bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Security Rules and Mechanisms to Protect Data from Assaults
Authors: Zahra Syeda Wajiha , Arshad Ali , Nadeem Muhammad , Riaz Saman , Dutta Ashit Kumar , Alzaid Zaid , Alabdan Rana , Almutairi Badr , Almotairi Sultan
Published: December 2022

Cloud cryptography is the art of converting plain text into an unreadable format, which protects data and prevents the data from being misused by the attacker. Different researchers designed various Caesar cipher algorithms for data security. With the help of these algorithms, the data can be converted into a nonreadable format, but the data cannot be completely secured. In this paper, data security is provided in different phases. Firstly, data are secured through a bit-reversing mechanism in which those replace the actual values with no relation to the original data. Then the four-bit values are added at the beginning and end of bits using a salting mechanism to interlink the salting and existing bit-values and hide the original data. A Caesar cipher value is obtained by applying the Caesar cipher algorithm to the resulting bits. The Caesar cipher algorithm is used to implement number-of-shifting on the obtained values. An efficient cipher matrix algorithm is then developed in which different rules are designed to encrypt the data. Afterward, a secure cipher value is obtained by implementing Cipher XORation rules on the result obtained and the user-defined key. In the end, the proposed algorithm is compared with various papers. It identifies how much better the proposed algorithm performs than all the previous algorithms and how much the attack rate can be reduced if this algorithm is used for data security.
Leader-Following Formation Tracking Control of Nonholonomic Mobile Robots Considering Collision Avoidance: A System Transformation Approach
Authors: Wen Baoyu , Huang Jiangshuai
Published: December 2022

In this paper, the obstacle avoidance problem-based leader–following formation tracking of nonholonomic wheeled mobile robots with unknown parameters of desired trajectory is investigated. First, the under-actuated system is transformed into a fully-actuated system by obtaining an auxiliary control variable using the transverse function. Second, by introducing a potential function for each obstacle, the influence of obstacles is considered in trajectory tracking, and the effect of the potential field on mobile robots is taken into account in the system tracking error. Third, the adaptive laws are designed to estimate the unknown parameters of the desired trajectory. Fourth, the results show that the formation error with respect to the actual position and orientation can be arbitrarily small by selecting appropriate design parameters. Finally, simulation examples are used to demonstrate that the proposed control scheme is effective.
Walking Speed in a Motorbike Lane Considering the Density of Evacuees and Motorbikes
Authors: Cheng Cheng-Chung , Chung Hung-Chieh , Kawabata Nobuyoshi , Seike Miho , Hasegawa Masato , Chien Shen-Wen , Shen Tzu-Sheng
Published: December 2022

In countries with a high motorbike utilization rate, road tunnels can feature motorbike lanes, bringing an additional risk to evacuation from tunnels during a fire or emergency. To better understand the walking speed in motorbike lanes to enhance risk assessment in tunnels, in the present study, we conducted evacuation experiments to investigate the influence of motorbike and evacuee density on the walking speed of motorbike users. According to the experimental results, the walking speed was slightly reduced even when the evacuee density was relatively lower (around 0.1 person/m2). To further analyze the influence of motorbikes in the lane, the walking speed decreased significantly with the increase in motorbike density. The decrease in walking speed presented an exponential relationship with evacuee and motorbike density. Considering this exponential relationship, nonlinear regression was applied to estimate the parameters of the walking speed model. The proposed model consisting of the evacuee density, motorbike density, and free walking speed as variables can serve as an approach to describe the walking speed of motorbike lane evacuation in tunnels.
Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
Authors: Alkwai Lamia , Belghith Abdelfettah , Gazdar Achraf , Al-Ahmadi Saad
Published: December 2022

Seamless management of producer mobility in named data networks (NDNs) has become an inherent requirement to satisfy the ever-increasing number of mobile user devices and the streaming of widespread real-time multimedia content. In this paper, we first classify the various producer mobility management (MM) schemes into four different approaches. Then, we select a representative scheme from each approach and conduct a comparative analysis between them to suggest the most suitable producer MM approach for a broad class of latency sensitive applications, such as video and audio streaming and broadcasting over NDNs. To assess and compare the efficiency and effectiveness of the representative schemes, we implemented them in the NDN defacto NdnSIM simulator and used the same network scenarios and mobility settings. The results show the superiority of the producer MM scheme that follows the data plane-based approach, which yielded lower data loss rates, lower data delivery delays and lower signaling overheads.
An Evaluation of Surface-Active Agent Hexadecyltrimethylammonium Bromide with Vertical Self-Alignment Properties to Align Liquid Crystals for Various Cell Gap Conditions
Authors: Ma Jun-Seok , Choi Jin-Young , Shin Hyun-Ji , Lee Jae-Hwan , Oh Seung-Won , Kim Wook-Sung
Published: December 2022

We evaluated hexadecyltrimethylammonium bromide (HTAB) for liquid crystals (LCs) in layered ITO cells with various cell gap conditions. HTAB is a surfactant that can self-align vertically on the surface of indium tin oxide (ITO) substrates and induce homeotropic alignment of the LC molecules. For implementing RF devices with HTAB and LCs, we should consider limitations caused by the design conditions which are different from conventional liquid crystal displays such as cell gap. We quantified the concentration of HTAB ([HTAB]) that is necessary to form and maintain a sufficiently dense vertical alignment of 5CB (4-Cyano-4′-pentylbiphenyl). The required [HTAB] for full-homeotropic alignment was increased to the cell gap until it was too large to support the transfer of the surface alignment to the LC molecules, due to the weak anchoring nature of HTAB. We also showed the phase-change characteristic of the LC mixture related to [HTAB] for the design of RF devices driven by light or heat. This study may help to guide the development of new approaches to designing efficient RF devices that use LCs.
Geospatial Evaluation of Cropping Pattern and Cropping Intensity Using Multi Temporal Harmonized Product of Sentinel-2 Dataset on Google Earth Engine
Authors: Sonia  , Ghosh Tathagata , Gacem Amel , Alsufyani Taghreed , Alam M. , Yadav Krishna , Amanullah Mohammed , Cabral-Pinto Marina
Published: December 2022

Due to the declining land resources over the past few decades, the intensification of land uses has played a significant role in balancing the ever-increasing demand for food in developing nations such as India. To optimize agricultural land uses, one of the crucial indicators is cropping intensity, which measures the number of times a single parcel of land is farmed. Therefore, it is imperative to create a timely and accurate cropping intensity map so that landowners and agricultural planners can use it to determine the best course of action for the present and for the future. In the present study, we have developed an algorithm on Google Earth Engine (GEE) to depict cropping patterns and further fused it with a GIS environment to depict cropping intensity in the arid western plain zone of Rajasthan, India. A high-resolution multi-temporal harmonized product of the Sentinel-2 dataset was incorporated for depicting the growth cycle of crops for the year 2020–2021 using the greenest pixel composites. Kharif and Rabi accounted for 73.44% and 26.56% of the total cultivated area, respectively. Only 7.42% was under the double-cropped area to the total cultivated area. The overall accuracy of the classified image was 90%. For the Kharif crop, the accuracy was 95%, while for Rabi and the double-cropped region, the accuracy was 88%, with a kappa coefficient of 0.784. The present study was able to depict the seasonal plantation system in arid arable land with higher accuracy. The proposed work can be used to monitor cropping patterns and cost-effectively show cropping intensities.
Non-Probabilistic Reliability Bounds Method for Series Structural Systems Considering Redundant Failure Modes
Authors: Qiao Xinzhou , Zhang Fan , Zhao Jiangbin , Fang Xiurong
Published: December 2022

Non-probabilistic structural reliability analysis is based on the convex model and more applicable for practical engineering problems with limited samples. Recently, the authors proposed a non-probabilistic reliability bounds method (NRBM) for series structural systems as an effective means for the assessment of systems’ non-probabilistic reliability. A novel non-probabilistic reliability bounds method considering redundant failure modes is proposed in this paper for series structural systems to further improve the efficiency and accuracy of the NRBM. By decomposing the system into several subsystems with two or three failure modes, three identification criteria for redundant failure modes are developed for these subsystems. A bounding formula for the system’s non-probabilistic failure degree is then derived after removing the redundant failure modes. An investigation of three numerical examples indicates that the proposed method has a higher efficiency and at least equivalent accuracy compared to the NRBM.
A Critical Review Using CO2 and N2 of Enhanced Heavy-Oil-Recovery Technologies in China
Authors: He Xujiao , Zhao Liangdong , Lu Xinqian , Ding Fei , Wang Zijian , Han Ruijing , Liu Pengcheng
Published: December 2022

Thermal recovery technology is generally suitable for shallow lays due to the higher thermal loss for the deep heavy-oil reservoirs. Non-thermal recovery technologies, such as the non-condensate gas injection technology, are not limited by the reservoir depth and could be extensively applied for the heavy-oil reservoir. Many experimental studies and field applications of non-condensate gas injection have been conducted in heavy-oil reservoirs. The injected non-condensate gas could achieve dynamic miscibility with heavy oil through multiple contacts, which has a significant viscosity-reduction effect under the reservoir conditions. In addition, the equipment involved in the gas injection operation is simple. There are many kinds of non-condensate gases, and common types of gases include N2 and CO2 due to abundant gas sources and lower prices. Moreover, CO2 is a greenhouse gas and the injection of CO2 into the reservoir would have environmental benefits. The non-thermodynamic method is to inject N2 and CO2 separately to produce heavy oil based on the mechanism of the volume expansion of crude oil to form elastic flooding and reduce crude oil viscosity and foamy oil flow. Steam injection recovery of the thermodynamics method has the disadvantages of large wellbore heat loss and inter-well steam channeling. The addition of N2, CO2, and other non-condensate gases to the steam could greatly improve the thermophysical properties of the injected fluid, and lead to higher expansion performance. After being injected into the reservoir, the viscosity of heavy oil could be effectively reduced, the seepage characteristics of heavy oil would be improved, and the reservoir development effect could be improved. Non-condensate gas injection stimulation technology can not only effectively improve oil recovery, but also help to achieve carbon neutrality, which has a very broad application prospect in the future oil recovery, energy utilization, environmental improvement, and other aspects.
Grouting Additives and Information-Based Construction of Jet Grouting in the Water-Rich Sand Stratum
Authors: Qian Xiaqing , Zhang Peng , Wang Shengnian , Guo Shuangfeng , Hou Xinyu
Published: December 2022

The dynamic action of groundwater in the water-rich sand stratum carries away injected cement slurry before it becomes solidified, which seriously affects the determination of the diameter and strength of the column. Grouting additives and information-based construction are two main actions to control the quality of jet grouting construction. This study introduced a solution of grouting additives and information-based construction of jet grouting in the water-rich sand stratum. First, high-polymer cement grout (HPCG), red mud and phosphogypsum cement grout (RPCG) and metakaolin cement grout (MKCG) additives were screened with a series of laboratory tests on solidification time and permeability; moreover, the mix proportion of grouting fluids was developed in order to adapt for water-rich sand strata. Secondly, information-based construction of jet grouting was conducted to control grouting quantity with real-time monitoring of drill rotational velocity, drill lifting speed and injection pressure equipped with the monitoring system on the double fluid jet grouting systems. Lastly, the validity of grouting additives and information-based construction in the water-rich sand stratum was verified via a test pile in situ, and a series of material tests on drilling core samples on permeability with SEM observation. The results indicate that the high polymer is the preferred additive of grouting fluids because the solidification time can be controlled in the range of 10 min to 20 min; the permeability of drilling core samples can reach the order of 10−7, with the mix proportion being A:B = 2:1, high-polymer additive:water > 1:3, with a water-cement ratio of 0.8. The specifications of information-based construction are a drill rotational velocity of 10 r/min, a drill lifting speed of 0.2 m/min, an injection pressure of 20 MPa and a grouting quantity of 40 L/min.
Bi-LSTM-Based Neural Source Code Summarization
Authors: Aljumah Sarah , Berriche Lamia
Published: December 2022

Code summarization is a task that is often employed by software developers for fixing code or reusing code. Software documentation is essential when it comes to software maintenance. The highest cost in software development goes to maintenance because of the difficulty of code modification. To help in reducing the cost and time spent on software development and maintenance, we introduce an automated comment summarization and commenting technique using state-of-the-art techniques in summarization. We use deep neural networks, specifically bidirectional long short-term memory (Bi-LSTM), combined with an attention model to enhance performance. In this study, we propose two different scenarios: one that uses the code text and the structure of the code represented in an abstract syntax tree (AST) and another that uses only code text. We propose two encoder-based models for the first scenario that encodes the code text and the AST independently. Previous works have used different techniques in deep neural networks to generate comments. This study’s proposed methodologies scored higher than previous works based on the gated recurrent unit encoder. We conducted our experiment on a dataset of 2.1 million pairs of Java methods and comments. Additionally, we showed that the code structure is beneficial for methods’ signatures featuring unclear words.
Parameter Estimation for Some Probability Distributions Used in Hydrology
Authors: Anghel Cristian Gabriel , Ilinca Cornel
Published: December 2022

Estimating the parameters of probability distributions generally involves solving a system of nonlinear equations or a nonlinear equation, being a technical difficulty in their usual application in hydrology. The choice of probability distributions for the calculation of extreme values in hydrology is, in most cases, made according to the ease of calculation of the estimated parameters and the explicit form of the inverse probability function. This article presents improved approximations and, in some cases, new approximations for the estimation with the method of ordinary moments and the method of linear moments, which are useful for the direct calculation of the parameters, because the errors in the approximate estimation are similar to the use of iterative numerical methods. Thirteen probability distributions of two and three parameters frequently used in hydrology are presented, for which parameter estimation was laborious. Thus, the approximate estimation of the parameters by the two methods is simple but also precise and easily applicable by hydrology researchers. The new and improved approximate forms presented in this article are the result of the research conducted within the Faculty of Hydrotechnics to update the Romanian normative standards in the hydrotechnical field.
Back Analysis of Surrounding Rock Parameters of Large-Span Arch Cover Station Based on GP-DE Algorithm
Authors: Zheng Fu , Jiang Annan , Guo Xinping , Min Qinghua , Yin Qingfeng
Published: December 2022

Due to the characteristics of soil–rock composites and large-span arches, the surrounding rock parameters of stations are difficult to obtain accurately under soft upper and hard lower geological conditions when the arch cover method is used to carry out the construction of a large-span underground excavation station. To optimize the design of stations and guide the next step of construction, an intelligent inverse analysis method, the Gaussian process differential evolution co-optimization algorithm (GP-DE algorithm), is proposed for the arch cover method for station construction. Taking the Shikui Road station of the Dalian Metro Line Five as the engineering background, the finite element model of FLAC3D is established. By combining the measured data of the sensor and the monitoring data obtained using the orthogonal scheme, this algorithm is used for the joint back analysis of displacement stress and the accuracy of the inversion parameters is verified by forwarding the calculation for FLAC3D. By using the obtained surrounding rock parameters, the demolition length of the center diaphragm to the Shikui Road station is optimized. Under different numbers of training samples, the inversion effect of the GP-DE algorithm and the other three common back-analysis algorithms is compared and analyzed. Finally, based on the iteration rate and convergence effect, the value range of the differential evolution algorithm parameters F and CR is given. The results show that the forward calculation results of the parameters obtained from the back analysis are in good agreement with the actual values, and the accuracy of the back-analysis results is high.
HDL-ODPRs: A Hybrid Deep Learning Technique Based Optimal Duplication Detection for Pull-Requests in Open-Source Repositories
Authors: Alotaibi Saud S.
Published: December 2022

Recently, open-source repositories have grown rapidly due to volunteer contributions worldwide. Collaboration software platforms have gained popularity as thousands of external contributors have contributed to open-source repositories. Although data de-duplication decreases the size of backup workloads, this causes poor data locality (fragmentation) and redundant review time and effort. Deep learning and machine learning techniques have recently been applied to identify complex bugs and duplicate issue reports. It is difficult to use, but it increases the risk of developers submitting duplicate pull requests, resulting in additional maintenance costs. We propose a hybrid deep learning technique in this work on the basis of an optimal duplication detection is for pull requests (HDL-ODPRs) in open-source repositories. An algorithm used to extract textual data from pull requests is hybrid leader-based optimization (HLBO), which increases the accuracy of duplicate detection. Following that, we compute the similarities between pull requests by utilizing the multiobjective alpine skiing optimization (MASO) algorithm, which provides textual, file-change, and code-change similarities. For pull request duplicate detection, a hybrid deep learning technique (named GAN-GS) is introduced, in which the global search (GS) algorithm is used to optimize the design metrics of the generative adversarial network (GAN). The proposed HDL-ODPR model is validated against the public standard benchmark datasets, such as DupPR-basic and DupPR-complementary data. According to the simulation results, the proposed HDL-ODPR model can achieve promising results in comparison with existing state-of-the-art models.
Back Analysis of Geotechnical Engineering Based on Data-Driven Model and Grey Wolf Optimization
Authors: Zhao Lihong , Liu Xinyi , Zang Xiaoyu , Zhao Hongbo
Published: December 2022

Geomaterial mechanical parameters are critical to implementing construction design and evaluating stability through feedback analysis in geotechnical engineering. The back analysis is widely utilized to identify and calibrate the geomaterial mechanical properties in geotechnical engineering. This study developed a novel back-analysis framework by combining a reduced-order model (ROM), grey wolf optimization (GWO), and numerical technology. The ROM was adopted to evaluate the response of the geotechnical structure based on a numerical model. GWO was used to search and identify the geomaterials properties based on the ROM. The developed back analysis framework was applied to a circular tunnel and a practical tunnel for determining the mechanical property of the surrounding rock mass. The results showed that the ROM could be an excellent surrogated model and replaced it with the numerical model. The obtained geomaterial properties were in excellent agreement with the actual properties. The deformation behavior captured by the developed framework was consistent with the theoretical solution in a circular rock tunnel. The developed framework provides a practical, accurate, and convenient approach for calibrating the geomaterial properties based on field monitoring data in practical geotechnical engineering applications.
Instability and Atomization of Liquid Cylinders after Shock Wave’s Impacting
Authors: Chen Chen-Yuan , Dong Ruo-Ling , Shi Hong-Hui , Liu Yi
Published: December 2022

This paper describes an experimental study on the instability and atomization of liquid cylinders after the impact of shock waves. Single row water column, in-line double rows water columns and alongside triple rows water columns were evaluated in a horizontal shock tube. The diameter of water column and the Mach number in the experiments were 2.0–4.14 mm and 1.10–1.25, respectively. The global instability along the axial direction of water cylinders was focused. Using a high-speed camera, the developments of spike height, bubble depth and turbulent mixing zone, width were measured. Some comparison was also made between the present experimental results and the existing theoretical model.
New Aspects of Solution Feasibility in a Context of Personalized Therapy Optimization
Authors: Drąg Paweł , Drąg Marlena
Published: December 2022

In this work, the feasibility of a personalized therapy design is considered. We attempt to determine whether all of the obtained results of computer simulations should be presented to medical personnel. For this purpose, a two-drug displacement problem was used, which is the starting point of this research work. The relationships that can be used to characterize the progress as well as the efficiency of treatment in advanced cases can be modeled by a system of nonlinear dynamical equations with additional algebraic dependencies (differential-algebraic equations, DAEs). Then, to improve the efficiency of the therapy, an optimization task needs to be formulated and solved. The solution should meet all the assumed requirements and expectations. Therefore, a control vector parametrization (CVP) procedure for a DAE model is often suggested as an appropriate tool for solving the optimization-based therapy design tasks. In this work, a general iterative optimization framework is discussed in detail together with the proposed three levels of solution feasibility which try to decide if the iteratively obtained solution is trustworthy. The CVP optimization procedure with the designed levels of solution feasibility are implemented and tested. The obtained results are discussed from the perspective of their practical use in the treatment process. It is worth noting that solutions that are valuable from the perspective of creating new optimization algorithms may be rejected by the final recipient as devoid of application possibilities. Some of the presented solutions can be considered as a reference in further clinical research.
Effects of Contrast Enhancement Phase on the Reproducibility and Predictivity of CT-Based Renal Lesions Radiomic Features
Authors: Alkhafaji Hayder , Ibrahim Abdalla
Published: December 2022

The extraction of quantitative medical imaging features, or radiomics, has been an exponentially growing research field in recent decades. Nonetheless, more studies are investigating the limitations of the quantitative imaging features, especially the reproducibility of RFs across different scanning settings. In this experiment, we investigate the reproducibility of renal cell carcinoma (RCC) RFs between the non-contrast, arterial, and late phases contrast-enhanced computed tomography (CE-CT) scans; and the ability of ComBat technique to harmonize these RFs. In addition, we assessed the predictive performance of the RFs extracted from the different phases. A total of 69 CECT scans with the three different phases were analyzed. Original RFs were extracted from the segmented lesions on each phase using Pyradiomics toolbox. The agreement in RF values before and after harmonization was evaluated with the concordance correlation coefficient (CCC). Our results show that most RFs are not reproducible across different imaging phases. In addition, ComBat harmonization did not significantly increase the number of reproducible RFs in any of the three scenarios. Furthermore, RFs extracted from the arterial phase were, on average, the most predictive of overall survival in RCC patients. The findings can guide the analysis of retrospective RCC heterogeneous data acquired in different phases and add to the call for radiomics-specific harmonization techniques.
Towards the Evaluation of Augmented Reality in the Metaverse: Information Presentation Modes
Authors: Gattullo Michele , Laviola Enricoandrea , Evangelista Alessandro , Fiorentino Michele , Uva Antonio Emmanuele
Published: December 2022

In the future, many activities will be carried out in the Metaverse: hybrid offices and video-based education are just some examples. The way research is carried out could change, too. In this context, this work investigates the possibility of simulating Augmented Reality (AR) user studies on information presentation in a virtual environment. Organizing an industrial setup is complex; thus, most studies are executed in laboratories. However, lab experiments present limitations, e.g., the number and variety of participants and the availability of facilities. User studies may also be carried out by exploiting simulated AR, as an initial step for the Metaverse, where people are connected regardless of their location. This alternative could be used to carry out experiments on AR information presentation to solve common issues, such as the lack of physical equipment to perform component location tasks and the long time required to collect a large sample of users. Indeed, researchers could propose CAD models with information that simulates the same visual realism achieved with true AR. Moreover, multiple tests could be conducted in parallel by not relying on a limited amount of physical equipment per user. In this work, we developed and evaluated a desktop-simulated testing environment (DSTE) to conduct AR information presentation experiments remotely. We applied it in a pilot user study, revealing that the proposed DSTE was effective for the related research goals. Furthermore, 40 participants reported a positive user experience. The evaluation confirms that using a DSTE is promising for collecting and analyzing data from a wide range of people.
Special Issue on Advances in Metamaterials for Sound and Vibration Control
Authors: He Qingbo , Yang Tianzhi , Xia Baizhan , Jiang Tianxi
Published: December 2022

Sound and vibration control represent critical issues in our society and research community [...]
Effect of the Addition of Soy Beverage and Propionic Bacteria on Selected Quality Characteristics of Cow’s Milk Yoghurt Products
Authors: Ziarno Małgorzata , Zaręba Dorota , Dryzek Wiktoria , Hassaliu Rozeta , Florowski Tomasz
Published: December 2022

Many diet trends have emerged over the last few years, including plant-based diets with soya as an important component, while fermented milk beverages have been an integral part of the human diet for thousands of years. However, there is little research on the potential of using propionic bacteria for dairy or plant-based food fermentation. The aim of this study was to evaluate the effects of soy beverage addition and propionic bacterium application on the quality of dairy yoghurt products. Three variants of the products—based on cow’s milk, soya beverages, and mixtures of both—were prepared and then fermented with yoghurt bacteria, propionic bacteria or a mixture thereof. It was found that it is possible to obtain functional yoghurt products based on cow’s milk, as well as milk–soya blends, using a mixture of yoghurt and propionic cultures. The resulting milk, milk–soya, and soya yoghurt products displayed pH values in the correct range and a stable bacterial population during refrigerated storage for 21 days. The refrigerated storage time and the type of culture starter used significantly determined the quality characteristics of the milk, milk–soya and soya yoghurt products, such as their firmness, adhesiveness, and water-holding capacity.
Detection of Miss-Seeding of Sweet Corn in a Plug Tray Using a Residual Attention Network
Authors: Gao Lulu , Bai Jinqiang , Xu Jingyao , Du Baoshuai , Zhao Jingbo , Ma Dexin , Hao Fengqi
Published: December 2022

With the promotion of artificial intelligence in agriculture and the popularization of plug tray seedling-raising technology, seedling raising and transplanting have become the most popular planting modes. Miss-seeding is one of the most serious problems affecting seedling raising and transplanting. It not only affects the germination rate of seeds but also reduces the utilization rate of the plug tray. The experimental analysis of traditional machine vision-based miss-seeding showed that because of uneven lighting, the plug tray was wrongly identified as a seed under bright light, but the seeds in the dark were not easy to identify. When using the seeding area to identify seeds and noise, sweet corn seeds in a small area can be easily screened out. This paper proposes a method using the ResNet network with an attention mechanism to solve the above-mentioned problems. In this paper, the captured image was segmented into the images of a single plug tray, and a residual attention network was built; the detection scheme of miss-seeding was also converted into a dichotomous picture recognition task. This paper demonstrates that the residual attention network can effectively recognize and detect the seed images of sweet corn with very high accuracy. The results of the experiment showed that the average accuracy of this recognition model was 98%. The feature visualization method was used to analyze the features, further proving the effectiveness of the classification method of plug tray seedlings.
Evaluation of Braking Timing Sequence of Semi-Trailer Train Based on Fuzzy Analytic Hierarchy Process
Authors: Feng Shiyuan , Zhang Libin , Shan Hongying , Zhang Minghang
Published: December 2022

A reasonable braking timing sequence plays an important role in the braking stability of a semi-trailer train, but there is still a lack of objective and comprehensive scientific evaluations of braking stability as shown by the braking timing sequence. Aiming at this problem, through the analysis of relevant regulations and standards at home and abroad, an evaluation index hierarchy model of the semi-trailer train braking timing sequence is constructed. The fuzzy analytic hierarchy process is used to determine the weight of the evaluation index of each level of the braking timing sequence, and a comprehensive evaluation is obtained. In order to determine the optimal braking timing sequence of the train during braking, a simulation model was established and simulated. Through the combination of index weight and simulation data, the model can provide a theoretical basis for the subsequent study of the optimal braking timing sequence of semi-trailer trains running under different working conditions.
Deep Learning and Bayesian Hyperparameter Optimization: A Data-Driven Approach for Diamond Grit Segmentation toward Grinding Wheel Characterization
Authors: Sicard Damien , Briois Pascal , Billard Alain , Thevenot Jérôme , Boichut Eric , Chapellier Julien , Bernard Frédéric
Published: December 2022

Diamond grinding wheels (DGWs) have a central role in cutting-edge industries such as aeronautics or defense and spatial applications. Characterizations of DGWs are essential to optimize the design and machining performance of such cutting tools. Thus, the critical issue of DGW characterization lies in the detection of diamond grits. However, the traditional diamond detection methods rely on manual operations on DGW images. These methods are time-consuming, error-prone and inaccurate. In addition, the manual detection of diamond grits remains challenging even for a subject expert. To overcome these shortcomings, we introduce a deep learning approach for automatic diamond grit segmentation. Due to our small dataset of 153 images, the proposed approach leverages transfer learning techniques with pre-trained ResNet34 as an encoder of U-Net CNN architecture. Moreover, with more than 8600 hyperparameter combinations in our model, manually finding the best configuration is impossible. That is why we use a Bayesian optimization algorithm using Hyperband early stopping mechanisms to automatically explore the search space and find the best hyperparameter values. Moreover, considering our small dataset, we obtain overall satisfactory performance with over 53% IoU and 69% F1-score. Finally, this work provides a first step toward diamond grinding wheel characterization by using a data-driven approach for automatic semantic segmentation of diamond grits.
Microalgal Growth in Aquaculture Effluent: Coupling Biomass Valorisation with Nutrients Removal
Authors: Esteves Ana F. , Soares Sara M. , Salgado Eva M. , Boaventura Rui A. R. , Pires José C. M.
Published: December 2022

Natural resources are becoming increasingly scarce, and the need to control their consumption and recycle their use is growing. Water is one of the essential resources for human survival. Therefore, there has been an increasing interest in ways to save, recycle and treat water supplies. Aquaculture is one of the most polluting activities as it produces a significant wastewater volume, which needs proper treatment before being discharged into the environment or recycled. Microalgae are a potential solution for wastewater treatment. Due to their numerous advantages, the use of microalgal biomass is being studied, and, at present, there is already a market and room for profit in the sale of microalgal components in various forms, such as animal and human supplements. From a biorefinery point of view, it is important to take advantage of all the qualities and benefits that microalgae have by combining their great capacity to treat wastewater and exploit the produced biomass, analysing its composition for subsequent valorisation, for example. In this study, Chlorella vulgaris was used to treat aquaculture wastewater from a trout farm aquaculture facility, and the treatment efficiency was evaluated. To valorise the resulting biomass, its composition was also assessed. C. vulgaris successfully grew in the effluent with growth rates of 0.260 ± 0.014 d−1 and with average productivity of 32.9 ± 1.6 mg L−1 d−1. The achieved removal efficiencies were 93.5 ± 2.1% for total nitrogen, 98.0 ± 0.1% for nitrate-nitrogen and 92.7 ± 0.1% for phosphate-phosphorus. Concerning biomass composition, the lipids (15.82 ± 0.15%), carbohydrates (48.64 ± 0.83%), and pigment contents (0.99 ± 0.04% for chlorophyll a + b and 0.21 ± 0.04% for carotenoids) were similar to the values of similar studies. However, the protein content obtained (17.93 ± 1.21%) was lower than the ones mentioned in the literature.
Influencing Factors of Steel States in Concrete Based on Electrochemical Impedance Spectroscopic Measurements
Authors: Kim Jaehwan , Park Ki-Tae , Kwon Tae Ho
Published: December 2022

To cope with the premature deterioration of reinforced concrete structures caused by chloride-induced corrosion, the corrosion rate is required to be estimated and the transport parameters calculated. The electrochemical impedance spectroscopy (EIS) technique can evaluate steel corrosion behaviors at different frequencies. However, its capacity to interpret the impedance response of the system (steel embedded in concrete) is still challenged. Although concrete is a good barrier used to secure structural performance, one of the main obstacles associated with electrical measurements relates to the cases in which concrete contains unexpected or invisible variables, such as changes in pore structure induced by the continuous hydration process. In addition, the fact that steel embedded in concrete is controlled during experiments is technically challenging. Due to these limitations, various circuits have been proposed to explain the corrosion behavior of steel in concrete. EIS measurements are very sensitive to small perturbations. During the analysis process of EIS data, it is possible to introduce unexpected errors attributed to variables; thus, the estimated corrosion values based on the analysis rate may be misleading. To overcome the limitations, it is necessary to confirm the impedance responses first in controlled variable cases. In this study, EIS measurements were conducted for active/passive states of steel in concrete in various conditions to minimize variable errors which are likely induced by operators.
Drone and Controller Detection and Localization: Trends and Challenges
Authors: Yousaf Jawad , Zia Huma , Alhalabi Marah , Yaghi Maha , Basmaji Tasnim , Shehhi Eiman Al , Gad Abdalla , Alkhedher Mohammad , Ghazal Mohammed
Published: December 2022

Unmanned aerial vehicles (UAVs) have emerged as a rapidly growing technology seeing unprecedented adoption in various application sectors due to their viability and low cost. However, UAVs have also been used to perform illegal and malicious actions, which have recently increased. This creates a need for technologies capable of detecting, classifying, and deactivating malicious and unauthorized drones. This paper reviews the trends and challenges of the most recent UAV detection methods, i.e., radio frequency-based (RF), radar, acoustic, and electro-optical, and localization methods. Our research covers different kinds of drones with a major focus on multirotors. The paper also highlights the features and limitations of the UAV detection systems and briefly surveys the UAV remote controller detection methods.
Practice Promotes Learning: Analyzing Students’ Acceptance of a Learning-by-Doing Online Programming Learning Tool
Authors: Iftikhar Sidra , Guerrero-Roldán Ana-Elena , Mor Enric
Published: December 2022

Learning-by-doing is a pedagogical approach that helps in learning skills through practice. An online learning-by-doing tool, CodeLab, has been introduced to students undertaking the digital design and creation bachelor’s degree program at the Universitat Oberta de Catalunya. The tool has been used to facilitate and engage students, not well-acquainted with problem-solving techniques, in an introductory programming course. The aim of this study was to examine the factors that play vital roles in students’ acceptance of learning-by-doing tools that facilitate the development of problem-solving skills. The Unified Theory of Acceptance and the Use of Technology (UTAUT) model was used for this purpose and extended by adding the factor of motivation, which is essential for educational contexts. The results highlight that there is a strong relationship between acceptance and motivation, implying that students would use online learning-by-doing tools, such as CodeLab, depending on the amount of motivation and engagement while practicing the learning activities. A positive relationship between motivation and acceptance clearly supports the primary aim of using learning-by-doing tools in problem-solving courses.
PointSwin: Modeling Self-Attention with Shifted Window on Point Cloud
Authors: Jiang Cheng , Peng Yuanxi , Tang Xuebin , Li Chunchao , Li Teng
Published: December 2022

As a pioneering work that directly applies deep learning methods to raw point cloud data, PointNet has the advantages of fast convergence speed and high computational efficiency. However, its feature learning in local areas has a certain defect, which limits the expressive ability of the model. In order to enhance the feature representation in the local area, this paper proposes a new point cloud processing model, which is called PointSwin. By applying the Self-Attention with Shifted-Window mechanism to learn the correlation between mixed features and points, PointSwin encourages features to enhance their interactions with each other to achieve the effect of feature enhancement. At the same time, PointSwin also achieves a better balance between higher accuracy results and less time overhead by adopting the Mask mechanism to reduce redundant computations. In addition, this paper also proposes an efficient model called PointSwin-E. It can maintain good performance while greatly reducing the computational overhead. The results of the comparative experiments on ModelNet40 dataset show that PointSwin and PointSwin-E are better than PointNet and PointNet++ in terms of accuracy, and the effectiveness verification experiments on the Self-Attention with Shifted-Window mechanism also prove the superiority of this model.
Research on Control Strategy of a Magnetorheological Fluid Brake Based on an Enhanced Gray Wolf Optimization Algorithm
Authors: Dai Lili , Lu He , Hua Dezheng , Liu Xinhua , Wang Lifeng , Li Qiang
Published: December 2022

In order to improve the response characteristics of magnetorheological fluid brake (MRB) and reduce the braking fluctuation rate, an improved grey wolf optimization algorithm was proposed to adjust the parameters of the proportion integration differentiation (PID) controller. Firstly, an MRB system was designed and constructed, and its transfer function was determined. Moreover, by adopting the iterative method of logistic curve, an enhanced grey wolf optimization algorithm (EGWOA) was presented. Using the EGWOA, the parameters of the PID controller were optimized to improve the control performance of the system. Finally, the simulation and experiment were carried out. The results showed that EGWOA has a faster response output and overall better performance without overshoot compared with the conventional PID and grey wolf optimization algorithm (GWOA) PID controller.
Wavefront Shaping-Assisted Forward-Viewing Photoacoustic Endomicroscopy Based on a Transparent Ultrasound Sensor
Authors: Zhao Tianrui , Zhang Mengjiao , Ourselin Sebastien , Xia Wenfeng
Published: December 2022

Photoacoustic endoscopy (PAE) can provide 3D functional, molecular and structural information of tissue deep inside the human body, and thus could be well suited for guiding minimally invasive procedures such as tumour biopsy and fetal surgery. One of the major challenges in the development of miniature PAE probes, in particular, forward-viewing PAE probes, is the integration of a sensitive and broadband ultrasound sensor with the light delivery and scanning system into a small footprint. In this work, we developed a forward-viewing PAE probe enabling optical-resolution microscopy imaging based on a transparent ultrasound sensor coated on the distal end of a multimode optical fibre. The transparent sensor comprised a transparent polyvinylidene fluoride (PVDF) thin film coated with indium tin oxide (ITO) electrodes with a diameter of 2 mm. Excitation laser light was focused and raster-scanned across the facet of the probe tip through the multimode fibre and the PVDF-ITO thin film via wavefront shaping. The sensor had an optical transmission rate of 55–72% in the wavelength range of 400 to 800 nm, a centre frequency of 17.5 MHz and a −10 dB bandwidth of 25 MHz. Singular value decomposition was used to remove a prominent trigger-induced noise, which enabled imaging close to the probe tip with an optically defined lateral resolution of 2 µm. The performance of the imaging probe was demonstrated by obtaining high-fidelity photoacoustic microscopy images of carbon fibres. With further optimisation of the sensitivity, the probe promises to guide minimally invasive procedures by providing in situ, in vivo characterisation of tissue.
Fire Behavior and Adhesion of Magnesium Phosphate Coatings for the Protection of Steel Structures
Authors: Cirstea Nicoleta Florentina , Badanoiu Alina , Voicu Georgeta , Ciocoiu Robert Catalin , Boscornea Aurelian Cristian
Published: December 2022

This paper presents the main properties of magnesium phosphate cements (MPCs) to be used as coatings for passive fire protection of steel structures. The influence of various additions, i.e., waste glass powder, fly ash, a styrene–acrylic dispersion, and expandable graphite, on the fire behavior and the adhesion to steel substrates of magnesium phosphate coatings is presented in this paper. The setting time of studied cements is extended when magnesia, the main component of MPCs, is partially replaced with fly ash or/and waste glass powder. The mineralogical composition of these cements, before and after thermal treatment at 1050 °C, was assessed by X-ray diffraction and could explain the changes in compressive strength, volume, and mass recorded for the thermally treated specimens. The studied magnesium phosphate coatings have a good adherence to the steel substrate (assessed by a pull-off test) both before and after direct contact with a flame (fire test) and decrease the temperature of the steel substrate by 30% with respect to the one recorded for the uncoated steel plate.
Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
Authors: Han Yan , Wang Jiangjiang , Zhang Enlai
Published: December 2022

To ensure that the cementitious grouting material (CGM) layer and existing concrete work together, the interfacial bond strength between CGM and normal concrete substrate was experimentally investigated by conducting direct and slant shear tests. The effects of connection interface form, concrete strength, interface shear key, and shear angle on the bond strength of CGM and normal concrete were discussed in detail. The results showed that for composite specimens, the interfacial bond strength was the highest for the triangular interface form, followed by the trapezoidal interface and smooth interface, which had the lowest. The interfacial bond strength increased as the existing concrete’s strength increased; the interfacial bond strength could be greatly improved under shear load in the slant section or when the shear key was set at the interface. Moreover, the interfacial bond strength increased with the increase in the shear angle. The interfacial bond–slip curves were analyzed, and a bond–slip model of the interface between CGM and concrete was proposed. A certain value of interfacial bond stiffness was also recommended.