ObjectiveCompared with traditional concrete construction, the application of prefabricated assembly construction based on digital twin technology in urban rail transit station construction can effectively ensure component production quality, reduce environmental pollution and lower resource consumption. Therefore, an in-depth research on digital twin technology suitable for prefabricated assembly station construction should be conducted. MethodFirst, in station construction, the overall architecture featuring "4 horizontal + 4 vertical + N platforms" for the application of digital twin technologies, such as BIM (building information modeling) and IoT (Internet of things) is proposed. Second, the modeling process and methodology of BIM are presented. By adopting methods such as mathematical model separation, lightweight processing, and mathematical model association, the established BIM data are imported into the platform, and a technical workflow for uploading IoT monitoring data to the BIM platform is established. Finally, taking a certain underground prefabricated assembly superimposed station in the Phase I project of Jinan Urban Rail Transit Line 8 as a case study, the application effect of the digital twin technology for prefabricated assembly superimposed stations based on BIM+IoT integration is analyzed. Result & Conclusion The proposed digital twin technology shows good application effects in the case station, achieving design goals such as construction progress query, structural safety monitoring, quality management control, and process auxiliary design, and realizing data management interaction and sharing throughout the components full life cycle.
DESIDOC Journal of Library & Information Technology (DJLIT) formerly known as DESIDOC Bulletin of Information Technology is a peer-reviewed, open access, bimonthly journal. This paper presents a Scientometric analysis of the DESIDOC Journal. The paper analyses the pattern of growth of the research output published in the journal, pattern of authorship, author productivity, and, subjects covered to the papers over the period (2013-2017). It is found that 227 papers were published during the period of study (2001-2012). The maximum numbers of articles were collaborative in nature. The subject concentration of the journal noted is Scientometrics. The maximum numbers of articles (65%) have ranged their thought contents between 6 and 10 pages. The study applied standard formula and statistical tools to bring out the factual result.
Gus Henry Smith, Colin Knizek, Daniel Petrisko
et al.
State-of-the-art hardware compilers for FPGAs often fail to find efficient mappings of high-level designs to low-level primitives, especially complex programmable primitives like digital signal processors (DSPs). New approaches apply sketch-guided program synthesis to more optimally map designs. However, this approach has two primary drawbacks. First, sketch-guided program synthesis requires the user to provide sketches, which are challenging to write and require domain expertise. Second, the open-source SMT solvers which power sketch-guided program synthesis struggle with the sorts of operations common in hardware -- namely multiplication. In this paper, we address both of these challenges using an equality saturation (eqsat) framework. By combining eqsat and an existing state-of-the-art program-synthesis-based tool, we produce Churchroad, a technology mapper which handles larger and more complex designs than the program-synthesis-based tool alone, while eliminating the need for a user to provide sketches.
Long wheelbase design of vehicle can effectively increase the standing area without increasing the body length, thus increasing the passenger capacity. Today, with the development of urbanization, the long wheelbase vehicle design has become a trend, but this poses new challenges to the low-speed trafficability and high-speed stability of vehicles. This paper takes the long wheelbase commercial vehicle as the research object. Based on the vehicle dynamics and suspension design theory, the author first designed key parameters of long wheelbase vehicle and built an 18 degrees of freedom (DOF) dynamics simulation model for the vehicle; then designed the four-wheel steering (4WS) control algorithm according to the design parameters of the vehicle, for achieving the control target that the side slip angle tends to zero; and finally researched the influence of front-wheel steering (FWS) control and four-wheel steering control on the dynamic performance of the vehicle under the steady-state circumferential conditions with different turning radiuses and steering wheel angle pulse conditions with different speeds. The simulation results show that, under the steady-state circumferential turning condition with a low-speed turning radius of R15, the four-wheel steering design reduces the passing space from 4.6 m to 3.9 m, effectively improving the tracking ability of the front and rear axles of the vehicle and enhancing the trafficability and safety of the vehicle, and under the pulse condition with a maximum speed of 100 km/h, reduces the peak lateral acceleration from 4 m/s2 to 1.5 m/s2 and the peak yaw rate from 11°/s to 3°/s. Therefore, under high-speed steering wheel angle pulse conditions, the four-wheel steering design can effectively reduce the dynamic indicators of the vehicle, such as side slip angle, lateral acceleration and yaw rate, and improve the safety, stability and comfort of the vehicle at high speed.
Control engineering systems. Automatic machinery (General), Technology
Elena Laura Mazzoldi, Giulia Gaudenzi, Paola Serena Ginestra
et al.
IntroductionTissue engineering has advanced significantly in recent years, owing primarily to additive manufacturing technology and the combination of biomaterials and cells known as 3D cell printing or Bioprinting. Nonetheless, various obstacles remain developing adequate 3D printed structures for biomedical applications, including bioinks optimization to meet biocompatibility and printability standards. Hydrogels are among the most intriguing bioinks because they mimic the natural extracellular matrix found in connective tissues and can create a highly hydrated environment that promotes cell attachment and proliferation; however, their mechanical properties are weak and difficult to control, making it difficult to print a proper 3D structure.MethodsIn this research, hydrogels based on Alginate and Gelatin are tested to evaluate the metabolic activity, going beyond the qualitative evaluation of cell viability. The easy-to-make hydrogel has been chosen due to the osmotic requirements of the cells for their metabolism, and the possibility to combine temperature and chemical crosslinking. Different compositions (%w/v) are tested (8% gel-7% alg, 4% gel-4% alg, 4% gel-2% alg), in order to obtain a 3D structure up to 10.3 ± 1.4 mm.ResultsThe goal of this paper is to validate the obtained cell-laden 3D structures in terms of cell metabolic activity up to 7 days, further highlighting the difference between printed and not printed cell-laden hydrogels. To this end, MS5 cells viability is determined by implementing the live/dead staining with the analysis of the cellular metabolic activity through ATP assay, enhancing the evaluation of the actual cells activity over cells number.DiscussionThe results of the two tests are not always comparable, indicating that they are not interchangeable but provide complementary pieces of information.
Jiayun Xin, Mohammad Khishe, Diyar Qader Zeebaree
et al.
Deep Convolutional Neural Networks (DCNNs) have shown remarkable success in image classification tasks, but optimizing their hyperparameters can be challenging due to their complex structure. This paper develops the Adaptive Habitat Biogeography-Based Optimizer (AHBBO) for tuning the hyperparameters of DCNNs in image classification tasks. In complicated optimization problems, the BBO suffers from premature convergence and insufficient exploration. In this regard, an adaptable habitat is presented as a solution to these problems; it would permit variable habitat sizes and regulated mutation. Better optimization performance and a greater chance of finding high-quality solutions across a wide range of problem domains are the results of this modification's increased exploration and population diversity. AHBBO is tested on 53 benchmark optimization functions and demonstrates its effectiveness in improving initial stochastic solutions and converging faster to the optimum. Furthermore, DCNN-AHBBO is compared to 23 well-known image classifiers on nine challenging image classification problems and shows superior performance in reducing the error rate by up to 5.14%. Our proposed algorithm outperforms 13 benchmark classifiers in 87 out of 95 evaluations, providing a high-performance and reliable solution for optimizing DNNs in image classification tasks. This research contributes to the field of deep learning by proposing a new optimization algorithm that can improve the efficiency of deep neural networks in image classification.
Aleksei Cherkasov, Kirill M. Gerke, Aleksey Khlyupin
Structures are abundant in both natural and human-made environments and usually studied in the form of images or scattering patterns. To characterize structures a huge variety of descriptors is available spanning from porosity to radial and correlation functions. In addition to morphological structural analysis, such descriptors are necessary for stochastic reconstructions, stationarity and representativity analysis. The most important characteristic of any such descriptor is its information content - or its ability to describe the structure at hand. For example, from crystallography it is well known that experimentally measurable $S_2$ correlation function lacks necessary information content to describe majority of structures. The information content of this function can be assessed using Monte-Carlo methods only for very small 2D images due to computational expenses. Some indirect quantitative approaches for this and other correlation function were also proposed. Yet, to date no methodology to obtain information content for arbitrary 2D or 3D image is available. In this work, we make a step toward developing a general framework to perform such computations analytically. We show, that one can assess the entropy of a perturbed random field and that stochastic perturbation of fields correlation function decreases its information content. In addition to analytical expression, we demonstrate that different regions of correlation function are in different extent informative and sensitive for perturbation. Proposed model bridges the gap between descriptor-based heterogeneous media reconstruction and information theory and opens way for computationally effective way to compute information content of any descriptor as applied to arbitrary structure.
Accreditation is an assessment activity to determine the eligibility of a department and higher education. Accreditation aims to determine the eligibility of departments and universities based on criteria that refer to the national higher education standards and guarantee the quality of departments and universities externally in both academic and non-academic fields to protect the interests of students and the community. All accreditation agencies have different accreditation instruments. The scoring calculation can be done automatically through the application of the respective accreditation agency and can only be seen by the assessment team, while departments and universities cannot see it. so far, universities and departments can only calculate accreditation scores manually using the excell application based on accreditation assessment instruments and matrices. Therefore, the research team wants to create a web-based application that can calculate accreditation scores independently to calculate accreditation assessment scores quickly and accurately.
In order to accurately predict the risk level of coal mine gas explosion accidents, based on the feature vector in line with the actual situation, the particle swarm optimization probabilistic neural network (RWPSO-PNN) is improved to realize the prediction model of gas explosion risk level. Firstly, the cause of coal mine gas explosion accident is extracted by Chinese word segmentation, and the input feature vector of the model is selected by grey correlation analysis (GRA). Aiming at the problem of low recognition rate caused by smoothing factor in probabilistic neural network (PNN), RWPSO-PNN is proposed to adjust the smoothing factor adaptively. Finally, RWPSO-PNN is analyzed and compared with extreme learning machine algorithm, BP neural network and support vector machine algorithm. The results show that the prediction accuracy of RWPSO-PNN is 90 %, and the average absolute error is 0.133, which is obviously better than the comparison algorithm.
Over the past two decades, digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis, drug discovery, and immunoassays, among other areas. However, for complex bioassays, finding routes for the transportation of droplets in an electrowetting-on-dielectric digital biochip while maintaining their discreteness is a challenging task. In this study, we propose a deep reinforcement learning-based droplet routing technique for digital microfluidic biochips. The technique is implemented on a distributed architecture to optimize the possible paths for predefined source–target pairs of droplets. The actors of the technique calculate the possible routes of the source–target pairs and store the experience in a replay buffer, and the learner fetches the experiences and updates the routing paths. The proposed algorithm was applied to benchmark suites I and III as two different test benches, and it achieved significant improvements over state-of-the-art techniques.
With the advent of globalization and the evergrowing rate of technology, the volume of production as well as ease of procuring counterfeit goods has become unprecedented. Be it food, drug or luxury items, all kinds of industrial manufacturers and distributors are now seeking greater transparency in supply chain operations with a view to deter counterfeiting. This paper introduces a decentralized Blockchain based application system (DApp) with a view to identifying counterfeit products in the supply chain system. With the rapid rise of Blockchain technology, it has become known that data recorded within Blockchain is immutable and secure. Hence, the proposed project here uses this concept to handle the transfer of ownership of products. A consumer can verify the product distribution and ownership information scanning a Quick Response (QR) code generated by the DApp for each product linked to the Blockchain.