bluuebunny/crossref_metadata_2025 huggingface dataset:
Study of Patterns of Movement of Groups of Seed Potatoes in Conical Seed Box Based on the Dem-Model of the Process
Authors: Cai Hui , Hu Bin , Chen Yong , Luo Xin , Wang Jian , Mao Zibin , Yuan Chenglin
Published: February 2022

To reduce the missing rate caused by the arching phenomenon of seed potatoes in the traditional seed boxes of cup-belt potato planters, a conical-shaped seed box comprising a seed-metering box and a reseeding box connected together was proposed. The method in this paper combined the discrete-element-analysis method and response-surface method and took the movement of the seed-potato group in the seed box as the research object. First, by analyzing the force and speed of seed potatoes, the main structural parameters affecting the seed-box seed-metering performance were determined, and the indices for evaluating the seed-box seed-metering performance were established. Additionally, a single-factor simulation test and orthogonal test were carried out for the main structural parameters of the seed box. Using the optimized structural parameters of the seed box, a trial conical seed box was produced, and bench-verification tests were carried out. The results showed that the multiple rate, missing rate and coefficient of the variation of the plant distance for the conical seed box were reduced by 4.76%, 4.0% and 9.18%, respectively. The research results have practical significance as guidance for improving the sowing performance of cup-belt potato planters. At the same time, the research results have reference value for solving the arching problem for granular materials in the box.
Mini-Hydropower Plant Based on Lenyov Hydrobelt and Volume-Sectional Hydraulic Engine
Authors: Zhilkashinova Almira , Abilev Madi , Ocheredko Igor , Tuyakbayev Baurzhan , Nurbayev Mergen , Azamatov Bagdat
Published: February 2022

The use of the energy of small watercourses with the help of small hydropower plants is one of the promising directions for the development of renewable energy. This article presents the designs of two different hydraulic engines, each of which has its own advantage. Therefore, the task of calculating the real parameters of the design of a mini-hydropower plant based on Lenyov hydrobelt has been solved. Theoretical calculations were validated numerically by the finite volume method and computational fluid dynamics modeling; both methods gave similar results. According to the results of calculations, this design based on the Lenyov hydrobelt with the capacity of 16 kW is advisable to place in a river with a flow velocity of at least 4.5 m/s. The article also presents an alternative type of developed mini-hydropower plant, -a volume-sectional hydraulic engine. The proposed rotary-type positive displacement hydraulic engine can operate at low pressure on a flat surface. The advantage of the hydraulic engine is the sectional operation of several working chambers. It was established that a high water velocity and a large volume of passing water was not required. The total force acting in the hydraulic engine is 5430.19 N. Due to the use of conical inlet channels, the water flow velocity was increased and the water flow became directional. The frequency of rotation of the hydraulic engine shaft at a river flow velocity of 4 m/s was 60.43 rpm. The received power in these modes was 22.25 kW.
Attracting Potential Customers in E-Commerce Environments: A Comparative Study of Metaheuristic Algorithms
Authors: Yazdani Reza , Taghipourian Mohammad Javad , Pourpasha Mohammad Mahdi , Hosseini Seyed Shamseddin
Published: February 2022

Internet technology has provided an indescribable new way for businesses to attract new customers, track their behaviour, customise services, products, and advertising. Internet technology and the new trend of online shopping have resulted in the establishment of numerous websites to sell products on a daily basis. Products compete to be displayed on the limited pages of a website in online shopping because it has a significant impact on sales. Website designers carefully select which products to display on a page in order to influence the customers’ purchasing decisions. However, concerns regarding appropriate decision making have not been fully addressed. As a result, this study conducts a comprehensive comparative analysis of the performance of ten different metaheuristics. The ant lion optimiser (ALO), Dragonfly algorithm (DA), Grasshopper optimisation algorithm (GOA), Harris hawks optimisation (HHO), Moth-flame optimisation algorithm (MFO), Multi-verse optimiser (MVO), sine cosine algorithm (SCA), Salp Swarm Algorithm (SSA), The whale optimisation algorithm (WOA), and Grey wolf optimiser (GWO) are some of the recent algorithms that were chosen for this study. The results show that the MFO outperforms the other methods in all sizes. MFO has an average normalised objective function of 81%, while ALO has a normalised objective function of 77%. In contrast, HHO has the worst performance of 16%. The study’s findings add new theoretical and practical insights to the growing body of knowledge about e-commerce environments and have implications for planners, policymakers, and managers, particularly in companies where an unplanned advertisement wastes the budget.
Embodied Energy in Pyrolysis and Solvolysis Approaches to Recycling for Carbon Fiber-Epoxy Reinforced Composite Waste Streams
Authors: Kooduvalli Komal , Unser John , Ozcan Soydan , Vaidya Uday K.
Published: February 2022

Carbon fiber composites are increasingly used in aerospace, motorcycles, sporting, and high-performance vehicles, and their end of life recycling is of growing interest. This study deals with the life cycle assessment (LCA) of carbon fiber reinforced plastics (CFRP) waste streams. The embodied energy (EE) of recycling CFRP via two viable methods—i.e., pyrolysis and solvolysis—is studied. Both pyrolysis and solvolysis were studied for EE with different variants. Alongside fiber recovery from CFRP, the pyrolysis process calculations consider energy recovery from syngas and oil produced within the system. For pyrolysis, electric furnace and natural gas were primarily considered. For solvolysis, different solvent scenarios were considered, including (a) deionized water, (b) water and potassium hydroxide, (c) acetone and water, and (d) water with acetic acid and potassium hydroxide. Energy reduction from one generation to the next has also been highlighted. The EE for recycling CFRP is quantified and discussed for these scenarios in this paper.
Target Localization Based on High Resolution Mode of MIMO Radar with Widely Separated Antennas
Authors: Lu Jiaxin , Liu Feifeng , Liu Hongjie , Liu Quanhua
Published: February 2022

Coherent processing of multiple-input multiple-output (MIMO) radar with widely separated antennas has high resolution capability, but it also brings ambiguity in target localization. In view of the ambiguity problem, different from other signal processing sub-directions such as array configuration optimization or continuity of phase in space/time, this paper analyzes it from the information level, that is, the tracking method is adopted. First, by using the state equation and measurement equation, the echo data of multiple coherent processing intervals (CPI) are collected to improve the target localization accuracy as much as possible. Second, the non-coherent joint probability data association filter (JPDAF) is used to achieve stable tracking of spatial cross targets without ambiguity measurements. Third, based on the tracking results of the non-coherent JPDAF, the ambiguity of coherent measurement is resolved, that is, the coherent JPDAF is realized. By means of non-coherent and coherent alternating JPDAF (NCCAF) algorithms, high accuracy localization of multiple targets is achieved. Finally, numerical simulations are carried out to validate the effectiveness of the proposed NCCAF algorithm.
Determination of the Optimal Orientation of Chinese Solar Greenhouses Using 3D Light Environment Simulations
Authors: Liu Anhua , Xu Demin , Henke Michael , Zhang Yue , Li Yiming , Liu Xingan , Li Tianlai
Published: February 2022

With the continuous use of resources, solar energy is expected to be the most used sustainable energy. To improve the solar energy efficiency in Chinese Solar Greenhouses (CSG), the effect of CSG orientation on intercepted solar radiation was systematically studied. By using a 3D CSG model and a detailed crop canopy model, the light environment within CSG was optimized. Taking the most widely used Liao-Shen type Chinese solar greenhouse (CSG-LS) as the prototype, the simulation was fully verified. The intercepted solar radiation of the maintenance structures and crops was used as the evaluation index. The results showed that the highest amount of solar radiation intercepted by the maintenance structures occurred in the CSG orientations of 4–6° south to west (S-W) in 36.8° N and 38° N areas, 8–10° S-W in 41.8° N areas, and 2–4° south to east (S-E) in 43.6° N areas. The solar radiation intercepted by the crop canopy displayed the highest value at an orientation of 2–4° S-W in 36.8° N, 38° N, 43.6° N areas, and 4–6° S-W in the 41.8° N area. Furthermore, the proposed model could provide scientific guidance for greenhouse crop modelling.
RSMT: A Remote Sensing Image-to-Map Translation Model via Adversarial Deep Transfer Learning
Authors: Song Jieqiong , Li Jun , Chen Hao , Wu Jiangjiang
Published: February 2022

Maps can help governments in infrastructure development and emergency rescue operations around the world. Using adversarial learning to generate maps from remote sensing images is an emerging field. As we now know, the urban construction styles of different cities are diverse. The current translation methods for remote sensing image-to-map tasks only work on the specific regions with similar styles and structures to the training set and perform poorly on previously unseen areas. We argue that this greatly limits their use. In this work, we intend to seek a remote sensing image-to-map translation model that approaches the challenge of generating maps for the remote sensing images of unseen areas. Our remote sensing image-to-map translation model (RSMT) achieves universal and general applicability to generate maps over multiple regions by combining adversarial deep transfer training schemes with novel attention-based network designs. Extracting the content and style latent features from remote sensing images and a series of maps, respectively, RSMT generalizes a pattern applied to the remote sensing images of new areas. Meanwhile, we introduce feature map loss and map consistency loss to reinforce generated maps’ precision and geometry similarity. We critically analyze qualitative and quantitative results using widely adopted evaluation metrics through extensive validation and comparisons with previous remote sensing image-to-map approaches. The results of experiment indicate that RSMT can translate remote sensing images to maps better than several state-of-the-art methods.
A Nondestructive Measurement Method of Optical Fiber Young’s Modulus Based on OFDR
Authors: Li Pengfei , Fu Cailing , Zhong Huajian , Du Bin , Guo Kuikui , Meng Yanjie , Du Chao , He Jun , Wang Lei , Wang Yiping
Published: February 2022

A nondestructive measurement method based on an Optical frequency domain reflectometry (OFDR) was demonstrated to achieve Young’s modulus of an optical fiber. Such a method can be used to measure, not only the averaged Young’s modulus within the measured fiber length, but also Young’s modulus distribution along the optical fiber axis. Moreover, the standard deviation of the measured Young’s modulus is calculated to analyze the measurement error. Young’s modulus distribution of the coated and uncoated single mode fiber (SMF) samples was successfully measured along the optical fiber axis. The average Young’s modulus of the coated and uncoated SMF samples was 13.75 ± 0.14, and 71.63 ± 0.43 Gpa, respectively, within the measured fiber length of 500 mm. The measured Young’s modulus distribution along the optical fiber axis could be used to analyze the damage degree of the fiber, which is very useful to nondestructively estimate the service life of optical fiber sensors immersed into smart engineer structures.
Keypoint-Aware Single-Stage 3D Object Detector for Autonomous Driving
Authors: Xu Wencai , Hu Jie , Chen Ruinan , An Yongpeng , Xiong Zongquan , Liu Han
Published: February 2022

Current single-stage 3D object detectors often use predefined single points of feature maps to generate confidence scores. However, the point feature not only lacks the boundaries and inner features but also does not establish an explicit association between regression box and confidence scores. In this paper, we present a novel single-stage object detector called keypoint-aware single-stage 3D object detector (KASSD). First, we design a lightweight location attention module (LLM), including feature reuse strategy (FRS) and location attention module (LAM). The FRS can facilitate the flow of spatial information. By considering the location, the LAM adopts weighted feature fusion to obtain efficient multi-level feature representation. To alleviate the inconsistencies mentioned above, we introduce a keypoint-aware module (KAM). The KAM can model spatial relationships and learn rich semantic information by representing the predicted object as a set of keypoints. We conduct experiments on the KITTI dataset. The experimental results show that our method has a competitive performance with 79.74% AP on a moderate difficulty level while maintaining 21.8 FPS inference speed.
A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer’s Disease
Authors: Aqeel Anza , Hassan Ali , Khan Muhammad Attique , Rehman Saad , Tariq Usman , Kadry Seifedine , Majumdar Arnab , Thinnukool Orawit
Published: February 2022

The early prediction of Alzheimer’s disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.
High-Frequency Ultrasound Dataset for Deep Learning-Based Image Quality Assessment
Authors: Czajkowska Joanna , Juszczyk Jan , Piejko Laura , Glenc-Ambroży Małgorzata
Published: February 2022

This study aims at high-frequency ultrasound image quality assessment for computer-aided diagnosis of skin. In recent decades, high-frequency ultrasound imaging opened up new opportunities in dermatology, utilizing the most recent deep learning-based algorithms for automated image analysis. An individual dermatological examination contains either a single image, a couple of pictures, or an image series acquired during the probe movement. The estimated skin parameters might depend on the probe position, orientation, or acquisition setup. Consequently, the more images analyzed, the more precise the obtained measurements. Therefore, for the automated measurements, the best choice is to acquire the image series and then analyze its parameters statistically. However, besides the correctly received images, the resulting series contains plenty of non-informative data: Images with different artifacts, noise, or the images acquired for the time stamp when the ultrasound probe has no contact with the patient skin. All of them influence further analysis, leading to misclassification or incorrect image segmentation. Therefore, an automated image selection step is crucial. To meet this need, we collected and shared 17,425 high-frequency images of the facial skin from 516 measurements of 44 patients. Two experts annotated each image as correct or not. The proposed framework utilizes a deep convolutional neural network followed by a fuzzy reasoning system to assess the acquired data’s quality automatically. Different approaches to binary and multi-class image analysis, based on the VGG-16 model, were developed and compared. The best classification results reach 91.7% accuracy for the first, and 82.3% for the second analysis, respectively.
Efficient Plant Types and Coverage Rates for Optimal Green Roof to Reduce Urban Heat Island Effect
Authors: Park Jinsil , Shin Yeeun , Kim Suyeon , Lee Sang-Woo , An Kyungjin
Published: February 2022

Green roofs are implemented to reduce the urban heat island effect; however, studies are limited to comparing the reduction in urban heat island effect before and after implementation, and the focus is on the structural stability of the building rather than urban heat island reduction. In this study, using the sky view factor (SVF) in ENVI-met, a 3D microclimate modeling program, urban spaces were classified as closed, semi-open, and open areas. Meanwhile, the green roof types were subdivided according to the vegetation coverage rates, which included grass, shrubs, and trees. The vegetation ratio was evaluated using ENVI-met to determine which of the 10 scenarios was most effective for each urban space. The thermal environment was most comfortable in semi-open areas. Therefore, the green roof scenario with 70% grass and 30% trees was effective in closed areas, 50% shrubs and 50% trees were best in semi-open areas, and 70% grass with 30% trees, or 30% grass and 70% trees, was best in open areas. This study provides a basis for creating green roof guidelines aimed at improving the urban thermal environment, as well as creating other green infrastructure elements in cities.
Drying of Carrot Strips in Indirect Solar Dehydrator with Photovoltaic Cell and Thermal Energy Storage
Authors: Cerezal-Mezquita Pedro , Bugueño-Muñoz Waldo
Published: February 2022

Minimizing agricultural losses, accompanied by employing technologies capable of taking advantage of solar energy, are the current challenges of the Antofagasta region of Chile due to having an average solar irradiance of 7.2 kWh/m2 per day. With this objective, using an indirect solar dryer with storage of thermal energy in the form of sensible heat, the effect of drying on the quality of carrot strips was studied using chromatic coordinates CIEL*a*b*, the color difference (ΔE), the relationship between redness/yellowness (R = a*/b*), browning index (BI), whiteness index (WI), chroma (C), hue angle (h°) and drying kinetics. The experimental drying data were fitted to 15 typically employed nonlinear regression models. The ΔE = 14.11 ± 0.14 between the carrots in the dry and fresh conditions represented a detectable color change, the R ratio increased from 0.75 to 0.89, the BI index decreased from 209.82 ± 0.62 to 148.38 ± 0.26 and the WI index increased from 24.5 ± 0.11 to 31.8 ± 0.17, indicating color affectations due to the process. The coefficients of determination, (R2) close to 1 and the values closest to 0 of χ2, SSE and RMSE, led to the best fit corresponding to the Weibull distribution model. In addition, it was found that the operation of the drying installation in the hours without incident solar radiation maintained the drying temperature values throughout the process.
Experimental Study on the Hydroponics of Wetland Plants for the Treatment of Acid Mine Drainage
Authors: Wu Aijing , Zhang Yongbo , Zhao Xuehua , Li Jiamin , Zhang Guowei , Shi Hong , Guo Lina , Xu Shuyuan
Published: February 2022

Acid Mine Drainage (AMD) has become an important issue due to its significant ecological pollution. In this paper, phytoremediation technology and mechanism for AMD were investigated by hydroponic experiments, using six wetland plants (Phragmites australis, Typha orientalis, Cyperus glomeratus, Scirpus validus, Iris wilsonii, Juncus effusus) as research objects. The results showed that (1) the removal of sulfate from AMD was highest for Juncus effusus (66.78%) and Iris wilsonii (40.74%) and the removal of Mn from AMD was highest for Typha orientalis (>99%) and Phragmites australis (>99%). In addition, considering the growth condition of the plants, Juncus effusus, Iris wilsonii, and Phragmites australis were finally selected as the dominant plants for the treatment of AMD. (2) The removal pathway of pollutants in AMD included two aspects: one part was absorbed by plants, and the other part was removed through hydrolysis and precipitation processes. Our findings provide a theoretical reference for phytoremediation technology for AMD.
Effectiveness of Chemical- and Hazardous-Waste-Based MEAs in Sustaining Life and Land: Analysis of Implementing Legislations and Practice in Ethiopia
Authors: Jalleta Abebe Kebede , Tianbao Qin
Published: February 2022

The general purpose of regulating chemical and hazardous waste in international legal frameworks is to protect against life-threatening and adverse impacts on the environment during its generation and use at the domestic level and global transfer. Implementing these global regimes can be effective at the national level when the top leadership makes environmental protection a priority agenda in its policy and legislation, shifting from a traditional economic development attitude to harmonizing environmental, human rights, and economic growth needs. To this end, the joint plans and actions of institutions, which should be augmented by public involvement and judicial activism, play crucial roles. Mining companies should also be part of this cooperative framework, changing their enclave attitude and exclusionary approach. This paper analyzes the implementation of chemical- and hazardous-waste-based global regimes in Ethiopia by adopting a comparative method as a lesson from other jurisdictions. It argues that realizing multilateral agreements remains in the dock due to legal and practical gaps. The applicable domestic laws are flawed, exhibiting inconsistency, fragmentation, and inadequacy. Additionally, the state’s conduct does not balance its or investors’ economic interests with those of indigenous people, including their health, livelihood, and the environment. Thus, it recommends legal rectification and practical compliance with international law to realize the sustainable viability of human health and environmental media.
Role of Energy Mix in Determining Climate Change Vulnerability in G7 Countries
Authors: Dai Hui , Mamkhezri Jamal , Arshed Noman , Javaid Anam , Salem Sultan , Khan Yousaf Ali
Published: February 2022

Anthropogenic activities are responsible for greenhouse gas emissions, causing extreme events like soil erosion, droughts, floods, forest fires and tornadoes. Fossil fuel consumption produces CO2, and trapping heat is the major reason for a rapid increase in global temperature, and electricity generation is responsible for 25% of greenhouse gas emissions. Fossil fuel consumption, CO2 emissions and their adverse impact have become the focus of efforts to mitigate climate change vulnerability. This study explores empirical determinants of vulnerability to climate change such as ecosystem, food, health and infrastructure. The sustainable use of energy is necessary for development, and a source of response to climate change. The present study focuses on renewable energy consumption to determine climate vulnerability in G7 countries between 1995 and 2019. The panel ARDL approach showed that the renewable to non-renewable energy mix showed a quadratic effect on vulnerability, whereby a minimum threshold of renewable energy is required to witness a reduction in food, health and infrastructure vulnerability. Other results indicate that trade openness and development expenditures reduce health vulnerability. Development expenditures also decrease ecosystem vulnerability, while trade openness increases it. However, both of these variables increase infrastructure vulnerability. Avoiding severe food and water crises requires investment to tackle climate change, conserve energy and water resources, reform global trade and food markets, and adapting and adopting climate-resilient responses to change.
Weather Route Optimization Method of Unmanned Ship Based on Continuous Dynamic Optimal Control
Authors: Wang Xiaoyuan , Zhao Xinyue , Wang Gang , Wang Quanzheng , Feng Kai
Published: February 2022

Intelligent weather route optimization method was an essential guarantee for safe and efficient navigation of ships. In traditional methods, the multi-stage decision-making method was generally used to solve route dynamic optimization problems, in which it was impossible to achieve dynamic optimal routes, due to the temporal and spatial complexity of route design. In this paper, an unmanned ship weather route optimization model was established based on a method of continuous dynamic optimal control. Marine meteorological information was analyzed. Navigation safety, energy consumption, and sailing time were integrated considered. Dual-target route evaluation function of energy consumption and sailing time was established. The problem about the multi-stage decision was transformed into that of one-step optimal control, and an improved ant colony algorithm was adopted. Simulation results showed that compared with some traditional methods, the proposed method was better performed, which can be applied to dynamic route optimization of the unmanned ship in a large marine area under complex meteorological conditions.
Questioning the Sustainability of English-Medium Instruction Policy in Science Classrooms: Teachers’ and Students’ Experiences at a Hong Kong Secondary School
Authors: Pun Jack , Thomas Nathan , Bowen Neil Evan Jon Anthony
Published: February 2022

Teaching science through English as a medium of instruction (EMI) is a growing phenomenon around the world. In Hong Kong, this was realised on a large scale in 2010, with the implementation of a “fine-tuning” compulsory language policy. This allowed Chinese-medium schools to adopt EMI fully. Yet, despite such rapid and widespread adoption, an adequate understanding of key stakeholders’ experiences in relation to their perceptions of what constitutes effective EMI science education remains scarce. Thus, we question the sustainability of EMI programs that are driven by top-down policy. In this case study, we explore the perspectives and experiences of six EMI science teachers and thirteen of their students as their secondary school transitions from partial to full EMI. From in-depth interviews (complemented by classroom observations), findings reveal that the transition to full EMI has presented challenges that appear to hinder students’ development of scientific knowledge and the language of science in English. This directly counters the primary goal of the fine-tuning policy. Nevertheless, findings also illuminate a number of coping strategies teachers and students use to deal with their changing curricula. Overall, we offer insights into this under-researched context of transitioning EMI programs and provide recommendations for future research and practice.
BlueNavi: A Microservices Architecture-Styled Platform Providing Maritime Information
Authors: Liu Hongze , Jurdana Irena , Lopac Nikola , Wakabayashi Nobukazu
Published: February 2022

Traditional methods of marine navigation are undergoing a revolution brought about by the almost universal adoption of the Automatic Identification System (AIS). AIS exchanges a wealth of navigational information among vessels and between ships to shore through Very High Frequency (VHF). With AIS data integrated into the Electronic Chart Display and Information System (ECDIS), the identification and navigational information of surrounding vessels as well as aids to navigation can be reflected on the electronic charts in real time, despite some problems such as the low AIS carriage rate on small vessels where it is not mandatory and the high cost of ECDIS preventing such vessels from installing it. In this paper, we introduce BlueNavi, a lower cost but sustainable maritime information providing platform built with microservices architecture allowing flexible on-demand scalability and cross-platform adaptability. Applications served by BlueNavi can provide users with data either stored in a remote data center through the internet or received locally by devices connected to the station without the need for the internet. From our land test, we show that users with only an internet connection but without any AIS equipment can also obtain live AIS data collected by other stations. Conversely, with access to the internet, BlueNavi can also send data back to the land stations, enabling other ships to identify non-AIS ships as well. Through the live-ship test, we demonstrate that BlueNavi works well offline in cooperation with shipborne AIS equipment. We also look at some possible application scenarios for BlueNavi with other data sources and means of communication other than AIS and VHF that can be expanded to the platform. BlueNavi will enable inexpensive ship identification for small vessels and provide an extension of functionality to ECDIS for large ships.
Physicochemical Characterization of Natural Rocks and Their Applications for Wastewater Treatment
Authors: Ahmed Abdelkader T. , Farooq Qazi Umar , Almohamadi Hamad
Published: February 2022

Arid countries such as Arabian Gulf countries are suffering from a water shortage, especially with the recent high-water demand. The best solution for this shortage is the management of currently available water resources, through the reuse of treated wastewater for irrigation purposes. This solution also solves the problem of regularization of wastewater discharge, with positive impacts on the environment. This study aimed to apply an innovative, advanced method for treating wastewater with a favorable environment, low economic cost, and less energy consumption. The research investigated the possibility of using natural rocks such as volcanic and zeolite for advanced treatment of wastewater effluent. The research methodology relied on an experimental work in the lab scales and applied on materials available in Saudi Arabia. The experiments included a leaching batch test to first examine what leaches out from these rocks into water. Then, the materials were tested with wastewater effluent. The main mechanism of treatment was based on the absorption process. The results exhibited significant improvement in the water quality of treated wastewater. On the other hand, the results of the leaching tests showed many ions being dissolved from both rocks into water; thus, it is recommended to soak and flush these solid materials with clean water before using them for the treatment process. Further research is required to determine the best pretreated methods to be applied on these rocks to improve their performance as absorbents.
Tripartite Evolutionary Game Analysis for Plastic Pollution Prevention and Control under the Background of China’s Plastic Ban
Authors: Ouyang Chenlu , Jiang Huiqi , Sheng Qing , Liu Guannan , Jiang Minghui
Published: February 2022

Controlling plastic pollution is an important issue in the field of sustainable development and is of great concern for numerous countries’ governments. After investigating the implementation of relevant policies, this paper analyzes the tripartite mainstay under the background of China’s Plastic Ban by constructing an evolutionary game model involving governments, enterprises and the public. Through simulation modeling, the paper then investigates the possible regulatory effects of different policies at the micro level. According to the results, the government should pay more attention to how it reacts to enterprises’ compliance strategies, both for the sake of the public and itself. It is vital to increase enterprises’ compliance rewards and decrease their compliance costs through various means such as policy changes and technological innovation. The policy’s effect can be improved if supervision and regulations are both enforced.
Capturing Changes in Residential Occupant Behavior Due to Work from Home in Japan as a Consequence of the COVID-19 Pandemic
Authors: Ueno Takahiro
Published: February 2022

Since there is a possibility that work from home will become one of the major working styles in the era of a new normal after the COVID-19 pandemic, it is necessary to examine an effect of work from home on life of residents at home and on energy consumption. Therefore, a web-based questionnaire survey was conducted at the end of October 2020 for households in which any of the family members was a work from home worker. In regard to the changes in life schedule, work from home helped 21.5% of respondents to sleep 30 min more or even longer. This shows that the loss of commuting time to work due to work from home improved the health of respondents in terms of sleep. In terms of the changes in energy consumption, more than a quarter of households experienced an increase in electricity consumption. Around 40~50% of households whose monthly electricity consumption increased from last year were not aware of the reasons for the increase or realized it for the first time when they answered the questionnaire survey. This indicates that feedback using home energy management systems, etc., play an important role in promoting energy-saving behavioral changes. An analysis of the causes of the increase in energy consumption by households reveals that the increase in hours of use of air-conditioners is significantly correlated with the increase in household electricity consumption in August (p = less than 0.01). The large-scale questionnaire survey on Japanese housing during the COVID-19 pandemic in this study elucidated that work from home as a countermeasure against COVID-19 infection affected the residents’ lives and the energy consumption of each household. This paper also showed the causes of the increase in household energy consumption and the importance of feedback, such as HEMS, in promoting energy-saving behavior. These results are expected to help to improve health of residents and energy saving in the era of a new normal.
An Exact Solution to a Quaternion Matrix Equation with an Application
Authors: Liu Long-Sheng , Wang Qing-Wen , Chen Jiang-Feng , Xie Yu-Zhu
Published: February 2022

In this paper, we establish the solvability conditions and the formula of the general solution to a Sylvester-like quaternion matrix equation. As an application, we give some necessary and sufficient conditions for a system of quaternion matrix equations to be consistent, and present an expression of the general solution of the system when it is solvable. We present an algorithm and an example to illustrate the main results of this paper. The findings of this paper generalize the known results in the literature.
Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to Handle Binary Class Imbalanced Dataset Classification
Authors: Choudhary Roshani , Shukla Sanyam
Published: February 2022

Class imbalance is a phenomenon of asymmetry that degrades the performance of traditional classification algorithms such as the Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Various modifications of SVM and ELM have been proposed to handle the class imbalance problem, which focus on different aspects to resolve the class imbalance. The Universum Support Vector Machine (USVM) incorporates the prior information in the classification model by adding Universum data to the training data to handle the class imbalance problem. Various other modifications of SVM have been proposed which use Universum data in the classification model generation. Moreover, the existing ELM-based classification models intended to handle class imbalance do not consider the prior information about the data distribution for training. An ELM-based classification model creates two symmetry planes, one for each class. The Universum-based ELM classification model tries to create a third plane between the two symmetric planes using Universum data. This paper proposes a novel hybrid framework called Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to handle the classification of binary class-imbalanced problems. The proposed RKWELM-UFS combines the Universum learning method with a Reduced-Kernelized Weighted Extreme Learning Machine (RKWELM) for the first time to inherit the advantages of both techniques. To generate efficient Universum samples in the feature space, this work uses the kernel trick. The performance of the proposed method is evaluated using 44 benchmark binary class-imbalanced datasets. The proposed method is compared with 10 state-of-the-art classifiers using AUC and G-mean. The statistical t-test and Wilcoxon signed-rank test are used to quantify the performance enhancement of the proposed RKWELM-UFS compared to other evaluated classifiers.
A New Study on Optimization of Four-Bar Mechanisms Based on a Hybrid-Combined Differential Evolution and Jaya Algorithm
Authors: Nguyen-Van Sy , Lieu Qui X. , Xuan-Mung Nguyen , Nguyen Thi Thanh Nga
Published: February 2022

In mechanism design with symmetrical or asymmetrical motions, obtaining high precision of the input path given by working requirements of mechanisms can be a challenge for dimensional optimization. This study proposed a novel hybrid-combined differential evolution (DE) and Jaya algorithm for the dimensional synthesis of four-bar mechanisms with symmetrical motions, called HCDJ. The suggested algorithm uses modified initialization, a hybrid-combined mutation between the classical DE and Jaya algorithm, and the elitist selection. The modified initialization allows generating initial individuals, which are satisfied with Grashof’s condition and consequential constraints. In the hybrid-combined mutation, three differential groups of mutations are combined. DE/best/1 and DE/best/2, DE/current to best/1 and Jaya operator, and DE/rand/1, and DE/rand/2 belong to the first, second, and third groups, respectively. In the second group, DE/current to best/1 is hybrid with the Jaya operator. Additionally, the elitist selection is also applied in HCDJ to find the best solutions for the next generation. To validate the feasibility of HCDJ, the numerical examples of the symmetrical motion of four-bar mechanisms are investigated. From the results, the proposed algorithm can provide accurate optimal solutions that are better than the original DE and Jaya methods, and its solutions are even better than those of many other algorithms that are available in the literature.
Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms
Authors: Kronhardt Kirill , Rübner Stephan , Pascher Max , Goldau Felix Ferdinand , Frese Udo , Gerken Jens
Published: February 2022

Robot arms are one of many assistive technologies used by people with motor impairments. Assistive robot arms can allow people to perform activities of daily living (ADL) involving grasping and manipulating objects in their environment without the assistance of caregivers. Suitable input devices (e.g., joysticks) mostly have two Degrees of Freedom (DoF), while most assistive robot arms have six or more. This results in time-consuming and cognitively demanding mode switches to change the mapping of DoFs to control the robot. One option to decrease the difficulty of controlling a high-DoF assistive robot arm using a low-DoF input device is to assign different combinations of movement-DoFs to the device’s input DoFs depending on the current situation (adaptive control). To explore this method of control, we designed two adaptive control methods for a realistic virtual 3D environment. We evaluated our methods against a commonly used non-adaptive control method that requires the user to switch controls manually. This was conducted in a simulated remote study that used Virtual Reality and involved 39 non-disabled participants. Our results show that the number of mode switches necessary to complete a simple pick-and-place task decreases significantly when using an adaptive control type. In contrast, the task completion time and workload stay the same. A thematic analysis of qualitative feedback of our participants suggests that a longer period of training could further improve the performance of adaptive control methods.
Effect of Insertion and Deletion in the Meq Protein Encoded by Highly Oncogenic Marek’s Disease Virus on Transactivation Activity and Virulence
Authors: Sato Jumpei , Murata Shiro , Yang Zhiyuan , Kaufer Benedikt B. , Fujisawa Sotaro , Seo Hikari , Maekawa Naoya , Okagawa Tomohiro , Konnai Satoru , Osterrieder Nikolaus , Parcells Mark S. , Ohashi Kazuhiko
Published: February 2022

Marek’s disease virus (MDV) causes malignant lymphoma in chickens (Marek’s disease, MD). Although MD is currently controlled by vaccination, MDV strains have continuously increased in virulence over the recent decades. Polymorphisms in Meq, an MDV-encoded oncoprotein that serves as a transcription factor, have been associated with the enhanced virulence of the virus. In addition, insertions and deletions in Meq have been observed in MDV strains of higher virulence, but their contribution to said virulence remains elusive. In this study, we investigated the contribution of an insertion (L-Meq) and a deletion in the Meq gene (S-Meq) to its functions and MDV pathogenicity. Reporter assays revealed that both insertion and deletion enhanced the transactivation potential of Meq. Additionally, we generated RB-1B-based recombinant MDVs (rMDVs) encoding each Meq isoform and analyzed their pathogenic potential. rMDV encoding L-Meq indueced the highest mortality and tumor incidence in infected animals, whereas the rMDV encoding S-Meq exhibited the lowest pathogenicity. Thus, insertion enhanced the transactivation activity of Meq and MDV pathogenicity, whereas deletion reduced pathogenicity despite having increased transactivation activity. These data suggest that other functions of Meq affect MDV virulence. These data improve our understanding of the mechanisms underlying the evolution of MDV virulence.
In Silico Prediction and Selection of Target Sequences in the SARS-CoV-2 RNA Genome for an Antiviral Attack
Authors: Hussein Mouraya , Andrade dos Ramos Zaria , Berkhout Ben , Herrera-Carrillo Elena
Published: February 2022

The SARS-CoV-2 pandemic has urged the development of protective vaccines and the search for specific antiviral drugs. The modern molecular biology tools provides alternative methods, such as CRISPR-Cas and RNA interference, that can be adapted as antiviral approaches, and contribute to this search. The unique CRISPR-Cas13d system, with the small crRNA guide molecule, mediates a sequence-specific attack on RNA, and can be developed as an anti-coronavirus strategy. We analyzed the SARS-CoV-2 genome to localize the hypothetically best crRNA-annealing sites of 23 nucleotides based on our extensive expertise with sequence-specific antiviral strategies. We considered target sites of which the sequence is well-conserved among SARS-CoV-2 isolates. As we should prepare for a potential future outbreak of related viruses, we screened for targets that are conserved between SARS-CoV-2 and SARS-CoV. To further broaden the search, we screened for targets that are conserved between SARS-CoV-2 and the more distantly related MERS-CoV, as well as the four other human coronaviruses (OC43, 229E, NL63, HKU1). Finally, we performed a search for pan-corona target sequences that are conserved among all these coronaviruses, including the new Omicron variant, that are able to replicate in humans. This survey may contribute to the design of effective, safe, and escape-proof antiviral strategies to prepare for future pandemics.
In-Silico Analysis of Monoclonal Antibodies against SARS-CoV-2 Omicron
Authors: Hu Ye-Fan , Hu Jing-Chu , Chu Hin , Yau Thomas , Zhang Bao-Zhong , Huang Jian-Dong
Published: February 2022

Omicron was designated by the WHO as a VOC on 26 November 2021, only 4 days after its sequence was first submitted. However, the impact of Omicron on current antibodies and vaccines remains unknown and evaluations are still a few weeks away. We analysed the mutations in the Omicron variant against epitopes. In our database, 132 epitopes of the 120 antibodies are classified into five groups, namely NTD, RBD-1, RBD-2, RBD-3, and RBD-4. The Omicron mutations impact all epitopes in NTD, RBD-1, RBD-2, and RBD-3, with no antibody epitopes spared by these mutations. Only four out of 120 antibodies may confer full resistance to mutations in the Omicron spike, since all antibodies in these three groups contain one or more epitopes that are affected by these mutations. Of all antibodies under EUA, the neutralisation potential of Etesevimab, Bamlanivimab, Casirivimab, Imdevima, Cilgavimab, Tixagevimab, Sotrovimab, and Regdanvimab might be dampened to varying degrees. Our analysis suggests the impact of Omicron on current therapeutic antibodies by the Omicron spike mutations may also apply to current COVID-19 vaccines.
Modelling Intense Combined Load Transport in Open Channel
Authors: Matoušek Václav
Published: February 2022

Granular flow is modelled under the following conditions: Steady-state uniform turbulent open-channel solid–liquid flow carrying combined load at high solids concentration above a plane mobile bed. In the combined load, a portion of transported particles is transported as collisional bed load and the rest as suspended load supported by carrier turbulence. In our modelling approach, we consider one-dimensional flow and take into account a layered structure of the flow with the intense combined load. Principles of kinetic theory of granular flow are employed together with the mixing-length theory of flow turbulence in order to predict distributions of solids concentration and velocity in sediment-water flow of the given flow depth and longitudinal slope in an open channel. Components of the model are tested and calibrated by results of our laboratory experiments with lightweight sediment in a recirculating tilting flume.
Effect of Different Sowing Methods on Water Use Efficiency and Grain Yield of Wheat in the Loess Plateau, China
Authors: Noor Hafeez , Sun Min , Lin Wen , Gao Zhiqiang
Published: February 2022

Research has revealed that summer fallow sowing improves the water use efficiency (WUE) and grain yield of winter wheat. However, wheat yields differ yearly with crop management. A field experiment over 8 years was established in the Loess Plateau to determine the role of precipitation and soil water storage in wheat yield formation under conservation tillage. The average WUE values were 7.8, 11.0, and 12.6 t·ha−1, while the average evapotranspiration (ET) values were 334.7, 365.5, and 410 mm when the yields were 3.0, 3.0–4.5, and over 4.5 t·ha−1, respectively. Compared to drill sowing (DS), high water consumption during early growth increased the spike number, grain number, and yield. In years of intermediate or low yields, wide-space sowing (WS) and furrow sowing (FS) improved the ET, WUE, spike number, grain number, and yield of wheat compared to (DS) drill sowing. When the wheat yield was 3.0–4.5 t·ha−1, higher soil water intake during jointing, anthesis, and anthesis–maturity increased the tiller number, 1000-grain weight, and yield, related to the use of suitable tillers. Synchronous increases in grain number per spike and 1000-grain weight were observed with increased soil water content at jointing, maturity, and anthesis, as well as consumption of soil water in the latter part during the growing season.