bluuebunny/crossref_metadata_2025 huggingface dataset:
Altitudinal Vascular Plant Richness and Climate Change in the Alpine Zone of the Lefka Ori, Crete
Authors: Kazakis George , Ghosn Dany , Remoundou Ilektra , Nyktas Panagiotis , Talias Michael A. , Vogiatzakis Ioannis N.
Published: January 2021

High mountain zones in the Mediterranean area are considered more vulnerable in comparison to lower altitudes zones. Lefka Ori massif, a global biodiversity hotspot on the island of Crete is part of the Global Observation Research Initiative in Alpine Environments (GLORIA) monitoring network. The paper examines species and vegetation changes with respect to climate and altitude over a seven-year period (2001–2008) at a range of spatial scales (10 m Summit Area Section-SAS, 5 m SAS, 1 m2) using the GLORIA protocol in a re-survey of four mountain summits (1664 m–2339 m). The absolute species loss between 2001–2008 was 4, among which were 2 endemics. At the scale of individual summits, the highest changes were recorded at the lower summits with absolute species loss 4 in both cases. Paired t-tests for the total species richness at 1 m2 between 2001–2008, showed no significant differences. No significant differences were found at the individual summit level neither at the 5 m SAS or the 10 m SAS. Time series analysis reveals that soil mean annual temperature is increasing at all summits. Linear regressions with the climatic variables show a positive effect on species richness at the 5 m and 10 m SAS as well as species changes at the 5 m SAS. In particular, June mean temperature has the highest predictive power for species changes at the 5 m SAS. Recorded changes in species richness point more towards fluctuations within a plant community’s normal range, although there seem to be more significant diversity changes in higher summits related to aspects. Our work provides additional evidence to assess the effects of climate change on plant diversity in Mediterranean mountains and particularly those of islands which remain understudied.
Dynamics of a Predator–Prey Model with the Effect of Oscillation of Immigration of the Prey
Authors: Alebraheem Jawdat
Published: January 2021

In this article, the use of predator-dependent functional and numerical responses is proposed to form an autonomous predator–prey model. The dynamic behaviors of this model were analytically studied. The boundedness of the proposed model was proven; then, the Kolmogorov analysis was used for validating and identifying the coexistence and extinction conditions of the model. In addition, the local and global stability conditions of the model were determined. Moreover, a novel idea was introduced by adding the oscillation of the immigration of the prey into the model which forms a non-autonomous model. The numerically obtained results display that the dynamic behaviors of the model exhibit increasingly stable fluctuations and an increased likelihood of coexistence compared to the autonomous model.
Genome Insights into the Novel Species Jejubacter calystegiae, a Plant Growth-Promoting Bacterium in Saline Conditions
Authors: Jiang Ling Min , Lee Yong Jae , Han Ho Le , Lee Myoung Hui , Jeong Jae Cheol , Kim Cha Young , Kim Suk Weon , Lee Ji Young
Published: January 2021

Jejubacter calystegiae KSNA2T, a moderately halophilic, endophytic bacterium isolated from beach morning glory (Calystegia soldanella), was determined to be a novel species in a new genus in the family Enterobacteriaceae. To gain insights into the genetic basis of the salinity stress response of strain KSNA2T, we sequenced its genome using two complementary sequencing platforms (Illumina HiSeq and PacBio RSII). The genome contains a repertoire of metabolic pathways, such as those for nitrogen, phosphorus, and some amino acid metabolism pathways. Functional annotation of the KSNA2T genome revealed several genes involved in salt tolerance pathways, such as those encoding sodium transporters, potassium transporters, and osmoprotectant enzymes. Plant growth-promoting bacteria-based experiments indicated that strain KSNA2T promotes the germination of vegetable seeds in saline conditions. Overall, the genetic and biological analyses of strain KSNA2T provide valuable insights into bacteria-mediated salt tolerance in agriculture.
Diagnostic Task Specific Activations in Functional MRI and Aberrant Connectivity of Insula with Middle Frontal Gyrus Can Inform the Differential Diagnosis of Psychosis
Authors: Stoyanov Drozdstoy , Aryutova Katrin , Kandilarova Sevdalina , Paunova Rositsa , Arabadzhiev Zlatoslav , Todeva-Radneva Anna , Kostianev Stefan , Borgwardt Stefan
Published: January 2021

We constructed a novel design integrating the administration of a clinical self-assessment scale with simultaneous acquisition of functional Magnetic Resonance Imaging (fMRI), aiming at cross-validation between psychopathology evaluation and neuroimaging techniques. We hypothesized that areas demonstrating differential activation in two groups of patients (the first group exhibiting paranoid delusions in the context of paranoid schizophrenia—SCH—and second group with a depressive episode in the context of major depressive disorder or bipolar disorder—DEP) will have distinct connectivity patterns and structural differences. Fifty-one patients with SCH (n = 25) or DEP (n = 26) were scanned with three different MRI sequences: a structural and two functional sequences—resting-state and task-related fMRI (the stimuli represent items from a paranoid-depressive self-evaluation scale). While no significant differences were found in gray matter volumes, we were able to discriminate between the two clinical entities by identifying two significant clusters of activations in the SCH group—the left Precuneus (PreCu) extending to the left Posterior Cingulate Cortex (PCC) and the right Angular Gyrus (AG). Additionally, the effective connectivity of the middle frontal gyrus (MFG), a part of the Dorsolateral Prefrontal Cortex (DLPFC) to the Anterior Insula (AI), demonstrated a significant difference between the two groups with inhibitory connection demonstrated only in SCH. The observed activations of PreCu, PCC, and AG (involved in the Default Mode Network DMN) might be indirect evidence of the inhibitory connection from the DLPFC to AI, interfering with the balancing function of the insula as the dynamic switch in the DMN. The findings of our current study might suggest that the connectivity from DLPFC to the anterior insula can be interpreted as evidence for the presence of an aberrant network that leads to behavioral abnormalities, the manifestation of which depends on the direction of influence. The reduced effective connectivity from the AI to the DLPFC is manifested as depressive symptoms, and the inhibitory effect from the DLPFC to the AI is reflected in the paranoid symptoms of schizophrenia.
Aerobic Vaginitis—Underestimated Risk Factor for Cervical Intraepithelial Neoplasia
Authors: Plisko Olga , Zodzika Jana , Jermakova Irina , Pcolkina Kristine , Prusakevica Amanda , Liepniece-Karele Inta , Donders Gilbert G. G. , Rezeberga Dace
Published: January 2021

The aim of this study is to analyse the association between vaginal microbiota and the histological finding of CIN. From July 2016 until June 2017, we included 110 consecutive patients with abnormal cervical cytology results referred for colposcopy to Riga East Clinical University Hospital Outpatient department in the study group. 118 women without cervical pathology were chosen as controls. Certified colposcopists performed interviews, gynaecological examinations and colposcopies for all participants. Material from the upper vaginal fornix was taken for pH measurement and wet-mount microscopy. Cervical biopsy samples were taken from all subjects in the study group and in case of a visual suspicion for CIN in the control group. Cervical pathology was more often associated with smoking (34.6% vs. 11.0%, p < 0.0001), low education level (47.2% vs. 25.5%, p = 0.001), increased vaginal pH (48.2% vs. 25.4%, p < 0.0001), abnormal vaginal microbiota (50% vs. 31.4%, p = 0.004) and moderate to severe aerobic vaginitis (msAV) (13.6% vs. 5.9%, p = 0.049) compared to controls. The most important independent risk factors associated with CIN2+ were smoking (OR 3.04 (95% CI 1.37–6.76), p = 0.006) and msAV (OR 3.18 (95% CI 1.13–8.93), p = 0.028). Bacterial vaginosis (BV) was found more often in CIN1 patients (8/31, 25.8%, p = 0.009) compared with healthy controls (8/118, 6.8%), or CIN2+ cases (8/79, 10.1%). In the current study msAV and smoking were the most significant factors in the development of CIN in HPV-infected women, especially high grade CIN. We suggest that AV changes are probably more important than the presence of BV in the pathogenesis of CIN and progression to cervix cancer and should not be ignored during the evaluation of the vaginal microbiota.
Liver Fibrosis Biomarkers Accurately Exclude Advanced Fibrosis and Are Associated with Higher Cardiovascular Risk Scores in Patients with NAFLD or Viral Chronic Liver Disease
Authors: Ballestri Stefano , Mantovani Alessandro , Baldelli Enrica , Lugari Simonetta , Maurantonio Mauro , Nascimbeni Fabio , Marrazzo Alessandra , Romagnoli Dante , Targher Giovanni , Lonardo Amedeo
Published: January 2021

Liver fibrosis predicts liver-related and cardiovascular outcomes in chronic liver disease patients. We compared the diagnostic performance of various liver fibrosis biomarkers for identifying histological significant/advanced fibrosis. Additionally, the correlations of such liver fibrosis biomarkers with cardiovascular risk (CVR) scores were evaluated. 173 patients with viral hepatitis (157 HCV and 16 HBV) and 107 with a non-alcoholic fatty liver disease (NAFLD) were consecutively enrolled. Various liver fibrosis biomarkers: aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio (ARR), AST to Platelet Ratio Index (APRI), Fibrosis-4 (FiB-4), Forns index, NAFLD fibrosis score (NFS), BARD (body mass index (BMI), AAR, Diabetes) score, and Hepamet fibrosis score (HFS), were used to identify significant/advanced fibrosis. CVR was assessed by using the SCORE, the Progetto CUORE, or the Framingham risk scoring systems. Liver fibrosis biomarkers performed better in predicting advanced rather than significant liver fibrosis in all patients, regardless of chronic liver disease aetiology. Forns index and HFS performed best in predicting advanced fibrosis in patients with viral chronic liver disease and NAFLD. Lower cut-offs of these liver fibrosis biomarkers had high negative predictive values for advanced fibrosis overall, as well as in patients with NAFLD or viral chronic liver disease. FIB-4, Forns index, NFS, and HFS were positively correlated with SCORE and Framingham risk scores. In conclusion, liver fibrosis biomarkers accurately exclude advanced fibrosis and positively correlate with CVR scores in patients with chronic liver disease.
Identification of MicroRNAs as Diagnostic Biomarkers for Breast Cancer Based on the Cancer Genome Atlas
Authors: Kim Jungho
Published: January 2021

Breast cancer is the most common cancer among women worldwide. MicroRNAs (miRNAs or miRs) play an important role in tumorigenesis, and thus, they have been identified as potential targets for translational research with diagnostic, prognostic, and therapeutic markers. This study aimed to identify differentially expressed (DE) miRNAs in breast cancer using the Cancer Genome Atlas. The miRNA profiles of 755 breast cancer tissues and 86 adjacent non-cancerous breast tissues were analyzed using Multi Experiment Viewer; miRNA–mRNA network analyses and constructed KEGG pathways with the predicted target genes were performed. The clinical relevance of miRNAs was investigated using area under the receiver operating characteristic curve (AUC) analysis, sensitivity, and specificity. The analysis identified 28 DE miRNAs in breast cancer tissues, including nine upregulated and 19 downregulated miRNAs, compared to non-cancerous breast tissues (p < 0.001). The AUC for each DE miRNA, miR-10b, miR-21, miR-96, miR-99a, miR-100, miR-125b-1, miR-125b-2, miR-139, miR-141, miR-145, miR-182, miR-183, miR-195, miR-200a, miR-337, miR-429, and let-7c, exceeded 0.9, indicating excellent diagnostic performance in breast cancer. Moreover, 1381 potential target genes were predicted using the prediction database tool, miRNet. These genes are related to PD-L1 expression and PD-1 checkpoint in cancer, MAPK signaling, apoptosis, and TNF pathways; hence, they regulate the development, progression, and immune escape of cancer. Thus, these 28 miRNAs can serve as prospective biomarkers for the diagnosis of breast cancer. Taken together, these results provide insight into the pathogenic mechanisms and potential therapies for breast cancer.
Complex Implant-Prosthetic Rehabilitation Following Sports Trauma with 14 Years of Follow-Up: Case Report
Authors: Murri dello Diago Alberto , Apponi Roberto , Colombini Vittorio , Mordini Lorenzo , Ideo Francesca
Published: January 2021

Tooth loss after traumatic dental injuries (TDI) often requires rehabilitation with a multidisciplinary treatment plan. In growing patients, the therapeutic approach may be different than in adults; the scientific literature offers alternative solutions even if they involve long, complex and uncomfortable treatments. Among the possible therapeutic options, implant-prosthetic treatment through the use of mini-implants is presented in this complex case report with a 14-year follow-up.
Entropy Analysis of COVID-19 Cardiovascular Signals
Authors: Bajić Dragana , Đajić Vlado , Milovanović Branislav
Published: January 2021

The world has faced a coronavirus outbreak, which, in addition to lung complications, has caused other serious problems, including cardiovascular. There is still no explanation for the mechanisms of coronavirus that trigger dysfunction of the cardiac autonomic nervous system (ANS). We believe that the complex mechanisms that change the status of ANS could only be solved by advanced multidimensional analysis of many variables, obtained both from the original cardiovascular signals and from laboratory analysis and detailed patient history. The aim of this paper is to analyze different measures of entropy as potential dimensions of the multidimensional space of cardiovascular data. The measures were applied to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy controls. Methods that indicate a statistically significant difference between patients with different levels of infection and healthy controls will be used for further multivariate research. As a result, it was shown that a statistically significant difference between healthy controls and patients with COVID-19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic dynamics entropy, and copula parameters. Statistical significance between serious and mild patients with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure. This result contributes to the hypothesis that the severity of COVID-19 disease is associated with ANS disorder and encourages further research.
Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers
Authors: Ćwiklinski Bartosz , Giełczyk Agata , Choraś Michał
Published: January 2021

Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.
Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State
Authors: Chen Yuchao , Tang Haoyue , Wang Jintao , Song Jian
Published: January 2021

In this paper, we consider a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying and error-prone channels. We characterize the data freshness at the terminal end through a class of monotone increasing functions related to Age of information (AoI). Our goal is to design an optimal policy to minimize the average age penalty of all sensors in infinite horizon under bandwidth and power constraint. By formulating the scheduling problem into a constrained Markov decision process (CMDP), we reveal the threshold structure for the optimal policy and approximate the optimal decision by solving a truncated linear programming (LP). Finally, a bandwidth-truncated policy is proposed to satisfy both power and bandwidth constraint. Through theoretical analysis and numerical simulations, we prove the proposed policy is asymptotic optimal in the large sensor regime.
Application of Information Technologies and Programming Methods of Embedded Systems for Complex Intellectual Analysis
Authors: Emelianov Vitalii , Emelianova Nataliia , Zhilenkov Anton , Chernyi Sergei
Published: January 2021

An information model is outlined, which represents an intelligent system of metallographic analysis in the form of a set of subsystems, the interaction of which ensures the performance of metallographic analysis functions. The structure of the information storage subsystem for metallographic analysis is presented. The deployment model of an intelligent metallographic analysis system is proposed and described. The paper outlines the approach to the presentation of an expert subsystem for metallographic quality control of metals based on a neural network. The process of finding a close precedent in metallographic analysis with reference to a multilayer neural network is described. An intelligent metallographic analysis system is described, which based on proposed information model. A specialized software of an intelligent metallographic analysis system is presented. The functioning results of the developed system for processing images of steel microstructures to determine the steel quantitative parameters is presented.
Deep Neural Network Model for Approximating Eigenmodes Localized by a Confining Potential
Authors: Grubišić Luka , Hajba Marko , Lacmanović Domagoj
Published: January 2021

We study eigenmode localization for a class of elliptic reaction-diffusion operators. As the prototype model problem we use a family of Schrödinger Hamiltonians parametrized by random potentials and study the associated effective confining potential. This problem is posed in the finite domain and we compute localized bounded states at the lower end of the spectrum. We present several deep network architectures that predict the localization of bounded states from a sample of a potential. For tackling higher dimensional problems, we consider a class of physics-informed deep dense networks. In particular, we focus on the interpretability of the proposed approaches. Deep network is used as a general reduced order model that describes the nonlinear connection between the potential and the ground state. The performance of the surrogate reduced model is controlled by an error estimator and the model is updated if necessary. Finally, we present a host of experiments to measure the accuracy and performance of the proposed algorithm.
A Study of the Emotional Intelligence and Personality Traits of University Finance Students
Authors: Rosales-Pérez Ana M. , Fernández-Gámez Manuel A. , Torroba-Díaz Macarena , Molina-Gómez Jesús
Published: January 2021

Studies on financial behavior indicate that emotional intelligence (EI) and personality traits (PTs) explain much of the bias in financial activity. This study aims to identify in which dimensions of theEI and PTs of university students in finance further training is needed to avoid financial behavior bias. To this end, the EI and PT levels of a sample of university finance students and financial industry professionals were compared using the Trait Emotional Intelligence Questionnaire (TEIQue) and Big Five Inventory questionnaire. Subsequently, the dimensions of EI and PTs in which students have a deficit compared to professionals were identified, and the impacts that this deficit causes on the financial behavior of students were determined. The results indicate that students are deficient in the EI competencies related to empathy, emotion regulation, self-motivation, stress management, optimism, and self-esteem. Furthermore, PTs are related to kindness, awareness, openness, and extraversion. This deficit makes students more likely to have financial behavior biases such as risk tolerance, endowment, optimism, self-control, and loss aversion. These findings suggest that universities should be aware of providing financial students with full training in EI and PTs to help them successfully address their professional future.
Reliability-Aware Multipath Routing of Time-Triggered Traffic in Time-Sensitive Networks
Authors: Huang Kai , Wan Xinming , Wang Ke , Jiang Xiaowen , Chen Junjian , Deng Qingtang , Xu Wenyuan , Peng Yonggang , Liu Zhili
Published: January 2021

With the development of industrial networks, the demands for strict timing requirements and high reliability in transmission become more essential, which promote the establishment of a Time-Sensitive Network (TSN). TSN is a set of standards with the intention of extending Ethernet for safety-critical and real-time applications. In general, frame replication is used to achieve fault-tolerance, while the increased load has a negative effect on the schedule synthesis phase. It is necessary to consider schedulability and reliability jointly. In this paper, a heuristic-based routing method is proposed to achieve fault tolerance by spatial redundancy for TSNs containing unreliable links. A cost function is presented to evaluate each routing set, and a heuristic algorithm is applied to find the solution with higher schedulability. Compared to the shortest path routing, our method can improve the reliability and the success rate of no-wait scheduling by 5–15% depending on the scale of topology.
Data Analytics and Mathematical Modeling for Simulating the Dynamics of COVID-19 Epidemic—A Case Study of India
Authors: Gupta Himanshu , Kumar Saurav , Yadav Drishti , Verma Om Prakash , Sharma Tarun Kumar , Ahn Chang Wook , Lee Jong-Hyun
Published: January 2021

The global explosion of the COVID-19 pandemic has created worldwide unprecedented health and economic challenges which stimulated one of the biggest annual migrations globally. In the Indian context, even after proactive decisions taken by the Government, the continual growth of COVID-19 raises questions regarding its extent and severity. The present work utilizes the susceptible-infected-recovered-death (SIRD) compartment model for parameter estimation and fruitful prediction of COVID-19. Further, various optimization techniques such as particle swarm optimization (PSO), gradient (G), pattern search (PS) and their hybrid are employed to solve the considered model. The simulation study endorse the efficiency of PSO (with or without G) and G+PS+G over other techniques for ongoing pandemic assessment. The key parametric values including characteristic time of infection and death and reproduction number have been estimated as 60 days, 67 days and 4.78 respectively by utilizing the optimum results. The model assessed that India has passed its peak duration of COVID-19 with more than 81% recovery and only a 1.59% death rate. The short duration analysis (15 days) of obtained results against reported data validates the effectiveness of the developed models for ongoing pandemic assessment.
An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric Vehicles
Authors: Faia Ricardo , Soares João , Vale Zita , Corchado Juan Manuel
Published: January 2021

Electric vehicles have emerged as one of the most promising technologies, and their mass introduction may pose threats to the electricity grid. Several solutions have been proposed in an attempt to overcome this challenge in order to ease the integration of electric vehicles. A promising concept that can contribute to the proliferation of electric vehicles is the local electricity market. In this way, consumers and prosumers may transact electricity between peers at the local community level, reducing congestion, energy costs and the necessity of intermediary players such as retailers. Thus, this paper proposes an optimization model that simulates an electric energy market between prosumers and electric vehicles. An energy community with different types of prosumers is considered (household, commercial and industrial), and each of them is equipped with a photovoltaic panel and a battery system. This market is considered local because it takes place within a distribution grid and a local energy community. A mixed-integer linear programming model is proposed to solve the local energy transaction problem. The results suggest that our approach can provide a reduction between 1.6% to 3.5% in community energy costs.
A Segmented Preprocessing Method for the Vibration Signal of an On-Load Tap Changer
Authors: Shang Rongyan , Peng Changqing , Fang Ruiming
Published: January 2021

The vibration signal of an on-load tap changer (OLTC) consists of a series of sharp vibration bursts, and its fault feature in certain periods is easily missed. This study considered that preprocessing the vibration signal of the OLTC in segments could effectively solve the aforementioned problem. First, the collection of the signal is discussed, the waveform characteristics of the vibration signal when the OLTC was in normal action was described, and the selection of the signal was analyzed. Second, the time domain characteristics and frequency spectrum analyses were carried out to demonstrate the necessity of segmented preprocessing. Further, the segmented preprocessing method for the vibration signal of the OLTC was presented. Finally, the main mechanical faults of the OLTC were simulated, and the vibration signals were collected to carry out the fault diagnosis experiment on the OLTC. The experimental results showed that the accuracy of the fault diagnosis increased from 94.30% of the nonsegmented preprocessing to 98.46% of the segmented preprocessing. The increase was greater, especially for contact wear faults. The method was successfully applied to the actual project.
Life-Cycle Expectation Using Fault-Tree Analysis for Improved Hybrid Submodule in HVDC System
Authors: Kang Feel-Soon , Song Sung-Geun
Published: January 2021

An improved hybrid submodule employs a direct current (DC) short current protection function to improve the reliability of a high-voltage direct current (HVDC) system. However, it increases the number of circuit components to implement the protection. So, we need to evaluate the relationship between the protection function and the increased number of circuit components to assess whether the improved hybrid submodule (IHSM) is suitable to practical application or not from the viewpoint of reliability. Although conventional part count failure analysis considers the type and the number of parts, it cannot reflect the operational characteristics of the submodule. To overcome this problem, we design a fault tree that reflects the operational characteristics of IHSM and calculates the failure rate by using MIL-HDBK-217F. By part count failure analysis (PCA) and fault-tree analysis (FTA), we prove the high reliability of IHSM compared to half-bridge, full-bridge, and clamped-double submodules.
A Universal Machine-Learning-Based Automated Testing System for Consumer Electronic Products
Authors: Siddiqui Atif , Zia Muhammad Yousuf Irfan , Otero Pablo
Published: January 2021

Consumer electronic manufacturing (CEM) companies face a constant challenge to maintain quality standards during frequent product launches. A manufacturing test verifies product functionality and identifies manufacturing defects. Failure to complete testing can even result in product recalls. In this research, a universal automated testing system has been proposed for CEM companies to streamline their test process in reduced test cost and time. A universal hardware interface is designed for connecting commercial off-the-shelf (COTS) test equipment and unit under test (UUT). A software application, based on machine learning, is developed in LabVIEW. The test site data for around 100 test sites have been collected. The application automatically selects COTS test equipment drivers and interfaces on UUT and test measurements for test sites through a universal hardware interface. Further, it collects real-time test measurement data, performs analysis, generates reports and key performance indicators (KPIs), and provides recommendations using machine learning. It also maintains a database for historical data to improve manufacturing processes. The proposed system can be deployed standalone as well as a replacement for the test department module of enterprise resource planning (ERP) systems providing direct access to test site hardware. Finally, the system is validated through an experimental setup in a CEM company.
Rotation Invariant Networks for Image Classification for HPC and Embedded Systems
Authors: Rodriguez Salas Rosemberg , Dokladal Petr , Dokladalova Eva
Published: January 2021

Convolutional Neural Network (CNNs) models’ size reduction has recently gained interest due to several advantages: energy cost reduction, embedded devices, and multi-core interfaces. One possible way to achieve model reduction is the usage of Rotation-invariant Convolutional Neural Networks because of the possibility of avoiding data augmentation techniques. In this work, we present the next step to obtain a general solution to endowing CNN architectures with the capability of classifying rotated objects and predicting the rotation angle without data-augmentation techniques. The principle consists of the concatenation of a representation mapping transforming rotation to translation and a shared weights predictor. This solution has the advantage of admitting different combinations of various basic, existing blocks. We present results obtained using a Gabor-filter bank and a ResNet feature backbone compared to previous other solutions. We also present the possibility to select between parallelizing the network in several threads for energy-aware High Performance Computing (HPC) applications or reducing the memory footprint for embedded systems. We obtain a competitive error rate on classifying rotated MNIST and outperform existing state-of-the-art results on CIFAR-10 when trained on up-right examples and validated on random orientations.
Experimental Parameter Tuning of a Portable Water Generator System Based on a Thermoelectric Cooler
Authors: Casallas Ingrid , Pérez Manuel , Fajardo Arturo , Paez-Rueda Carlos-Ivan
Published: January 2021

Atmospheric Water Generators (AWG) are a promising technology solution to the water scarcity in the world. However, their main drawback is the high power consumption. This paper presents the experimental optimization process of a Portable Atmospheric Water Generator (PAWG) prototype based on a thermometric cooler. This process was developed by an exhaustive search of the experimental solution space, which was generated by parametric sweeps of two parameters (i.e., control voltages in the PAWG), which are related to the power consumption of the PAWG and the physical variables involved in the water condensation process (i.e., the airflow and the temperature on the water condenser element). As a result, we found the existence of two optimal operation points under a constant value of relative humidity; one of them maximizes the amount of water generated, and the other one maximizes the system performance (i.e., the ratio between the generated water and consumed power in mL/Wh). The resulting Figures of Merit (FoMs) of the PAWG prototype were 0.33 mL/h of generated water and 0.22 mL/Wh for the system performance.
Design Optimisation Strategies for Solid Rammed Earth Walls in Mediterranean Climates
Authors: Giuffrida Giada , Detommaso Maurizio , Nocera Francesco , Caponetto Rosa
Published: January 2021

The renewed attention paid to raw earth construction in recent decades is linked to its undoubted sustainability, cost-effectiveness, and low embodied energy. In Italy, the use of raw earth as a construction material is limited by the lack of a technical reference standard and is penalised by the current energy legislation for its massive behaviour. Research experiences, especially transoceanic, on highly performative contemporary buildings made with natural materials show that raw earth can be used, together with different types of reinforcements, to create safe, earthquake-resistant, and thermally efficient buildings. On the basis of experimental data of an innovative fibre-reinforced rammed earth material, energy analyses are developed on a rammed earth building designed for a Mediterranean climate. The paper focuses on the influences that different design solutions, inspired by traditional bioclimatic strategies, and various optimised wall constructions have in the improvement of the energy performance of the abovementioned building. These considerations are furthermore compared with different design criteria aiming at minimising embodied carbon in base material choice, costs, and discomfort hours. Results have shown the effectiveness of using the combination of massive rammed earth walls, night cross ventilation, and overhangs for the reduction of energy demand for space cooling and the improvement of wellbeing. Finally, the parametric analysis of thermal insulation has highlighted the economic, environmental, and thermophysical optimal solutions for the rammed earth envelope.
Influence of Powertrain Topology and Electric Machine Design on Efficiency of Battery Electric Trucks—A Simulative Case-Study
Authors: Wolff Sebastian , Kalt Svenja , Bstieler Manuel , Lienkamp Markus
Published: January 2021

The advancement of electric mobility as a measure to comply with international climate targets and sustain renewable resources in the future has led to an electrification of the mobility sector in recent years. This trend has not been spared in the logistics and commercial vehicle sector. Emerging electric powertrain concepts for long-haul vehicles have since been developed and adapted to different use cases and axle concepts. In this paper, the authors show the influence of the powertrain topology and the associated design of the electric machine on the efficiency and energy consumption of commercial vehicles. For this, existing series or prototype long-haul axle topologies are analyzed regarding their efficiency and operating points within four driving cycles. Additionally, a sensitivity analysis on the influence of the total gearbox ratio tests the assumed designs. We find that single-machine topologies offer efficiency advantages over multiple-machine topologies. However, this study highlights a joint consideration of application-specific machine design and topology to realize the full technological potential.
Empirical Research on the Relationship amongst Renewable Energy Consumption, Economic Growth and Foreign Direct Investment in Kazakhstan and Uzbekistan
Authors: Grabara Janusz , Tleppayev Arsen , Dabylova Malika , Mihardjo Leonardus W. W. , Dacko-Pikiewicz Zdzisława
Published: January 2021

In this contemporary era, environmental problems spread at different levels in all countries of the world. Economic growth does not just depend on prioritizing the environment or improving the environmental situation. If the foreign direct investment is directed to the polluting industries, they will increase pollution and damage the environment. The purpose of the study is to consider the relationship between foreign direct investment in Kazakhstan and Uzbekistan and economic growth and renewable energy consumption. The study is based on data obtained from 1992 to 2018. The results show that there is a two-way link between foreign direct investment and renewable energy consumption in the considered two countries. The Granger causality test approach is applied to explore the causal relationship between the variables. The Johansen co-integration test approach is also employed to test for a relationship. The empirical results verify the existence of co-integration between the series. The main factors influencing renewable energy are economic growth and electricity consumption. To reduce dependence on fuel-based energy sources, Kazakhstan and Uzbekistan need to attract energy to renewable energy sources and implement energy efficiency based on rapid progress. This is because renewable energy sources play the role of an engine that stimulates the production process in the economy for all countries.
Mooring Angle Study of a Horizontal Rotor Wave Energy Converter
Authors: Meng Zhongliang , Liu Yanjun , Qin Jian , Sun Shumin
Published: January 2021

The horizontal rotor wave energy converter is a newly designed wave energy converter. While the mooring system plays a vital role in keeping the device floating stably, the selection of the mooring angle has immediate effects on the device’s floating stability and energy generation efficiency. Given the properties of wave energy along the coast in Shandong Province, this study combines wave statistics gathered from field measurements of a certain area in the Bohai Sea with hydrological data obtained in a field test in the same sea area and adopts Stokes’ fifth-order wave theory to theoretically design and simulate the mooring system for the new type of power generating device. With the help of AQWA software, data on the dynamics of the device at various angles are obtained to construct models and carry out regular wave experiments according to the most appropriate mooring angles to show the validity of the selected mooring angles. The consistency of the results between the experiment and simulation confirms that under the same working conditions of regular waves, as the mooring angle increases, the roll angle decreases first and then increases, the pitch angle barely varies, and the yaw angle decreases first and then increases. The adoption of this simulation method and the gathered experimental data help to provide theoretical and practical bases for choosing the mooring method for the engineering prototype and obtaining a reliable supply of power.
The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company
Authors: Sowinski Janusz
Published: January 2021

Forecasting of daily loads is crucial for the Distribution System Operators (DSO). Contemporary short-term load forecasting models (STLF) are very well recognized and described in numerous articles. One of such models is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which requires a large set of historical data. A well-recognized issue both for the ANFIS and other daily load forecasting models is the selection of exogenous variables. This article attempts to verify the statement that an appropriate selection of exogenous variables of the ANFIS model affects the accuracy of the forecasts obtained ex post. This proposal seems to be a return to the roots of the Polish econometrics school and the use of the Hellwig method to select exogenous variables of the ANFIS model. In this context, it is also worth asking whether the use of the Hellwig method in conjunction with the ANFIS model makes it possible to investigate the significance of weather variables on the profile of the daily load in an energy company. The functioning of the ANFIS model was tested for some consumers exhibiting high load randomness located within the area under supervision of the examined power company. The load curves featuring seasonal variability and weekly similarity are suitable for forecasting with the ANFIS model. The Hellwig method has been used to select exogenous variables in the ANFIS model. The optimal set of variables has been determined on the basis of integral indicators of information capacity H. Including an additional variable, i.e., air temperature, has also been taken into consideration. Some results of ex post daily load forecast are presented.
Impact of Intake and Exhaust Ducts on the Recovery Efficiency of Heat Recovery Ventilation Systems
Authors: Marsik Tom , Bickford Riley , Dennehy Conor , Garber-Slaght Robbin , Kasper Jeremy
Published: January 2021

The heat recovery efficiency of ventilation systems utilizing heat recovery ventilators (HRVs) depends not only on the heat recovery efficiency of the HRV units themselves but also on the intake and exhaust ducts that connect the HRV units to the outside environment. However, these ducts are often neglected in heat loss calculations, as their impact on the overall heat recovery efficiency of HRV systems is often not understood and, to the knowledge of the authors, a mathematical model for the overall heat recovery efficiency of HRV systems that accounts for these ducts has not been published. In this research, a mathematical model for the overall heat recovery efficiency of HRV systems that accounts for the intake and exhaust ducts was derived and validated using real-life data. The model-predicted decrease in heat recovery efficiency due to the ducts was in reasonable agreement (relative error within 20%) with the real-life measurements. The results suggest that utilizing this model allows for more correct ventilation heat loss calculations compared to using the heat recovery efficiency of the HRV unit alone, but more field studies are needed to verify the accuracy of this model in a wide range of applications.
Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders
Authors: Khan Saad Ullah , Mehmood Khawaja Khalid , Haider Zunaib Maqsood , Rafique Muhammad Kashif , Khan Muhammad Omer , Kim Chul-Hwan
Published: January 2021

In this paper, a coordination method of multiple electric vehicle (EV) aggregators has been devised to flatten the system load profile. The proposed scheme tends to reduce the peak demand by discharging EVs and fills the valley gap through EV charging in the off-peak period. Upper level fair proportional power distribution to the EV aggregators is exercised by the system operator which provides coordination among the aggregators based on their aggregated energy demand or capacity. The lower level min max objective function is implemented at each aggregator to distribute power to the EVs. Each aggregator ensures that the EV customers’ driving requirements are not relinquished in spite of their employment to support the grid. The scheme has been tested on IEEE 13-node distribution system and an actual distribution system situated in Seoul, Republic of Korea whilst utilizing actual EV mobility data. The results show that the system load profile is smoothed by the coordination of aggregators under peak shaving and valley filling goals. Also, the EVs are fully charged before departure while maintaining a minimum energy for emergency travel.
A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images
Authors: Hou Yu , Volk Rebekka , Soibelman Lucio
Published: January 2021

Multi-sensor imagery data has been used by researchers for the image semantic segmentation of buildings and outdoor scenes. Due to multi-sensor data hunger, researchers have implemented many simulation approaches to create synthetic datasets, and they have also synthesized thermal images because such thermal information can potentially improve segmentation accuracy. However, current approaches are mostly based on the laws of physics and are limited to geometric models’ level of detail (LOD), which describes the overall planning or modeling state. Another issue in current physics-based approaches is that thermal images cannot be aligned to RGB images because the configurations of a virtual camera used for rendering thermal images are difficult to synchronize with the configurations of a real camera used for capturing RGB images, which is important for segmentation. In this study, we propose an image translation approach to directly convert RGB images to simulated thermal images for expanding segmentation datasets. We aim to investigate the benefits of using an image translation approach for generating synthetic aerial thermal images and compare those approaches with physics-based approaches. Our datasets for generating thermal images are from a city center and a university campus in Karlsruhe, Germany. We found that using the generating model established by the city center to generate thermal images for campus datasets performed better than using the latter to generate thermal images for the former. We also found that using a generating model established by one building style to generate thermal images for datasets with the same building styles performed well. Therefore, we suggest using training datasets with richer and more diverse building architectural information, more complex envelope structures, and similar building styles to testing datasets for an image translation approach.
A Study on the Numerical Performances of Diffuse Interface Methods for Simulation of Melting and Their Practical Consequences
Authors: König-Haagen Andreas , Franquet Erwin , Faden Moritz , Brüggemann Dieter
Published: January 2021

This work is the final one in a series of three papers devoted to shedding light on the performance of fixed grid methods, also known as enthalpy methods, for the modeling and the simulation of solid/liquid phase transition. After a detailed analysis of five of the most common enthalpy methods for conductive-dominated and conductive-convective problems and then a study concerning the formulation of the advective term in the energy balance equation, the aim of the present paper is to extend the above-mentioned studies by an investigation of the numerical performance. Such a goal is achieved by comparing the required iterations and, even if it is shown to be only a rough guide, the simulation time of each method, for a great variety of parameter variations. In terms of contribution, the main conclusions of this overall work are to demonstrate that almost all solvers give similar results when stable. However, there are still distinctive deviations with the experiments, highlighting the need for a proper validation experiment. The second important assessment concerns resilience: almost all solvers work well, with only the applied apparent heat capacity method being the major exception as it often leads to unrealistic results. As a rule of thumb, models are more resilient when only the sensible enthalpy is advected. As far as the average of the required iterations is concerned, the so-called optimum approach needs the least. The order of the other solvers depends on the advective formulation, whereas source-based methods perform averagely and the tested apparent heat capacity method poorly. Cases with only sensible enthalpy advected need fewer iterations for four of the five solvers and less computational time for all solvers.