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AbstractThe concept of a ‘just transition’ to a low‐carbon economy is firmly embedded in mainstream global discourses about mitigating climate change. Drawing on Karl Polanyi's political economy elaborated inThe Great Transformation, we interrogate the idea of a just transition and place it within its historical context. We address a major contradiction at the core of global energy transition debates: the rapid shift to low‐carbon energy‐systems will require increased extraction of minerals and metals. In doing so, we argue that extractive industries are energy and carbon‐intensive, and will enlarge and intensify social and ecological injustice. Our findings reveal the importance of understanding how the idea of a just transition is used, and by who, and the type of justice that underpins this concept. We demonstrate the need to ground just transition policies and programmes in a notion of justice as fairness.

Abstract Background : To design an effective workflow scheduling optimization of web servers that will bring good trade-offs to meet the workflow task delay prerequisite and performance requirement. Methods: This study presents a delayaware and performance-efficient energy optimization (DAPEEO) technique for workflow execution in a heterogeneous environment (i.e., edge-cloud environment). This technique provides a workflow execution model which meets the application delay prerequisite and performance requirement. Findings: Our model has been designed to reduce the energy consumption, increase throughput, reduce computational cost and computational time to provide a delay-aware and performance-efficient workflow model for the web servers in hybrid cloud computing. Our model Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment (DAPEEO) has reduced the energy consumption by 4.217%, increased the throughput by 19.51%, and reduce the computational cost by 62.38% when compared with the existing Deadline-Constrained Cost Optimization for Hybrid Clouds (DCOH) models. Furthermore, the average energy consumption showed a reduction of 40.993% and 90.384% when compared with the DCOH and Self-Configuring and Self-Healing of Cloud-based Resources (RADAR) workload model respectively. Experiment outcome shows the DAPEEO technique achieves much superior energy efficiency, throughput and computation cost reduction when compared with the existing workflow execution model. Novelty: Existing model failed to balance reducing cost, and meeting workflow execution deadlines under a heterogeneous environment. On the other side, the DAPEEO is efficient in bringing trade-offs in reducing energy dissipation and meeting task deadlines with reduced cost under the edge-cloud computing model. Keywords: Cloud Computing; EdgeCloud; DAPEEO; Energy Efficiency; Throughput; Cost

<p>This paper presents cuckoo search algorithm (CSA) for solving non-convex economic load dispatch (ELD) problems of fossil fuel fired generators considering transmission losses and valve point loading effect. CSA is a new meta-heuristic optimisation technique inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. The strength of the proposed meta-heuristic optimization technique CSA has been tested and validated on the standard IEEE 14-bus, 26-bus and 30-bus system with several heuristic load patterns. The results have indicated that the proposed approach is able to obtain significant economic load dispatch solutions than those of Firefly Algorithm (FFA) and other soft computing techniques reported in the literature.</p>

Pedestrian inertial navigation technology plays an important role in indoor positioning technology. However, low-cost inertial sensors in smart devices are affected by bias and noise, resulting in rapidly increasing and accumulating errors when integrating double acceleration to obtain displacement. The data-driven class of pedestrian inertial navigation algorithms can reduce sensor bias and noise in IMU data by learning motion-related features through deep neural networks. Inspired by the RoNIN algorithm, this paper proposes a data-driven class algorithm, RBCN-Net. Firstly, the algorithm adds NAM and CBAM attention modules to the residual network ResNet18 to enhance the learning ability of the network for channel and spatial features. Adding the BiLSTM module can enhance the network’s ability to learn over long distances. Secondly, we construct a dataset VOIMU containing IMU data and ground truth trajectories based on visual inertial odometry (total distance of 18.53 km and total time of 5.65 h). Finally, the present algorithm is compared with CNN, LSTM, ResNet18 and ResNet50 networks in VOIMU dataset for experiments. The experimental results show that the RMSE values of RBCN-Net are reduced by 6.906, 2.726, 1.495 and 0.677, respectively, compared with the above networks, proving that the algorithm effectively improves the accuracy of pedestrian navigation.

This report summarizes the experimental work carried out at the Lawrence Livermore National Laboratory on the electronic structure and reactivity of uranium thin films on Pd, Pt, Si, graphite, Cu, and Au substrates from 1990 to 1993. The U-Pd system was studied in the most detail because it was the first to be chosen right after the completion of the experimental equipment. We first studied and characterized clean U overlayers and the possible surface reactions between this metal and the substrates studied. We then subjected these systems to reactive conditions such as heating and adsorbing corrosive gases (O{sub 2}, CO, CO{sub 2}, and C{sub 2}H{sub 4}). Finally we investigated the diffusion of U metal and some of its compounds into the substrates. A new technique was developed, based on Auger Electron Spectroscopy, to follow in real time the diffusion of U overlayers into the substrate. The temperature of the sample is ramped linearly up to 900{degrees}C while following the Auger peak intensities of the two components for a given system. Diffusion rates are obtained by differentiating the measured intensity curves, then peaks result corresponding to diffusion processes with different activation energies. This technique bears a strong similarity to thermal desorption spectroscopy (TDS), where the sample is heated linearly and the rate of desorption is measured as a function of temperature and heating rate.

Datacenter power demand has been continuously growing and is the key driver of its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and hardware types (servers, accelerators, etc.) to power consumption has emerged as a critical requirement for major Web and cloud service providers. With the global growth in datacenter capacity and associated power consumption, such models are essential for important decisions around datacenter design and operation. In this paper, we discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads across hyperscale datacenters of Google fleet. To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features. This performance matches the reported accuracy of the previous started-of-the-art methods, while using significantly less features and covering a wider range of use cases.

AbstractGold, silver, platinum and palladium typically crystallize with the face-centred cubic structure. Here we report the high-yield solution synthesis of gold nanoribbons in the 4H hexagonal polytype, a previously unreported metastable phase of gold. These gold nanoribbons undergo a phase transition from the original 4H hexagonal to face-centred cubic structure on ligand exchange under ambient conditions. Using monochromated electron energy-loss spectroscopy, the strong infrared plasmon absorption of single 4H gold nanoribbons is observed. Furthermore, the 4H hexagonal phases of silver, palladium and platinum can be readily stabilized through direct epitaxial growth of these metals on the 4H gold nanoribbon surface. Our findings may open up new strategies for the crystal phase-controlled synthesis of advanced noble metal nanomaterials.