bluuebunny/crossref_metadata_2025 huggingface dataset:
Position Control Study on Pump-Controlled Servomotor for Steam Control Valve
Authors: Yan Guishan , Jin Zhenlin , Zhang Tiangui , Zhao Penghui
Published: January 2021

In steam turbine control and actuation, the steam control valve plays a key role in operability and reliability. The electrohydraulic regulating system for the steam control valve, usually called the servomotor, needs to be reliable and high performing under nonlinear excitation interference in actual conditions. Currently, electrohydraulic servo valve control technology is widely used in servomotors. Although this technology has good control performance, it still has some technical defects, such as poor antipollution ability, low energy efficiency, large volume size, and limited installation space. Aiming at the abovementioned technical shortcomings of electrohydraulic servo valve control technology, a servomotor-pump-hydraulic cylinder volume control scheme is proposed in this paper, forming a pump-controlled servomotor for the steam control valve. By analyzing the working principle of the pump-controlled servomotor position control in the steam control valve, the mathematical model of a pump-controlled servomotor for the steam control valve is established. The sliding mode variable structure control strategy is proposed, and the variable structure control law is solved by constructing a switching function. To verify the performance of the proposed control method, experimental research was conducted. The research results show that the proposed sliding mode variable structure control strategy has a good control effect, which lays the theoretical and technical foundation for the engineering application and promotion of pump-controlled servomotors for steam control valves and helps the technical upgrade and product optimization of steam turbines.
Proteomics-Based Regression Model for Assessing the Development of Chronic Lymphocytic Leukemia
Authors: Bakhtina Varvara I. , Veprintsev Dmitry V. , Zamay Tatiana N. , Demko Irina V. , Mironov Gleb G. , Berezovski Maxim V. , Petrova Marina M. , Kichkailo Anna S. , Glazyrin Yury E.
Published: January 2021

The clinical course of chronic lymphocytic leukemia (CLL) is very ambiguous, showing either an indolent nature of the disease or having latent dangerous progression, which, if diagnosed, will require an urgent therapy. The prognosis of the course of the disease and the estimation of the time of therapy initiation are crucial for the selection of a successful treatment strategy. A reliable estimating index is needed to assign newly diagnosed CLL patients to the prognostic groups. In this work, we evaluated the comparative expressions of proteins in CLL blood cells using a label-free quantification by mass spectrometry and calculated the integrated proteomic indexes for a group of patients who received therapy after the blood sampling over different periods of time. Using a two-factor linear regression analysis based on these data, we propose a new pipeline for evaluating model development for estimation of the moment of therapy initiation for newly diagnosed CLL patients.
How to Protect the Credibility of Articles Published in Predatory Journals
Authors: Yamada Yuki
Published: January 2021

Predatory journals often prey on innocent researchers who are unaware of the threat they pose. This paper discusses what researchers can do if they unintentionally publish a paper in a predatory journal, including measures to take before submission, during peer review, and after the journal has accepted a manuscript. The specific recommendations discussed are pre-registration, pre-submission peer-review, open peer-review, topping up reviewers, post-publication peer review, open recommendation, and treatment as unrefereed. These measures may help to ensure the credibility of the article, even if it is published in a predatory journal. The present article suggests that an open and multi-layered assessment of research content enhances the credibility of all research articles, even those published in non-predatory journals. If applied consistently by researchers in various fields, the suggested measures may enhance reproducibility and promote the advancement of science.
Laughing about Religious Authority—But Not Too Loud
Authors: Richter Lena
Published: January 2021

In Facebook groups of young Moroccan non-believers, cartoons, memes, and jokes that mock religion are widely shared. By phrasing the messages in a humorous way, it is possible to express experiences and viewpoints that are more difficult to communicate in direct speech. Studying these forms of humor can reveal several themes, frames, and tropes that are important to many former Muslims, such as criticizing the legal restrictions of non-belief and countering stereotypes about non-believers. This leads to the following question: To what extent is humor being employed as an (online) tool for young Moroccan non-believers to challenge the religious status quo? To answer this question, the article analyzes numerous examples of religious-related humor during the COVID-19 pandemic and Ramadan. Hereby, it becomes clear that many jokes remain a limited and covert dissent strategy, as they are only shared among fellow non-believers. Yet, this article argues that jokes are an important method of differentiation, self-expression, and in-group identification that can build a fruitful ground for future activism.
An Experimental Characterization of TORVEastro, Cable-Driven Astronaut Robot
Authors: Samani Francesco , Ceccarelli Marco
Published: January 2021

TORVEastro robot design is presented with a built prototype in LARM2 (Laboratory of Robot Mechatronics) for testing and characterizing its functionality for service in space stations. Several robot astronauts are designed with bulky human-like structures that cannot be convenient for outdoor space service in monitoring and maintenance of the external structures of orbital stations. The design features of TORVEastro robot are discussed with its peculiar mechanical design with 3 arm-legs as agile service robot astronaut. A lab prototype is used to test the operation performance and the feasibility of its peculiar design. The robot weighs 1 kg, and consists of a central torso, three identical three-degree of freedom (DoF) arm–legs and one vision system. Test results are reported to discuss the operation efficiency in terms of motion characteristics and power consumption during lab experiments that nevertheless show the feasibility of the robot for outdoor space applications.
Traceability of the Sentinel-3 SLSTR Level-1 Infrared Radiometric Processing
Authors: Smith David , Hunt Samuel E. , Etxaluze Mireya , Peters Dan , Nightingale Tim , Mittaz Jonathan , Woolliams Emma R. , Polehampton Edward
Published: January 2021

Providing uncertainties in satellite datasets used for Earth observation can be a daunting prospect because of the many processing stages and input data required to convert raw detector counts to calibrated radiances. The Sea and Land Surface Temperature Radiometer (SLSTR) was designed to provide measurements of the Earth’s surface for operational and climate applications. In this paper the authors describe the traceability chain and derivation of uncertainty estimates for the thermal infrared channel radiometry. Starting from the instrument model, the contributing input quantities are identified to build up an uncertainty effects tree. The characterisation of each input effect is described, and uncertainty estimates provided which are used to derive the combined uncertainties as a function of scene temperature. The SLSTR Level-1 data products provide uncertainty estimates for fully random effects (noise) and systematic effects that can be mapped for each image pixel, examples of which are shown.
Automatic Image-Based Event Detection for Large-N Seismic Arrays Using a Convolutional Neural Network
Authors: Mężyk Miłosz , Chamarczuk Michał , Malinowski Michał
Published: January 2021

Passive seismic experiments have been proposed as a cost-effective and non-invasive alternative to controlled-source seismology, allowing body–wave reflections based on seismic interferometry principles to be retrieved. However, from the huge volume of the recorded ambient noise, only selected time periods (noise panels) are contributing constructively to the retrieval of reflections. We address the issue of automatic scanning of ambient noise data recorded by a large-N array in search of body–wave energy (body–wave events) utilizing a convolutional neural network (CNN). It consists of computing first both amplitude and frequency attribute values at each receiver station for all divided portions of the recorded signal (noise panels). The created 2-D attribute maps are then converted to images and used to extract spatial and temporal patterns associated with the body–wave energy present in the data to build binary CNN-based classifiers. The ensemble of two multi-headed CNN models trained separately on the frequency and amplitude attribute maps demonstrates better generalization ability than each of its participating networks. We also compare the prediction performance of our deep learning (DL) framework with a conventional machine learning (ML) algorithm called XGBoost. The DL-based solution applied to 240 h of ambient seismic noise data recorded by the Kylylahti array in Finland demonstrates high detection accuracy and the superiority over the ML-based one. The ensemble of CNN-based models managed to find almost three times more verified body–wave events in the full unlabelled dataset than it was provided at the training stage. Moreover, the high-level abstraction features extracted at the deeper convolution layers can be used to perform unsupervised clustering of the classified panels with respect to their visual characteristics.
Spatiotemporal Dynamics of Net Primary Productivity in China’s Urban Lands during 1982–2015
Authors: Zhang Helin , Sun Rui , Peng Dailiang , Yang Xiaohua , Wang Yan , Hu Yueming , Zheng Shijun , Zhang Jingyu , Bai Jia , Li Qi
Published: January 2021

The rapid urbanization process has threatened the ecological environment. Net primary productivity (NPP) can effectively indicate vegetation growth status in an urban area. In this paper, we evaluated the change in NPP in China and China’s urban lands and assessed the impact of temperature, precipitation, the sunshine duration, and vegetation loss due to urban expansion on NPP in China’s three fast-growing urban agglomerations and their buffer zones (~5–20 km). The results indicated that the NPP in China exhibited an increasing trend. In contrast, the NPP in China’s urban lands showed a decreasing trend. However, after 1997, China’s increasing trend in NPP slowed (from 9.59 Tg C/yr to 8.71 Tg C/yr), while the decreasing trend in NPP in China’s urban lands weakened. Moreover, we found that the NPP in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), and the Pearl River Delta urban agglomeration (PRDUA) showed a decreasing trend. The NPP in the BTHUA showed an increasing trend in the buffer zones, which was positively affected by temperature and sunshine duration. Additionally, nonsignificant vegetation loss could promote the increase of NPP. In the YRDUA, the increasing temperature was the main factor that promoted the increase of NPP. The effect of temperature on NPP could almost offset the inhibition of vegetation reduction on the increase of NPP as the buffer zone expanded. In PRDUA, sunshine duration and vegetation loss were the main factors decreasing NPP. Our results will support future urban NPP prediction and government policymaking.
Multi-Sensor InSAR Assessment of Ground Deformations around Lake Mead and Its Relation to Water Level Changes
Authors: Darvishi Mehdi , Destouni Georgia , Aminjafari Saeid , Jaramillo Fernando
Published: January 2021

Changes in subsurface water resources might alter the surrounding ground by generating subsidence or uplift, depending on geological and hydrogeological site characteristics. Improved understanding of the relationships between surface water storage and ground deformation is important for design and maintenance of hydraulic facilities and ground stability. Here, we construct one of the longest series of Interferometric Synthetic Aperture Radar (InSAR) to date, over twenty-five years, to study the relationships between water level changes and ground surface deformation in the surroundings of Lake Mead, United States, and at the site of the Hoover Dam. We use the Small Baseline Subset (SBAS) and Permanent scatterer interferometry (PSI) techniques over 177 SAR data, encompassing different SAR sensors including ERS1/2, Envisat, ALOS (PALSAR), and Sentinel-1(S1). We perform a cross-sensor examination of the relationship between water level changes and ground displacement. We found a negative relationship between water level change and ground deformation around the reservoir that was consistent across all sensors. The negative relationship was evident from the long-term changes in water level and deformation occurring from 1995 to 2014, and also from the intra-annual oscillations of the later period, 2014 to 2019, both around the reservoir and at the dam. These results suggest an elastic response of the ground surface to changes in water storage in the reservoir, both at the dam site and around the reservoir. Our study illustrates how InSAR-derived ground deformations can be consistent in time across sensors, showing the potential of detecting longer time-series of ground deformation.
Multi-Scale LG-Mod Analysis for a More Reliable SAR Sea Surface Wind Directions Retrieval
Authors: Rana Fabio Michele , Adamo Maria
Published: January 2021

An improved version of the Local-Gradient-Modified (LG-Mod) algorithm for Sea Surface Wind (SSW) directions retrieval by means of Synthetic Aperture Radar (SAR) images is presented. A “local” multi-scale analysis of wind-aligned SAR patterns is introduced to improve the LG-Mod sensitivity to SAR backscattering modulations, occurring locally with various spatial wavelengths. The Marginal Error parameter is redefined, and the adoption of the Directional Accuracy Maximization Criterion (DAMC) allows for the novel Multi-Scale (MS) LG-Mod to automatically select the local processing scale that may be regarded as optimal for pattern enhancement, once a discrete set of scales has been already fixed. Hence, this optimal scale successfully gives evidence to guarantee the best achievable local direction estimation. The assessment of the MS LG-Mod is carried on both simulated SAR images and a Sentinel-1 (S-1) dataset, consisting of 350 Interferometric Wide Swath Ground Range Multi-Look Detected High-Resolution images, which cover the region of the Gulf of Maine. In the latter case, the removal of artifacts and non-wind features from SAR amplitudes is mandatory before directional estimations. In situ wind observations gathered by the National Oceanic and Atmospheric Administration National Data Buoy Center (NOAA NDBC) are exploited for validation. The findings obtained from S-1 data confirm the ones from simulated patterns. The MS LG-Mod analysis performs better than each single-scale one in terms of both percentages of reliable directions and directional Root Mean Square Error (RMSE) values achieved.
Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables
Authors: Elshafei Mohamed , Shihab Emad
Published: January 2021

Fatigue is a naturally occurring phenomenon during human activities, but it poses a bigger risk for injuries during physically demanding activities, such as gym activities and athletics. Several studies show that bicep muscle fatigue can lead to various injuries that may require up to 22 weeks of treatment. In this work, we adopt a wearable approach to detect biceps muscle fatigue during a bicep concentration curl exercise as an example of a gym activity. Our dataset consists of 3000 bicep curls from twenty middle-aged volunteers at ages between 27 to 30 and Body Mass Index (BMI) ranging between 18 to 28. All volunteers have been gym-goers for at least 1 year with no records of chronic diseases, muscle, or bone surgeries. We encountered two main challenges while collecting our dataset. The first challenge was the dumbbell’s suitability, where we found that a dumbbell weight (4.5 kg) provides the best tradeoff between longer recording sessions and the occurrence of fatigue on exercises. The second challenge is the subjectivity of RPE, where we average the reported RPE with the measured heart rate converted to RPE. We observed from our data that fatigue reduces the biceps’ angular velocity; therefore, it increases the completion time for later sets. We extracted a total of 33 features from our dataset, which have been reduced to 16 features. These features are the most overall representative and correlated with bicep curl movement, yet they are fatigue-specific features. We utilized these features in five machine learning models, which are Generalized Linear Models (GLM), Logistic Regression (LR), Random Forests (RF), Decision Trees (DT), and Feedforward Neural Networks (FNN). We found that using a two-layer FNN achieves an accuracy of 98% and 88% for subject-specific and cross-subject models, respectively. The results presented in this work are useful and represent a solid start for moving into a real-world application for detecting the fatigue level in bicep muscles using wearable sensors as we advise athletes to take fatigue into consideration to avoid fatigue-induced injuries.
Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification
Authors: Huang Zhiwen , Zhou Quan , Zhu Xingxing , Zhang Xuming
Published: January 2021

In many medical image classification tasks, there is insufficient image data for deep convolutional neural networks (CNNs) to overcome the over-fitting problem. The light-weighted CNNs are easy to train but they usually have relatively poor classification performance. To improve the classification ability of light-weighted CNN models, we have proposed a novel batch similarity-based triplet loss to guide the CNNs to learn the weights. The proposed loss utilizes the similarity among multiple samples in the input batches to evaluate the distribution of training data. Reducing the proposed loss can increase the similarity among images of the same category and reduce the similarity among images of different categories. Besides this, it can be easily assembled into regular CNNs. To appreciate the performance of the proposed loss, some experiments have been done on chest X-ray images and skin rash images to compare it with several losses based on such popular light-weighted CNN models as EfficientNet, MobileNet, ShuffleNet and PeleeNet. The results demonstrate the applicability and effectiveness of our method in terms of classification accuracy, sensitivity and specificity.
Wearable Devices for Ergonomics: A Systematic Literature Review
Authors: Stefana Elena , Marciano Filippo , Rossi Diana , Cocca Paola , Tomasoni Giuseppe
Published: January 2021

Wearable devices are pervasive solutions for increasing work efficiency, improving workers’ well-being, and creating interactions between users and the environment anytime and anywhere. Although several studies on their use in various fields have been performed, there are no systematic reviews on their utilisation in ergonomics. Therefore, we conducted a systematic review to identify wearable devices proposed in the scientific literature for ergonomic purposes and analyse how they can support the improvement of ergonomic conditions. Twenty-eight papers were retrieved and analysed thanks to eleven comparison dimensions related to ergonomic factors, purposes, and criteria, populations, application and validation. The majority of the available devices are sensor systems composed of different types and numbers of sensors located in diverse body parts. These solutions also represent the technology most frequently employed for monitoring and reducing the risk of awkward postures. In addition, smartwatches, body-mounted smartphones, insole pressure systems, and vibrotactile feedback interfaces have been developed for evaluating and/or controlling physical loads or postures. The main results and the defined framework of analysis provide an overview of the state of the art of smart wearables in ergonomics, support the selection of the most suitable ones in industrial and non-industrial settings, and suggest future research directions.
Optimization of a Low-Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis
Authors: Gaiardo Andrea , Novel David , Scattolo Elia , Crivellari Michele , Picciotto Antonino , Ficorella Francesco , Iacob Erica , Bucciarelli Alessio , Petti Luisa , Lugli Paolo , Bagolini Alvise
Published: January 2021

The substrate plays a key role in chemoresistive gas sensors. It acts as mechanical support for the sensing material, hosts the heating element and, also, aids the sensing material in signal transduction. In recent years, a significant improvement in the substrate production process has been achieved, thanks to the advances in micro- and nanofabrication for micro-electro-mechanical system (MEMS) technologies. In addition, the use of innovative materials and smaller low-power consumption silicon microheaters led to the development of high-performance gas sensors. Various heater layouts were investigated to optimize the temperature distribution on the membrane, and a suspended membrane configuration was exploited to avoid heat loss by conduction through the silicon bulk. However, there is a lack of comprehensive studies focused on predictive models for the optimization of the thermal and mechanical properties of a microheater. In this work, three microheater layouts in three membrane sizes were developed using the microfabrication process. The performance of these devices was evaluated to predict their thermal and mechanical behaviors by using both experimental and theoretical approaches. Finally, a statistical method was employed to cross-correlate the thermal predictive model and the mechanical failure analysis, aiming at microheater design optimization for gas-sensing applications.
Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition
Authors: Kreuzer David , Munz Michael
Published: January 2021

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.
Efficient and Practical Correlation Filter Tracking
Authors: Zhu Chengfei , Jiang Shan , Li Shuxiao , Lan Xiaosong
Published: January 2021

Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz).
Anti-Asthmatic Effects of Saffron Extract and Salbutamol in an Ovalbumin-Induced Airway Model of Allergic Asthma
Authors: Nair Pranav , Prabhavalkar Kedar
Published: January 2021

Introduction: Asthma is a chronic inflammatory disorder of the airways often characterized by airway remodeling and influx of inflammatory cells into the airways. Saffron (C. sativus) has been reported to possess anti-inflammatory, anti-allergic and immunomodulatory properties. Salbutamol is known to relax airway smooth muscles. Objective: To investigate the combined anti-asthmatic effect of C. sativus extract (CSE) and salbutamol in an ovalbumin (OVA)-induced asthma in rats. Materials and methods: Airway hyperresponsiveness (AHR) was induced in male Sprague-Dawley rats by OVA challenge and treated with CSE (30 mg/kg and 60 mg/kg i.p.) and salbutamol (0.5 mg/kg p.o) for 28 days. After the induction period, various hematological, biochemical, molecular (ELISA) and histological analyses were performed. Results: OVA-induced alterations observed in hematological parameters (total and differential cell counts observed in Bronchoalveolar Lavage Fluid (BALF) were significantly attenuated (p < 0.01) by CSE (30 mg/kg and 60 mg/kg) and salbutamol (0.5 mg/kg). The treatment combination also significantly decreased (p < 0.01) the levels of total protein and albumin in serum, BALF and lung tissues. Treatment with CSE and salbutamol significantly attenuated (p < 0.01) increase in OVA induced Th2 cytokine levels (TNF-α, IL-1β, IL-4, IL-13). Histopathological analysis of lung tissue showed that combined effect of CSE and salbutamol treatment ameliorated OVA-induced inflammatory influx and ultrastructural aberrations. Conclusion: The results obtained from this study show that the combined effect of CSE and salbutamol exhibited anti-asthmatic properties via its anti-inflammatory effect and by alleviating Th2 mediated immune response. Thus, this treatment combination could be considered as a new therapeutic strategy for management of asthma.
A Comparison of PlayerLoadTM and Heart Rate during Backwards and Forwards Locomotion during Intermittent Exercise in Rugby League Players
Authors: Barnes Matthew R. , Guy Joshua H. , Elsworthy Nathan , Scanlan Aaron T.
Published: January 2021

Limited research has examined the demands of backward locomotion at various speeds using common load monitoring metrics in team sport athletes. Consequently, this study compared the external and internal loads between backward and forward locomotion during intermittent exercise in team sport athletes. Semi-professional, male rugby league players (n = 29) completed the same exercise protocol on two occasions in backward and forward directions. On each occasion, participants performed separate 20 m trials at self-selected walking, jogging, running, and sprinting speeds and then completed a 15 min modified Loughborough intermittent shuttle test (mLIST). Common external and internal load metrics were gathered across testing. Faster speeds (p < 0.001) were attained at all speeds during forward locomotion in the 20 m trials. Non-significant differences in accumulated PlayerLoadTM were found between directions across the mLIST; however, higher relative (per min) PlayerLoadTM (p < 0.001) was apparent during backward locomotion when walking and during forward locomotion when sprinting during the mLIST. RPE and mean heart rate were higher (p < 0.001) during backward locomotion across the mLIST. These data highlight the unique loading patterns experienced during backward locomotion and suggest practitioners should consider the discernment in loading imposed between backward and forward locomotion when measuring athlete demands using common metrics.
The Imperative to Address Sustainability Skills Gaps in Tourism in Wales
Authors: Carlisle Sheena , Zaki Karam , Ahmed Mohamed , Dixey Louise , McLoughlin Emmet
Published: January 2021

Tourism is fundamental to the economy of Wales, and the government tourism plan 2020–2025 centres on sustainable development. This paper presents a quantitative analysis of research undertaken by the Next Tourism Generation Alliance (NTGA) project in Wales evidencing sustainability skills gaps. Survey data collected from five tourism sectors, accommodation, food and beverage, destination management, visitor attractions, travel agents and tour operators, show that the highest green and social skills gaps were identified in food and beverage businesses, whilst accommodation reported the lowest skills gaps. The effect of the type of tourism sector, business size and job level on green skills gaps perceptions was found to be insignificant, which indicates that training in sustainability skills is considered important regardless of these variables. The effect of type of tourism activity on perceptions of social skills gaps including personal and communication was significant. Addressing skills gaps is crucial given the significance of the industry in the national economy and the need for recovery from COVID-19. Tourism is one of the worst affected industries in the pandemic, and global market research and policy guidance highlight the importance of sustainable tourism development to ensure a resilient recovery.
Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm
Authors: Qin Guangyu , Yan Qingyou , Zhu Jingyao , Xu Chuanbo , Kammen Daniel M.
Published: January 2021

Accurate wind power forecasting is essential to reduce the negative impact of wind power on the operation of the grid and the operation cost of the power system. Day-ahead wind power forecasting plays an important role in the day-ahead electricity spot trading market. However, the instability of the wind power series makes the forecast difficult. To improve forecast accuracy, a hybrid optimization algorithm is established in this study, which combines variational mode decomposition (VMD), maximum relevance & minimum redundancy algorithm (mRMR), long short-term memory neural network (LSTM), and firefly algorithm (FA) together. Firstly, the original historical wind power sequence is decomposed into several characteristic model functions with VMD. Then, mRMR is applied to obtain the best feature set by analyzing the correlation between each component. Finally, the FA is used to optimize the various parameters LSTM. Adding the forecasting results of all sub-sequences acquires the forecasting result. It turns out that the proposed hybrid algorithm is superior to the other six comparison algorithms. At the same time, an additional case is provided to further verify the adaptability and stability of the proposed hybrid model.
Knowledge Management in Entrepreneurship Education as the Basis for Creative Business Development
Authors: Frolova Yuliya , Alwaely Suad A. , Nikishina Olga
Published: January 2021

Despite numerous studies dedicated to business and entrepreneurship education, there is a lack of research dedicated to students studying creativity in entrepreneurial and business-related disciplines through knowledge management tools and practices. The objectives of the study were to determine the key factors of creative motivation for entrepreneurship among students, to build an appropriate universal practical model of learner creativeness motivation, and to create a knowledge management concept based on this model. By way of comparative, descriptive, qualitative, and quantitative analysis methods, we investigated previous research in the field of motivation, educational approaches, and methodologies, together with the data of the Program for International Student Assessment of the Organization for Economic Co-operation and Development. In order to compare international experience of knowledge management in modern approaches to education, we analyzed the curricular of business and entrepreneurship programs in three higher education entities from different countries: the Russian Presidential Academy of National Economy and Public Administration, KIMEP University, and Al Ain University. As a result of the research, we developed knowledge management that can be used for the learner creativity and motivation model. Recommendations developed in the course of the study would allow for the ability to make business and entrepreneurship education more sustainable.
Optimization for a Multi-Constraint Truck Appointment System Considering Morning and Evening Peak Congestion
Authors: Xu Bowei , Liu Xiaoyan , Yang Yongsheng , Li Junjun , Postolache Octavian
Published: January 2021

Gate and yard congestion is a typical type of container port congestion, which prevents trucks from traveling freely and has become the bottleneck that constrains the port productivity. In addition, urban traffic increases the uncertainty of the truck arrival time and additional congestion costs. More and more container terminals are adopting a truck appointment system (TAS), which tries to manage the truck arrivals evenly all day long. Extending the existing research, this work considers morning and evening peak congestion and proposes a novel approach for multi-constraint TAS intended to serve both truck companies and container terminals. A Mixed Integer Nonlinear Programming (MINLP) based multi-constraint TAS model is formulated, which explicitly considers the appointment change cost, queuing cost, and morning and evening peak congestion cost. The aim of the proposed multi-constraint TAS model is to minimize the overall operation cost. The Lingo commercial software is used to solve the exact solutions for small and medium scale problems, and a hybrid genetic algorithm and simulated annealing (HGA-SA) is proposed to obtain the solutions for large-scale problems. Experimental results indicate that the proposed TAS can not only better serve truck companies and container terminals but also more effectively reduce their overall operation cost compared with the traditional TASs.
The Importance of Social Value in Agroecological Farms: Adjusting the Common Good Balance Sheet to Improve Their Sustainable Management
Authors: Campos Vanessa , Sanchis Joan R. , Talavera Carmen
Published: January 2021

Organic farming offers the producers the possibility of focusing on their business while obtaining a competitive advantage. Nevertheless, this is centered in specific activities of the production process, thus only creating environmental value. Therefore, it is not an approach based on sustainability. As a consequence of this, it becomes necessary to enlarge this approach toward people (social value) and profitability (economic value). The objective of this work is to describe a tool based on the Economy for the Common Good model applied to the agroecological sector. We propose to adapt the Common Good Matrix to the characteristics of agroecological farms to offer a useful tool to manage and monitor Mediterranean small farms following a sustainability approach. The methodology consists of: (1) A bibliometric analysis of agroecological publications to identify sustainability indicators; (2) Delphi method application to perform a screening among the identified indicators; and (3) the establishment of peer groups of producers to carry out jointly their Common Good Balance Sheets. Eighteen experts participated in the Delphi study, and 15 companies participated in peer groups, of which seven obtained certification as companies of the common good. The bibliometric analysis shows that there is a gap of publications on agroecology and sustainability. The results obtained from peer study confirm that those agroecological companies that apply the adapted Common Good Matrix can implement corporate sustainability more easily.
Exploration of Sharing Accommodation Platform Airbnb Using an Extended Technology Acceptance Model
Authors: Jung Jiwoo , Park Eunkyung , Moon Joonho , Lee Won Seok
Published: January 2021

The purpose of this research is to investigate the accountability of the extended technology acceptance model (TAM) in the domain of sharing accommodation platform service. Based on TAM, this research derived attributes such as network externalities, trust, interactivity, ease of use, usefulness, and intention to repurchase. This study selects Airbnb as the context. A survey was adopted as the main instrument of this research. The total number of valid observations is 450. For the data analysis, this study conducted frequency analysis, confirmatory factor analysis, correlation matrix, reliability test, and path analysis using structural equation modeling. The results show that network externalities are essential to account for trust and interactivity. In addition, the results show that interactivity is an influential element to both ease of use and usefulness. Moreover, usefulness is affected by trust and interactivity. Furthermore, this research reveals the positive association between usefulness and intention to repurchase.
Application of Sustainable Prefabricated Wall Technology for Energy Efficient Social Housing
Authors: Chippagiri Ravijanya , Gavali Hindavi R. , Ralegaonkar Rahul V. , Riley Mike , Shaw Andy , Bras Ana
Published: January 2021

Under the India “Housing for all” scheme, 20 million urban houses have to be constructed by 2022, which requires the rate of construction to be around 8000 houses/day. Previous results by the team show that present design methods for affordable buildings and structures in India need improvement. The challenges are the disposal of solid waste generated from agro-industrial activities and the energy peak demand in extremely hot and cold seasons. The development of bio-based urban infrastructure which can adapt to the climatic conditions has been proposed. Inclusion of sustainable materials such as agro-industrial by-products and insulation materials has resulted in effective environmental sustainability and climate change adaptability. Precast components are highlighted as a suitable solution for this purpose as well as to fulfil the need of mass housing. India has a lesser record in implementing this prefab technology when compared to a global view. For the first time, a novel and sustainable prefab housing solution is tested for scale-up using industrial waste of co-fired blended ash (CBA) and the results are presented here. A model house of real scale measuring 3 × 3 × 3 m3 was considered as a base case and is compared with 17 other combinations of model house with varying alignment of prefab panels. Comparison was made with commercially available fly ash brick and CBA brick with a conventional roof slab. A simulation study was conducted regarding cost and energy analysis for all the 18 cases. Various brick and panel compositions with CBA for housing were tried and the superior composition was selected. Similarly, 18 model houses of real scale were simulated, with different combinations of walls made of bricks or panels and different building orientations, to check the impact on energy peak cooling and cost. Results show that peak cooling load can be reduced by six times with bio-based prefab panels. Prefab construction can be considered for mass housing ranging above 100 housing units, each consisting of an area of 25 m2.
A Cultural and Environmental Assessment of a Landscape Archetype with Dispersed Settlements in Čadca Cadastral District, Slovakia
Authors: Belčáková Ingrid , Olah Branislav , Slámová Martina , Pšenáková Zuzana
Published: January 2021

Special types of rural settlements in Slovakia, so-called dispersed settlements, are typical of several regions in the country. They are recognized as specific elements in a landscape and have a strong effect on local identity. They are a part of a historical landscape structure, constituting a unique natural and cultural heritage. For this reason, they deserve special attention in planning and management processes. Decision-making processes about the landscape that do not take into consideration that the inherent value of those structures could lead to their irreversible loss. This paper aims at the evaluation of specific landscape elements in the case study area and describes their effect in terms of the sociohistorical, environmental, and visual context and their influence on sustainability. Both cultural and environmental inventories were interpreted in relation to spatiotemporal land cover/use changes. The field inventory and geospatial analysis, using geographic information systems (GIS) tools, resulted in the categorization and evaluation of 63 dispersed settlement units in the study area of Čadca. We propose a management method, giving reasonable detail to proposed incentives, for each dispersed settlement unit category. The proposed methodology is intended to create a classification of the dispersed settlement units from the perspective of landscape archetypes. The cultural and environmental assessment of dispersed settlement units resulted in the definition of indicators signaling the presence of a particular archetype.
Design and Implementation of a Highly Scalable, Low-Cost Distributed Traffic Violation Enforcement System in Phuket, Thailand
Authors: Limsoonthrakul Somphop , Dailey Matthew N. , Marikhu Ramesh , Timtong Vasan , Chairat Aphinya , Suphavilai Anant , Seetamanotch Wiwat , Ekpanyapong Mongkol
Published: January 2021

The number of global road traffic accidents is rising every year and remains undesirably high. One of the main reasons for this trend is that, in many countries, road users violate road safety regulations and traffic laws. Despite improvements in road safety legislation, enforcement is still a major challenge in low- and middle-income countries. Information technology solutions have emerged for automated traffic enforcement systems in the last decade. They have been tested on a small scale, but until now, the cost of deployment of these systems is generally too high for nation-wide adoption in low- and middle-income countries that need them the most. We present the architectural design of a traffic violation enforcement system that can optimize the cost of deployment and resource utilization. Based on the proposed architecture, we describe the implementation and deployment of the system, and perform a comparison of two different versions of the video-based enforcement system, one using classical computer vision methods and another using deep learning techniques. Finally, we analyze the impact of the system deployed in Phuket, Thailand from 2017 to the present in terms of local road users’ compliance and the road safety situation. We conclude that the system has had a positive impact on road safety in Phuket at a moderate cost.
Source Apportionment and Ecological Risk Assessment of Potentially Toxic Elements in Cultivated Soils of Xiangzhou, China: A Combined Approach of Geographic Information System and Random Forest
Authors: Huang He , Zhou Yong , Liu Yu-Jie , Xiao Liang , Li Ke , Li Meng-Yao , Tian Yang , Wu Fei
Published: January 2021

Soil is both an important sink and a source for contaminants in the agricultural ecosystem. To research the sources and ecological risk of potentially toxic elements in Xiangzhou, China, 326 soil samples from arable land were collected and analyzed for five potentially toxic elements: cadmium (Cd), mercury (Hg), arsenic (As), lead (Pb), and chromium (Cr). In this research, ecological risk assessment was used to determine the degree of contamination in the research area, the outcome of the Geographic Information System was as used to study the spatial distribution characteristics of potentially toxic elements, and random forest was used to evaluate the natural and artificial influencing factors. We surveyed the sources of potentially toxic elements through quantifying the indicators, which gave further opinions. The results were as follows: (1) The average contents of potentially toxic elements were 0.14 mg/kg (Cd), 0.05 mg/kg (Hg), 12.33 mg/kg (As), 28.39 mg/kg (Pb), and 75.21 mg/kg (Cr), respectively. The results compared with the background value of Hubei, neighboring regions, and countries for Cd, As, Pb, and Cr showed mild pollution. (2) The total evaluation of soil pollution via the comprehensive pollution index indicated slight contamination by Cd. Assessment by the potential ecological risk index indicated low ecological risk due to Cd and moderate contamination by Hg. Evaluation through the geo-accumulation index evinced the low ecological risk for Cd, As, and Pb and moderate contamination by Hg. (3) We found that in addition to natural factors (such as soil parent material, soil pH, etc.), long-term industrial pollution, mineral mining and processing, exhaust emissions from transportation, the application of manure from farms as farmyard manure, and sewage irrigation were the primary anthropogenic sources of potentially toxic element contamination in the soil.
UltraBrix: A Device for Measuring the Soluble Solids Content in Sugarcane
Authors: Costa Marcus V. Americano da , Fontes Cristiano Hora , Carvalho Gregor , Júnior Edison Camilo de Moraes
Published: January 2021

Human interference in production systems becomes feasible through the use of specific devices that, among other functions, allow the monitoring, control and optimization of processes. One of the drawbacks encountered in the sugar and ethanol industry is the lack of instruments for the measurement of the °Brix concentration in sugarcane that are low-cost and non-destructive. The measurement and monitoring of the °Brix concentration in the cane are necessary to identify the appropriate time for harvesting in order to reach the peak sucrose content in the cane, obtaining maximum yield from a given crop. Based on several measurement devices available in the current market that involve non-destructive technologies for the monitoring of physical-chemical properties of food, this paper proposes a new low-cost meter to measure °Brix in sugarcane. The device uses the continuous wave technique consisting of two piezoelectric transducers (signal transmitter and receiver), a pulse generator, an instrumentation amplifier with a bandpass filter and an amplitude meter. The developed measuring instrument is feasible and represents a promising alternative for the sugar and alcohol industry.