bluuebunny/crossref_metadata_2025 huggingface dataset:
Universal Filter Based on Compact CMOS Structure of VDDDA
Authors: Jaikla Winai , Khateb Fabian , Kulej Tomasz , Pitaksuttayaprot Koson
Published: March 2021

This paper proposes the simulated and experimental results of a universal filter using the voltage differencing differential difference amplifier (VDDDA). Unlike the previous complementary metal oxide semiconductor (CMOS) structures of VDDDA that is present in the literature, the present one is compact and simple, owing to the employment of the multiple-input metal oxide semiconductor (MOS) transistor technique. The presented filter employs two VDDDAs, one resistor and two grounded capacitors, and it offers low-pass: LP, band-pass: BP, band-reject: BR, high-pass: HP and all-pass: AP responses with a unity passband voltage gain. The proposed universal voltage mode filter has high input impedances and low output impedance. The natural frequency and bandwidth are orthogonally controlled by using separated transconductance without affecting the passband voltage gain. For a BP filter, the root mean square (RMS) of the equivalent output noise is 46 µV, and the third intermodulation distortion (IMD3) is −49.5 dB for an input signal with a peak-to peak of 600 mV, which results in a dynamic range (DR) of 73.2 dB. The filter was designed and simulated in the Cadence environment using a 0.18-µm CMOS process from Taiwan semiconductor manufacturing company (TSMC). In addition, the experimental results were obtained by using the available commercial components LM13700 and AD830. The simulation results are in agreement with the experimental one that confirmed the advantages of the filter.
Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems
Authors: Akbarzadeh Aida , Katsikas Sokratis
Published: March 2021

Contemporary Critical Infrastructures (CIs), such as the power grid, comprise cyber physical systems that are tightly coupled, to form a complex system of interconnected components with interacting dependencies. Modelling methodologies have been suggested as proper tools to provide better insight into the dependencies and behavioural characteristics of these complex systems. In order to facilitate the study of interconnections in and among critical infrastructures, and to provide a clear view of the interdependencies among their cyber and physical components, this paper proposes a novel method, based on a graphical model called Modified Dependency Structure Matrix (MDSM). The MDSM provides a compact perspective of both inter-dependency and intra-dependency between subsystems of one complex system or two distinct systems. Additionally, we propose four parameters that allow the quantitative assessment of the characteristics of dependencies, including multi-order dependencies in large scale CIs. We illustrate the workings of the proposed method by applying it to a micro-distribution network based on the G2ELAB 14-Bus model. The results provide valuable insight into the dependencies among the network components and substantiate the applicability of the proposed method for analyzing large scale cyber physical systems.
Vibration Analysis for Fault Detection of Wind Turbine Drivetrains—A Comprehensive Investigation
Authors: Teng Wei , Ding Xian , Tang Shiyao , Xu Jin , Shi Bingshuai , Liu Yibing
Published: March 2021

Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of wind turbine drivetrains. It enables the defect location of mechanical subassemblies and health indicator construction for remaining useful life prediction, which is beneficial to reducing the operation and maintenance costs of wind farms. This paper analyzes the structure features of different drivetrains of mainstream wind turbines and introduces a vibration data acquisition system. Almost all the research on the vibration-based diagnosis algorithm for wind turbines in the past decade is reviewed, with its effects being discussed. Several challenging tasks and their solutions in the vibration-based fault detection of wind turbine drivetrains are proposed from the perspective of practicality for wind turbines, including the fault detection of planetary subassemblies in multistage wind turbine gearboxes, fault feature extraction under nonstationary conditions, fault information enhancement techniques and health indicator construction. Numerous naturally damaged cases representing the real operational features of industrial wind turbines are given, with a discussion of the failure mechanism of defective parts in wind turbine drivetrains as well.
Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps
Authors: Betti Alessandro , Tucci Mauro , Crisostomi Emanuele , Piazzi Antonio , Barmada Sami , Thomopulos Dimitri
Published: March 2021

In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.
Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures
Authors: Ali Luqman , Alnajjar Fady , Jassmi Hamad Al , Gocho Munkhjargal , Khan Wasif , Serhani M. Adel
Published: March 2021

This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.
Speciation and Determination of Selenium Oxyanions at the Drinking Water Pollution Concentration Levels
Authors: Kalaitzidou Kyriaki , Bidiou Evangelia , Zouboulis Anastasios , Mitrakas Manassis
Published: March 2021

The research on selenium presence in water resources has revealed the need to determine the respective aquatic species. As selenium oxyanions SeO32− (SeIV) and SeO42− (SeVI) predominate in natural waters, their determination is essential, mainly due to different ecotoxicity properties, as well as to different removal options from relevant-polluted waters. This study focuses on the SeO32−/SeO42− speciation/separation and determination through the selective adsorption of SeO32− only onto specific iron oxy-hydroxides (FeOOHs). For this purpose, the laboratory prepared FeOOHs examined along with the commercially available relevant material (Bayoxide), which was found to present optimum results for the speciation of selenium oxyanions, at the low concentration range 10–100 μg/L, using a dose of 0.5 g/L of adsorbent and gently stirring for 30 min at the usually encountered pH value of 7.3 ± 0.2. Moreover, the relevant experiments showed that the other major ions Cl−, HCO3−, NO3−, SO42−, Ca2+, Mg2+, Na+, possibly found in most natural waters at the concentration range 0–200 mg/L, as well as silicon, total organic carbon (TOC) of natural organic matter (NOM) and iron at the concentration range 0–50 mg/L, 0–5 mg/L and 0–1 mg/L, respectively, did not interfere with the selective adsorption of Se(IV). Furthermore, the most important advantage of this selective speciation method is its implementation/combination with all commonly applied analytical methods for the determination of total selenium.
Smart Cities and Big Data Analytics: A Data-Driven Decision-Making Use Case
Authors: Shahat Osman Ahmed M. , Elragal Ahmed
Published: March 2021

Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of SCs, BDA offers potential as a data-driven decision-making enabler. Although there are abundant articles in the literature addressing BDA as a decision-making enabler in SCs, mainstream research addressing BDA and SCs focuses on either the technical aspects or smartening specific SC domains. A small fraction of these articles addresses the proposition of developing domain-independent BDA frameworks. This paper aims to answer the following research question: how can BDA be used as a data-driven decision-making enabler in SCs? Answering this requires us to also address the traits of domain-independent BDA frameworks in the SC context and the practical considerations in implementing a BDA framework for SCs’ decision-making. This paper’s main contribution is providing influential design considerations for BDA frameworks based on empirical foundations. These foundations are concluded through a use case of applying a BDA framework in an SC’s healthcare setting. The results reveal the ability of the BDA framework to support data-driven decision making in an SC.
A Grounded Theory Exploration of Language Massive Open Online Courses (LMOOCs): Understanding Students’ Viewpoints
Authors: Hsu Robert Li-Wei
Published: March 2021

Massive open online courses (MOOCs) have been called the biggest innovation in education in 200 years for their unique attribute of being open and free to any individual with Internet access; however, their high dropout rate has led many people to be concerned or dubious about their effectiveness and applicability. The applicability of MOOCs in English for specific purposes (ESP) courses (in this case, hospitality English) needs more empirical evidence; the present study intends to help fill this gap and extend our current understanding of this issue. This study followed a grounded theory methodology to develop a theoretical model based on a constant dialogue between collected data and the literature. The results suggest that most participants had positive perceptions of language MOOCs (LMOOCs) in general, but some doubted their applicability. Most participants said they would continue to use LMOOCs for learning, depending on the attributes of specific courses. Based on the extracted data, a conceptual model for the applicability of LMOOCs is proposed.
Training and Support for Inclusive Practices: Transformation from Cooperation in Teaching and Learning
Authors: Muñoz-Martínez Yolanda , Gárate-Vergara Francisco , Marambio-Carrasco Cecilia
Published: March 2021

In this article, we present the four-year actions carried out at a public school in Meco (Madrid, Spain). Through the research contract (LOU, Art. 83) “Cooperative teaching and learning to respond to the diversity of students”, during the years 2014 and 2018, various actions were carried out by the school in its process of methodological and organizational transformation towards a more inclusive model based on the cooperation of teachers and students. A journey was made from the actions aimed at traditional training through courses and seminars to the accompaniment actions in the classroom to implement the cooperative learning methodology. Results of the actions and analysis of the interviews carried out with teachers participating in the process during the four years of work as well as field observation notes are presented. The results show the transformation and improvement of teaching practices based on inclusion and the cooperative learning methodology. Among the central conclusions, there is evidence of internal improvement in organizational management and inclusive teaching practices from the involvement, support, and continuous training of teachers in educational quality towards students.
Towards More Sustainable Materials for Geo-Environmental Engineering: The Case of Geogrids
Authors: Cislaghi Alessio , Sala Paolo , Borgonovo Gigliola , Gandolfi Claudio , Bischetti Gian Battista
Published: March 2021

Plastic materials are widely used in geotechnical engineering, especially as geosynthetics. The use of plastic-based products involves serious environmental risks caused by their degradation. Innovative research has been focusing on biodegradable polymers of natural origin, especially on poly(lactic acid) (PLA), to reduce the use of plastics. This study aims to explore the potentiality of biopolymers for the production of geogrids, measuring the chemical and mechanical characteristics of raw materials and of prototype samples, similar to those available on the market. First, chemical composition and optical purity were determined by hydrogen nuclear magnetic resonance (1H-NMR) and polarimetry. Furthermore, samples of uniaxial and biaxial geogrids were custom-molded using a professional 3D printer. Mechanical properties were measured both on the filament and on the prototype geogrids. The maximum tensile resistance was 6.76 kN/m for the neat-PLA filament and 10.14 kN/m for uniaxial prototype geogrids produced with PLA-based polymer mixed with titanium dioxide. PLA-based materials showed higher tensile properties than polypropylene (PP), the most common petroleum derivative. Conversely, such biomaterials seem to be more brittle and with scarce elongation rate respect PP. Nonetheless, these results are encouraging and can support the use of PLA-based materials for innovative biodegradable geosynthetics production, especially if used in combination with live plants.
Validation of a Simulation-Based Pre-Assessment Process for Solar Photovoltaic Technology Implemented on Rooftops of South African Shopping Centres
Authors: van Vuuren Dirk Johan , Marnewick Annlizé L. , Pretorius Jan Harm C.
Published: March 2021

The existing processes that determines the yield of a photovoltaic (PV) system before construction commences are fairly unstructured. Research that defines a structured process for simulation purposes is limited. This article builds on prior research where a proposed structured pre-assessment process, which may be applied before construction commences, was generated so that electricity yield from a PV system can be predicted with a higher degree of accuracy, and then subsequently optimized. By implementing the proposed pre-assessment process, calculating the future return on investment (ROI) by private investors is simplified, given that the existing process is restrictive. The research used the results from a South African case study over 24 months to ascertain the validity of the proposed pre-assessment process. The validation process includes analyzing the load demand of the shopping centre before and after the PV system was constructed, comparing the electricity yield from the PV system to the simulation results obtained in the preceding research, and amending the proposed pre-assessment process accordingly for improved electricity estimation. The case study shopping centre operates in Johannesburg, Gauteng, and consumes approximately 5000 kVA under maximum load.
Latent Heat Phase Change Heat Transfer of a Nanoliquid with Nano–Encapsulated Phase Change Materials in a Wavy-Wall Enclosure with an Active Rotating Cylinder
Authors: Mehryan S. A. M. , Raahemifar Kaamran , Gargari Leila Sasani , Hajjar Ahmad , El Kadri Mohamad , Younis Obai , Ghalambaz Mohammad
Published: March 2021

A Nano-Encapsulated Phase-Change Material (NEPCM) suspension is made of nanoparticles containing a Phase Change Material in their core and dispersed in a fluid. These particles can contribute to thermal energy storage and heat transfer by their latent heat of phase change as moving with the host fluid. Thus, such novel nanoliquids are promising for applications in waste heat recovery and thermal energy storage systems. In the present research, the mixed convection of NEPCM suspensions was addressed in a wavy wall cavity containing a rotating solid cylinder. As the nanoparticles move with the liquid, they undergo a phase change and transfer the latent heat. The phase change of nanoparticles was considered as temperature-dependent heat capacity. The governing equations of mass, momentum, and energy conservation were presented as partial differential equations. Then, the governing equations were converted to a non-dimensional form to generalize the solution, and solved by the finite element method. The influence of control parameters such as volume concentration of nanoparticles, fusion temperature of nanoparticles, Stefan number, wall undulations number, and as well as the cylinder size, angular rotation, and thermal conductivities was addressed on the heat transfer in the enclosure. The wall undulation number induces a remarkable change in the Nusselt number. There are optimum fusion temperatures for nanoparticles, which could maximize the heat transfer rate. The increase of the latent heat of nanoparticles (a decline of Stefan number) boosts the heat transfer advantage of employing the phase change particles.
Lifestyle Experiences: Exploring Key Attributes of Lifestyle Hotels Using Instagram User-Created Contents in South Korea
Authors: Han Yoojin , Lee Hyunsoo
Published: March 2021

This study aims to investigate the key attributes of a steadily growing hotel sector (lifestyle hotels), which has shown great success in the global competitive market, by analyzing user-created content on Instagram. The dataset used in this study were prepared from a total of 20,999 lifestyle hotel posts and 24,262 boutique hotel posts created from 2013 to 2020 and retrieved using a Python web crawler. The locations, hashtags, and image data were analyzed based on frequency analysis using social network analysis methods and computer vision technology, after which they were visualized with a geographical information system and Gephi. The results demonstrated that lifestyle hotels share key attributes that differentiate them from others in terms of physical, geospatial, and experiential contexts. Design, location, and management type are the main attributes that comprise the distinct identity of each lifestyle hotel. Moreover, a lifestyle hotel is distinct from a boutique hotel in that staying in the former means consuming experiences with continuous changes. The information and knowledge gained from this research will contribute to bridging the gap between theoretical literature and the practical development of lifestyle hospitality.
Strategic Development of Product-Service Systems (PSS) through Archetype Assessment
Authors: Frederiksen Trine Brink , P. P. Pieroni Marina , Pigosso Daniela C. A. , McAloone Tim C.
Published: March 2021

Product-service systems (PSS) enable product-oriented manufacturing companies to differentiate their offerings and become more competitive, while creating more value for customers along with improved economic performance and, in some cases, reduced environmental impact. However, PSS development remains difficult for manufacturing companies due to limited prior knowledge about PSS and the strategic characteristics it entails. To guide the PSS development process, this paper presents a prescriptive approach composed of a tool and a method based on the use of PSS archetypes in the initial development stages of new PSS business models (BM). The proposed tool builds upon three PSS archetypes, combined with 10 factors for assessment of the best suiting archetype. The proposed method and supporting tool guide companies in five steps, from the beginning of a servitisation process to the initial PSS development. Based on an evaluation with a Danish manufacturing company, an improved version of the method and tool was developed and applied with 60 students specialised in PSS development. Results from the evaluation indicate that the method and tool support scoping new PSS BM through the selection and modification of a PSS archetype, encouraging servitisation and PSS development in companies with different levels of PSS experience.
Cultural Landscape Preservation and Social–Ecological Sustainability
Authors: Schmitz María Fe , Herrero-Jáuregui Cristina
Published: March 2021

Cultural landscapes are the result of social–ecological processes that have co-evolved throughout history, shaping high-value sustainable systems [...]
Fostering Communicative Competence and Motivation through ComunicARTE Program
Authors: Lara Sonia , González-Torres María Carmen , Ibarrola-García Sara , Zúñiga Ana
Published: March 2021

The objective of this article is to analyse the potential of ComunicARTE, an innovative Spanish Language teaching/learning program that uses project-based learning to develop dialogic spaces which promote the communicative competence of Spanish students, together with social, emotional and motivational outcomes. Two schools have been observed with this in mind: an experimental one using this program and a control one. This is a longitudinal study with pre-and post-test data which analyses 170 children at the beginning of their fifth year and the end of their sixth year in primary school. Quantitative tests have been used to assess their communicative competence and motivational orientation in the classroom. The results obtained are discussed.
Exploring the Effectiveness of Clustering Algorithms for Capturing Water Consumption Behavior at Household Level
Authors: Ioannou Alexandra E. , Creaco Enrico F. , Laspidou Chrysi S.
Published: March 2021

As water scarcity becomes more prevalent, the analysis of urban water consumption patterns at the consumer level and the estimation of the corresponding water demand for water utility are expected to be among the top priorities of water companies in the near future. This study proposes a comprehensive methodology for water managers to achieve an efficient operation of urban water networks, by successfully detecting residential water consumption patterns corresponding to different household needs and behaviors. The methodology uses Self Organizing Maps as the main clustering algorithm in combination with K-means and Hierarchical Agglomerative Clustering. The objective is to create clusters in a literature dataset that includes water consumption from 21 customers located in Milford, Ohio, USA, for a 7-month period. Originally, water consumption data was recorded for every water use incident in the household, while for this analysis, the information is converted to half-hourly water consumption. Individual customers with similar consumption behavior are clustered and water-consumption curves are calculated for each cluster; these curves can be used by the water utility to obtain estimates of the spatio-temporal distribution of demand, thus giving insight into peak demands at different locations. Statistical indices of agreement are used to confirm a good agreement between the estimated and observed water use, when clustering is employed. The resulting curves show a clear improvement in capturing water consumption behavior at household level, when compared to corresponding curves obtained without clustering. This analysis offers water utilities an innovative solution that relies on real time data and uses data science principles for optimizing water supply and network operation and provides tools for the efficient use of water resources.
Coping Strategies against Peer Victimization: Differences According to Gender, Grade, Victimization Status and Perceived Classroom Social Climate
Authors: Cava María-Jesús , Ayllón Ester , Tomás Inés
Published: March 2021

The Sustainable Development Goals (SDGs) include “Good Health and Well-being” (SDG3) and “Quality Education” (SDG4). Nevertheless, many students cannot achieve these goals if they suffer peer victimization at their schools, and intervention programs to reduce it are necessary. These programs should consider the possible differences in the coping strategies preferred by students according to some personal (e.g., gender, grade, victimization status) and contextual (e.g., perceived classroom social climate) factors to be more effective. Therefore, the objective of this study was to analyze the possible differences in the coping strategies preferred by students (ask a friend for help, ask a teacher for help, ask parents for help, not ask anyone for help, fighting back, avoid the aggressor and ask the aggressor why) to handle situations of relational, physical and verbal peer victimization according to their gender, school grade, victimization status and perceived classroom social climate. The sample comprised 479 students (52.2% boys, 47.8% girls) aged from 9 to 14 years (M = 11.21, SD = 1.52). The results showed that girls chose the strategies of asking friends or adults for help and asking the aggressor why more than boys, while boys chose the strategies of fighting back and not ask anyone for help more than girls. The coping strategy of asking a teacher for help was preferred more by students of lower school grades and by students with a positive perception of the classroom climate. Victimized students preferred the strategy of not asking anyone for help. These results may be useful for developing more effective intervention programs. These programs should aim to enhance the teacher–student relationship in upper school grades, help victimized students to inform about peer aggression situations and improve perceived classroom social climate.
Analysis of the Use and Integration of the Flipped Learning Model, Project-Based Learning, and Gamification Methodologies by Secondary School Mathematics Teachers
Authors: Hossein-Mohand Hossein , Trujillo-Torres Juan-Manuel , Gómez-García Melchor , Hossein-Mohand Hassan , Campos-Soto Antonio
Published: March 2021

In the didactics of mathematics, many technological resources can be used with teaching strategies, techniques, models, and methodologies that facilitate the teaching–learning process. Pedagogical models such as Flipped Learning and active methodologies such as Project-Based Learning and Gamification have a relevant role in education. Objectives. (1) To identify the variables of teaching practices, ratios, and mathematics teacher training indicators that could influence the choice of different study models or methodologies. Method. A total of 73 teachers from the Autonomous City of Melilla were included in a cross-sectional study. Teachers filled out a validated questionnaire with 35 questions regarding mathematics and teacher training, information and communication technology (ICT) uses, resources and mastery, communication, and collaboration. Results. The statistical analyses revealed significant positive influences between the Flipped Learning model, Project-Based Learning, and Gamification with the evaluated items. Furthermore, Project-Based Learning showed a negative association with two items of the teaching practice indicator. Conclusion. The exchange of information and content through online spaces, participation, and collaboration in center projects related to digital technologies and educational software to teach mathematics had a significant impact on the choice of Flipped Learning model and active methodologies.
Social Inclusion, Innovation and Food Security in West Africa
Authors: Khalid Anser Muhammad , Iqbal Godil Danish , Aderounmu Busayo , Onabote Ademola , Osabohien Romanus , Ashraf Junaid , Yao-Ping Peng Michael
Published: March 2021

To achieve the United Nations Sustainable Goals (SDGs) by 2030, especially goal 2 (SDG-2) which is to “end hunger, achieve sustainable food security, improved nutrition and promote agriculture” this study examines how innovation and social inclusion affect food security in West Africa. The study applies the system Generalised Method of Moments (GMM) on a panel data of 15 West African countries for the period 2005–2018. The result from system GMM shows that innovation and social inclusion are drivers of food security. The implication of this is that increased level of social inclusion and innovation in West African may increase the level of food security by about 41.5% and 13.6% respectively. Therefore, the study concludes that to feed the growing African population, social inclusion should be improved to mitigate risk, vulnerability and socioeconomic shocks faced by farming households. In addition, innovation in agricultural should be enhanced to drive productivity, thereby leading to a sustainable food security.
A Two-Stream CNN Model with Adaptive Adjustment of Receptive Field Dedicated to Flame Region Detection
Authors: Lu Peng , Zhao Yaqin , Xu Yuan
Published: March 2021

Convolutional neural networks (CNN) have yielded state-of-the-art performance in image segmentation. Their application in video surveillance systems can provide very useful information for extinguishing fire in time. The current studies mostly focused on CNN-based flame image classification and have achieved good accuracy. However, the research of CNN-based flame region detection is extremely scarce due to the bulky network structures and high hardware configuration requirements of the state-of-the-art CNN models. Therefore, this paper presents a two-stream convolutional neural network for flame region detection (TSCNNFlame). TSCNNFlame is a lightweight CNN architecture including a spatial stream and temporal stream for detecting flame pixels in video sequences captured by fixed cameras. The static features from the spatial stream and dynamic features from the temporal stream are fused by three convolutional layers to reduce the false positives. We replace the convolutional layer of CNN with the selective kernel (SK)-Shuffle block constructed by integrating the SK convolution into the deep convolutional layer of ShuffleNet V2. The SKnet blocks can adaptively adjust the size of one receptive field with the proportion of one region of interest (ROI) in it. The grouped convolution used in Shufflenet solves the problem in which the multi-branch structure of SKnet causes the network parameters to double with the number of branches. Therefore, the CNN network dedicated to flame region detection balances the efficiency and accuracy by the lightweight architecture, the temporal–spatial features fusion, and the advantages of the SK-Shuffle block. The experimental results, which are evaluated by multiple metrics and are analyzed from many angles, show that this method can achieve significant performance while reducing the running time.
A New Simplification Algorithm for Scattered Point Clouds with Feature Preservation
Authors: Gong Miao , Zhang Zhijiang , Zeng Dan
Published: March 2021

High-precision and high-density three-dimensional point cloud models usually contain redundant data, which implies extra time and hardware costs in the subsequent data processing stage. To analyze and extract data more effectively, the point cloud must be simplified before data processing. Given that point cloud simplification must be sensitive to features to ensure that more valid information can be saved, in this paper, a new simplification algorithm for scattered point clouds with feature preservation, which can reduce the amount of data while retaining the features of data, is proposed. First, the Delaunay neighborhood of the point cloud is constructed, and then the edge points of the point cloud are extracted by the edge distribution characteristics of the point cloud. Second, the moving least-square method is used to obtain the normal vector of the point cloud and the valley ridge points of the model. Then, potential feature points are identified further and retained on the basis of the discrete gradient idea. Finally, non-feature points are extracted. Experimental results show that our method can be applied to models with different curvatures and effectively avoid the hole phenomenon in the simplification process. To further improve the robustness and anti-noise ability of the method, the neighborhood of the point cloud can be extended to multiple levels, and a balance between simplification speed and accuracy needs to be found.
A Modern View of the Equation of State in Nuclear and Neutron Star Matter
Authors: Burgio G. Fiorella , Schulze Hans-Josef , Vidaña Isaac , Wei Jin-Biao
Published: March 2021

Background: We analyze several constraints on the nuclear equation of state (EOS) currently available from neutron star (NS) observations and laboratory experiments and study the existence of possible correlations among properties of nuclear matter at saturation density with NS observables. Methods: We use a set of different models that include several phenomenological EOSs based on Skyrme and relativistic mean field models as well as microscopic calculations based on different many-body approaches, i.e., the (Dirac–)Brueckner–Hartree–Fock theories, Quantum Monte Carlo techniques, and the variational method. Results: We find that almost all the models considered are compatible with the laboratory constraints of the nuclear matter properties as well as with the largest NS mass observed up to now, 2.14−0.09+0.10M⊙ for the object PSR J0740+6620, and with the upper limit of the maximum mass of about 2.3–2.5M⊙ deduced from the analysis of the GW170817 NS merger event. Conclusion: Our study shows that whereas no correlation exists between the tidal deformability and the value of the nuclear symmetry energy at saturation for any value of the NS mass, very weak correlations seem to exist with the derivative of the nuclear symmetry energy and with the nuclear incompressibility.
Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things
Authors: Waleed Muhammad , Um Tai-Won , Kamal Tariq , Usman Syed Muhammad
Published: March 2021

In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.
Population Genetic Structure and Chemotype Diversity of Fusarium graminearum Populations from Wheat in Canada and North Eastern United States
Authors: Oghenekaro Abbot O. , Oviedo-Ludena Maria A. , Serajazari Mitra , Wang Xiben , Henriquez Maria A. , Wenner Nancy G. , Kuldau Gretchen A. , Navabi Alireza , Kutcher Hadley R. , Fernando W. G. Dilantha
Published: March 2021

Fusarium head blight (FHB) is a major disease in wheat causing severe economic losses globally by reducing yield and contaminating grain with mycotoxins. In Canada, Fusarium graminearum is the principal etiological agent of FHB in wheat, producing mainly the trichothecene mycotoxin, deoxynivalenol (DON) and its acetyl derivatives (15-acetyl deoxynivalenol (15ADON) and 3-acetyl deoxynivalenol (3ADON)). Understanding the population biology of F. graminearum such as the genetic variability, as well as mycotoxin chemotype diversity among isolates is important in developing sustainable disease management tools. In this study, 570 F. graminearum isolates collected from commercial wheat crops in five geographic regions in three provinces in Canada in 2018 and 2019 were analyzed for population diversity and structure using 10 variable number of tandem repeats (VNTR) markers. A subset of isolates collected from the north-eastern United States was also included for comparative analysis. About 75% of the isolates collected in the Canadian provinces of Saskatchewan and Manitoba were 3ADON indicating a 6-fold increase in Saskatchewan and a 2.5-fold increase in Manitoba within the past 15 years. All isolates from Ontario and those collected from the United States were 15ADON and isolates had a similar population structure. There was high gene diversity (H = 0.803–0.893) in the F. graminearum populations in all regions. Gene flow was high between Saskatchewan and Manitoba (Nm = 4.971–21.750), indicating no genetic differentiation between these regions. In contrast, less gene flow was observed among the western provinces and Ontario (Nm = 3.829–9.756) and USA isolates ((Nm = 2.803–6.150). However, Bayesian clustering model analyses of trichothecene chemotype subpopulations divided the populations into two clusters, which was correlated with trichothecene types. Additionally, population cluster analysis revealed there was more admixture of isolates among isolates of the 3ADON chemotypes than among the 15ADON chemotype, an observation that could play a role in the increased virulence of F. graminearum. Understanding the population genetic structure and mycotoxin chemotype variations of the pathogen will assist in developing FHB resistant wheat cultivars and in mycotoxin risk assessment in Canada.
Role of TNF-α-Inducing Protein Secreted by Helicobacter pylori as a Tumor Promoter in Gastric Cancer and Emerging Preventive Strategies
Authors: Suganuma Masami , Watanabe Tatsuro , Sueoka Eisaburo , Lim In Kyoung , Fujiki Hirota
Published: March 2021

The tumor necrosis factor-α (TNF-α)-inducing protein (tipα) gene family, comprising Helicobacter pylori membrane protein 1 (hp-mp1) and tipα, has been identified as a tumor promoter, contributing to H. pylori carcinogenicity. Tipα is a unique H. pylori protein with no similarity to other pathogenicity factors, CagA, VacA, and urease. American H. pylori strains cause human gastric cancer, whereas African strains cause gastritis. The presence of Tipα in American and Euro-Asian strains suggests its involvement in human gastric cancer development. Tipα secreted from H. pylori stimulates gastric cancer development by inducing TNF-α, an endogenous tumor promoter, through its interaction with nucleolin, a Tipα receptor. This review covers the following topics: tumor-promoting activity of the Tipα family members HP-MP1 and Tipα, the mechanism underlying this activity of Tipα via binding to the cell-surface receptor, nucleolin, the crystal structure of rdel-Tipα and N-terminal truncated rTipα, inhibition of Tipα-associated gastric carcinogenesis by tumor suppressor B-cell translocation gene 2 (BTG2/TIS21), and new strategies to prevent and treat gastric cancer. Thus, Tipα contributes to the carcinogenicity of H. pylori by a mechanism that differs from those of CagA and VacA.
Quantum Potentiality in Inhomogeneous Cosmology
Authors: Paliathanasis Andronikos
Published: March 2021

For the Szekeres system which describes inhomogeneous and anisotropic spacetimes we make use of a point-like Lagrangian, which describes the evolution of the physical variables of the Szekeres model, in order to perform a canonical quantization and to study the quantum potentiality of the Szekeres system in the content of de Broglie–Bohm theory. We revise previous results on the subject and we find that for a specific family of trajectories with initial conditions which satisfy a constraint equation, there exists additional conservation laws for the classical Szekeres system which are used to define differential operators and to solve the Wheeler–DeWitt equation. From the new conservation laws we construct a wave function which provides a nonzero quantum potential term that modifies the Szekeres system. The quantum potential corresponds to new terms in the dynamical system such that new asymptotic solutions with a nonzero energy momentum tensor of an anisotropic fluid exist. Therefore, the silent property of the Szekeres spacetimes is violated by quantum correction terms, which results in the quantum potential adding pressure to the solution.
A New Generation of Functional Tagged Proteins for HIV Fluorescence Imaging
Authors: Mamede João I. , Griffin Joseph , Gambut Stéphanie , Hope Thomas J.
Published: March 2021

During the last decade, there was a marked increase in the development of tools and techniques to study the molecular mechanisms of the HIV replication cycle by using fluorescence microscopy. Researchers often apply the fusion of tags and fluorophores to viral proteins, surrogate proteins, or dyes to follow individual virus particles while they progress throughout infection. The inclusion of such fusion motifs or surrogates frequently disrupts viral infectivity or results in a change of the wild-type phenotype. Here, we detail the construction and functional characterization of two new constructs where we fused fluorescent proteins to the N-terminus of HIV-1 Integrase. In the first, IN is recruited into assembling particles via a codon optimized Gag to complement other viral constructs, while the second is fused to a Gag-Pol expression vector fully capable of integration. Our data shows that N-terminal tagged IN is functional for integration by both recovery of integration of catalytically inactive IN and by the successful infectivity of viruses carrying only labeled IN. These tools will be important to study the individual behavior of viral particles and associate such behavior to infectivity.
Lipid Droplets Accumulation during Hepatitis C Virus Infection in Cell-Culture Varies among Genotype 1–3 Strains and Does Not Correlate with Virus Replication
Authors: Galli Andrea , Ramirez Santseharay , Bukh Jens
Published: March 2021

Liver steatosis is a common complication of chronic hepatitis C virus (HCV) infection, which can result in accelerated liver fibrosis development, especially in patients infected with genotype 3a. The precise mechanisms of HCV-induced liver steatosis remain unclear, but it is often posited that increased intracellular lipid accumulation is the underlying cause of steatosis. To study experimentally how HCV infection in human liver derived cells by different genotypes and subtypes might affect lipid accumulation, we performed detailed cytofluorimetric and microscopy analyses of intracellular lipid droplets (LDs) in relation to the viral Core and to cell endoplasmic reticulum proteins. Following culture infection with HCV genotype 1a, 2a, 2b, 2c, and 3a strains, we found variable levels of intracellular LDs accumulation, associated to the infecting strain rather than to the specific genotype. Although two genotype 3a strains showed high levels of lipid accumulation, as previously observed, some strains of other genotypes displayed a similar phenotype. Moreover, the analyses of LDs size, number, and shape indicated that the apparent increase in lipid accumulation is due to an increase in the overall number rather than in the size of droplets. Finally, differences in total lipid content across genotypes did not correlate to differences in Core distribution nor Core levels. In conclusion, our study provides a quantitative in-depth analysis of the effect of HCV infection on LDs accumulation in cell-culture.
SARS-CoV-2 Variant of Concern 202012/01 Has about Twofold Replicative Advantage and Acquires Concerning Mutations
Authors: Grabowski Frederic , Preibisch Grzegorz , Giziński Stanisław , Kochańczyk Marek , Lipniacki Tomasz
Published: March 2021

The novel SARS-CoV-2 Variant of Concern (VOC)-202012/01 (also known as B.1.1.7), first collected in United Kingdom on 20 September 2020, is a rapidly growing lineage that in January 2021 constituted 86% of all SARS-CoV-2 genomes sequenced in England. The VOC has been detected in 40 out of 46 countries that reported at least 50 genomes in January 2021. We have estimated that the replicative advantage of the VOC is in the range 1.83–2.18 [95% CI: 1.71–2.40] with respect to the 20A.EU1 variant that dominated in England in November 2020, and in range 1.65–1.72 [95% CI: 1.46–2.04] in Wales, Scotland, Denmark, and USA. As the VOC strain will likely spread globally towards fixation, it is important to monitor its molecular evolution. We have estimated growth rates of expanding mutations acquired by the VOC lineage to find that the L18F substitution in spike has initiated a fast growing VOC substrain. The L18F substitution is of significance because it has been found to compromise binding of neutralizing antibodies. Of concern are immune escape mutations acquired by the VOC: E484K, F490S, S494P (in the receptor binding motif of spike) and Q677H, Q675H (in the proximity of the polybasic cleavage site at the S1/S2 boundary). These mutants may hinder efficiency of existing vaccines and expand in response to the increasing after-infection or vaccine-induced seroprevalence.
Temporal Variability of Suspended-Solids Concentration in the Estuarine Channel of Patos Lagoon, Southern Brazil
Authors: Ávila Rafael André , Orozco Priscila Mulattieri Suarez , Andrade Mauro Michelena , Möller Osmar Olinto
Published: March 2021

The assessment of suspended-solids dynamics is crucial for the effective monitoring of estuarine environments. As the recurring in-situ sampling is usually problematic, the calibration of the backscattering from acoustic Doppler profilers has shown to be a reliable technique to estimate the suspended-solids concentration (SSC) in estuaries and rivers. In this study, we obtained a linear model that provides SSC estimates for the estuarine channel of Patos Lagoon by calibrating turbidity and acoustic data with in-situ concentration samples. The model output was analyzed in terms of its relationship with estuarine hydrodynamics and temporal variability. In this estuary, the supply of suspended solids is known to be due the runoff from its main tributaries, but also through the exchanges between the estuary and the coastal ocean. Both sources provide sediments and organic solids which affect water quality, geomorphology, and harbor operations. Results show that SSC is strongly linked to estuarine hydrodynamics, where concentrations increase with streamflow. During outflow periods, higher concentrations are associated with river runoff, whereas with inflow conditions they are induced by southern and southwesterly winds. However, relationship between SSC and streamflow is asymmetrical, meaning that the largest concentrations are majorly linked to outflow currents and downstream transport.