention. To address these concerns, a blockchain-based solution is proposed that brings breeders, farmers, warehouse owners, transporters, and food corporations to a single platform to enhance transparency, traceability, and trust among trust-less parties. A smart contract updates the status of seeds from a breeder from $submitted$ to $approved$. Then, a non-fungible token (NFT) corresponding to approved seeds is minted for the breeder, which records the date of cultivation and its owner (breeder). The NFT enables farmers to keep track of seeds right from the date of their cultivation and their owner, which helps them to make better decisions about picking seeds from the correct owner. Farmers directly interact with warehouses to purchase seeds, which removes the need for middlemen and improves the trust among trust-less entities. Furthermore, a tender for the transportation of seeds is auctioned on the basis of the priority location $lo{c}_p$, Score, and bid_amount of every transporter, which provides a fair chance to every transporter to restrict the monopoly of a single transporter. The proposed system achieves immutability, decentralization, and efficiency inherently from the blockchain. We implemented the proposed scheme and deployed it on the Ethereum network. Smart contracts deployed over the Ethereum network interact with React-based web pages. The analysis and results of the proposed model indicate that it is viable and secure, as well as superior to the current seed supply chain system. Full article

Agricultural peatlands are the most productive soils in the UK for the cultivation of many food crops. Historical drainage of peat for agriculture (i.e., cropland and managed grassland), without consideration of other associated environmental and climatic impacts, has resulted in a significant emission of greenhouse gases (GHGs). There is a need to reduce GHG emissions without compromising the rural economy and jeopardizing food security in the UK to a greater extent than is currently being experienced. In March 2023, in a bid to identify alternative land management systems for agricultural peatlands to support the UK’s commitment to achieving net zero GHG emissions by 2050, a group of forty investigators met at a workshop convened by the AgriFood4NetZero Network+. The workshop reviewed the state of knowledge surrounding the Fens of Eastern England and their importance for food provision, the economy, cultural identity, and climate change mitigation. A broad consensus emerged for research into how GHG emissions from agricultural peatlands could be reduced, whether alternative farming methods, such as seasonal farming or paludiculture, would offer a solution, and how a localized approach for the Fens could be defined. The development of a holistic, inclusive, and plausible land use scenario that considers all aspects of ecosystem services provided by the Fens is urgently needed. Full article

E-Learning is a popular computer-based teaching–learning system that has been rapidly gaining global attention during and post COVID-19. The leaping changes in digital technology have enabled E-Learning to become more effective in recent years. It offers freedom from restrictions caused by geographical boundaries and provides time flexibility in the teaching–learning process. Apart from its numerous advantages, the success of E-Learning depends upon many critical success factors (CSFs) and barriers. If the barriers that lie in the way of successful E-Learning implementation are not addressed diligently, it will limit E-Learning success. It has been revealed through past research that these barriers are serious threats that need immediate attention in their redressal. This paper attempts to reveal sixteen barriers under four different dimensions by going through a comprehensive review of the literature and engaging decision makers. Furthermore, it uses the Analytic Hierarchy Process-Group Decision Making (AHP-GDM) methodology to evaluate and prioritize them. The results obtained show that barriers related to the Institutional Management Dimension (BIMD), Infrastructure and Technological Dimension (BITD), Student Dimension (BSD), and Instructor Dimension (BID) pose the greatest challenges in the successful implementation of E-Learning. The AHP-GDM methodologies reveal the comparative relationship among these barriers as BIMD > BITD > BSD > BID and further quantify their negative effects as 46.35%, 29.88%, 12.30%, and 11.4%, respectively, on successful E-Learning systems (‘>’ indicates comparative challenges). Full article

The changeable nature of renewable sources creates difficulties in system security and stability. Therefore, it is necessary to study system risk in several power system scenarios. In a wind-integrated deregulated power network, the wind farm needs to submit the bid for its power-generating quantities a minimum of one day ahead of the operation. The wind farm submits the data based on the expected wind speed (EWS). If any mismatch occurs between real wind speed (RWS) and expected wind speed, ISO enforces the penalty/rewards to the wind farm. In a single word, this is called the power market imbalance cost, which directly distresses the system profit. Here, solar PV and battery energy storage systems are used along by the wind farm to exploit system profit by grasping the negative outcome of imbalance cost. Along with system profit, the focus has also been on system risk. The system risk has been calculated using the risk assessment factors, i.e., Value-at-Risk (VaR) and Cumulative Value-at-risk (CVaR). The work is performed on a modified IEEE 14 and modified IEEE 30 bus test system. The solar PV-battery storage system can supply the demand locally first, and then the remaining power is given to the electrical grid. By using this concept, the system risk can be minimized by the incorporation of solar PV and battery storage systems, which have been studied in this work. A comparative study has been performed using three dissimilar optimization methods, i.e., Artificial Gorilla Troops Optimizer Algorithm (AGTO), Artificial Bee Colony Algorithm (ABC), and Sequential Quadratic Programming (SQP) to examine the consequence of the presented technique. The AGTO has been used for the first time in the risk assessment and alleviation problem, which is the distinctiveness of this work. Full article

The increased penetration of renewables in power distribution networks has motivated significant interest in local energy systems. One of the main goals of local energy markets is to promote the participation of small consumers in energy transactions. Such transactions in local energy markets can be modeled as a bi-level optimization problem in which players (e.g., consumers, prosumers, or producers) at the upper level try to maximize their profits, whereas a market mechanism at the lower level maximizes the energy transacted. However, the strategic bidding in local energy markets is a complex NP-hard problem, due to its inherently nonlinear and discontinued characteristics. Thus, this article proposes the application of a hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem. The proposed CE-CMAES uses cross entropy for global exploration of search space and covariance matrix adaptation evolution strategy for local exploitation. The CE-CMAES prevents premature convergence while efficiently exploring the search space, thanks to its adaptive step-size mechanism. The performance of the algorithm is tested through simulation in a practical distribution system with renewable energy penetration. The comparative analysis shows that CE-CMAES achieves superior results concerning overall cost, mean fitness, and Ranking Index (i.e., a metric used in the competition for evaluation) compared with state-of-the-art algorithms. Wilcoxon Signed-Rank Statistical test is also applied, demonstrating that CE-CMAES results are statistically different and superior from the other tested algorithms. Full article

In the bid to survive and thrive in an environmental setting, bacterial species constantly interact and compete for resources and space in the microbial ecosystem. Thus, they have adapted to use various antibiotics and toxins to fight their rivals. Simultaneously, they have evolved an ability to withstand weapons that are directed against them. Several bacteria harbor colicinogenic plasmids which encode toxins that impair the translational apparatus. One of them, colicin E3 ribotoxin, mediates cleavage of the 16S rRNA in the decoding center of the ribosome. In order to thrive upon deployment of such ribotoxins, competing bacteria may have evolved counter-conflict mechanisms to prevent their demise. A recent study demonstrated the role of PrfH and the RtcB2 module in rescuing a damaged ribosome and the subsequent re-ligation of the cleaved 16S rRNA by colicin E3 in vitro. The rtcB2-prfH genes coexist as gene neighbors in an operon that is sporadically spread among different bacteria. In the current study, we report that the RtcB2-PrfH module confers resistance to colicin E3 toxicity in E. coli ATCC25922 cells in vivo. We demonstrated that the viability of E. coli ATCC25922 strain that is devoid of rtcB2 and prfH genes is impaired upon action of colicin E3, in contrast to the parental strain which has intact rtcB2 and prfH genes. Complementation of the rtcB2 and prfH gene knockout with a high copy number-plasmid (encoding either rtcB2 alone or both rtcB2-prfH operon) restored resistance to colicin E3. These results highlight a counter-conflict system that may have evolved to thwart colicin E3 activity. Full article

Network slicing (NS) is an emerging technology in recent years, which enables network operators to slice network resources (e.g., bandwidth, power, spectrum, etc.) in different types of slices, so that it can adapt to different application scenarios of 5 g network: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low-latency communications (URLLC). In order to allocate these sliced network resources more effectively to users with different needs, it is important that manage the allocation of network resources. Actually, in the practical network resource allocation problem, the resources of the base station (BS) are limited and the demand of each user for mobile services is different. To better deal with the resource allocation problem, more effective methods and algorithms have emerged in recent years, such as the bidding method, deep learning (DL) algorithm, ant colony algorithm (AG), and wolf colony algorithm (WPA). This paper proposes a two tier slicing resource allocation algorithm based on Deep Reinforcement Learning (DRL) and joint bidding in wireless access networks. The wireless virtual technology divides mobile operators into infrastructure providers (InPs) and mobile virtual network operators (MVNOs). This paper considers a single base station, multi-user shared aggregated bandwidth radio access network scenario and joins the MVNOs to fully utilize base station resources, and divides the resource allocation process into two tiers. The algorithm proposed in this paper takes into account both the utilization of base station (BS) resources and the service demand of mobile users (MUs). In the upper tier, each MVNO is treated as an agent and uses a combination of bidding and Deep Q network (DQN) allows the MVNO to get more resources from the base station. In the lower tier allocation process, each MVNO distributes the received resources to the users who are connected to it, which also uses the Dueling DQN method for iterative learning to find the optimal solution to the problem. The results show that in the upper tier, the total system utility function and revenue obtained by the proposed algorithm are about 5.4% higher than double DQN and about 2.6% higher than Dueling DQN; In the lower tier, the user service quality obtained by using the proposed algorithm is more stable, the system utility function and Se are about 0.5–2.7% higher than DQN and Double DQN, but the convergence is faster. Full article

The integration of renewable energy sources with active thermal power plants contributes to the green environment all over the globe. To achieve maximum reliability and sustainability of the renewable-thermal hybrid system, plentiful constraints need to be considered for minimizing the situation, which creates due to the unpredictable nature of renewable energy. In wind integrated deregulated system, wind farms need to submit the power generation scenario for future days to Independent System Operator (ISO) before the date of operation. Based on their submitted bid, ISO scheduled the power generation from different generating stations, including thermal and renewable. Due to the uncertain nature of the wind flow, there is always a chance of not fulfilling the scheduling amount of power from the wind farm. This violation in the market can impose an economic burden (i.e., imbalance cost) on the generating companies. The solar photovoltaic cell can be used to decrease the adverse economic effects of unpredicted wind saturation in the deregulated system. This paper presents consistent, competent, and effective operating schemes for the hybrid operation of solar PV and wind farms to maximize the economic profit by minimizing the imbalance cost, which occurs due to the mismatch between the actual and predicted wind speed. Modified IEEE 14-bus and modified IEEE 30-bus test systems have been used to check the usefulness of the proposed approach. Three optimization techniques (i.e., Sequential Quadratic Programming (SQP), Smart Flower Optimization Algorithm (SFOA), Honey Badger Algorithm (HBA)) have been used in this work for the comparative study. Bus Loading Factor (BLF) has been proposed here to identify the most sensitive bus in the system, used to place wind farms. The SFOA and HBA optimization technique has been used first time in this type of economic assessment problem, which is the novelty of this paper. The Bus Loading Factor (BLF) has been introduced here to identify the most sensitive bus in the system. After implementing the work, it has been seen that the operation of the solar PV system has reduced the adverse effect of imbalance cost on the renewable integrated deregulated power system. Full article

The Ab-Bid deposit, located in the Tabas-Posht e Badam metallogenic belt (TPMB) in Central Iran, is the largest Pb-Zn (±Cu) deposit in the Behadad-Kuhbanan mining district. Sulfide mineralization in the Ab-Bid deposit formed in Middle Triassic carbonate rocks and contains galena and sphalerite with minor pyrite, chalcopyrite, chalcocite, and barite. Silicification and dolomitization are the main wall-rock alteration styles. Structural and textural observations indicate that the mineralization occurs as fault fills with coarse-textured, brecciated, and replacement sulfides deposited in a bookshelf structure. The Ab-Bid ore minerals precipitated from high temperature (≈180–200 °C) basinal brines within the dolomitized and silicified carbonates. The sulfur isotope values of ore sulfides suggest a predominant thermochemical sulfate reduction (TSR) process, and the sulfur source was probably Triassic-Jurassic seawater sulfate. Given the current evidence, mineralization at Ab-Bid resulted from focusing of heated, over-pressurized brines of modified basinal origin into an active fault system. The association of the sulfide mineralization with intensely altered wall rock represents a typical example of such features in the Mississippi Valley-type (MVT) metallogenic domain of the TPMB. According to the structural data, the critical ore control is a bookshelf structure having mineralized dextral strike-slip faults in the northern part of the Ab-Bid reverse fault, which seems to be part of a sinistral brittle shear zone. Structural relationships also indicate that the strata-bound, fault-controlled Ab-Bid deposit was formed after the Middle Jurassic, and its formation may be related to compressive and deformation stages of the Mid-Cimmerian in the Middle Jurassic to Laramide orogenic cycle in the Late Cretaceous-Tertiary. Full article