bluuebunny/crossref_metadata_2025 huggingface dataset:
Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China
Authors: Chen Wei , Zhou Yuyu , Wu Qiusheng , Chen Gang , Huang Xin , Yu Bailang
Published: August 2020

The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.83 and 0.71, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level.
Low-Power Voltage Transformer Smart Frequency Modeling and Output Prediction up to 2.5 kHz, Using Sinc-Response Approach
Authors: Ghaderi Abbas , Mingotti Alessandro , Peretto Lorenzo , Tinarelli Roberto
Published: August 2020

The instrument transformers scenario is moving towards the adoption of a new generation of low-power instrument transformers. This disruptive change also requires that the modeling, characterization, and testing of those devices must be improved. Therefore, this study focuses on a smart approach developed by the authors in a previous study to estimate the output of low-power voltage transformers (LPVT). The approach—which is based on a sort of modeling in the frequency domain (the so-called sinc-response)—allows obtaining the behavior of the LPVT at rated and distorted conditions. Experimental tests performed on off-the-shelf devices confirm the applicability and effectiveness of the proposed approach when estimating the output response of LPVTs.
Metal-Organic-Framework FeBDC-Derived Fe3O4 for Non-Enzymatic Electrochemical Detection of Glucose
Authors: Abrori Syauqi Abdurrahman , Septiani Ni Luh Wulan , Nugraha  , Anshori Isa , Suyatman  , Suendo Veinardi , Yuliarto Brian
Published: August 2020

Present-day science indicates that developing sensors with excellent sensitivity and selectivity for detecting early signs of diseases is highly desirable. Electrochemical sensors offer a method for detecting diseases that are simpler, faster, and more accurate than conventional laboratory analysis methods. Primarily, exploiting non-noble-metal nanomaterials with excellent conductivity and large surface area is still an area of active research due to its highly sensitive and selective catalysts for electrochemical detection in enzyme-free sensors. In this research, we successfully fabricate Metal-Organic Framework (MOF) FeBDC-derived Fe3O4 for non-enzymatic electrochemical detection of glucose. FeBDC synthesis was carried out using the solvothermal method. FeCl2.4H2O and Benzene-1,4-dicarboxylic acid (H2BDC) are used as precursors to form FeBDC. The materials were further characterized utilizing X-ray Powder Diffraction (XRD), Scanning Electron Microscopy (SEM), and Fourier-Transform Infrared Spectroscopy (FTIR). The resulting MOF yields good crystallinity and micro-rod like morphology. Electrochemical properties were tested using Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV) with a 0.1 M of Phosphate Buffer Saline (PBS pH 7.4) solution as the supporting electrolyte. The measurement results show the reduction and oxidation peaks in the CV curve of FeBDC, as well as Fe3O4. Pyrolysis of FeBDC to Fe3O4 increases the peak of oxidation and reduction currents. The Fe3O4 sample obtained has a sensitivity of 4.67 µA mM−1.cm−2, a linear range between 0.0 to 9.0 mM, and a glucose detection limit of 15.70 µM.
Deep Anomaly Detection for CNC Machine Cutting Tool Using Spindle Current Signals
Authors: Li Guang , Fu Yan , Chen Duanbing , Shi Lulu , Zhou Junlin
Published: August 2020

In recent years, industrial production has become more and more automated. Machine cutting tool as an important part of industrial production have a large impact on the production efficiency and costs of products. In a real manufacturing process, tool breakage often occurs in an instant without warning, which results a extremely unbalanced ratio of the tool breakage samples to the normal ones. In this case, the traditional supervised learning model can not fit the sample of tool breakage well, which results to inaccurate prediction of tool breakage. In this paper, we use the high precision Hall sensor to collect spindle current data of computer numerical control (CNC). Combining the anomaly detection and deep learning methods, we propose a simple and novel method called CNN-AD to solve the class-imbalance problem in tool breakage prediction. Compared with other prediction algorithms, the proposed method can converge faster and has better accuracy.
An Effective Dense Co-Attention Networks for Visual Question Answering
Authors: He Shirong , Han Dezhi
Published: August 2020

At present, the state-of-the-art approaches of Visual Question Answering (VQA) mainly use the co-attention model to relate each visual object with text objects, which can achieve the coarse interactions between multimodalities. However, they ignore the dense self-attention within question modality. In order to solve this problem and improve the accuracy of VQA tasks, in the present paper, an effective Dense Co-Attention Networks (DCAN) is proposed. First, to better capture the relationship between words that are relatively far apart and make the extracted semantics more robust, the Bidirectional Long Short-Term Memory (Bi-LSTM) neural network is introduced to encode questions and answers; second, to realize the fine-grained interactions between the question words and image regions, a dense multimodal co-attention model is proposed. The model’s basic components include the self-attention unit and the guided-attention unit, which are cascaded in depth to form a hierarchical structure. The experimental results on the VQA-v2 dataset show that DCAN has obvious performance advantages, which makes VQA applicable to a wider range of AI scenarios.
Stochastic Recursive Gradient Support Pursuit and Its Sparse Representation Applications
Authors: Shang Fanhua , Wei Bingkun , Liu Yuanyuan , Liu Hongying , Wang Shuang , Jiao Licheng
Published: August 2020

In recent years, a series of matching pursuit and hard thresholding algorithms have been proposed to solve the sparse representation problem with ℓ0-norm constraint. In addition, some stochastic hard thresholding methods were also proposed, such as stochastic gradient hard thresholding (SG-HT) and stochastic variance reduced gradient hard thresholding (SVRGHT). However, each iteration of all the algorithms requires one hard thresholding operation, which leads to a high per-iteration complexity and slow convergence, especially for high-dimensional problems. To address this issue, we propose a new stochastic recursive gradient support pursuit (SRGSP) algorithm, in which only one hard thresholding operation is required in each outer-iteration. Thus, SRGSP has a significantly lower computational complexity than existing methods such as SG-HT and SVRGHT. Moreover, we also provide the convergence analysis of SRGSP, which shows that SRGSP attains a linear convergence rate. Our experimental results on large-scale synthetic and real-world datasets verify that SRGSP outperforms state-of-the-art related methods for tackling various sparse representation problems. Moreover, we conduct many experiments on two real-world sparse representation applications such as image denoising and face recognition, and all the results also validate that our SRGSP algorithm obtains much better performance than other sparse representation learning optimization methods in terms of PSNR and recognition rates.
Data-Driven Design and Optimization for Smart Logistics Parks: Towards the Sustainable Development of the Steel Industry
Authors: Lv Yaqiong , Xiang Shangjia , Zhu Tianyi , Zhang Shuzhu
Published: August 2020

The design of steel logistics parks acts as fundamental infrastructure supporting the operations of storage, allocation, and distribution of steel products in the steel logistics industry, which actually lags behind the development of other logistics industries, such as e-commerce logistics, due to its large lot bulk storage, low turnover rate, and costly transportation and operations. This research proposes a data-driven approach for a specific steel logistics park, aiming to improve its operational efficiency in terms of product layout and allocation in multiple yards. The entry and delivery order data are analyzed comprehensively so as to determine the products with high operational frequency and the corresponding relevancy among them. Experimental results show that, among the 69 steel specifications, 14 high-frequency products are identified, and the correlation among the 14 identified high-frequency products possesses evident distribution characteristics concerning their brands and specifications. The identified frequency and correlation among various products can not only facilitate the product layout and allocation in steel logistics parks, but also advance the vehicle scheduling efficiency for product pick-up and delivery. Moreover, the research methodology and framework can provide managerial insights for other industries with mass data processing requirements.
A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)
Authors: Huang Pei , Zhang Xingxing , Copertaro Benedetta , Saini Puneet Kumar , Yan Da , Wu Yi , Chen Xiangjie
Published: August 2020

The deployment of solar photovoltaics (PV) and electric vehicles (EVs) is continuously increasing during urban energy transition. With the increasing deployment of energy storage, the development of the energy sharing concept and the associated advanced controls, the conventional solar mobility model (i.e., solar-to-vehicles (S2V), using solar energy in a different location) and context are becoming less compatible and limited for future scenarios. For instance, energy sharing within a building cluster enables buildings to share surplus PV power generation with other buildings of insufficient PV power generation, thereby improving the overall PV power utilization and reducing the grid power dependence. However, such energy sharing techniques are not considered in the conventional solar mobility models, which limits the potential for performance improvements. Therefore, this study conducts a systematic review of solar mobility-related studies as well as the newly developed energy concepts and techniques. Based on the review, this study extends the conventional solar mobility scope from S2V to solar-to-buildings, vehicles and storage (S2BVS). A detailed modeling of each sub-system in the S2BVS model and related advanced controls are presented, and the research gaps that need future investigation for promoting solar mobility are identified. The aim is to provide an up-to-date review of the existing studies related to solar mobility to decision makers, so as to help enhance solar power utilization, reduce buildings’ and EVs’ dependence and impacts on the power grid, as well as carbon emissions.
Increasing Energy Efficiency in the Food and Beverage Industry: A Human-Centered Design Approach
Authors: Millán Gema , Llano Erudino , Globisch Joachim , Durand Antoine , Hettesheimer Tim , Alcalde Eduardo
Published: August 2020

Energy efficiency in companies is addressed by established tools or procedures, such as energy audits or the implementation of an energy management system in accordance with the Energy Efficiency Directive. Both options indistinctly provide sufficient information for companies to identify opportunities for improvement and savings in their energy costs. However, the influence of cultural and behavioral aspects on employees and the organizational and structural configuration of companies impacts the effectiveness of these tools. In this regard, within the framework of the INDUCE “Towards a sustainable agro-food Industry: Capacity building programmes in Energy Efficiency” project, which is an H2020 research project focused on the food and beverage industry, an innovative methodology based on the human-centered design approach has been designed to motivate and empower the key actors of companies by creating an energy culture that increases energy efficiency. This methodology, which internalizes the procedures and philosophy of continuous improvement of an energy management system, allows companies to adopt different models adapted to each situation in order to acquire an energy-efficiency culture that enables actions to be carried out in companies more effectively and with long-term impact. This paper describes the methodology and its implementation and validation within 11 pilot companies in France, Germany, Spain, and The Netherlands, providing some representative results.
Monitoring Methods for Systems-Strengthening Activities Toward Sustainable Water and Sanitation Services in Low-Income Settings
Authors: Hollander Daniel , Ajroud Brittany , Thomas Evan , Peabody Shawn , Jordan Elizabeth , Javernick-Will Amy , Linden Karl
Published: August 2020

To address the complex challenge of sustaining basic water and sanitation services in low income settings, international organizations and local and national government entities are beginning to design and implement interventions explicitly aimed at addressing system weaknesses. Often referred to as “systems approaches,” these interventions seek to understand, engage with, and positively influence the network of actors and the interacting factors that deliver services. As WASH sector assistance and support activities shift toward systems approaches, many associated intermediate results and desired outcomes become less quantifiable than those of more traditional WASH activities. This paper reviews systems approaches, evaluation methodologies, and several applications in East Africa, at varying geographic scales. Early findings from the application of outcome mapping and system-wide assessments within the USAID-funded Sustainable WASH Systems Learning Partnership (SWS) indicate the importance of including both within an overall monitoring approach to support systems strengthening of water and sanitation services. The views and opinions expressed in this paper are those of the author(s) and not necessarily the views and opinions of the United States Agency for International Development, or the U.S. Government.
Challenges for Open Education with Educational Innovation: A Systematic Literature Review
Authors: Ramirez-Montoya Maria Soledad
Published: August 2020

Open education can enrich learning environments and support the processes of educational innovation. The objective of this article is to analyze the evidence published about open education in the period 2014–2019 to identify future challenges. We performed a Systematic Literature Review (SLR), identifying 245 articles on the topic of open education in the databases, Web of Science (WOS), and Scopus, from January 2014 to June 2019. We filtered to obtain the most relevant data by applying inclusion, exclusion, and quality criteria. The results yield information on (1) the characteristics of the publications; (2) the methodological trends; (3) the central and transversal dimensions of the topic; and (4) the innovative educational practices that have been implemented. The article concludes with a presentation of the challenges that are linked to coverage, empirical evidence, co-creation, and innovation. The contribution of this research is to add value to the body of knowledge available to trainers, researchers, and decision-makers interested in open education and educational innovation.
A Trade-off Analysis of Economic and Environmental Aspects of a Disruption Based Closed-Loop Supply Chain Network
Authors: Khan Abdul Salam , Pruncu Catalin Iulian , Khan Razaullah , Naeem Khawar , Ghaffar Abdul , Ashraf Pakeeza , Room Shah
Published: August 2020

Closed-loop supply chain networks are gaining research popularity due to environmental, economic and social concerns. Such networks are primarily designed to overcome carbon footprints and to retrieve end of life products from customers. This study considers a multi echelon closed-loop supply chain in the presence of machine disruption. A multi-objective model is presented to optimize the total cost, the total time and emissions in a closed-loop supply chain network. The aim is to analyze the trade-off between the objectives of cost, time, and emissions and how these decisions are impacted by the selection of different available machines. A number of solution approaches are tested on a case study from the tire industry. The results suggest the improved performance of the hybrid heuristic and the importance of controlling disruption in a closed-loop supply chain network. Furthermore, there is a trade-off between the different objective functions which can help the decision maker to choose a particular solution according to the preference of an organization. Finally, conclusion and future research avenues are provided.
Hydrocracking: A Perspective towards Digitalization
Authors: Iplik Esin , Aslanidou Ioanna , Kyprianidis Konstantinos
Published: August 2020

In a world of fast technological advancements, it is increasingly important to see how hydrocracking applications can benefit from and adapt to digitalization. A review of hydrocracking processes from the perspective of modeling and characterization methods is presented next to an investigation on digitalization trends. Both physics-based and data-based models are discussed according to their scope of use, needs, and capabilities based on open literature. Discrete and continuous lumping, structure-oriented lumping, and single event micro-kinetic models are reported as well as artificial neural networks, convolutional neural networks, and surrogate models. Infrared, near-infrared, ultra-violet and Raman spectroscopic methods are given with their examples for the characterization of feed or product streams of hydrocracking processes regarding boiling point curve, API, SARA, sulfur, nitrogen and metal content. The critical points to consider while modeling the system and the soft sensor are reported as well as the problems to be addressed. Optimization, control, and diagnostics applications are presented together with suggested future directions of interdisciplinary studies. The links required between the models, soft sensors, optimization, control, and diagnostics are suggested to achieve the automation goals and, therefore, a sustainable operation.
Lean Management Framework for Healthcare Facilities Integrating BIM, BEPS and Big Data Analytics
Authors: Demirdöğen Gökhan , Işık Zeynep , Arayici Yusuf
Published: August 2020

An increase in the usage of information and communication technologies (ICT) and the Internet of Things (IoT) in Facility Management (FM) induces a huge data stack. Even though these data bring opportunities such as cost savings, time savings, increase in user comfort, space optimization, energy savings, inventory management, etc., these data sources cannot be managed and manipulated effectively to increase efficiency at the FM stage. In addition to data management issues, FM practices, or developed solutions, need to be supported with the implementation of lean management philosophy to reveal organizational and managerial wastes. In the literature, some researchers performed studies about awareness about building information modeling (BIM)-FM, and FM-related data management problems in terms of lean philosophy. However, the comprehensive solution for effective FM has not been investigated with the application of lean management philosophy yet. Therefore, this study aims to develop an FM framework for healthcare facilities by considering lean management philosophy since more stable workflow, continuous improvement, and creating more value to customers will help to deliver a more acceptable solution for the FM industry. Within this context, the integration of BIM, Building Energy Performance Simulations, and Big Data Analytics are proposed as a solution. In the study, the Design Science Research (DSR) methodology was followed to develop the FM framework. Depending on the DSR methodology, two scenarios were used to investigate the issue in a real healthcare facility and develop the FM framework. The developed framework was evaluated by four experts, and the revisions of the proposed framework were realized.
Approaching Monetary Integration in the Context of the Imperative to Ensure the Sustainable Growth in the EU
Authors: Bostan Ionel , Oprea Otilia-Roxana , Stoica Ovidiu
Published: August 2020

Sustainable economic growth is an essential objective at the European Union level. The purpose of this paper is to investigate the impact of monetary integration on economic growth, assuming that the introduction of the euro significantly stimulated the process of European financial integration. We used a fixed-effects methodology for panel data for the EU 28 countries for the period 2004–2018. We find that the main factors through which monetary integration contributessignificantly and positively to economic growth areeconomic growth Single Euro Payments Area (SEPA)cards, trade, monetary freedom, convergence of interest rates, convergence of exchange rates and cross-border holdings of short-term debt, with significant differences between Eurozone and non-euro countries, which confirms the hypothesis that the introduction of the euro had a significant impact on economic and financial integration.
Ecotoxicity Responses of the Macrophyte Algae Nitellopsis obtusa and Freshwater Crustacean Thamnocephalus platyurus to 12 Rare Earth Elements
Authors: Manusadžianas Levonas , Vitkus Rimantas , Gylytė Brigita , Cimmperman Reda , Džiugelis Mindaugas , Karitonas Rolandas , Sadauskas Kazys
Published: September 2020

Due to unique chemical properties, rare earth elements (REEs) are increasingly used in versatile technological applications. They are considered emerging environmental contaminants, since they become mobile instead of being bound in rocks. At present, the information on REE effects to aquatic biota is scarce and contradictory. This study aims to explore the ecotoxicity of 11 lanthanides (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Er, and Lu) and yttrium (Y) to charophyte algae Nitellopsis obtusa and microcrustaceans Thamnocephalus platyurus. Median lethal concentrations (LC50) were assessed in characean cells at 8, 12, 16, 20, and 24 days of exposure, and 24-h LC50s were determined in shrimps. According to the EU−Directive 93/67/EEC hazard classification scheme and 24-day LC50 values generated for N. obtusa, REE effects were assigned from “harmful” to “very toxic” (Gd), while 24-h LC50s for T. platyurus were classified as “harmful” or “toxic” (based on nominal concentrations) and as “toxic” or “very toxic” (based on REE free ion concentrations calculated with CHEAQS Next software). The data obtained for algae showed correlations with the REE atomic numbers (r = −0.68, p < 0.05) and ionic radii (r = 0.65, p < 0.05) at the most extended 24-day exposure only. The analysis of the trends of concentration−response (c–r) curves obtained at increasing exposure durations (8–24 days), alongside the 24-day LC50s ranging within almost two orders of magnitude, allowed a more-toxic heavy REE group to be distinguished, and somewhat different modes REE actions to be envisioned for N. obtusa.
Hardware Implementation of a Softmax-Like Function for Deep Learning
Authors: Kouretas Ioannis , Paliouras Vassilis
Published: August 2020

In this paper a simplified hardware implementation of a CNN softmax-like layer is proposed. Initially the softmax activation function is analyzed in terms of required numerical accuracy and certain optimizations are proposed. A proposed adaptable hardware architecture is evaluated in terms of the introduced error due to the proposed softmax-like function. The proposed architecture can be adopted to the accuracy required by the application by retaining or eliminating certain terms of the approximation thus allowing to explore accuracy for complexity trade-offs. Furthermore, the proposed circuits are synthesized in a 90 nm 1.0 V CMOS standard-cell library using Synopsys Design Compiler. Comparisons reveal that significant reduction is achieved in area × delay and power × delay products for certain cases, respectively, over prior art. Area and power savings are achieved with respect to performance and accuracy.
Clinical Effectiveness and Adverse Events of Bee Venom Therapy: A Systematic Review of Randomized Controlled Trials
Authors: Jang Soobin , Kim Kyeong Han
Published: August 2020

Bee venom has been used to treat many diseases because of its anti-inflammatory and analgesic effects. However, the secretions of bee venom can also cause life-threatening adverse reactions. The objective of this paper was to review the clinical effectiveness of bee venom and adverse events induced by bee venom, regardless of the disease. Four electronic databases were searched in April 2020. The reference lists of the retrieved articles and previous review articles were also hand-searched. Randomized controlled trials (RCTs) using any type of bee venom other than live bee stings for the clinical treatment of any disease other than cancer were included. The studies were selected, the data were extracted, and the quality of the studies was assessed by two authors. Risk of bias was assessed using the Cochrane risk of bias standards. Twelve RCTs were included in this review—three on Parkinson’s disease, four on arthralgia, four on musculoskeletal disorders, and one on polycystic ovary syndrome. The types of bee venom used were acupuncture injections, ultrasound gel, and an ointment. Six studies reported adverse events, and skin reactions such as pruritus and swelling were the most common. The large-scale clinical trials of bee venom therapy are needed to verify the statistical difference, and the reporting system for adverse events is also required to increase the safety of bee venom therapy.
Maladaptive Planning and the Pro-Innovation Bias: Considering the Case of Automated Vehicles
Authors: Ferreira António , von Schönfeld Kim , Tan Wendy , Papa Enrica
Published: September 2020

This article argues that a more critical approach to innovation policy within planning is needed and offers recommendations for achieving this. These recommendations entail rethinking the values, focus, speed, and legitimacy of innovations. It takes a critical perspective on how contemporary societies treat rapid innovation as having necessarily positive results in the achievement of objectives such as sustainability and justice. This critical perspective is needed because innovation can both contribute to and drive a form of maladaptive planning: a collective approach to reality that imposes constant and rapid changes to societal configurations due to an obsession with the new and with too little rapport with the problems in place or that it creates. A maladaptive direction for transport planning is used as a sectorial illustration of the broader conceptual ideas presented: for both sustainability and social justice reasons, it would be desirable to see peak car occurring. However, the car industry is presenting driving automation as an innovation with the potential to restore the vitality of the private vehicles market while creating effective means to dismiss alternatives to car dominance.
Human Endogenous Retrovirus Expression Is Associated with Head and Neck Cancer and Differential Survival
Authors: Kolbe Allison R. , Bendall Matthew L. , Pearson Alexander T. , Paul Doru , Nixon Douglas F. , Pérez-Losada Marcos , Crandall Keith A.
Published: August 2020

Human endogenous retroviruses (HERVs) have been implicated in a variety of human diseases including cancers. However, technical challenges in analyzing HERV sequence data have limited locus-specific characterization of HERV expression. Here, we use the software Telescope (developed to identify expressed transposable elements from metatranscriptomic data) on 43 paired tumor and adjacent normal tissue samples from The Cancer Genome Atlas Program to produce the first locus-specific retrotranscriptome of head and neck cancer. Telescope identified over 3000 expressed HERVs in tumor and adjacent normal tissue, and 1078 HERVs were differentially expressed between the two tissue types. The majority of differentially expressed HERVs were expressed at a higher level in tumor tissue. Differentially expressed HERVs were enriched in members of the HERVH family. Hierarchical clustering based on HERV expression in tumor-adjacent normal tissue resulted in two distinct clusters with significantly different survival probability. Together, these results highlight the importance of future work on the role of HERVs across a range of cancers.
Components and Architecture of the Rhabdovirus Ribonucleoprotein Complex
Authors: Riedel Christiane , Hennrich Alexandru A. , Conzelmann Karl-Klaus
Published: August 2020

Rhabdoviruses, as single-stranded, negative-sense RNA viruses within the order Mononegavirales, are characterised by bullet-shaped or bacteroid particles that contain a helical ribonucleoprotein complex (RNP). Here, we review the components of the RNP and its higher-order structural assembly.
The Virome of Acute Respiratory Diseases in Individuals at Risk of Zoonotic Infections
Authors: Thi Kha Tu Nguyen , Thi Thu Hong Nguyen , Thi Han Ny Nguyen , My Phuc Tran , Thi Thanh Tam Pham , Doorn H. Rogier van , Dang Trung Nghia Ho , Thao Huong Dang , An Han Duong , Thi Thu Ha Luu , Deng Xutao , Thwaites Guy , Delwart Eric , Virtala Anna-Maija K. , Vapalahti Olli , Baker Stephen , Van Tan Le
Published: August 2020

The ongoing coronavirus disease 2019 (COVID-19) pandemic emphasizes the need to actively study the virome of unexplained respiratory diseases. We performed viral metagenomic next-generation sequencing (mNGS) analysis of 91 nasal-throat swabs from individuals working with animals and with acute respiratory diseases. Fifteen virus RT-PCR-positive samples were included as controls, while the other 76 samples were RT-PCR negative for a wide panel of respiratory pathogens. Eukaryotic viruses detected by mNGS were then screened by PCR (using primers based on mNGS-derived contigs) in all samples to compare viral detection by mNGS versus PCR and assess the utility of mNGS in routine diagnostics. mNGS identified expected human rhinoviruses, enteroviruses, influenza A virus, coronavirus OC43, and respiratory syncytial virus (RSV) A in 13 of 15 (86.7%) positive control samples. Additionally, rotavirus, torque teno virus, human papillomavirus, human betaherpesvirus 7, cyclovirus, vientovirus, gemycircularvirus, and statovirus were identified through mNGS. Notably, complete genomes of novel cyclovirus, gemycircularvirus, and statovirus were genetically characterized. Using PCR screening, the novel cyclovirus was additionally detected in 5 and the novel gemycircularvirus in 12 of the remaining samples included for mNGS analysis. Our studies therefore provide pioneering data of the virome of acute-respiratory diseases from individuals at risk of zoonotic infections. The mNGS protocol/pipeline applied here is sensitive for the detection of a variety of viruses, including novel ones. More frequent detections of the novel viruses by PCR than by mNGS on the same samples suggests that PCR remains the most sensitive diagnostic test for viruses whose genomes are known. The detection of novel viruses expands our understanding of the respiratory virome of animal-exposed humans and warrant further studies.
Numerical and Physical Analysis on the Response of a Dam’s Radial Gate to Extreme Loading Performance
Authors: Faridmehr Iman , Farokhi Nejad Ali , Baghban Mohammad Hajmohammadian , Ghorbani Reza
Published: August 2020

Maintaining the reservoir safety of large dams has considerable importance for the public where they are constructed in heavily populated and industrialized areas. The extreme hydrodynamic force caused by ground acceleration, cavitation damage, and vibration are among concerns that threaten the safety of the spillway and its conveyance structures when subjected to a natural disaster, such as earthquakes and severe floods. Current research investigates the hydrostatic and hydrodynamic performance of the Karkheh Dam spillway radial gate through 3-D finite element (FE) models using ABAQUS/Explicit. The common loads applied on the radial gate were reviewed and stress–strain in the skin plate and trunnion were investigated as a result of developed hydrodynamic pressures. The performance of conveyance structures subjected to significant discharge was also investigated through a small-scale model to evaluate the cavitation damage index. The results of this research will help researchers in the field of civil and hydraulic engineering for the risk analysis of the radial gates and conveyance structures.
Hydrometeorological and Socio-Economic Impact Assessment of Stream Flooding in Southeast Mediterranean: The Case of Rafina Catchment (Attica, Greece)
Authors: Giannaros Christos , Kotroni Vassiliki , Lagouvardos Konstantinos , Oikonomou Christina , Haralambous Haris , Papagiannaki Katerina
Published: August 2020

The CyFFORS (Cyprus Flood Forecasting System) project aims at increasing flood risk awareness and promoting preparedness against flooding by developing and validating a pilot flood forecasting system targeted over three river/stream basins in the Larnaca region, Cyprus, and Attica region, Greece. The present study demonstrates the analysis of flood-associated information, based on ground-based and ERA5 model reanalysis data, which is a necessary procedure prior to the development of the hydrometeorological modeling tool, in one of the study areas, namely in the Rafina catchment in Attica, Greece. The analysis focusses on 12 stream flood episodes that occurred in the period 2008–2014. The results show that most events were associated with a typical, for the study area, wet-season cyclonic activity. The detailed investigation of two case studies highlighted important spatiotemporal differences in the generation and development of rainfall, as well as in the flooding magnitude, which were related to specific characteristics of the synoptic-scale forcing, topography and soil moisture preconditioning. Moreover, highly correlated positive relationships were found between the observed maximum stream discharge and the duration and maximum total accumulation of precipitation. A strong positive correlation was also evident between the peak discharge and the flooding impacts, leading to the identification of preliminary discharge thresholds for impact-based warnings, which can be applied to the pilot CyFFORS forecasting system.
River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques
Authors: Üneş Fatih , Demirci Mustafa , Zelenakova Martina , Çalışıcı Mustafa , Taşar Bestami , Vranay František , Kaya Yunus Ziya
Published: August 2020

Accurate determination of river flows and variations is used for the efficient use of water resources, the planning of construction of water structures, and preventing flood disasters. However, accurate flow prediction is related to a good understanding of the hydrological and meteorological characteristics of the river basin. In this study, flow in the river was estimated using Multi Linear Regression (MLR), Artificial Neural Network (ANN), M5 Decision Tree (M5T), Adaptive Neuro-Fuzzy Inference System (ANFIS), Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) models. The Stilwater River in the Sterling region of the USA was selected as the study area and the data obtained from this region were used. Daily rainfall, river flow, and water temperature data were used as input data in all models. In the paper, the performance of the methods is evaluated based on the statistical approach. The results obtained from the generated models were compared with the recorded values. The correlation coefficient (R), Mean Square Error (MSE), and Mean Absolute Error (MAE) statistics are computed separately for each model. According to the comparison criteria, as a final result, it is considered that Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model have better performance in river flow estimation than the other models.
Performance of Anaerobic Digestion of Acidified Palm Oil Mill Effluent under Various Organic Loading Rates and Temperatures
Authors: Fikri Hamzah Muhammad Arif , Abdul Peer Mohamed , Mahmod Safa Senan , Azahar Azratul Madihah , Jahim Jamaliah Md.
Published: August 2020

This study compared the performance of thermophilic and mesophilic digesters of an anaerobic digestion system from palm oil mill effluent (POME), in which temperature is a key parameter that can greatly affect the performance of anaerobic digestion. The digesters were incubated at two distinct temperatures of 55 and 37 °C, and operated with varying organic loading rates (OLRs) of 2.4, 3.2, and 4.0 g COD/L.d by altering the chemical oxygen demand (COD) of acidified POME during feeding. The results indicated that the performance of anaerobic digestion increased as the OLR increased from 2.4 to 4.0 g COD/L.d. At the OLR of 4.0 g COD/L.d, the thermophilic condition showed the highest methane yield of 0.31 ± 0.01 L/g COD, accompanied by the highest COD removal and volatile solid reduction, which were found to be higher than the mesophilic condition. Microbial community analysis via denaturing gradient gel electrophoresis (DGGE) revealed that Methanothermobacter sp. emerges as the dominant microbe, which is known to utilize the carbon dioxide pathway with hydrogen acting as an electron donor for methane formation
Decision-Making of LID-BMPs for Adaptive Water Management at the Boise River Watershed in a Changing Global Environment
Authors: Kim JungJin , Ryu Jae Hyeon
Published: August 2020

We conducted a study on water management at the Boise River Watershed in a changing global environment potentially induced by climate variability and urbanization. Environmental ‘hotspots’ associated with water quality and quantity were first identified to select suitable management options, such as Low Impact Development (LID is commonly used for urban storm water management to reduce impacts induced by flash flood in urban environment while improving water quality standard by filtering non-point source pollutants from predominant, impervious land segments in urban settings.) and Best Management Practices (BMPs) for urban and rural land segments, respectively. A decision-making process was employed to evaluate the cost-effectiveness for each management option based on multiple criteria, including water quality, financial challenges, and other environmental concerns. The results show that LID/BMPs were useful to control water quality in the watershed. The effectiveness of LID/BMPs implementation was subject to change with the placement location and consideration objectives associated with economic or environmental aspects. It appears that about 10% of the study area is required to implement water management options (LID/BMP) to improve water quality potentially driven by climate variability and urbanization. We anticipate that this study will make a case toward developing a sustainable water management plan in a changing global environment, especially for the urban–rural interface settings.
Comparing Devices for Concurrent Measurement of AC Current and DC Injection during Electric Vehicle Charging
Authors: Mironenko Olga , Kempton Willett
Published: August 2020

Widespread adoption of electric vehicles (EVs) requires additional safety countermeasures to prevent DC injection from EVs into the AC grid via Electric Vehicle Supply Equipment (EVSE). Moreover, for energy purchase, and even more so for vehicle-to-grid (V2G) services, the EVSE must conduct high precision bidirectional power and energy measurements. This paper introduces operating principles, structure, performance, and cost comparison of three current sensing technologies—current transformer, shunt and fluxgate—for metering and protection within an EVSE, concluding with recommendations among those sensors for the most beneficial applications concerning EV charging.
DISTANCE EDUCATION IN UKRAINE: REALITIES OF TODAY
Authors: Zadorozhna Olha
Published: September 2020

The first attempts to introduce distance education technologies in the education sector were made in the 60s of the twentieth century in Western Europe and the United States. The rapid development of information technology and computerization of the population have led to an increase in demand for new, specific technologies in education, which allows applicants to study at considerable distances from educational institutions. It is the criteria to which the distance learning conforms, and which has been and remains the subject of active scientific discussion both abroad and in our country.
Such a system of acquiring knowledge can significantly reduce the cost of education, as distance education is cheaper than traditional forms of higher education, and also reduces transport costs for travel to the place of study and back. Moreover, distance education can be obtained in any rhythm convenient for the applicant, distributing the workload at will. With the correct organization of classes and control of knowledge high efficiency of this method of professional education can be achieved through the use of new software and hardware platforms and electronic courses.
On the other hand, critics of distance education say that its quality cannot be compared even with extramural education, and such educational technologies allow only to obtain a diploma with minimal investment of effort and resources without acquiring factual knowledge. The low quality of distance education, according to some experts, can be explained by the lack of quality methods of distance teaching, the average quality of e-courses, as well as the e-learning environments themselves.
Núcleo de Sustentabilidade e Educação Ambiental: a atuação da engenharia ambiental na trans-formação humana
Authors: Anhê Ana Carolina Borella Marfil , Coêlho Bruna Lopes , Martins Patrícia Diniz , Curado Ana Luísa , Corrêa Thiago Henrique Barnabé , Senhuk Ana Paula Milla dos Santos
Published: September 2020

O Núcleo de Sustentabilidade e Educação Ambiental da UFTM visa a aproximação do saber acadêmico com a realidade social, permitindo que a comunidade se beneficie do conhecimento promovido pela universidade na busca por soluções de problemas ambientais. Nesse sentido, tem sido desenvolvidas atividades como arborização, coleta seletiva, compostagem, monitoramento da qualidade da água e do ar e práticas sustentáveis na universidade e em escolas públicas; algumas delas descritas ao longo do artigo. O Núcleo se preocupa em não assumir a EA como receita prescrita, mas como um instrumento de trans-formação humana capaz de gerar impacto social e ambiental, ao incorporar na cultura acadêmica um novo nível de consciência: a consciência ecológica.
Ações de sensibilização na implementação da coleta seletiva solidária em um campus universitário
Authors: Ribeiro Elaine Nolasco , Almeida Filipe Ribeiro de , Oliveira Maria Cristina de
Published: September 2020

O estudo teve como objetivo avaliar se as ações educativas realizadas pelo grupo Recicla FUP da Faculdade UnB Planaltina têm sido exitosas em envolver a comunidade acadêmica no processo de descarte seletivo dos resíduos. A coleta de dados foi realizada através de questionário estruturado. Observou-se que mais de 80,0% dos entrevistados sabem o que é a coleta seletiva. Os murais e as redes sociais são as formas de comunicação apontadas como as de maior impacto. A falta de motivação pessoal e o desconhecimento sobre como fazer o descarte seletivo foram os fatores negativos. Para maior engajamento da comunidade acadêmica, é fundamental que ações educativas de sensibilização sejam contínuas e permanentes, além da institucionalização do projeto no campus.
SARS-CoV2 Virüsünün Potansiyel Rezervuarları ve Yarasalarla Bulaşabilecek Diğer Zoonotik Virüsler
Authors: KIRDAR Sevin , KOÇ Bahattin Taylan
Published: September 2020

Coronaviridae, COVID-19 pandemik salgını ile global düzeyde dikkat çeken bir virüs ailesi haline gelmiştir. Yapılan araştırmaların artışı ve gelişen laboratuvar teknolojisi sayesinde bu virus ailesine birçok yeni virüs katılarak son 20 yılda oldukça genişlemiştir. Coronaviridae ailesine katılan bu yeni virusların çoğu ya direkt hayvanları enfekte eden ya da son konakçısı insan, rezervuarı ise hayvan olan virüslerdır. Özellikle SARS ve MERS coronavirus salgınlarında rol oynadığı çeşitli çalışmalarla kanıtlanan yarasaların SARS-CoV-2 virusu için de rezervuar olma ihtimali yüksektir. Genişleyen coronavirusun SARS-CoV-2 özelinde zoonotik potansiyeli, muhtemel rezervuarlığı ve yarasalar ile bulaşabilen zoonotik viral enfeksiyonlar bu derlememizde konu edilmiştir. Dünyada ve ülkemizde gelecekte söz konusu virusların ve ilgili rezervuar/vektörlerin araştırılması gereklidir. Böylece söz konusu salgın tehdidi en başında engellenebilecektir.
Evaluation of complications in implant-supported locator retained removable dentures: 24 Months Retrospective Study
Authors: FADHİL Sadeq Mohammed Taqi , MUMCU Emre
Published: September 2020

Aim: The purpose is to evaluate the complications that occur in implanted locator supported removable dentures for 2 years clinically and radiologically.Materials and methods: Study between the years of 2014-2019, was performed on 121 patients who underwent 284 dental implants and came to their controls at the 12th and 24th months. Patients included in the retrospective study were 52 men and 69 women. Complications detected in the first 24 months after the implant supported prosthesis applied; relining and rebasing, loss of retention (housing removal, wear of the retaining clip) and fracture in the base of prosthesis were recorded and evaluated. Data were analyzed using One‐way ANOVA and Tukey's HSD tests. A P value of < 0.05 was considered statistically significant.Results: In implant-supported removable prostheses, there are significant differences were found between the increasing complication rates at 12th and 24th months (p <0.05). The most common complication at the end of 24th month in overdenture prosthesis with implant supported locator holder applied to the maxilla and mandible, respectively; relining of prosthesis (11.57%), rebasing of prosthesis (6.6%), loss of retention (5.79%) and prosthetic base fracture (4.13%).Conclusion: Based on the data analyzed in our study, removable prostheses showed different types and frequency of complications. Treatment of implant-supported overdenture prostheses, routine control of the prostheses is important.