Experimental study on a single cement-fracture using CO\u3csub\u3e2\u3c/sub\u3e rich brine

The efficiency of Carbon Capture and Storage (CCS) projects is directly related to the long term sealing efficiency of barrier systems and of wellbore cement in wellbores penetrating storage reservoirs. The microfractures inside the wellbore cement provide possible pathways for CO leakage to the surface and/or fresh water aquifers, impairing the long-term containment of CO in the subsurface. The purpose of this experimental study is to understand the dynamic alteration process in the cement caused by the acidic brine. The first experiment, at ambient temperature and pressure, was conducted by flowing CO -rich brine through 1 in. by 2 in. (25.4 mm by 50.8 mm) cement cores for 4 and 8 weeks durations. The second experiment was a 4 weeks long flow-through experiment conducted at ambient conditions using a 1 in by 12 in.(25.4 mm by 304.8 mm) cement core and CO -rich brine with a core flooding system under 600 psi (4.13 MPa) confining stress. Post-experiment material analysis from both experiments confirmed leaching of Ca from reacted cement, as reported in literature. However for the first time, porosity of the reacted regions was semi-quantified applying micro-CT images. © 2010 Elsevier Ltd. © 2011 Published by Elsevier Ltd. 2 2 2 2 2

A ‘quiet revolution’? The impact of Training Schools on initial teacher training partnerships

This paper discusses the impact on initial teacher training of a new policy initiative in England: the introduction of Training Schools. First, the Training School project is set in context by exploring the evolution of a partnership approach to initial teacher training in England. Ways in which Training Schools represent a break with established practice are considered together with their implications for the dominant mode of partnership led by higher education institutions (HEIs). The capacity of Training Schools to achieve their own policy objectives is examined, especially their efficacy as a strategy for managing innovation and the dissemination of innovation. The paper ends by focusing on a particular Training School project which has adopted an unusual approach to its work and enquires whether this alternative approach could offer a more profitable way forward. During the course of the paper, five different models of partnership are considered: collaborative, complementary, HEI-led, school-led and partnership within a partnership

Design of Multi-Layer Protocol Architecture using Hybrid Optimal Link State Routing (HOLSR) Protocol for CR Networks

There is a lack of spectrum due to the rising demand for sensing device communication and the inefficient use of the existing available spectrum. Through opportunistic access to licenced bands, which does not obstruct the primary sensory users (PU), it is feasible to enhance the inefficient use of the current sensor device frequency spectrum. Cognitive settings are a demanding environment in which to carry out tasks like sensor network routing and spectrum access since it is difficult to access channels due to the presence of PUs. The basic goal of the routing problem in sensor networks is to establish and maintain wireless sensor multihop paths between cognitive sensor nodes. The frequency to be used as well as the number of hops at each sensor node along the path must be determined for this assignment. In order to improve performance while using less energy, scientists suggested a unique adaptive cross-layer optimisation subcarrier distribution technique with the HOLSR protocol for wireless sensor nodes. Throughput and energy consumption parameters are used to analyse the sensor network architecture protocol that has been developed. The energy usage of the sensor nodes in the network has increased by 50%. The performance of the proposed HOLSR algorithm is assessed using the simulation results, and the results are contrasted with those of a conventional multicarrier (MC) system in terms of bit error rate and throughput

Sensitivity analysis of the dynamic CO2 storage capacity estimate for the Bunter Sandstone of the UK Southern North Sea

Carbon capture and storage (CCS) in subsurface reservoirs has been identified as a potentially cost-effective way to reduce CO2 emissions to the atmosphere. Global emissions reductions on the gigatonne scale using CCS will require regional or basin-scale deployment of CO2 storage in saline aquifers. Thus the evaluation of both the dynamic and ultimate CO2 storage capacity of formations is important for policy makers to determine the viability of CCS as a pillar of the greenhouse gas mitigation strategy in a particular region. We use a reservoir simulation model representing the large-scale Bunter Sandstone in the UK Southern North Sea to evaluate the dynamics and sensitivities of regional CO2 plume transport and storage. At the basin-scale, we predict hydrogeological changes in the storage reservoir in response to multiple regional carbon sequestration development scenarios. We test the sensitivity of injection capacity to a range of target CO2 injection rates and fluctuations in CO2 supply. Model sensitivities varying the target injection rates indicate that in the absence of pressure management up to 3.7 Gt of CO2 can be stored in the Bunter region over 50 years given the pressure constraints set to avoid fracturing the formation. Long-term (approx. 1000 years), our results show that up to 16 Gt of CO2 can be stored in the Bunter region without pressure management. With pressure management, the estimate rises to 32 Gt. However, consideration must be given to the additional operational and economic requirements of pressure management using brine production

5G Enabled Moving Robot Captured Image Encryption with Principal Component Analysis Method

Estimating the captured image of moving robots is very difficult. These images are vital in analyzing earth's surface objects for many applications like studying environmental conditions, Land use and Land Cover changes, and change detection studies of worldwide change. Multispectral robot-captured images have a massive amount of low-resolution data, which is lost due to a lack of capture efficiency due to artificial and atmospheric reasons. The image transformation is required in a 5G network with effective transmission by reducing noise, inconsistent lighting, and low resolution, degrading image quality. In this paper, the authors proposed the machine learning dimensionality reduction technique i.e. Principle Component Analysis (PCA) and which is used for metastasizing the 5 G-enabled moving robot captured image to enrich the image's visual perception to analyze the exact information of global or local data. The encryption algorithm implanted for data reduction and transmission over the 5G network gives sophisticated results compared with other standard methods. This proposed algorithm gives better performance in developing data reduction, network convergence speed, reduces the training time for object classification, and improves accuracy for multispectral moving robot-captured images by the support of 5G network

STRIDE: Single-video based Temporally Continuous Occlusion Robust 3D Pose Estimation

The capability to accurately estimate 3D human poses is crucial for diverse fields such as action recognition, gait recognition, and virtual/augmented reality. However, a persistent and significant challenge within this field is the accurate prediction of human poses under conditions of severe occlusion. Traditional image-based estimators struggle with heavy occlusions due to a lack of temporal context, resulting in inconsistent predictions. While video-based models benefit from processing temporal data, they encounter limitations when faced with prolonged occlusions that extend over multiple frames. This challenge arises because these models struggle to generalize beyond their training datasets, and the variety of occlusions is hard to capture in the training data. Addressing these challenges, we propose STRIDE (Single-video based TempoRally contInuous occlusion Robust 3D Pose Estimation), a novel Test-Time Training (TTT) approach to fit a human motion prior for each video. This approach specifically handles occlusions that were not encountered during the model's training. By employing STRIDE, we can refine a sequence of noisy initial pose estimates into accurate, temporally coherent poses during test time, effectively overcoming the limitations of prior methods. Our framework demonstrates flexibility by being model-agnostic, allowing us to use any off-the-shelf 3D pose estimation method for improving robustness and temporal consistency. We validate STRIDE's efficacy through comprehensive experiments on challenging datasets like Occluded Human3.6M, Human3.6M, and OCMotion, where it not only outperforms existing single-image and video-based pose estimation models but also showcases superior handling of substantial occlusions, achieving fast, robust, accurate, and temporally consistent 3D pose estimates