bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced DNA Repair Pathway is Associated with Cell Proliferation and Worse Survival in Hepatocellular Carcinoma (HCC)
Authors: Oshi Masanori , Kim Tae Hee , Tokumaru Yoshihisa , Yan Li , Matsuyama Ryusei , Endo Itaru , Cherkassky Leonid , Takabe Kazuaki
Published: January 2021

Hepatocellular carcinoma (HCC) is one of the most common malignancies and a leading cause of cancer-related deaths worldwide. In this study, a total of 749 HCC patients from 5 cohorts were studied to examine the relationships between enhancement of DNA repair and cancer aggressiveness, tumor immune microenvironment, and patient survival in HCC, utilizing a DNA repair pathway score. Our findings suggest that the DNA repair pathway was not only enhanced by the stepwise carcinogenic process of HCC, but also significantly enhanced in grade 3 HCC compared with grade 1 and 2 tumors. DNA repair high HCC was associated with worse survival, elevated intratumor heterogeneity, and mutation load, but not with the fraction of immune cell infiltration nor immune response. HCC tumors with a DNA repair high score enriched the cell proliferation- and other cancer aggressiveness-related gene sets. Interestingly, these features were more pronounced in grade 1 and 2 HCC compared to grade 3 HCC. To our knowledge, this is the first study to use DNA repair pathway-related gene set expression data to examine and validate the clinical relevance of DNA repair pathway activity in HCC. The DNA repair score may be used to better understand and predict prognosis in HCC.
Resistance to Antiandrogens in Prostate Cancer: Is It Inevitable, Intrinsic or Induced?
Authors: Maitland Norman
Published: January 2021

Increasingly sophisticated therapies for chemical castration dominate first-line treatments for locally advanced prostate cancer. However, androgen deprivation therapy (ADT) offers little prospect of a cure, as resistant tumors emerge rather rapidly, normally within 30 months. Cells have multiple mechanisms of resistance to even the most sophisticated drug regimes, and both tumor cell heterogeneity in prostate cancer and the multiple salvage pathways result in castration-resistant disease related genetically to the original hormone-naive cancer. The timing and mechanisms of cell death after ADT for prostate cancer are not well understood, and off-target effects after long-term ADT due to functional extra-prostatic expression of the androgen receptor protein are now increasingly being recorded. Our knowledge of how these widely used treatments fail at a biological level in patients is deficient. In this review, I will discuss whether there are pre-existing drug-resistant cells in a tumor mass, or whether resistance is induced/selected by the ADT. Equally, what is the cell of origin of this resistance, and does it differ from the treatment-naïve tumor cells by differentiation or dedifferentiation? Conflicting evidence also emerges from studies in the range of biological systems and species employed to answer this key question. It is only by improving our understanding of this aspect of treatment and not simply devising another new means of androgen inhibition that we can improve patient outcomes.
On the Support Effect and the Cr Promotion of Co Based Catalysts for the Acetic Acid Steam Reforming
Authors: Cortese Marta , Ruocco Concetta , Palma Vincenzo , Megía Pedro J. , Carrero Alicia , Calles José A.
Published: January 2021

This work focuses on the support effect of the performances of Co based catalysts for acetic acid steam reforming. SBA-15, a well ordered hexagonal mesoporous silica structure, and CeO2 have been selected as the supports, with the impact of chromium addition also being investigated. Better acetic acid steam reforming performances have been recorded for CeO2 compared to SBA-15 supported catalysts and, in particular, the 7Co/CeO2 catalyst showed the highest values of acetic acid conversions with enhanced H2 yields below 480 °C, in comparison to the other investigated catalytic formulations. In addition, more pronounced coke depositions and acetone concentrations have been obtained with CeO2 supported catalysts, due to the tendency of ceria to catalyse the ketonization reaction. Chromium addition to Co/SBA-15 catalysts led to an enhancement in the activity towards acetic acid steam reforming, while on CeO2 supported catalysts no improvement in the catalysts’ activity was observed. However, on both SBA-15 and CeO2 supported catalysts, Cr addition reduced the amount of coke deposited on the catalysts surface.
Unravelling Genetic Factors Underlying Corticobasal Syndrome: A Systematic Review
Authors: Arienti Federica , Lazzeri Giulia , Vizziello Maria , Monfrini Edoardo , Bresolin Nereo , Saetti Maria Cristina , Picillo Marina , Franco Giulia , Di Fonzo Alessio
Published: January 2021

Corticobasal syndrome (CBS) is an atypical parkinsonian presentation characterized by heterogeneous clinical features and different underlying neuropathology. Most CBS cases are sporadic; nevertheless, reports of families and isolated individuals with genetically determined CBS have been reported. In this systematic review, we analyze the demographical, clinical, radiological, and anatomopathological features of genetically confirmed cases of CBS. A systematic search was performed using the PubMed, EMBASE, and Cochrane Library databases, included all publications in English from 1 January 1999 through 1 August 2020. We found forty publications with fifty-eight eligible cases. A second search for publications dealing with genetic risk factors for CBS led to the review of eight additional articles. GRN was the most common gene involved in CBS, representing 28 out of 58 cases, followed by MAPT, C9ORF72, and PRNP. A set of symptoms was shown to be significantly more common in GRN-CBS patients, including visuospatial impairment, behavioral changes, aphasia, and language alterations. In addition, specific demographical, clinical, biochemical, and radiological features may suggest mutations in other genes. We suggest a diagnostic algorithm to help in identifying potential genetic cases of CBS in order to improve the diagnostic accuracy and to better understand the still poorly defined underlying pathogenetic process.
A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture
Authors: Alqithami Saad
Published: January 2021

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures.
Evolution of Shape and Volume Fraction of Superconducting Domains with Temperature and Anion Disorder in (TMTSF)2ClO4
Authors: Kesharpu Kaushal K. , Kochev Vladislav D. , Grigoriev Pavel D.
Published: January 2021

In highly anisotropic organic superconductor (TMTSF)2ClO4, superconducting (SC) phase coexists with metallic and spin-density wave phases in the form of domains. Using the Maxwell-Garnett approximation (MGA), we calculate the volume ratio and estimate the shape of these embedded SC domains from resistivity data at various temperature and anion disorder, controlled by the cooling rate or annealing time of (TMTSF)2ClO4 samples. We found that the variation of cooling rate and of annealing time affect differently the shape of SC domains. In all cases the SC domains have oblate shape, being the shortest along the interlayer z-axis. This contradicts the widely assumed filamentary superconductivity along the z-axis, used to explain the anisotropic superconductivity onset. We show that anisotropic resistivity drop at the SC onset can be described by the analytical MGA theory with anisotropic background resistance, while the anisotropic Tc can be explained by considering a finite size and flat shape of the samples. Due to a flat/needle sample shape, the probability of percolation via SC domains is the highest along the shortest sample dimension (z-axis), and the lowest along the sample length (x-axis). Our theory can be applied to other heterogeneous superconductors, where the size d of SC domains is much larger than the SC coherence length ξ, e.g., cuprates, iron-based or organic superconductors. It is also applicable when the spin/charge-density wave domains are embedded inside a metallic background, or vice versa.
The Role of Deposition Temperature in the Photovoltaic Properties of RF-Sputtered CdSe Thin Films
Authors: Rosly Hasrul Nisham , Rahman Kazi Sajedur , Abdullah Siti Fazlili , Harif Muhammad Najib , Doroody Camellia , Chelvanathan Puvaneswaran , Misran Halina , Sopian Kamaruzzaman , Amin Nowshad
Published: January 2021

Cadmium selenide (CdSe) thin films were grown on borosilicate glass substrates using the RF magnetron sputtering method. In this study, CdSe thin film was deposited at a deposition temperature in the range of 25 °C to 400 °C. The influence of deposition or growth temperature on the structural, morphological, and opto-electrical properties of CdSe films was investigated elaborately to achieve a good-quality window layer for solar-cell applications. The crystal structure, surface morphology, and opto-electrical characteristics of sputtered CdSe films were determined using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), UV–Vis spectrophotometry, and Hall effect measurement, respectively. The XRD results revealed the polycrystalline nature of CdSe, with a hexagonal structure having a strong preferential orientation toward the (002) plane. As evident from the FESEM images, the average grain size and surface morphology of the films were dependent on deposition temperatures. The carrier concentration was obtained as 1014 cm−3. The band gap in the range of 1.65–1.79 eV was found. The explored results suggested that sputtered CdSe thin film deposited at 300 °C has the potential to be used as a window layer in solar cells.
Feasibility and Safety of Transjugular Liver Biopsy for Japanese Patients with Chronic Liver Diseases
Authors: Iijima Makoto , Arisaka Takahiro , Yamamiya Akira , Tominaga Keiichi , Nagashima Kazunori , Kanamori Akira , Masuyama Satoshi , Majima Yuichi , Goda Kenichi , Ishida Kazuyuki , Irisawa Atsushi
Published: January 2021

Background and study aim: Transjugular liver biopsy (TJLB) can be used in patients who are ineligible for percutaneous liver biopsy (PLB) with acute and chronic hepatic disease. This study aimed to evaluate the usefulness and safety of TJLB in patients who were not indicated for PLB. Methods: Between July 2014 and February 2019, a total of 134 patients underwent liver biopsies at our institution. Among these, PLB was performed in 110 patients and TJLB in 24 patients. A retrospective comparison of clinical results in these patients was then performed. The primary endpoints of this study were the utility and safety of TJLB in patients who were not indicated for PLB. Results: The procedural success rate was 100% in both groups. The clinical response rate and the effective tissue sampling rate were 100% in the TJLB group and 97% in the PLB group (p = 0.55). There was no difference in the number of portal fields examined retrospectively between the two groups. No serious adverse events were observed in either group. Conclusions: It is suggested that TJLB is useful because it can be safely performed in patients with poor general condition who are not indicated for PLB.
Soluble ST2 as a Biomarker for Early Complications in Patients with Chronic Thromboembolic Pulmonary Hypertension Treated with Balloon Pulmonary Angioplasty
Authors: Banaszkiewicz Marta , Pietrasik Arkadiusz , Florczyk Michał , Kędzierski Piotr , Piłka Michał , Mańczak Rafał , Kochman Janusz , Opolski Grzegorz , Torbicki Adam , Kurzyna Marcin , Darocha Szymon
Published: January 2021

Background: The aim of the study was to assess soluble ST2 (sST2) concentration and its dynamic changes in the periprocedural period in patients with chronic thromboembolic pulmonary hypertension (CTEPH) treated with balloon pulmonary angioplasty (BPA). Methods: We prospectively analyzed 57 procedures of BPA performed in 37 patients with CTEPH. Biomarkers, such as N-terminal pro B-type natriuretic peptide (NT-proBNP), troponin T (TnT), and sST2 were assessed at four time points: Before the BPA procedure, 24 h and 48 h after the procedure, and at the discharge from hospital. Each postprocedural period was assessed for complications. Results: Before the BPA procedure, median sST2 concentration was 26.56 ng/mL (IQR: 16.66–40.83 ng/mL). sST2 concentration was significantly higher 24 h and 48 h after the BPA compared to the baseline measurements (33.31 ng/mL (IQR: 20.81–62.56), p = 0.000 and 27.45 ng/mL (IQR: 17.66–54.45), p = 0.028, respectively). sST2 level 24 h after the BPA procedure was significantly higher in the group with complications compared to the group without complications in the postprocedural period (97.66 ng/mL (IQR: 53.07–126.18) vs. 26.86 ng/mL (IQR: 19.10–40.12), p = 0.000). Conclusions: sST2 concentration in patients with CTEPH treated with BPA changes significantly in the postprocedural period and is significantly higher in the group with complications in postprocedural period.
Student Burnout: A Case Study about a Portuguese Public University
Authors: Salgado Sofia , Au-Yong-Oliveira Manuel
Published: January 2021

Burnout is increasingly present in organizations and in the most diverse professions, namely, in university students. Burnout can have negative repercussions on their well-being and can even lead them to abandon their studies. The objective of the study focuses on academic burnout and taking medication as a consequence of the requirements of the academic path of students at a Portuguese public university. To achieve this goal, a quantitative methodology was used, consisting of the distribution of a questionnaire to a sample of students from the analyzed university. The first study questionnaire obtained 207 responses, all valid. To perform the analysis of the quantitative data, the program IBM SPSS Statistics, version 25 was used. Inferential statistics were used, namely, Student t-test and one-way ANOVA (parametric tests), Spearman’s correlation coefficient, and the Chi-square test, to test the previously defined research hypotheses. Among the variables for which statistically significant relationships with burnout were found, the following stand out: the arithmetic mean (course average); the professional situation; participation in extracurricular activities; the practice and frequency of physical exercise; the choice and expectations regarding the course; the uncertainty felt about the professional future; the evaluation of the relationship with colleagues.
When Authenticity Goes Missing: How Monocultural Children’s Literature Is Silencing the Voices and Contributing to Invisibility of Children from Minority Backgrounds
Authors: Adam Helen
Published: January 2021

The importance of recognising, valuing and respecting a child’s family, culture, language and values is central to socially just education and is increasingly articulated in educational policy worldwide. Inclusive children’s literature can support children’s human rights and contribute to equitable and socially just outcomes for all children. However, evidence suggests many educational settings provide monocultural book collections which are counterproductive to principles of diversity and social justice. Further, that educators’ understandings and beliefs about diversity can contribute to inequitable provision and use of diverse books and to inequitable outcomes of book sharing for many children. This paper reports on a larger study investigating factors and relationships influencing the use of children’s literature to support principles of cultural diversity in the kindergarten rooms of long day care centres. The study was conducted within an ontological perspective of constructivism and an epistemological perspective of interpretivism informed by sociocultural theory. A mixed methods approach was adopted, and convergent design was employed interpret significant relationships and their meanings. Twenty-four educators and 110 children from four long day care centres in Western Australia participated. Data were collected through semi-structured interviews, video-based observations, field notes, document analysis and a book audit. This study firstly identified that current book collections in kindergarten rooms of long day care centres promote mono-cultural viewpoints and ‘othering’ of minority groups through limited access to books portraying inclusive and authentic cultural diversity. Secondly, that educators had limited understandings of the role of literature in acknowledging and valuing diversity and rarely used it to promote principles of diversity, resulting in a practice of “othering” those from minority group backgrounds. The key challenges which emerged from the study concerned beliefs, understanding and confidence of educators about diversity and inclusion, and the impact of these on their approaches to promoting principles of diversity through the use of children’s books. This research contributes to discussion on the value of children’s literature in achieving international principles of diversity. These findings have important social justice implications. The outcomes of this study have implications for educators, policy makers, early childhood organisations and those providing higher education and training for early childhood educators.
Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise
Authors: Lu Jun , Zhang Qunfei , Shi Wentao , Zhang Lingling , Shi Juan
Published: January 2021

Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.
Examination of Abnormal Behavior Detection Based on Improved YOLOv3
Authors: Fang Meng-ting , Chen Zhong-ju , Przystupa Krzysztof , Li Tao , Majka Michal , Kochan Orest
Published: January 2021

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.
Sentiment Level Evaluation of 3D Handicraft Products Application for Smartphones Usage
Authors: Jinsakul Natinai , Tsai Cheng-Fa , Wang Paohsi
Published: January 2021

Three-dimensional (3D) technology has attracted users’ attention because it creates objects that can interact with a given product in a system. Nowadays, Thailand’s government encourages sustainability projects through advertising, trade shows and information systems for small rural entrepreneurship. However, the government’s systems do not include virtual products with a 3D display. The objective of this study was four-fold: (1) develop a prototype of 3D handicraft product application for smartphones; (2) create an online questionnaire to collect user usage assessment data in terms of five sentiment levels—strongly negative, negative, neutral, positive and strongly positive—in response to the usage of the proposed 3D application; (3) evaluate users’ sentiment level in 3D handicraft product application usage; and (4) investigate attracting users’ attention to handicraft products after using the proposed 3D handicraft product application. The results indicate that 78.87% of participants’ sentiment was positive and strongly positive under accept using 3D handicraft product application, and evaluations in terms of assessing attention paid by participants to the handicraft products revealed that positive and strongly positive sentiment was described by 79.61% of participants. The participants’ evaluation results in this study prove that our proposed 3D handicraft product application affected users by attracting their attention towards handicraft products.
Influence of Active Channel Layer Thickness on SnO2 Thin-Film Transistor Performance
Authors: Kim Do Won , Kim Hyeon Joong , Lee Changmin , Kim Kyoungdu , Bae Jin-Hyuk , Kang In-Man , Jang Jaewon
Published: January 2021

Sol-gel processed SnO2 thin-film transistors (TFTs) were fabricated on SiO2/p+ Si substrates. The SnO2 active channel layer was deposited by the sol-gel spin coating method. Precursor concentration influenced the film thickness and surface roughness. As the concentration of the precursor was increased, the deposited films were thicker and smoother. The device performance was influenced by the thickness and roughness of the SnO2 active channel layer. Decreased precursor concentration resulted in a fabricated device with lower field-effect mobility, larger subthreshold swing (SS), and increased threshold voltage (Vth), originating from the lower free carrier concentration and increase in trap sites. The fabricated SnO2 TFTs, with an optimized 0.030 M precursor, had a field-effect mobility of 9.38 cm2/Vs, an SS of 1.99, an Ion/Ioff value of ~4.0 × 107, and showed enhancement mode operation and positive Vth, equal to 9.83 V.
A Non-Linear Convolution Network for Image Processing
Authors: Marsi Stefano , Bhattacharya Jhilik , Molina Romina , Ramponi Giovanni
Published: January 2021

This paper proposes a new neural network structure for image processing whose convolutional layers, instead of using kernels with fixed coefficients, use space-variant coefficients. The adoption of this strategy allows the system to adapt its behavior according to the spatial characteristics of the input data. This type of layers performs, as we demonstrate, a non-linear transfer function. The features generated by these layers, compared to the ones generated by canonical CNN layers, are more complex and more suitable to fit to the local characteristics of the images. Networks composed by these non-linear layers offer performance comparable with or superior to the ones which use canonical Convolutional Networks, using fewer layers and a significantly lower number of features. Several applications of these newly conceived networks to classical image-processing problems are analyzed. In particular, we consider: Single-Image Super-Resolution (SISR), Edge-Preserving Smoothing (EPS), Noise Removal (NR), and JPEG artifacts removal (JAR).
NDF of Scattered Fields for Strip Geometries
Authors: Akbari Sekehravani Ehsan , Leone Giovanni , Pierri Rocco
Published: January 2021

Solving inverse scattering problems by numerical methods requires investigating the number of independent pieces of information that can be reconstructed stably. To this end, we address the evaluation of the Number of Degrees of Freedom (NDF) of far-zone scattered fields for some strip geometries under the first-order Born approximation. The analysis is performed by employing the Singular Value Decomposition (SVD) of the scattering operator in the two-dimensional scalar geometry of one or more strips illuminated by a TM polarized plane wave. It is known that investigating the scattering scene at different incident plane waves (multi-view configuration) enhances the NDF. Therefore we mean to examine the minimum number of incident plane waves providing the NDF of the scattered fields both by theoretical estimations and numerical verifications.
Full Digital Control of an All-Si On-Board Charger Operating in Discontinuous Conduction Mode
Authors: Cittanti Davide , Gregorio Matteo , Mandrile Fabio , Bojoi Radu
Published: January 2021

This paper deals with the design, tuning and implementation of a digital controller for an all-Si electric vehicle (EV) on-board battery charger operated in discontinuous conduction mode (DCM). This charger consists of two cascaded conversion stages: a front-end power factor corrector (PFC) with two interleaved legs and an isolated phase-shifted full bridge DC/DC converter. Both stages operate in DCM over the complete battery charging power range, allowing lower inductance values for both the PFC and the DC/DC filtering elements. Moreover, DCM operation ensures a large reduction of the reverse-recovery losses in the power diodes, enabling the adoption of relatively cheap Si devices. The main goal of the work is to address the well-known DCM control challenges, leveraging a novel control strategy for both converter stages. This control scheme counteracts the DCM system non-linearities with a proper feed-forward contribution and an open-loop gain adjustment, ensuring consistent dynamical performance over the complete operating range. The designed controllers are tuned analytically, taking into account the delay components related to the digital implementation. Finally, the proposed control strategy is implemented on a single general purpose microcontroller unit (MCU) and its performance is experimentally validated on a 3.3 kW battery charger prototype.
Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches
Authors: Niccolai Alessandro , Dolara Alberto , Ogliari Emanuele
Published: January 2021

Accurate photovoltaic (PV) prediction has a very positive effect on many problems that power grids can face when there is a high penetration of variable energy sources. This problem can be addressed with computational intelligence algorithms such as neural networks and Evolutionary Optimization. The purpose of this article is to analyze three different hybridizations between physical models and artificial neural networks: the first hybridization combines neural networks with the output of the five-parameter physical model of a photovoltaic module in which the parameters are obtained from a datasheet. In the second hybridization, the parameters are obtained from a matching procedure with historical data exploiting Social Network Optimization. Finally, the third hybridization is PHANN, in which clear sky irradiation is used as an input. These three hybrid methods are compared with two physical approaches and simple neural network-based forecasting. The results show that the hybridization is very effective for achieving good forecasting results, while the performance of the three hybrid methods is comparable.
An Open-Source Monte Carlo Ray-Tracing Simulation Tool for Luminescent Solar Concentrators with Validation Studies Employing Scattering Phosphor Films
Authors: Smith Duncan E. , Hughes Michael D. , Patel Bhakti , Borca-Tasciuc Diana-Andra
Published: January 2021

Luminescent solar concentrators enhance the power output of solar cells through wave-guided luminescent emission and have great potential as building-integrated photovoltaics. Luminescent solar concentrators with a variety of geometries and absorbing–emitting materials have been reported in the literature. As the breadth of available experimental configurations continues to grow, there is an increasing need for versatile Monte Carlo ray-tracing simulation tools to analyze the performance of these devices for specific applications. This paper presents the framework for a Monte Carlo ray-tracing simulation tool that can be used to analyze a host of three-dimensional geometries. It incorporates custom radiative transport models to consider the effects of scattering from luminescent media, while simultaneously modeling absorption and luminescent emission. The model is validated using experimental results for three-dimensional planar and wedge-shaped luminescent solar concentrators employing scattering phosphor films. Performance was studied as a function of length, wavelength, and the angle of incidence of incoming light. The data for the validation studies and the code (written using the Python programming language) associated with the described model are publically available.
Parallel PV Configuration with Magnetic-Free Switched Capacitor Module-Level Converters for Partial Shading Conditions
Authors: Kampitsis Georgios , Batzelis Efstratios , van Erp Remco , Matioli Elison
Published: January 2021

In this paper, a module-level photovoltaic (PV) architecture in parallel configuration is introduced for maximum power extraction, under partial shading (PS) conditions. For the first time, a non-regulated switched capacitor (SC) nX converter is a used at the PV-side conversion stage, whose purpose is just to multiply the PV voltage by a fixed ratio and accordingly reduce the input current. All the control functions, including the maximum power point tracking, are transferred to the grid-side inverter. The voltage-multiplied PV modules (VMPVs) are connected in parallel to a common DC-bus, which offers expandability to the system and eliminates the PS issues of a typical string architecture. The advantage of the proposed approach is that the PV-side converter is relieved of bulky capacitors, filters, controllers and voltage/current sensors, allowing for a more compact and efficient conversion stage, compared to conventional per-module systems, such as microinverters. The proposed configuration was initially simulated in a 5 kW residential PV system and compared against conventional PV arrangements. For the experimental validation, a 10X Gallium Nitride (GaN) converter prototype was developed with a flat conversion efficiency of 96.3% throughout the power range. This is particularly advantageous, given the power production variability of PV generators. Subsequently, the VMPV architecture was tested on a two-module 500 WP prototype, exhibiting an excellent power extraction efficiency of over 99.7% under PS conditions and minimal DC-bus voltage variation of 3%, leading to a higher total system efficiency compared to most state-of-the-art configurations.
An Overview of Flow Assurance Heat Management Systems in Subsea Flowlines
Authors: Sunday Nsidibe , Settar Abdelhakim , Chetehouna Khaled , Gascoin Nicolas
Published: January 2021

The enormous cost of handling the challenges of flow assurance in subsea wells, flowlines, and risers, especially in deepwater applications, has necessitated a proactive approach to prevent their risk of occurrence. To ensure that transportation of the hydrocarbon is economical and efficient from the subsea wellhead to the processing units, a flow assurance heat management system is relevant in the design and planning of a fluid transport system. Consequently, the advancement of new technologies to serve the increasing need by exploring the technologically challenging and hostile subsea fields is of great importance. A comparative study on heat management systems in flowlines was conducted from the top five publishers (Elsevier, Springer, Taylor & Francis, Wiley, and Sage) based on the number of publications to determine the level of work done by researchers in the last decade, the figures from the study showed the need for scientific research in the field of active heating. Additionally, a review was implemented to ascertain the likely advantages and drawbacks of each technique, its limitations concerning field applications and then recommend suitable cost-effective technique(s). The active heating system gives the most cost-effective solution for subsea deepwater fields.
Synchronous Reluctance Machines for Automotive Cooling Fan Systems: Numerical and Experimental Study of Different Slot-Pole Combinations and Winding Types
Authors: Pop-Pîgleşan Florin , Pop Adrian-Cornel , Marțiş Claudia
Published: January 2021

In this paper a side-by-side comparison between synchronous reluctance machines (SynRMs) with concentrated and distributed windings is performed. The characteristics, parameters, and the installation space of a permanent magnet synchronous machine (PMSM) with concentrated windings used in a 13 V automotive cooling fan system (CFMs) are used as requirements and specifications. For that, eight SynRMs with different stator and rotor topologies are investigated and optimized by means of FE-based electromagnetic optimization. Knowing the challenges associated with the development of mechanically stable SynRM rotor structures, for two selected cross-sections in view of being prototyped, designs checks are performed to ensure robust operation at up to two times the required operating speed. The simulated results were verified by means of measurements performed using two different types of loading systems, i.e., with the real ventilator and using a DC machine as a load. Based on this, the relative differences between all three motor technologies in terms of important quantities (e.g., torque-speed characteristic, torque ripple, efficiency, power factor and ultimately the size) are highlighted.
Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications
Authors: Furlani Bastos Alvaro , Santoso Surya
Published: January 2021

In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both synthetic signals and field data measurements.
A New Approach to the PWM Modulation for the Multiphase Matrix Converters Supplying Loads with Open-End Winding
Authors: Szczepankowski Pawel , Strzelecka Natalia , Romero-Cadaval Enrique
Published: January 2021

This article presents three variants of the Pulse Width Modulation (PWM) for the Double Square Multiphase type Conventional Matrix Converters (DSM-CMC) supplying loads with the open-end winding. The first variant of PWM offers the ability to obtain zero value of the common-mode voltage at the load’s terminals and applies only six switches within the modulation period. The second proposal archives for less Total Harmonic Distortion (THD) of the generated load voltage. The third variant of modulation concerns maximizing the voltage transfer ratio, minimizing the number of switching, and the common-mode voltage cancellation. The discussed modulations are based on the concept of sinusoidal voltage quadrature signals, which can be an effective alternative to the classic space-vector approach. In the proposed approach, the geometrical arrangement of basic vectors needed to synthesize output voltages is built from the less number of vectors, which is equal to the number of the matrix converter’s terminals. The PWM duty cycle computation is performed using only a second-order determinant of the voltages coordinate matrix without using trigonometric functions. A new approach to the PWM duty cycles computing and the load voltage synthesis by 5 × 5 and 12 × 12 topologies has been verified using the PSIM simulation software.
Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux
Authors: Gómez-Orellana Antonio Manuel , Fernández Juan Carlos , Dorado-Moreno Manuel , Gutiérrez Pedro Antonio , Hervás-Martínez César
Published: January 2021

Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always available in an appropriate format and its preparation and pre-processing implies a lot of time and effort by researchers. This paper presents a novel software tool with a user-friendly GUI to create datasets by means of management and data integration of meteorological observations from two data sources: the National Data Buoy Center and the National Centers for Environmental Prediction and for Atmospheric Research Reanalysis Project. Such datasets can be created using buoys and reanalysis data through customisable procedures, in terms of temporal resolution, predictive and objective variables, and can be used by SC and ML methodologies for prediction tasks (classification or regression). The objective is providing the research community with an automated and versatile system for the casuistry that entails well-formed and quality data integration, potentially leading to better prediction models. The software tool can be used as a supporting tool for coastal and ocean engineering applications, sustainable energy production, or environmental modelling; as well as for decision-making in the design and building of coastal protection structures, marine transport, ocean energy converters, and well-planned running of offshore and coastal engineering activities. Finally, to illustrate the applicability of the proposed tool, a case study to classify waves depending on their significant height and to predict energy flux in the Gulf of Alaska is presented.
Small-Scale Solar–Bio-Hybrid Power Generation Using Brayton and Rankine Cycles
Authors: Bustamante Mauricio , Engeda Abraham , Liao Wei
Published: January 2021

This study conducted a detailed technical analysis of small-scale solar–bio-hybrid power generation systems using Rankine (steam turbine) and Brayton (gas turbine) cycles. Thermodynamic models were developed to characterize the state of working fluid and select the most suitable solar collection technology for individual power generation systems. Net capacity factor of power generation and utilization efficiencies of solar and biogas energy were used as parameters to evaluate energy generation and conclude the preferred system configuration. The analysis concluded that the steam turbine system has better global efficiency (67.7%) than the gas turbine system (55.7%), while the gas turbine system has better electricity generation efficiency (27.0%) than that (5.6%) of the steam turbine system. The effects of different climates on the selection of suitable hybrid systems were also investigated to delineate suitability and feasibility of different hybrid systems. In addition, the method used in this study can also be applied to investigate and optimize other small-scale hybrid renewable energy generation systems.
Effect of Lactic Acid Fermentation on Quinoa Characteristics and Quality of Quinoa-Wheat Composite Bread
Authors: Cizeikiene Dalia , Gaide Ieva , Basinskiene Loreta
Published: January 2021

The application of selected starter cultures with specific properties for fermentation may determine steady lactic acid bacteria (LAB) variety and the characteristics of fermented products that influence nutritional value, the composition of biologically active compounds and quality. The aim of this research was to evaluate the influence of different LAB on the biochemical characteristics of fermented quinoa. Moreover, total phenolic content (TPC), and the antimicrobial and antioxidant activities of protein fractions isolated from quinoa previously fermented with LAB were investigated. Quinoa additives, including quinoa fermented with Lactobacillus brevis, were incorporated in a wheat bread recipe to make nutritionally fortified quinoa-wheat composite bread. The results confirmed that L. plantarum, L. brevis, and L. acidophilus were well adapted in quinoa medium, confirming its suitability for fermentation. LAB strains influenced the acidity, L/D-lactic acid content, enzyme activity, TPC and antioxidant activity of fermented quinoa. The maximum phytase activity was determined in quinoa fermented with L. brevis. The results obtained from the ABTS radical scavenging assay of protein fractions confirmed the influence of LAB strain on the antioxidant activity of protein fractions. The addition of 5 and 10% of quinoa fermented with L. brevis did not affect the total titratable acidity of wheat bread, while 10% of fermented quinoa with L. brevis resulted in a higher specific volume. Fermented quinoa additives increased the overall acceptability of bread compared with unfermented seed additives.
Sensory and Volatile Flavor Analysis of Beverages
Authors: Vilela Alice
Published: January 2021

Humans have used their senses to evaluate food for several thousands of years [...]
Pre-Operative Oral Health-Related Quality of Life in Patients Attending Surgical Removal of Mandibular Third Molar Teeth
Authors: Abramovitz Itzhak , Zakopay Evgeny , Zini Avraham , Chweidan Harry , Balakirski Daniel , Protter Noam E. , Almoznino Galit
Published: January 2021

The study aimed to measure the pre-operative oral health-related quality of life (OHRQoL) and to identify patient and teeth pathologies associated with worse OHRQoL among patients attending mandibular third molar tooth extraction. Data were collected preoperatively from 199 patients attending surgical removal of their mandibular third molar. To that end, we measured the Oral Health Impact Profile-14 (OHIP-14) and analyzed its association with: (1) demographics; (2) health-related behaviors such as smoking, alcohol consumption, physical activity, and dietary habits; (3) Plaque Index (PI); (4) Decay, Missing, and Filled Teeth (DMFT); and (5) clinical characteristics related to third molar extraction, such as the indication for extraction, tooth angulations, and radiographic pathology. The mean age of the study population was 21.5 ± 3.2 years and the mean OHIP-14 global score was 22.5 ± 8.3. The present study identified patient and teeth profiles that are associated with worse pre-operative OHRQoL in patients attending mandibular third molar extraction. The “vulnerable patient” profile includes poor health-related behaviors, particularly the performance of physical activity less than once a week (p = 0.028). The “disturbing teeth” profile includes higher plaque scores (p = 0.023) and specific characteristics of the third molar teeth, such as pericoronitis (p = 0.027) and radiolucency around third molars in panoramic radiography (p < 0.001). These findings support the hypothesis that OHRQoL is a complex phenomenon which is associated with the patient’s health-related behaviors as well as with specific tooth pathologies.
Analysis of Correlation between Climate Change and Human Health Based on a Machine Learning Approach
Authors: Pizzulli Vito Alberto , Telesca Vito , Covatariu Gabriela
Published: January 2021

Climate change increasingly affects every aspect of human life. Recent studies report a close correlation with human health and it is estimated that global death rates will increase by 73 per 100,000 by 2100 due to changes in temperature. In this context, the present work aims to study the correlation between climate change and human health, on a global scale, using artificial intelligence techniques. Starting from previous studies on a smaller scale, that represent climate change and which at the same time can be linked to human health, four factors were chosen. Four causes of mortality, strongly correlated with the environment and climatic variability, were subsequently selected. Various analyses were carried out, using neural networks and machine learning to find a correlation between mortality due to certain diseases and the leading causes of climate change. Our findings suggest that anthropogenic climate change is strongly correlated with human health; some diseases are mainly related to risk factors while others require a more significant number of variables to derive a correlation. In addition, a forecast of victims related to climate change was formulated. The predicted scenario confirms that a prevalently increasing trend in climate change factors corresponds to an increase in victims.