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Rapid increase in urbanization and industrialization due to increase in human population has led to increase in global warming resulting to climate change that affects the ecosystem. Unusual natural disasters such as cyclones, storms, floods, weather fluctuations, rising seal levels, transitional ecosystems and coastal disruptions are major adverse effects of climate change. There are huge worrisome concerns and uncertainties about the future of the planet earth due to climate change caused by high carbon emissions. Global aggressive policies in control of Carbon-dioxide ( $CO_{2}$ ) emissions from several combustion mechanisms have paved way for alternative/ clean energy sources. Green hydrogen is a promising means of clean energy sources for commercial power generation stations and other crucial systems such as hydrogen fuel cell vehicles (HFCVs), motors, drives, backup power for buildings, and also serving as a source of electrical energy for remote locations. The goal of this review paper is to review the current level of Sub-Saharan African nations’ preparedness to adopt green hydrogen as a way to mitigate local energy supply issues and strengthen their individual economies. The novelty of the paper is the light shedding on the progresses made by each member countries in SSA in terms of planning, funding, implementation, partnership and the gaps to be filled with regards to the production of green hydrogen in their local areas.

In contemporary manufacturing settings, adaptability and human-robot cooperation are crucial to addressing the challenges of high-mix, low-volume production. This paper presents a novel methodology to improve workcell ergonomics by optimally placing reconfigurable fixtures and other workcell elements. Based on the personalized upper body models of human workers, we define a comfortable workspace for each arm. Through the development of comprehensive optimization problems, we compute workcell configurations to align with individual ergonomic needs without compromising production goals. We validate our approach through case studies in the automotive sector, including the assembly of light housings and car starters. Results from these studies, based on objective and subjective metrics, affirm the potential of reconfigurable workcell elements to improve ergonomic conditions for workers, thereby contributing to safer and more productive manufacturing environments. This research contributes to the field by prioritizing ergonomics in the design of collaborative manufacturing systems through the implementation of cost-effective reconfigurable solutions.

At the end of a production process, the manufactured products must usually be visually inspected to ensure their quality. Often, it is necessary to inspect the final product from several viewpoints. However, the inspection of all possible aspects might take too long and thus create a bottleneck in the production process. In this paper we propose and evaluate a methodology for adaptive, robot-aided visual quality inspection. With the proposed method, the most probable defects are first predicted based on the production process parameters. A suitable classifier for defect prediction is learnt in an unsupervised manner from a database that includes the produced parts and the associated parameters. A robot then steers the camera only towards viewpoints associated with predicted defects, which implies that the trajectories of robot motion for the inspection might be different for every product. To enable dynamic planning of camera trajectories, we describe a methodology for evaluation and selection of the most appropriate autonomous motion planner. The proposed defect prediction approach was compared to other methods and evaluated on the products from a real-world production line for injection moulding, which was implemented for a producer of parts in the automotive industry.

Meta-learning is a learning-to-learn paradigm that leverages past learning experiences for quick adaptation to new learning tasks. It has a wide application, such as in few-shot learning, reinforcement learning, neural architecture search, federated learning, etc. It has been extended to the online learning setting where task data distribution arrives sequentially. This provides continuous lifelong learning. However, in the online meta-learning setting, a single agent has to learn many varieties of related tasks. Yet, a single agent is limited to its local task data and must collaborate with neighboring agents to improve its learning performance. Therefore, online decentralized meta-learning algorithms are designed to allow an agent to collaborate with neighboring agents in order to improve learning performance. Despite their advantages, online decentralized meta-learning algorithms are susceptible to Byzantine attacks caused by the diffusion of poisonous information from unidentifiable Byzantine agents in the network. This is a serious problem where normal agents are unable to learn and convergence to the global meta-initializer is thwarted. State-of-the-art algorithms, such as BRIDGE, designed to provide robustness against Byzantine attacks are slow and cannot work in online learning settings. Therefore, we propose an online decentralized meta-learning algorithm that works with two Byzantine-resilient aggregation techniques, which are modified coordinate-wise screening and centerpoint aggregation. The proposed algorithm provides faster convergence speed and guarantees both resiliency and continuous lifelong learning. Our simulation results show that the proposed algorithm performs better than state-of-the-art algorithms.

Sensor-based sorting has had a wide range of industrial use in automating and speeding up the process which requires substances or objects to be segregated from each other. The high demand for goods including raw materials, food, minerals, and waste recycling has increased the pressure for high-speed sorting. The first part of this paper presents a comprehensive theoretical and practical survey and comparison of current sorting methods relying on the use of the electromagnetic spectrum. Sorting methods are classified among other things, which portion of the EM spectrum is it, background noise rejection capacity, sample size limitations, sample chemistry limitations, sample surface cleanliness, spatial resolution capacity, spectral resolution, and feed rate limitations. The analysis focuses on color or visible light sorting, gamma-ray sorting, infra-red sorting, x-ray transmission-based sorting and x-ray fluorescence sorting, coupling the findings to the classification criteria outlined. We see a need to define a universal sorting scheme that will in general be applied to most sorting tasks. To do this, the final part of this paper re-looks at the x-ray transmission and x-ray fluorescence sorting scheme in line with the established limitations and proposes a dual x-ray transmission and fluorescence method to mitigate the challenge affecting the different schemes.

Modern-day diamond sorting is achieved through the application of X-ray luminescence (XRL) and X-ray transmission (XRT) techniques. Sorting with XRL is limited to the class range of 1.25 mm to 32 mm because of self-absorption associated with larger diamonds, greater than 32mm. The effect of self-absorption is also a high-energy phenomenon in XRL. XRT is limited to sorting large size diamonds as the technique suffers poor contrast for diamonds smaller than 10mm. XRT measurements are immune to self-absorption for all sample sizes, while XRL measurements have good contrast for particles smaller than 32mm. The applications of these techniques have hitherto been used independently of each other and have subsequently progressed mutually exclusively. Here we analytically show a new paradox of a dual-modality X-ray diamond sorting combining XRL and XRT techniques' strengths. Key features of our new paradoxical model performance are contrast mitigation for small particles and self-absorption rejection for a large particle at high energy as well as improved particle detectability and classification.

Long term planning in power transmission network expansion provides a well ordered and profitable extension of power equipment and facilities to meet the expected electric energy demand with an allowable degree of reliability. However, high quality and improved reliability in energy supply have to be balanced with the available funds. The need to expand transmission network can never be over emphasized. Transmission Network Expansion Planning (TNEP) is a periodical measure that must be carried out due to dynamic societies that attract extra energy demands. It is highly important to minimize the network reinforcement and operational costs while satisfying the increase in demand imposed by technical and economic conditions over the planning horizon. Several optimization algorithms for TNEP problems have been developed and applied over the past decades. This paper presents a comprehensive state-of-the-art survey on the TNEP optimization algorithms. The approach of this paper is in the area of highlights of the various available TNEP algorithms, their applications, viability, computational complexities and drawbacks, which can aid in the identifications of the proper methods that can yield an optimal solution to TNEP problem.

We propose a new approach to programming by the demonstration of finishing operations. Such operations can be carried out by industrial robots in multiple ways because an industrial robot is typically functionally redundant with respect to a finishing task. In the proposed system, a human expert demonstrates a finishing operation, and the demonstrated motion is recorded in the Cartesian space. The robot’s kinematic model is augmented with a virtual mechanism, which is defined according to the applied finishing tool. This way, the kinematic model is expanded with additional degrees of freedom that can be exploited to compute the optimal joint space motion of the robot without altering the essential aspects of the Cartesian space task execution as demonstrated by the human expert. Finishing operations, such as polishing and grinding, occur in contact with the treated workpiece. Since information about the contact point position is needed to control the robot during the operation, we have developed a novel approach for accurate estimation of contact points using the measured forces and torques. Finally, we applied iterative learning control to refine the demonstrated operations and compensate for inaccurate calibration and different dynamics of the robot and human demonstrator. The proposed method was verified on real robots and real polishing and grinding tasks. Note to Practitioners—This work was motivated by the need for automation of finishing operations, such as polishing and grinding, on contemporary industrial robots. Existing approaches are both too complex and too time-consuming to be applied in flexible and small-scale production, which often requires the frequent deployment of new applications. Our approach is based on programming by demonstration and enables the programming of finishing operations also for users who are not specialists in robot programming. Programming by demonstration is especially useful for teaching finishing operations because it enables the transfer of expert knowledge about finishing skills to robots without providing lengthy task descriptions or manual coding. Besides the human demonstration of the desired operation, the proposed approach also requires the availability of the kinematic model for the machine tool applied to carry out the finishing operation. We provide several practical examples of grinding and polishing tools and how to integrate them into our approach. Another feature of the proposed system is that user demonstrations of finishing operations can be transferred between different combinations of robots and machine tools. [ABSTRACT FROM AUTHOR]

Modern-day diamond sorting is achieved through the application of X-ray luminescence (XRL) and X-ray transmission (XRT) techniques. Sorting with XRL is limited to the class range of 1.25 mm to 32 mm because of self-absorption associated with larger diamonds, greater than 32mm. The effect of self-absorption is also a high-energy phenomenon in XRL. XRT is limited to sorting large size diamonds as the technique suffers poor contrast for diamonds smaller than 10mm. XRT measurements are immune to self-absorption for all sample sizes, while XRL measurements have good contrast for particles smaller than 32mm. The applications of these techniques have hitherto been used independently of each other and have subsequently progressed mutually exclusively. Here we analytically show a new paradox of a dual-modality X-ray diamond sorting combining XRL and XRT techniques' strengths. Key features of our new paradoxical model performance are contrast mitigation for small particles and self-absorption rejection for a large particle at high energy as well as improved particle detectability and classification. [ABSTRACT FROM AUTHOR]