Effect of CdO addition on photon,electron, and neutron attenuation properties of boro-tellurite glasses

This research article aims to study the effect of CdO addition on the radiation shielding characteristics of boro-tellurite glasses in the composition of 50B₂O₃ - (50-x) TeO2- xCdO, where x = 0, 10, 20, 30, 40 and 50 mol%. These glasses were exposed to gamma radiation and the transmitted gamma photons were evaluated for energies varying from 15 keV to 15 MeV using Geant4 simulation toolkit. The number of transmitted photons was then used to characterize the gamma shielding for the studied glasses in terms of linear/mass attenuation coefficients, MFP, Z_eff, and HVL. The simulation outcomes were theoretically confirmed by using Phy-X software. The beta (electron) shielding characterization of the involved glasses was also investigated by determining the projectile range and stopping power using ESTAR software. Additionally, the fast neutron shielding characterization of the glasses was achieved by evaluating removal cross-section (Σ_R). The results reveal that the CdO has a small influence on the shielding performance of the boro-tellurite glasses against gamma, beta, and neutron radiations. The shielding performance of the boro-tellurite glasses was compared with that of common shielding materials in terms of MFP. It can be concluded that the boro-tellurite glasses regardless of the concentration of CdO content have promising shielding performance to be used for radiation applications.     

Artificial Neural Network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approach

The rapid growth of vehicular pollution; mostly running on the diesel engine, emissions emerging are the concerns of the day. Owing to clean burn characteristics features, Hydrogen (H₂) as a fuel is the paradigm of the researcher. Extensive research presented in the literature on H₂ dual fueled diesel engine reveals, the significant role of H₂ in reducing emissions and enhancing the performance of a dual fueled diesel engine. With meager qualitative experiment data, the feasibility to develop an efficient Artificial Neural Network (ANN) model is investigated, the developed model can be utilized as a tool to investigate the H₂ dual fueled diesel engine further. In the process of developing an ANN model, engine load and H₂ flow rate are varied to register performance and emission characteristics. The creditability of the experiment is ascertained with uncertainty analysis of measurable and computed parameters. Leave-out-one method is adopted with 16 data sets; seven training algorithms are explored with eight transfer function combinations to evolve a competent ANN model. The efficacy of the developed model is adjudged with standard benchmark statistic indices. ANN model trained with Broyden, Fletcher, Goldfarb, & Shanno (BFGS) quasi-Newton backpropagation (trainbfg) stand out the best among other algorithms with regression coefficient ranging between 0.9869 and 0.9996.     

Erratum to: Conductivity Sensors Based System Development and Application to Investigate the Interfacial Behaviour between Supersonic Steam Jet and Water

Instruments and Experimental Techniques - An Erratum to this paper has been published: https://doi.org/10.1134/S0020441221050249     

Designing zinc oxide nanostructures (nanoworms,nanoflowers, nanowalls,and nanorods) by pulsed laser ablation technique for gas-sensing application

In this study, pulsed laser ablation technique, also known as pulsed laser deposition (PLD), is used to design and grow zinc oxide (ZnO) nanostructures (nanoworms, nanowalls, and nanorods) by template/seeding approach for gas-sensing applications. Conventionally, ZnO nanostructures used for gas-sensing have been usually prepared via chemical route, where the 3D/2D nanostructures are chemically synthesized and subsequently plated on an appropriate substrate. However, using pulsed laser ablation technique, the ZnO nanostructures are structurally designed and grown directly on a substrate using a two-step temperature-pressure seeding approach. This approach has been optimized to design various ZnO nanostructures by understanding the effect of substrate temperature in the 300-750°C range under O₂ gas pressure from 10-mTorr to 10 Torr. Using a thin ZnO seed layer as template that is deposited first at substrate temperature of ~300°C at background oxygen pressure of 10 mTorr on Si(100), ZnO nanostructures, such as nanoworms, nanowalls, and nanorods (with secondary flower-like growth) were grown at substrate temperatures and oxygen background pressures of (550°C and 2 Torr), (550°C and 0.5 Torr), and (650°C and 2 Torr), respectively. The morphology and the optical properties of ZnO nanostructures were examined by Scanning Electron Microscope (SEM-EDX), X-ray Diffraction (XRD), and photoluminescence (PL). The PLD-grown ZnO nanostructures are single-crystals and are highly oriented in the c-axis. The vapor-solid (VS) model is proposed to be responsible for the growth of ZnO nanostructures by PLD process. Furthermore, the ZnO nanowall structure is a very promising nanostructure due to its very high surface-to-volume ratio. Although ZnO nanowalls have been grown by other methods for sensor application, to this date, only a very few ZnO nanowalls have been grown by PLD for this purpose. In this regard, ZnO nanowall structures are deposited by PLD on an Al₂O₃ test sensor and assessed for their responses to CO and ethanol gases at 50 ppm, where good responses were observed at 350 and 400°C, respectively. The PLD-grown ZnO nanostructures are very excellent materials for potential applications such as in dye-sensitized solar cells, perovskite solar cells and biological and gas sensors.     

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

Recently, ground-penetrating radar (GPR) has been extended as a well-known area to investigate the subsurface objects. However, its output has a low resolution, and it needs more processing for more interpretation. This paper presents two algorithms for landmine detection from GPR images. The first algorithm depends on a multi-scale technique. A Gaussian kernel with a particular scale is convolved with the image, and after that, two gradients are estimated; horizontal and vertical gradients. Then, histogram and cumulative histogram are estimated for the overall gradient image. The bin values on the cumulative histogram are used for discrimination between images with and without landmines. Moreover, a neural classifier is used to classify images with cumulative histograms as feature vectors. The second algorithm is based on scale-space analysis with the number of speeded-up robust feature (SURF) points as the key parameter for classification. In addition, this paper presents a framework for size reduction of GPR images based on decimation for efficient storage. The further classification steps can be performed on images after interpolation. The sensitivity of classification accuracy to the interpolation process is studied in detail.     

Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language

The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos recognition tasks, with recognition rates of over 90% with little variations. CBAM-ResNet ‘Before Classifier’ is more efficient than ‘Within Blocks’ models of CBAM-ResNet. All experiment results indicated the CBAM-ResNet ‘Before Classifier’ efficiency in recognising Malaysian Sign Language and its worth of future research.     

Build-Operate-Transfer in Infrastructure Projects in the United States

While the infrastructure in the United States is in need of large and immediate investment, the funds provided by public agencies are not nearly sufficient to face such a challenge. Build-operate-transfer (BOT) is a delivery/financing system that can be a solution to this problem. In this system, a private sponsor finances the design, construction, maintenance, and operation of a public project for a specified concession period, at the end of which it transfers ownership to the government agency, hopefully after recouping its costs and achieving profits. A questionnaire survey of large municipalities and state departments of transportation was conducted to determine the extent to which they are using BOT in their large projects, to investigate the implementation of BOT, and the reasons why some government agencies avoid using BOT. The findings indicate that very few agencies use BOT. The reasons why most do not use BOT were reported by the respondents to be the availability of proven alternatives and enough funds, the existence of political barriers, and resistance to change both on the part of government agencies and private sponsors. When government agencies and private sponsors explore the use of BOT, they should avoid the pitfalls perceived by the respondents in this study.     

The Double Edge Sword Based Distributed Executor Service

Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.     

Perception,navigation, and manipulation in the team KAUST approach to the MBZIRC ground robotics challenge

The ground robotics challenge in the Mohammed Bin Zayed International Robotics Challenge required a ground vehicle equipped with a robotic arm to autonomously locate a panel, select a proper size wrench among several options mounted on the panel, and use the wrench to rotate a valve. Autonomy was the critical factor in this challenge, which required the teams to devise algorithms that can operate successfully in a semistructured environment without human supervision. This paper presents the approaches taken by team KAUST to meet this challenge, ranging from in‐house hardware designs to algorithm integration and customization. We separated the whole objective into three interconnected tasks: Navigation, perception, and manipulation. For the navigation task, we developed a basic robotic exploration scheme to find the panel front side where the wrenches were present. For the perception task, we integrated common object detection algorithms with neural networks to identify the proper size wrench precisely. For successful manipulation, we designed and built a custom gripper, which was inspired by the common grasping behavior of a human hand under tight clearance conditions. The modular structure of the proposed approach allowed the team to progress in several subtasks simultaneously. However, the interconnection between the subtasks necessitated a reliable integration framework between these modules for effective implementation. We tuned our algorithms in extensive experimental studies and eventually obtained 10 consecutive successful navigation runs, 96% true wrench detection rate, and high success rate in wrench grasping. Furthermore, successful complete tests proved the reliability and repeatability of our system.