bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning
Authors: Kim Sang Yeob , Nam Gyeong Hee , Heo Byeong Mun
Published: November 2020

Metabolic syndrome (MS) is an aggregation of coexisting conditions that can indicate an individual’s high risk of major diseases, including cardiovascular disease, stroke, cancer, and type 2 diabetes. We conducted a cross-sectional survey to evaluate potential risk factor indicators by identifying relationships between MS and anthropometric and spirometric factors along with blood parameters among Korean adults. A total of 13,978 subjects were enrolled from the Korea National Health and Nutrition Examination Survey. Statistical analysis was performed using a complex sampling design to represent the entire Korean population. We conducted binary logistic regression analysis to evaluate and compare potential associations of all included factors. We constructed prediction models based on Naïve Bayes and logistic regression algorithms. The performance evaluation of the prediction model improved the accuracy with area under the curve (AUC) and calibration curve. Among all factors, triglyceride exhibited a strong association with MS in both men (odds ratio (OR) = 2.711, 95% confidence interval (CI) [2.328–3.158]) and women (OR = 3.515 [3.042–4.062]). Regarding anthropometric factors, the waist-to-height ratio demonstrated a strong association in men (OR = 1.511 [1.311–1.742]), whereas waist circumference was the strongest indicator in women (OR = 2.847 [2.447–3.313]). Forced expiratory volume in 6s and forced expiratory flow 25–75% strongly associated with MS in both men (OR = 0.822 [0.749–0.903]) and women (OR = 1.150 [1.060–1.246]). Wrapper-based logistic regression prediction model showed the highest predictive power in both men and women (AUC = 0.868 and 0.932, respectively). Our findings revealed that several factors were associated with MS and suggested the potential of employing machine learning models to support the diagnosis of MS.
Evaluation of the Effect of the Microscopic Glass Surface Protonation on the Hard Tissue Thin Section Preparation
Authors: Szalóki Melinda , Hegedűs Viktória , Fodor Tamás , Martos Renáta , Radics Tünde , Hegedűs Csaba , Dezső Balázs
Published: November 2020

In this study, a new procedure for mounting tissue blocks was described while cutting and grinding the section remains tightly bound to the inert glass surface both chemically and micro mechanically allowing good quality specimens for staining and microscopic analysis. The micromechanical interlocking was achieved by using of frosted glass, the chemical binding was made with 10-methacryloyloxydecyl dihydrogen phosphate monomer (10-MDP) containing bond material. The glass surface activation was achieved by nitric acid etching and the surface was characterized by zeta potential, X-ray photoelectron spectroscopy (XPS), and contact angle measurements. Cylindrical samples were prepared from epoxy embedding materials, cortical bovine bone, and dental titanium to investigate the shear bond strengths (SBS) to microscopic glass slide compared to a routinely used thermoplastic adhesive. Based on the experiments it was found that the micromechanical retention combined with MDP containing bond material improved the SBS data compared to the thermoplastic adhesive. The acid etched glass became positively charged that significantly increased the SBS data of bone and titanium compared with the uncharged version. Therefore, the thickness of the undecalcified bone section with metal can safely reduce to improve histological microscopic analysis.
Circular Economy in Mexico’s Electronic and Cell Phone Industry: Recent Evidence of Consumer Behavior
Authors: Cordova-Pizarro Daniela , Aguilar-Barajas Ismael , Rodriguez Ciro A. , Romero David
Published: November 2020

Circular Economy (CE) models are increasingly referenced in international fora and on many countries’ climate action agendas. This emphasis is associated with the international environmental and climate crisis. The case of the electronics industry remains particularly relevant, given its background in the use of CE models and its potential to enhance their use. This work focuses on consumer behavior regarding electronics products in Mexico. This is a largely under-researched topic not only in Mexico but also in Latin America. This study demonstrates that, by trying to extend the lifespan of their electronic products, consumers have awareness of product circularity. However, there is a lack of incentives to capitalize on this consumer interest, compounded by the limited participation of manufacturers and distributors. It is concluded that with a well-designed public policy, the electronics industry (including the mobile phone sector) in Mexico can move toward a CE model more rapidly. A stronger initiative for CE by design should be part of these policies, not only in Mexico but also internationally. The role of better eco-labelling in promoting consumers’ environmental awareness is essential. The lessons of this case study might be of interest to other countries as well.
SIM-D: An Agent-Based Simulator for Modeling Contagion in Population
Authors: Waleed Muhammad , Um Tai-Won , Kamal Tariq , Khan Aftab , Zahid Zaka Ullah
Published: November 2020

The spread of infectious diseases such as COVID-19, flu influenza, malaria, dengue, mumps, and rubella in a population is a big threat to public health. The infectious diseases spread from one person to another person through close contact. Without proper planning, an infectious disease can become an epidemic and can result in large human and financial losses. To better respond to the spread of infectious disease and take measures for its control, the public health authorities need models and simulations to study the spread of such diseases. In this paper, an agent-based simulation engine is presented that models the spread of infectious diseases in the population. The simulation takes as an input the human-to-human interactions, population dynamics, disease transmissibility and disease states and shows the spread of disease over time. The simulation engine supports non-pharmaceutical interventions and shows its impact on the disease spread across locations. A unique feature of this tool is that it is generic; therefore, it can simulate a wide variety of infectious disease models (SIR), susceptible-infectious-susceptible (SIS) and susceptible-infectious (SI). The proposed simulation engine will help the policy-makers and public health authorities study the behavior of disease spreading; thus, allowing for better planning.
The Impact of Controlled Vocabularies on Requirements Engineering Activities: A Systematic Mapping Study
Authors: Ahmad Arshad , Justo José Luis Barros , Feng Chong , Khan Arif Ali
Published: November 2020

Context: The use of controlled vocabularies (CVs) aims to increase the quality of the specifications of the software requirements, by producing well-written documentation to reduce both ambiguities and complexity. Many studies suggest that defects introduced at the requirements engineering (RE) phase have a negative impact, significantly higher than defects in the later stages of the software development lifecycle. However, the knowledge we have about the impact of using CVs, in specific RE activities, is very scarce. Objective: To identify and classify the type of CVs, and the impact they have on the requirements engineering phase of software development. Method: A systematic mapping study, collecting empirical evidence that is published up to July 2019. Results: This work identified 2348 papers published pertinent to CVs and RE, but only 90 primary published papers were chosen as relevant. The process of data extraction revealed that 79 studies reported the use of ontologies, whereas the remaining 11 were focused on taxonomies. The activities of RE with greater empirical support were those of specification (29 studies) and elicitation (28 studies). Seventeen different impacts of the CVs on the RE activities were classified and ranked, being the two most cited: guidance and understanding (38%), and automation and tool support (22%). Conclusions: The evolution of the last 10 years in the number of published papers shows that interest in the use of CVs remains high. The research community has a broad representation, distributed across the five continents. Most of the research focuses on the application of ontologies and taxonomies, whereas the use of thesauri and folksonomies is less reported. The evidence demonstrates the usefulness of the CVs in all RE activities, especially during elicitation and specification, helping developers understand, facilitating the automation process and identifying defects, conflicts and ambiguities in the requirements. Collaboration in research between academic and industrial contexts is low and should be promoted.
Long-Term Deflection of Prestressed Concrete Bridge Considering Nonuniform Shrinkage and Crack Propagation by Equivalent Load Approach
Authors: Birhane Fiseha Nega , Kim Sung-Il , Jang Seung Yup
Published: November 2020

Long-span prestressed concrete (PSC) bridges often suffer excessive deflection during their service lives. The nonuniform shrinkage strains of concrete caused by uneven moisture distributions can induce significant additional deflections, when combined with the creep and cracking of the concrete. Current design practices usually overlook these factors, and the few proposed approaches to consider them are complex and computationally expensive. This study proposes a simplified approach for considering the effect of nonuniform shrinkage by using the equivalent load concept in combination with a nonlinear analysis of the creep and cracking using three-dimensional finite element models. The long-term deflections of short-, medium-, and long-span PSC bridges are calculated under the combined effects of creep, shrinkage, and cracking. The results show that the nonuniform shrinkage effect is significant in medium- to long-span bridges, and that the cracking of the concrete reduces the stiffness, thereby increasing the long-term deflection of the bridges (more severely so in combination with creep and shrinkage). The predicted long-term deflections reasonably agree with the measured data. Thus, the equivalent load approach is effective for calculating long-term deflections considering nonuniform shrinkage strains, without the complicated and expensive coupling of moisture transport and structural analyses.
Digital Twin for Monitoring Ergonomics during Manufacturing Production
Authors: Greco Alessandro , Caterino Mario , Fera Marcello , Gerbino Salvatore
Published: November 2020

Within the era of smart factories, concerning the ergonomics related to production processes, the Digital Twin (DT) is the key to set up novel models for monitoring the performance of manual work activities, which are able to provide results in near real time and to support the decision-making process for improving the working conditions. This paper aims to propose a methodological framework that, by implementing a human DT, and supports the monitoring and the decision making regarding the ergonomics performances of manual production lines. A case study, carried out in a laboratory, is presented for demonstrating the applicability and the effectiveness of the proposed framework. The results show how it is possible to identify the operational issues of a manual workstation and how it is possible to propose and test improving solutions.
Philosophic Money. The Contemporary Art System as a Market and Cultural Agent
Authors: Baia Curioni Stefano , Equi Pierazzini Marta , Forti Laura
Published: November 2020

Within the contemporary art system complex, constantly-changing cultural features coexist with stratified acts of dealing. The art market operates as a collective mediation structure, developing a multiple agency: financial and economic, educational, political and social. In this article, we offer the result of an empirical test dedicated to the identification of unseen changes in the informal organizational pattern of the market. Observing the behavior of selected samples, we focused, firstly, on the networks of artists and commercial galleries at the Art Basel fair; and, secondly, on the group and solo shows organized by a relevant sample of international contemporary art museums and exhibitions spaces. These analyses offer an insight into the changes that occurred from 2005 to 2013, encompassing the quantitative growth of the art system infrastructure and the effects of the crisis of 2008.
The Role of Samalas Mega Volcanic Eruption in European Summer Hydroclimate Change
Authors: Liu Bin , Liu Jian , Ning Liang , Sun Weiyi , Yan Mi , Zhao Chen , Chen Kefan , Wang Xiaoqing
Published: November 2020

In this study, the role of AD 1258 Samalas mega volcanic eruption in the summer hydroclimate change over Europe and the corresponding mechanisms are investigated through multi-member ensemble climate simulation experiments based on the Community Earth System Model (CESM). The results show that the CESM simulations are consistent with the reconstructed Palmer Drought Severity Index (PDSI) and the historical records of European climate. Europe experiences significant summer cooling in the first three years after the Samalas mega volcanic eruption, peaking at −3.61 °C, −4.02 °C, and −3.21 °C in year 1 over the whole Europe, Southern Europe, and Northern Europe, respectively. The summer surface air temperature (SAT, °C) changes over the European continent are mainly due to the direct weakening of shortwave solar radiation induced by volcanic aerosol. The summer precipitation over the European continent shows an obvious dipole distribution characteristic of north-south reverse phase. The precipitation increases up to 0.42 mm/d in year 1 over Southern Europe, while it decreases by −0.28 mm/d in year 1 over Northern Europe, respectively. Both simulations and reconstructions show that the centers with the strongest increase in precipitation have always been located in the Balkans and Apennine peninsulas along the Mediterranean coast over Southern Europe, and the centers with the strongest precipitation reduction are mainly located in the British Isles and Scandinavia over northwestern Europe. The negative response of North Atlantic Oscillation (NAO) with significant positive sea level pressure (SLP) anomaly in the north and negative SLP anomaly in the south is excited in summer. The low tropospheric wind anomaly caused by the negative phase of NAO in summer affects the water vapor transport to Europe, resulting in the distribution pattern of summer precipitation in Europe, which is drying in the north and wetting in the south. The knowledge gained from this study is crucial to better understand and predict the potential impacts of single mega volcanic eruption on the future summer hydroclimate change in Europe.
Therapeutic Potential of Ursonic Acid: Comparison with Ursolic Acid
Authors: Son Juhyeon , Lee Sang Yeol
Published: November 2020

Plants have been used as drugs to treat human disease for centuries. Ursonic acid (UNA) is a naturally occurring pentacyclic triterpenoid extracted from certain medicinal herbs such as Ziziphus jujuba. Since the pharmacological effects and associated mechanisms of UNA are not well-known, in this work, we attempt to introduce the therapeutic potential of UNA with a comparison to ursolic acid (ULA), a well-known secondary metabolite, for beneficial effects. UNA has a keto group at the C-3 position, which may provide a critical difference for the varied biological activities between UNA and ULA. Several studies previously showed that UNA exerts pharmaceutical effects similar to, or stronger than, ULA, with UNA significantly decreasing the survival and proliferation of various types of cancer cells. UNA has potential to exert inhibitory effects in parasitic protozoa that cause several tropical diseases. UNA also exerts other potential effects, including antihyperglycemic, anti-inflammatory, antiviral, and antioxidant activities. Of note, a recent study highlighted the suppressive potential of UNA against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Molecular modifications of UNA may enhance bioavailability, which is crucial for in vivo and clinical studies. In conclusion, UNA has promising potential to be developed in anticancer and antiprotozoan pharmaceuticals. In-depth investigations may increase the possibility of UNA being developed as a novel reagent for chemotherapy.
Neurodegeneration and Sensorimotor Function
Authors: Bologna Matteo , Paparella Giulia
Published: November 2020

Sensorimotor integration is an essential function for both motor control and learning. Over recent decades, a growing body of evidence has emerged in support of the role of altered sensorimotor integration in the pathophysiology of various neurological conditions and movement disorders, particularly bradykinesia, tremor, and dystonia. However, the various causes and mechanisms underlying altered sensorimotor integration in movement disorders are still not entirely understood. The lack of complete insight into the pathophysiological role of altered sensorimotor integration in movement disorders is certainly due to the heterogeneity of movement disorders as well as to the variable occurrence of neurodegenerative phenomena, even in idiopathic movement disorders, which contribute to pathophysiology in a complex and often not easily interpretable way. Clarifying the possible relationship between neurodegenerative phenomena and sensorimotor deficits in movement disorders and other neurological conditions may guide the development of a more detailed disease prognosis and lead, perhaps, to the implementation of novel and individualized therapeutic interventions.
On the Distribution in Height of Base Shear Forces in Linear Static Analysis of Base-Isolated Structures
Authors: Zinco Adamo , Fraternali Fernando , Benzoni Gianmario , Martinelli Enzo
Published: November 2020

Although base isolation is nowadays a well-established seismic-protection technique for both buildings and bridges, and several issues are still open and attract the interest of the research community. Among them, the formulation of computationally efficient and accurate analysis methods is a relevant aspect in structural design of seismic-isolated buildings. In fact, codes and guidelines currently in force in various parts of the world generally include the possibility for designers to utilize linear-elastic analysis methods based on equivalent linearization of the non-linear force-displacement response of isolators. This paper proposes a formula for defining the force distribution in height that should be considered in linear-static analyses to obtain a more accurate approximation of the actual structural response, supposedly simulated by means of non-linear time history analysis. To do that, it summarizes the results of a wide parametric analysis carried out on a batch of structures characterized by three different heights and various properties of base isolators. The reported results highlight that the equivalent static force distribution provided by both Italian and European codes tend to underestimate the actual seismic lateral forces acting on base-isolated buildings, whereas the inverted triangular distribution, proposed in various American codes and standards, is often conservative.
Modeling of Building Energy Consumption by Integrating Regression Analysis and Artificial Neural Network with Data Classification
Authors: Ridwana Iffat , Nassif Nabil , Choi Wonchang
Published: November 2020

With the constant expansion of the building sector as a major energy consumer in the modern world, the significance of energy-efficient building systems cannot be more emphasized. Most of the buildings are now equipped with an electric dashboard to record consumption data which presents a significant scope of research by utilizing those data in energy modeling. This paper investigates conventional regression modeling in building energy estimation and proposes three models with data classifications to improve their performance. The proposed models are regression models and an artificial neural network model with data classification for predicting hourly or sub-hourly energy usage in four different buildings. Energy data is collected from a building energy simulation program and existing buildings to develop the models for detailed analysis. Data classification is recommended according to the system operating schedules of the buildings and models are tested for their performance in capturing the data trends resulting from those schedules. Proposed regression models and an ANN model with the recommended classification show very accurate results in estimating energy demand compared to conventional regression models. Correlation coefficient and root mean squared error values improve noticeably for the proposed models and they can potentially be utilized for energy conservation purposes and energy savings in the buildings.
It’s Got Too Greedy. New Therapeutic Options for Metabolic[ally] Addicted NSCLC?
Authors: Caiola Elisa , Broggini Massimo
Published: November 2020

Accounting for more than 2 million new cases and around 1 [...]
Recent Discoveries of Diagnostic, Prognostic and Predictive Biomarkers for Pancreatic Cancer
Authors: Khomiak Andrii , Brunner Marius , Kordes Maximilian , Lindblad Stina , Miksch Rainer Christoph , Öhlund Daniel , Regel Ivonne
Published: November 2020

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a dismal prognosis that is frequently diagnosed at an advanced stage. Although less common than other malignant diseases, it currently ranks as the fourth most common cause of cancer-related death in the European Union with a five-year survival rate of below 9%. Surgical resection, followed by adjuvant chemotherapy, remains the only potentially curative treatment but only a minority of patients is diagnosed with locally resectable, non-metastatic disease. Patients with advanced disease are treated with chemotherapy but high rates of treatment resistance and unfavorable side-effect profiles of some of the used regimens remain major challenges. Biomarkers reflect pathophysiological or physiological processes linked to a disease and can be used as diagnostic, prognostic and predictive tools. Thus, accurate biomarkers can allow for better patient stratification and guide therapy choices. Currently, the only broadly used biomarker for PDAC, CA 19-9, has multiple limitations and the need for novel biomarkers is urgent. In this review, we highlight the current situation, recent discoveries and developments in the field of biomarkers of PDAC and their potential clinical applications.
Chronic Lymphocytic Leukemia-Induced Humoral Immunosuppression: A Systematic Review
Authors: Grywalska Ewelina , Zaborek Monika , Łyczba Jakub , Hrynkiewicz Rafał , Bębnowska Dominika , Becht Rafał , Sosnowska-Pasiarska Barbara , Smok-Kalwat Jolanta , Pasiarski Marcin , Góźdź Stanisław , Roliński Jacek , Niedźwiedzka-Rystwej Paulina
Published: November 2020

Secondary immunodeficiency is observed in all patients with chronic lymphocytic leukemia (CLL) in varying degrees. The aim of the study was to review the available literature data on patients with CLL, with particular regard to the pathogenesis of the disease and the impact of humoral immunity deficiency on the clinical and therapeutic approach. A systematic literature review was carried out by two independent authors who searched PubMed databases for studies published up to January 2020. Additionally, Google Scholar was used to evaluate search results and support manual research. The search resulted in 240 articles eligible for analysis. After all criteria and filters were applied, 22 studies were finally applied to the analysis. The data analysis showed that the clinical heterogeneity of CLL patients correlates with the diversity of molecular abnormalities determining the clinical picture of the disease, the analysis of which enables setting therapeutic targets. Additionally, in improving the therapeutic method, it is worth introducing supportive therapies with the use of vaccines, antibiotics and/or immunoglobins. Moreover, humoral immunodeficiency in CLL has a strong influence on the risk of infection in patients for whom infections are a major cause of morbidity and mortality.
Embedding Active Pedagogies within Pre-Service Teacher Education: Implementation Considerations and Recommendations
Authors: Lander Natalie , Mazzoli Emiliano , Cassar Samuel , Symington Naomi , Salmon Jo
Published: November 2020

The physical activity levels of children in Australia are critically low and correlate with reduced academic achievement and poor health outcomes. Schools provide an ideal setting for physical activity interventions to help children move more. Instead of targeting in-service teachers, this study embedded an evidence-based active pedagogy program called Transform-Ed! into pre-service teacher education. Pre/post surveys and post-program interviews and focus group discussions were conducted with key stakeholders (n = 5), lecturers (n = 6), and pre-service teachers (n = 274) involved with the 12-week program. The design, implementation, and evaluation of the study were systematically guided by all five dimensions of Glasgow and colleagues’ RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework. Linear mixed models, descriptive analysis and a framework approach were used to analyse the data. Significant improvements were observed in pre-service teachers’ willingness, confidence, and competence to implement physically active pedagogic strategies following the intervention. Pre-service teacher perceived effectiveness of such strategies on student outcomes also significantly increased and perceived barriers decreased. High adherence was consistently reported and the program was maintained after completion of the implementation trial by all lecturers. Four key themes spanning multiple dimensions and participant levels informed recommendations for program scalability: an “inter-systemic approach”, a “co-design” approach, “embedded in professional practice”, and “evidence of impact” on teacher practice. Anchored in real-world settings and tethered by implementation science, Transform-Ed! could have the potential to advance the teaching capability of teachers, and transform the learning experience and physical and academic outcomes of primary school students.
Edible Films of Whey and Cassava Starch: Physical, Thermal, and Microstructural Characterization
Authors: Andrade Martins Yasmine Ariadne , Ferreira Samuel Viana , Silva Nayane Matias , Sandre Marcella Fernandes Borges , Filho Josemar Gonçalves Oliveira , Leão Paulo Victor Toledo , Leão Karen Martins , Nicolau Edmar Soares , Plácido Geovana Rocha , Egea Mariana Buranelo , Silva Marco Antônio Pereira da
Published: November 2020

The present work aimed to obtain and characterize edible films produced with liquid whey and cassava starch. The films were produced with different proportions of whey (63.75–67.50%) and cassava starch (7.50–11.25%) and characterized in relation to physical, thermal, and microstructural properties. The films showed reduced solubility with increasing concentrations of cassava starch, and those with the highest proportions of whey were more stable to thermal decomposition. The increase in concentration of cassava starch altered the microstructure of the films, making them more irregular and with an accumulation of matter. The production of biodegradable polymer blend films is an important step in the development of films for use in packaging, with the formulation of 67.50/7.50% whey/cassava starch being the best film for continued future work.
Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature
Authors: Hillebrand Eric , Johansen Søren , Schmith Torben
Published: November 2020

We study the stability of estimated linear statistical relations of global mean temperature and global mean sea level with regard to data revisions. Using four different model specifications proposed in the literature, we compare coefficient estimates and long-term sea level projections using two different vintages of each of the annual time series, covering the periods 1880–2001 and 1880–2013. We find that temperature and sea level updates and revisions have a substantial influence both on the magnitude of the estimated coefficients of influence (differences of up to 50%) and therefore on long-term projections of sea level rise following the RCP4.5 and RCP6 scenarios (differences of up to 40 cm by the year 2100). This shows that in order to replicate earlier results that informed the scientific discussion and motivated policy recommendations, it is crucial to have access to and to work with the data vintages used at the time.
The Economic Impact of Participant Sports Events: A Case Study for the Winter World Masters Games 2020 in Tyrol, Austria
Authors: Lintumäki Petri , Winner Hannes , Scheiber Sabrina , Mederle Anna , Schnitzer Martin
Published: November 2020

The Winter World Masters Games (WWMGs) are a large sports event for 30+-year-old athletes. As there are neither competitive qualification requirements for participants, nor entrance fees for spectators, the event can be considered as a participatory sports tourism event rather than a spectator event. In 2020, the WWMGs were staged in Innsbruck, Tyrol. In this study, we estimate the payoff of the event for the regional economy by assessing the impacts generated by participant spending and organizational expenditure. Furthermore, we discuss the peculiarities of the masters sports concept. Our empirical work is based on three distinct analyses: (1) economic impact analysis of participant spending, (2) cost-benefit analysis of organizational resource flows, and (3) discussion of impacts with experts in a focus group setting. Our results support the previous findings that masters sports events attract rather affluent and consumption-oriented participants. Indeed, the WWMGs were found to have a regional economic impact of €6.18 million and an estimated yield of €4.40 for each publicly subsidized euro. For an audience interested in the economic impact of events, this paper presents a novel method for handling non-normal expenditure distributions and adds to the understanding of how visitor segmentation can be utilized in an assessment of event impacts.
Program Coherence and Integration of School- and Work-Based Learning in the Icelandic Dual Vocational Education and Training (VET) System
Authors: Eiríksdóttir Elsa
Published: November 2020

This study examines how recently graduated journeymen, vocational teachers, and workplace trainers view the integration between learning at school and at work in a dual vocational education and training (VET) system, and how these views might be influenced by the duration and the sequencing of school- and work-based learning periods. Research indicates that effective implementation of the dual VET system is contingent upon successful integration of learning experience at the two venues. Recent graduates, workplace trainers, and teachers in all 51 certified trades answered an electronic questionnaire (667 participants, response rate 24%). Factor analysis of responses to 22 statements resulted in the identification of five factors. Findings show that the learning venues were parallel rather than integrated. Communication and collaboration between teachers and workplace trainers were limited and recent graduates, in particular, did not experience the program as a coherent whole. The results also showed important variations in the perspectives of the three groups of participants. The duration of the workplace learning period was not found to be associated with perceptions of program integration. Some limited effects of sequencing were found, but no clear pattern emerged. This study adds to knowledge on learning and integration, and indicates possible ways of improving program coherence and integration in a dual VET system.
Training Deep Neural Networks Using Conjugate Gradient-like Methods
Authors: Iiduka Hideaki , Kobayashi Yu
Published: November 2020

The goal of this article is to train deep neural networks that accelerate useful adaptive learning rate optimization algorithms such as AdaGrad, RMSProp, Adam, and AMSGrad. To reach this goal, we devise an iterative algorithm combining the existing adaptive learning rate optimization algorithms with conjugate gradient-like methods, which are useful for constrained optimization. Convergence analyses show that the proposed algorithm with a small constant learning rate approximates a stationary point of a nonconvex optimization problem in deep learning. Furthermore, it is shown that the proposed algorithm with diminishing learning rates converges to a stationary point of the nonconvex optimization problem. The convergence and performance of the algorithm are demonstrated through numerical comparisons with the existing adaptive learning rate optimization algorithms for image and text classification. The numerical results show that the proposed algorithm with a constant learning rate is superior for training neural networks.
An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria
Authors: Fong-Mata  María Berenice , García-Guerrero  Enrique Efrén , Mejía-Medina David Abdel , López-Bonilla  Oscar Roberto , Villarreal-Gómez  Luis Jesús , Zamora-Arellano Francisco , López-Mancilla  Didier , Inzunza-González  Everardo
Published: November 2020

The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.
Blind Image Quality Assessment Based on Classification Guidance and Feature Aggregation
Authors: Cai Weipeng , Fan Cien , Zou Lian , Liu Yifeng , Ma Yang , Wu Minyuan
Published: November 2020

In this work, we present a convolutional neural network (CNN) named CGFA-CNN for blind image quality assessment (BIQA). A unique two-stage strategy is utilized which firstly identifies the distortion type in an image using Sub-Network I and then quantifies this distortion using Sub-Network II. Different from most deep neural networks, we extract hierarchical features as descriptors to enhance the image representation and design a feature aggregation layer in an end-to-end training manner applying Fisher encoding to visual vocabularies modeled by Gaussian mixture models (GMMs). Considering the authentic distortions and synthetic distortions, the hierarchical feature contains the characteristics of a CNN trained on the self-built dataset and a CNN trained on ImageNet. We evaluated our algorithm on four publicly available databases, and the results demonstrate that our CGFA-CNN has superior performance over other methods both on synthetic and authentic databases.
Automatic Method for Distinguishing Hardware and Software Faults Based on Software Execution Data and Hardware Performance Counters
Authors: Park Jihyun , Choi Byoungju
Published: November 2020

Debugging in an embedded system where hardware and software are tightly coupled and have restricted resources is far from trivial. When hardware defects appear as if they were software defects, determining the real source becomes challenging. In this study, we propose an automated method of distinguishing whether a defect originates from the hardware or software at the stage of integration testing of hardware and software. Our method overcomes the limitations of the embedded environment, minimizes the effects on runtime, and identifies defects by obtaining and analyzing software execution data and hardware performance counters. We analyze the effects of the proposed method through an empirical study. The experimental results reveal that our method can effectively distinguish defects.
Malicious PowerShell Detection Using Attention against Adversarial Attacks
Authors: Choi Sunoh
Published: November 2020

Currently, hundreds of thousands of new malicious files are created daily. Existing pattern-based antivirus solutions face difficulties in detecting such files. In addition, malicious PowerShell files are currently being used for fileless attacks. To prevent these problems, artificial intelligence-based detection methods have been suggested. However, methods that use a generative adversarial network (GAN) to avoid AI-based detection have been proposed recently. Attacks that use such methods are called adversarial attacks. In this study, we propose an attention-based filtering method to prevent adversarial attacks. Using the attention-based filtering method, we can obtain restored PowerShell data from fake PowerShell data generated by GAN. First, we show that the detection rate of the fake PowerShell data generated by GAN in an existing malware detector is 0%. Subsequently, we show that the detection rate of the restored PowerShell data generated by attention-based filtering is 96.5%.
GPU Accelerated Nonlinear Electronic Circuits Solver for Transient Simulation of Systems with Large Number of Components
Authors: Černý David , Dobeš Josef
Published: November 2020

GPU cards have been used for scientific calculations for many years. Despite their ever-increasing performance, there are cases where they may still have problems. This article addresses possible performance and memory issues and their solutions that may occur during GPU calculations of iterative algorithms. Specifically, the article focuses on the optimization of transient simulation of extra-large highly nonlinear time-dependent circuits in SPICE-like electronic circuit simulator core enhanced with NVIDIA/CUDA (Compute Unified Device Architecture) interface and iterative Krylov Subspace methods with emphasis on improved accuracy. The article presents procedures for solving problems that may occur during this integration and negatively affect either the simulation speed or the accuracy of the calculation. Finally, a comparison of the implementation of an iterative calculation procedure with the use of GPU cards, calculation by the direct method and calculation on the CPU only is presented.
Digital Marketing Attribution: Understanding the User Path
Authors: Romero Leguina Jesús , Cuevas Rumín Ángel , Cuevas Rumín Rubén
Published: November 2020

Digital marketing is a profitable business generating annual revenue over USD 200B and an inter-annual growth over 20%. The definition of efficient marketing investment strategies across different types of channels and campaigns is a key task in digital marketing. Attribution models are an instrument used to assess the return of investment of different channels and campaigns so that they can assist in the decision-making process. A new generation of more powerful data-driven attribution models has irrupted in the market in the last years. Unfortunately, its adoption is slower than expected. One of the main reasons is that the industry lacks a proper understanding of these models and how to configure them. To solve this issue, in this paper, we present an empirical study to better understand the key properties of user-paths and their impact on attribution models. Our analysis is based on a large-scale dataset including more than 95M user-paths from real advertising campaigns of an international hoteling group. The main contribution of the paper is a set of recommendation to build accurate, interpretable and computationally efficient attribution models such as: (i) the use of linear regression, an interpretable machine learning algorithm, to build accurate attribution models; (ii) user-paths including around 12 events are enough to produce accurate models; (iii) the recency of events considered in the user-paths is important for the accuracy of the model.
A Review of Energy Management Assessment Models for Industrial Energy Efficiency
Authors: Hasan A S M Monjurul , Trianni Andrea
Published: November 2020

The necessity to ensure energy efficiency in the industries is of significant importance to attain reduction of energy consumption and greenhouse gases emissions. Energy management is one of the effective features that ensure energy efficiency in the industries. Energy management models are the infancy in the industrial energy domain with practical guidelines towards implementation in the organizations. Despite the increased interest in energy efficiency, a gap exists concerning energy management literature and present application practices. This paper aims to methodologically review the energy management assessment models that facilitate the assessment of industrial energy management. In this context, the minimum requirements model, maturity model, energy management matrix model, and energy efficiency measures characterization framework are discussed with implications. The study concludes with interesting propositions for academia and industrial think tanks delineating few further research opportunities.
Solar Radiation Estimation Using Data Mining Techniques for Remote Areas—A Case Study in Ethiopia
Authors: Abebe Worke Bizuayehu , Bludszuweit Hans , Domínguez-Navarro José A.
Published: November 2020

High quality of solar radiation data is essential for solar resource assessment. For remote areas this is a challenge, as often only satellite data with low spatial resolution are available. This paper presents an interpolation method based on topographic data in digital elevation model format to improve the resolution of solar radiation maps. The refinement is performed with a data mining method based on first-order Sugeno type Adaptive Neuro-Fuzzy Inference System. The training set contains topographic characteristics such as terrain aspect, slope and elevation which may influence the solar radiation distribution. An efficient sampling method is proposed to obtain representative training sets from digital elevation model data. The proposed geographic information system based approach makes this method reproducible and adaptable for any region. A case study is presented on the remote Amhara region in North Shewa, Ethiopia. Results are shown for interpolation of solar radiation data from 10 km × 10 km to a resolution of 1 km × 1 km and are validated with data from the PVGIS and SWERA projects.
Voltage Stability Analysis in Medium-Voltage Distribution Networks Using a Second-Order Cone Approximation
Authors: Montoya Oscar Danilo , Gil-González Walter , Arias-Londoño Andrés , Rajagopalan Arul , Hernández Jesus C.
Published: November 2020

This paper addresses the voltage stability margin calculation in medium-voltage distribution networks in the context of exact mathematical modeling. This margin calculation is performed with a second-order cone (SOCP) reformulation of the classical nonlinear non-convex optimal power flow problems. The main idea around the SOCP approximation is to guarantee the global optimal solution via convex optimization, considering as the objective function the λ-coefficient associated with the maximum possible increment of the load consumption at all the nodes. Different simulation cases are considered in one test feeder, described as follows: (i) the distribution network without penetration of distributed generation; (ii) the distribution network with penetration of distributed generation; and (iii) the distribution grid with capacitive compensation. Numerical results in the test system demonstrated the effectiveness of the proposed SOCP approximation to determine the λ-coefficient. In addition, the proposed approximation is compared with nonlinear tools available in the literature. All the simulations are carried out in the MATLAB software with the CVX package and the Gurobi solver.
Outdoor Air-Cooling System for a Computer Room and Its Corresponding Energy-Saving Effect
Authors: Hwang JiHyun , Lee Taewon
Published: November 2020

The recent expansion of the internet network and rapid advancements in information and communication technology are expected to lead to a significant increase in power consumption and the number of data centers. However, these data centers consume a considerable amount of electric power all year round, regardless of working days or holidays; thus, energy saving at these facilities has become essential. A disproportionate level of power consumption is concentrated in computer rooms because air conditioners in these rooms are required to operate throughout the year to maintain a constant indoor environment for stable operation of computer equipment with high-heat release densities. Considerable energy-saving potential is expected in such computer rooms, which consume high levels of energy, if an outdoor air-cooling system and air conditioners are installed. These systems can reduce the indoor space temperature by introducing a relatively low outdoor air temperature. Therefore, we studied the energy-saving effect of introducing an outdoor air-cooling system in a computer room with a disorganized arrangement of servers and an inadequate air conditioning system in a research complex in Korea. The findings of this study confirmed that annual energy savings of up to approximately 40% can be achieved.
Enhancing Oil Recovery with Hydrophilic Polymer-Coated Silica Nanoparticles
Authors: Bila Alberto , Torsæter Ole
Published: November 2020

Nanoparticles (NPs) have been proposed for enhanced oil recovery (EOR). The research has demonstrated marvelous effort to realize the mechanisms of nanoparticles EOR. Nevertheless, gaps still exist in terms of understanding the nanoparticles-driven interactions occurring at fluids and fluid–rock interfaces. Surface-active polymers or other surface additive materials (e.g., surfactants) have shown to be effective in aiding the dispersion stability of NPs, stabilizing emulsions, and reducing the trapping or retention of NPs in porous media. These pre-requisites, together with the interfacial chemistry between the NPs and the reservoir and its constituents, can result in an improved sweep efficiency. This paper investigates four types of polymer-coated silica NPs for the recovery of oil from water-wet Berea sandstones. A series of flooding experiments was carried out with NPs dispersed at 0.1 wt.% in seawater in secondary and tertiary oil recovery modes at ambient conditions. The dynamic interactions of fluids, fluid–rock, and the transport behavior of injected fluid in the presence of NPs were, respectively, studied by interfacial tension (IFT), spontaneous imbibition tests, and a differential pressure analysis. Core flooding results showed an increase in oil recovery up to 14.8% with secondary nanofluid injection compared to 39.7% of the original oil in place (OOIP) from the conventional waterflood. In tertiary mode, nanofluids increased oil recovery up to 9.2% of the OOIP. It was found that no single mechanism could account for the EOR effect with the application of nanoparticles. Instead, the mobilization of oil seemed to occur through a combination of reduced oil/water IFT, change in the rock surface roughness and wettability, and microscopic flow diversion due to clogging of the pores.