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We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.

What drives performance of small- and medium-sized enterprises remains largely unanswered and this article is an attempt in that direction to fill in the gap and help evolve the body of knowledge. The article is designed to produce theoretical insights on how top management team (TMT) sharing leadership, market culture, and firm innovation capability, relates to firm performance. Drawing on the resource-based and the dynamic capabilities-based view, we propose that firm innovation capability mediates between the linkages of shared leadership and market-oriented culture with firm performance. In this article, we performe structural equation modeling on survey data collected from 336 small- and medium-sized enterprises in the United Arab Emirates to examine the proposed hypothesized model of the study. The results reveal that both shared leadership and market-oriented culture have positive effects on firm innovation capability. This article suggests that market-oriented culture mediates the relationships of TMT-shared leadership and firm innovation capability. Similarly, firm innovation capability mediates the influence of market-oriented culture and firm performance, and the influence of TMT-shared leadership and firm performance. This article contributes to advance theory and practices. This article also makes sound theoretical and practical contributions to the usage of the resource-based and the dynamic capabilities view in the domain of the small- and medium-sized enterprises.

Abstract Internet of things (IoT) provides connectivity between different smart devices. IoT systems aim to make data collection, and processing easier. Studies show that we can expect over 75 billion IoT devices to be active by 2025. The increasing great interest in IoT systems is due to their ability to provide quality of services to end users. However, critical challenges may arise when they are deployed in various areas and applications. Among these issues are security, bandwidth, scalability, and network latency. In fact, security is one of the most critical issues in IoT application, this is due to the fact that IoT devices can have different computational capabilities, and might be as simple as sensors nodes, or as complex as smart device. hence it is not feasible to have standard security methods adopted for IoT devices. In this paper, we intend to propose a fog computing based trust and reputation system for IoT. Using fog nodes, each IoT device evaluates trust towards other IoT devices and will only proceed with communication if it meets a certain threshold value. This evaluation is necessary to eliminate any malicious devices from affecting the system and quality of service. It will also help protect the system from many attacks such as bad mouthing, on off, and self promoting attacks. Simulation results are provided to highlight the behavior of the system under these attacks. Moreover, the fog based structure will also provide faster and instantaneous services to users and overcome bandwidth and network latency issues. A framework for comparison is also proposed to evaluate the proposed model in comparison to the related work.

Abstract It is an inevitable trend of sustainable manufacturing to replace flood and dry machining with minimum quantity lubrication (MQL) technology. Nevertheless, for aeronautical difficult-to-machine materials, MQL couldn’t meet the high demand of cooling and lubrication due to high heat generation during machining. Nano-biolubricants, especially non-toxic carbon group nano-enhancers (CGNs) are used, can solve this technical bottleneck. However, the machining mechanisms under lubrication of CGNs are unclear at complex interface between tool and workpiece, which characterized by high temperature, pressure, and speed, limited its application in factories and necessitates in-depth understanding. To fill this gap, this study concentrates on the comprehensive quantitative assessment of tribological characteristics based on force, tool wear, chip, and surface integrity in titanium alloy and nickel alloy machining and attempts to answer mechanisms systematically. First, to establish evaluation standard, the cutting mechanisms and performance improvement behavior covering antifriction, antiwear, tool failure, material removal, and surface formation of MQL were revealed. Second, the unique film formation and lubrication behaviors of CGNs in MQL turning, milling, and grinding are concluded. The influence law of molecular structure and micromorphology of CGNs was also answered and optimized options were recommended by considering diverse boundary conditions. Finally, in view of CGNs limitations in MQL, the future development direction is proposed, which needs to be improved in thermal stability of lubricant, activity of CGNs, controllable atomization and transportation methods, and intelligent formation of processing technology solutions.

This paper studies the technical, economic, and environmental feasibility of a standalone adsorption cooling system that is thermally driven by biomass combustion and solar photovoltaic energy. The developed cooling package was benchmarked against a baseline vapour compression refrigeration system, driven by grid electricity and the widely investigated adsorption cooling system driven by solar heat. TRNSYS was utilised to imitate the integrated systems, investigate their performance throughout the year, and optimise their designs by employing the meteorological data for Rwanda and an existing cold room (13 m 2 floor area × 2.9 m height) as a case study. The optimisation study for the system revealed that maximum chiller performance (COP = 0.62), minimum biomass daily consumption (36 kg), and desired cold room setting temperature (10 °C) throughout the year can be achieved if the boiler setting temperature, heat storage size, and heating water flow rate are 95.13 °C, 0.01 m 3 and 601.25 Kg/h. An optimal PV area/battery size combination of 12 modules / 16 kWh was observed from the economic, environmental, and technical viewpoints.

AbstractOxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level, and for more than 50 countries at the regional level. It is curated from a variety of (wherever available) official sources. Its purpose is to facilitate the analysis of the spread of SARS-CoV-2 virus and to assess the effects of non-pharmaceutical interventions to reduce the impact of the pandemic. Our database is a freely available, daily updated tool that provides unified and granular information across geographical regions. Design type Data integration objective Measurement(s) Coronavirus infectious disease, viral epidemiology Technology type(s) Digital curation Factor types(s) Sample characteristic(s) Homo sapiens

Binary neural networks (BNNs) have attracted broad research interest due to their efficient storage and computational ability. Nevertheless, a significant challenge of BNNs lies in handling discrete constraints while ensuring bit entropy maximization, which typically makes their weight optimization very difficult. Existing methods relax the learning using the sign function, which simply encodes positive weights into +1s, and -1s otherwise. Alternatively, we formulate an angle alignment objective to constrain the weight binarization to {0,+1} to solve the challenge. In this paper, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise. Therefore, a high-quality discrete solution is established in a computationally efficient manner without the sign function. We prove that the learned weights of binarized networks roughly follow a Laplacian distribution that does not allow entropy maximization, and further demonstrate that it can be effectively solved by simply removing the $\ell_2$ regularization during network training. Our method, dubbed sign-to-magnitude network binarization (SiMaN), is evaluated on CIFAR-10 and ImageNet, demonstrating its superiority over the sign-based state-of-the-arts. Our source code, experimental settings, training logs and binary models are available at https://github.com/lmbxmu/SiMaN. Comment: Accepted by IEEE TPAMI, 2022

Abstract Wireless Body Area Networks (WBAN) can provide continuous monitoring of patients’ health. Such monitoring can be a decisive factor in health and death situations. Fault management in WBANs is a key reliability component to make it socially acceptable and to overcome pertained challenges such as unpredicted faults, massive data streaming, and detection accuracy. Failures in fault detection due to hardware, software, and network issues may put human lives at risk. This paper focuses on detecting and predicting faults in sensors in the context of a WBAN. A framework is proposed to manage AI-based prediction models and fault detection using thresholds where four Machine learning techniques: Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Support Vector Machines (SVM), and Decision Trees (DT), are used. The framework also provides alarm notifications, prediction model deployment, version control, and sensing node profiling. As a proof of concept, a fault management prototype is implemented and validated. The prototype classifies faults, manages automation of sensing node profiling, training, and validation of new models. The obtained experimental results show an accuracy greater than 96% for detecting faults with an inferior false alarm rate.

Abstract Internet of things (IoT) is an essential technology in our life; the importance of IoT is yearly increasing because of the excellent usage value. IoT management can help stakeholders in analyzing and making the right decisions based on previous historical sensed data. However, some challenges emerge while using the IoT that will be more complicated in the future. Data management is one of the significant challenges that is facing IoT technology. The growth of the number of sensors will increase the generated data (Big Data). In a few years, the problem of analyzing, processing, and storing such data will become a highly complex process. Due to the mentioned challenges, in this paper, we propose a new schema to efficiently store the structured IoT data to improve the performance of analyzing and retrieving the data. The main idea about the proposed schema is performed in the data preprocessing step by grouping the data into different levels without losing any single value (lossless compression). We evaluate our proposed schema using eight other datasets in terms of storage size and processing time; our results show that the proposed schema outperforms the traditional storing method for all datasets.

Abstract Background Epigenetic regulatory mechanisms are divergent across the animal kingdom, yet these mechanisms are not well studied in non-model organisms. Unique features of cephalopods make them attractive for investigating behavioral, sensory, developmental, and regenerative processes, and recent studies have elucidated novel features of genome organization and gene and transposon regulation in these animals. However, it is not known how epigenetics regulates these interesting cephalopod features. We combined bioinformatic and molecular analysis of Octopus bimaculoides to investigate the presence and pattern of DNA methylation and examined the presence of DNA methylation and 3 histone post-translational modifications across tissues of three cephalopod species. Results We report a dynamic expression profile of the genes encoding conserved epigenetic regulators, including DNA methylation maintenance factors in octopus tissues. Levels of 5-methyl-cytosine in multiple tissues of octopus, squid, and bobtail squid were lower compared to vertebrates. Whole genome bisulfite sequencing of two regions of the brain and reduced representation bisulfite sequencing from a hatchling of O. bimaculoides revealed that less than 10% of CpGs are methylated in all samples, with a distinct pattern of 5-methyl-cytosine genome distribution characterized by enrichment in the bodies of a subset of 14,000 genes and absence from transposons. Hypermethylated genes have distinct functions and, strikingly, many showed similar expression levels across tissues while hypomethylated genes were silenced or expressed at low levels. Histone marks H3K27me3, H3K9me3, and H3K4me3 were detected at different levels across tissues of all species. Conclusions Our results show that the DNA methylation and histone modification epigenetic machinery is conserved in cephalopods, and that, in octopus, 5-methyl-cytosine does not decorate transposable elements, but is enriched on the gene bodies of highly expressed genes and could cooperate with the histone code to regulate tissue-specific gene expression.