bluuebunny/crossref_metadata_2025 huggingface dataset:
Two-Photon Imaging to Unravel the Pathomechanisms Associated with Epileptic Seizures: A Review
Authors: Khan Luqman , van Lanen Rick , Hoogland Govert , Schijns Olaf , Rijkers Kim , Kapsokalyvas Dimitrios , van Zandvoort Marc , Haeren Roel
Published: March 2021

Despite extensive research, the exact pathomechanisms associated with epileptic seizure formation and propagation have not been elucidated completely. Two-photon imaging (2PI) is a fluorescence-based microscopy technique that, over the years, has been used to evaluate pathomechanisms associated with epileptic seizures and epilepsy. Here, we review previous applications of 2PI in epilepsy. A systematic search was performed in multiple literature databases. We identified 38 publications that applied 2PI in epilepsy research. These studies described models of epileptic seizure propagation; anatomical changes and functional alterations of microglia, astrocytes, and neurites; and neurometabolic effects that accompany seizures. Moreover, various neurovascular alterations that accompany seizure onset and ictal events, such as blood vessel responses, have been visualized using 2PI. Lastly, imaging and quantitative analysis of oxidative stress and the aggregation of lipofuscin in the neurovasculature have been accomplished with 2PI. Cumulatively, these papers and their reported findings demonstrate that 2PI is an especially well-suited imaging technique in the domain of epilepsy research, and these studies have significantly improved our understanding of the disorder. The application of 2PI provides ample possibilities for future research, most interestingly on human brains, while also stretching beyond the field of epilepsy.
Metabolomics and Transcriptomics Analyses of Two Contrasting Cherry Rootstocks in Response to Drought Stress
Authors: Wan Tian , Feng Ying , Liang Chenglin , Pan Liuyi , He Ling , Cai Yuliang
Published: March 2021

Drought is one of the main factors affecting sweet cherry yields, and cherry rootstocks can provide a range of tree vigor levels to better match sweet cherries with the characteristics of the soil. To investigate the molecular events of the cherry to water deficiency, we performed transcriptomic and metabolomic analyses of Prunus mahaleb CDR-1 (drought-tolerant cherry rootstock (DT)) and P. cerasus × P. canescens Gisela 5 (drought-susceptible cherry rootstock (DS)), respectively. The results revealed 253 common drought-responsive genes in leaves and roots in DT and 17 in DS; 59 upregulated metabolites were explored in leaves in DT and 19 were explored in DS. Differentially expressed metabolites related to the cyanoamino acid metabolism pathway and phenylpropanoid biosynthesis pathway may be key factors in the difference in drought resistance in the two rootstocks. Moreover, six central metabolites—3-cyanoalanine, phenylalanine, quinic acid, asparagine, p-benzoquinone, and phytosphingosine—were identified as potential biological markers of drought response in cherries and may be key factors in the difference in drought resistance, along with caffeic acid and chlorogenic acid. We also selected 17 differentially expressed genes as core candidate genes and the mechanism of DT in response to drought is summarized.
Glycosaminoglycans: Carriers and Targets for Tailored Anti-Cancer Therapy
Authors: Berdiaki Aikaterini , Neagu Monica , Giatagana Eirini-Maria , Kuskov Andrey , Tsatsakis Aristidis M. , Tzanakakis George N. , Nikitovic Dragana
Published: March 2021

The tumor microenvironment (TME) is composed of cancerous, non-cancerous, stromal, and immune cells that are surrounded by the components of the extracellular matrix (ECM). Glycosaminoglycans (GAGs), natural biomacromolecules, essential ECM, and cell membrane components are extensively altered in cancer tissues. During disease progression, the GAG fine structure changes in a manner associated with disease evolution. Thus, changes in the GAG sulfation pattern are immediately correlated to malignant transformation. Their molecular weight, distribution, composition, and fine modifications, including sulfation, exhibit distinct alterations during cancer development. GAGs and GAG-based molecules, due to their unique properties, are suggested as promising effectors for anticancer therapy. Considering their participation in tumorigenesis, their utilization in drug development has been the focus of both industry and academic research efforts. These efforts have been developing in two main directions; (i) utilizing GAGs as targets of therapeutic strategies and (ii) employing GAGs specificity and excellent physicochemical properties for targeted delivery of cancer therapeutics. This review will comprehensively discuss recent developments and the broad potential of GAG utilization for cancer therapy.
Neuronal Differentiation from Induced Pluripotent Stem Cell-Derived Neurospheres by the Application of Oxidized Alginate-Gelatin-Laminin Hydrogels
Authors: Distler Thomas , Lauria Ines , Detsch Rainer , Sauter Clemens M. , Bendt Farina , Kapr Julia , Rütten Stephan , Boccaccini Aldo R. , Fritsche Ellen
Published: March 2021

Biodegradable hydrogels that promote stem cell differentiation into neurons in three dimensions (3D) are highly desired in biomedical research to study drug neurotoxicity or to yield cell-containing biomaterials for neuronal tissue repair. Here, we demonstrate that oxidized alginate-gelatin-laminin (ADA-GEL-LAM) hydrogels facilitate neuronal differentiation and growth of embedded human induced pluripotent stem cell (hiPSC) derived neurospheres. ADA-GEL and ADA-GEL-LAM hydrogels exhibiting a stiffness close to ~5 kPa at initial cell culture conditions of 37 °C were prepared. Laminin supplemented ADA-GEL promoted an increase in neuronal differentiation in comparison to pristine ADA-GEL, with enhanced neuron migration from the neurospheres to the bulk 3D hydrogel matrix. The presence of laminin in ADA-GEL led to a more than two-fold increase in the number of neurospheres with migrated neurons. Our findings suggest that laminin addition to oxidized alginate—gelatin hydrogel matrices plays a crucial role to tailor oxidized alginate-gelatin hydrogels suitable for 3D neuronal cell culture applications.
Prestressing Effect of Shape Memory Alloy Reinforcements under Serviceability Tensile Loads
Authors: Beßling Markus , Czaderski Christoph , Orlowsky Jeanette
Published: March 2021

Repairing and strengthening of existing aged steel-reinforced concrete structures is a major challenge. Today, much of the repair work completed is insufficient and brittle. A promising new solution for repair and strengthening tasks is the use of iron-based shape memory alloy (Fe-SMA). The pre-strained Fe-SMA components enable the pre-stressing of existing building components due to the heat-triggered contraction of the steel. Thus, deflections can be reduced or even recovered. In addition, the cracking process can be adapted, and an improvement in the load, under which the first crack appears, is possible. In this paper, the effects of pre-stress generated by activated Fe-SMA rebars, which were centrally embedded inside of a concrete specimen, are shown. The objective of the study is to quantify the improvement in the loads of the first crack and show the influences of the pre-stressing on the load-bearing behavior and the cracking process. For this purpose, axial tensile tests were performed on concrete bars with height, width, and length of 50 mm, 70 mm, and 900 mm, respectively. These were compared to usual construction steel rebars, pre-strained but nonactivated Fe-SMA rebars, and activated Fe-SMA steel rebars. The evaluation of crack patterns and openings was done using digital image correlation (DIC). The pre-stressing of the concrete causes an increase in the first crack loads of more than 150%, which indicates a clear improvement in the state of serviceability limit.
Special Issue on “Applied Biocatalysis in Europe: A Sustainable Tool for Improving Life Quality”
Authors: Alcántara Andrés R. , Plou Francisco J.
Published: March 2021

Applied biocatalysis, i [...]
Siblings’ Influence on the Motor Competence of Preschoolers
Authors: Rodrigues Luis P. , Luz Carlos , Cordovil Rita , Mendes Rui , Alexandre Rita , Lopes Vítor P.
Published: March 2021

The number of single-child families has been rising steadily in recent years, resulting in a childhood absent of sibling relationships. Being an only child has been shown to have a negative impact on physical fitness, somatic fitness, and motor development. In this study, we aimed to understand how living with and without siblings can impact the motor competence of children. One hundred and sixty-one children (87 boys, 74 girls) from 3.0 to 6.0 years of age (34 only children, 125 siblings) and with no known motor or cognitive disability were assessed using the Motor Competence Assessment (MCA). Their standardized results on the three MCA subscales (stability, locomotor, and manipulative) and total MCA were used to group them into high, average, and low motor competence groups. Motor competence percentile distribution of the sibling and only child group condition was compared using chi-square tests. Results showed a significative and positive association between the sibling condition and the distribution between the three MC groups (chi-square = 6.29; p = 0.043), showing that children in a household with siblings, independent of their age and sex, show a clear tendency for developing better motor competence.
Online Videogames Use and Anxiety in Children during the COVID-19 Pandemic
Authors: De Pasquale Concetta , Chiappedi Matteo , Sciacca Federica , Martinelli Valentina , Hichy Zira
Published: March 2021

Videogames use has constantly increased among children and adolescents, with uncertain consequences on their health. This study aimed to assess the prevalence of videogames use and addiction in a sample of Italian children during the COVID-19 pandemic and their association with anxiety symptoms. One hundred and sixty-two children (M/F:78/84; age range: 8–10 years; average age 9.4 ± 0.7 years) completed the Videogame Addiction Scale for Children (VASC), the Test of Anxiety and Depression (TAD) and the Children’s Anxiety Meter—State (CAM-S). Demographic variables and data on the access to electronic tools and games preferences were also collected. Overall, 96.3% of the study participants stated to have access to one or more device. They reported a low risk of videogame addiction (VASC score (mean ± SD): 46.7 ± 15.4), a moderate level of trait anxiety (TAD score (mean ± SD): 135 ± 16.8) and a low state anxiety (CAM-S score (mean ± SD): 2.2 ± 2.1). Males reported to spend more time on videogames, to perceive higher self-control and to be more influenced by reinforcement mechanisms; females described higher levels of trait anxiety. In the regression analysis, state anxiety was a predictor of videogame use and addiction (p = 0.01). Further research is needed to confirm these data and to maximize the developmentally positive effects of videogames and preventing the negative consequences.
Improved Influenza Diagnostics through Thermal Contrast Amplification
Authors: Liu Yilin , Zhan Li , Wang Yiru , Kangas Joseph , Larkin Daniel , Boulware David R. , Bischof John C.
Published: March 2021

Influenza poses a serious health threat and creates an economic burden for people around the world. The accurate diagnosis of influenza is critical to the timely clinical treatment of patients and the control of outbreaks to protect public health. Commercially available rapid influenza diagnostic tests (RIDTs) that are operated by visual readout are widely used in clinics to screen influenza infections, but RIDTs suffer from imperfect analytical sensitivity, especially when the virus concentration in the sample is low. Fortunately, the sensitivity can be simply improved through an add-on signal amplification step, i.e., thermal contrast amplification (TCA). To demonstrate the advantage of TCA for influenza diagnosis, we conducted a prospective cohort study on 345 clinical specimens collected for influenza A and B testing during the 2017–2018 influenza season. All samples were tested using the Quidel QuickVue Influenza A + B test, followed by a TCA readout, and then confirmatory polymerase chain reaction testing. Through the TCA detecting sub-visual weak positives, TCA reading improved the overall influenza sensitivity by 53% for influenza A and 33% for influenza B over the visual RIDTs readings. Even though the specificity was compromised slightly by the TCA protocol (relative decrease of 0.09% for influenza A and 0.01% for influenza B), the overall performance was still better than that achieved by visual readout based on comparison of their plots in receiver operating characteristic space and F1 scores (relative increase of 14.5% for influenza A and 12.5% for influenza B). Performing a TCA readout on wet RIDTs also improved the overall TCA performance (relative increase in F1 score of 48%). Overall, the TCA method is a simple and promising way to improve the diagnostic performance of commercial RIDTs for infectious diseases, especially in the case of specimens with low target analytes.
New Predictive Biomarkers for Ovarian Cancer
Authors: Atallah Ghofraan Abdulsalam , Abd. Aziz Nor Haslinda , Teik Chew Kah , Shafiee Mohamad Nasir , Kampan Nirmala Chandralega
Published: March 2021

Ovarian cancer is the eighth-most common cause of death among women worldwide. In the absence of distinctive symptoms in the early stages, the majority of women are diagnosed in advanced stages of the disease. Surgical debulking and systemic adjuvant chemotherapy remain the mainstays of treatment, with the development of chemoresistance in up to 75% of patients with subsequent poor treatment response and reduced survival. Therefore, there is a critical need to revisit existing, and identify potential biomarkers that could lead to the development of novel and more effective predictors for ovarian cancer diagnosis and prognosis. The capacity of these biomarkers to predict the existence, stages, and associated therapeutic efficacy of ovarian cancer would enable improvements in the early diagnosis and survival of ovarian cancer patients. This review not only highlights current evidence-based ovarian-cancer-specific prognostic and diagnostic biomarkers but also provides an update on various technologies and methods currently used to identify novel biomarkers of ovarian cancer.
Neuroendocrine Differentiation of Prostate Cancer Is Not Systematically Associated with Increased 18F-FDG Uptake
Authors: Bauckneht Matteo , Morbelli Silvia , Miceli Alberto , Rebuzzi Sara Elena , Fornarini Giuseppe
Published: March 2021

Neuroendocrine differentiation (NED) of prostate cancer represents an acknowledged predictor of resistant and more aggressive disease. NED can be functionally exploited in vivo using PET/CT imaging with somatostatin analogs radiolabeled with 68Ga. Many previous reports have shown that 18F-FDG PET/CT should also be used in cases such as guiding management, as NED is systematically associated with increased glycolysis. We hereby discuss the case of a metastatic prostate cancer patient in which 68Ga-Dotatoc PET/CT revealed the occurrence of NED with low FDG-avidity.
Altered Gene Expression Encoding Cytochines, Grow Factors and Cell Cycle Regulators in the Endometrium of Women with Chronic Endometritis
Authors: Cicinelli Ettore , Vitagliano Amerigo , Loizzi Vera , De Ziegler Dominique , Fanelli Margherita , Bettocchi Stefano , Nardelli Claudia , Trojano Giuseppe , Cicinelli Rossana , Minervini Crescenzio Francesco , Leronni Daniela , Viggiano Luigi
Published: March 2021

To evaluate the expression of genes encoding cytokines, grow factors and cell cycle regulators in the proliferative endometrium of women with chronic endometritis (CE) compared to controls. We performed a case-control study on seven women with CE as diagnosed by hysteroscopy and histology (Cases) compared to six women without CE (Controls). All women underwent diagnostic hysteroscopy plus endometrial biopsy during the mid-proliferative phase of the menstrual cycle. Endometrial samples were divided into two different aliquots for histological and molecular analyses. The endometrial expression profile of 16 genes encoding proteins involved in the inflammatory process, proliferation and cell cycle regulation/apoptosis was assessed by using high-throughput qPCR. Study endpoints were between-group differences in the expression of VEGF A, VEGF B, VEGF C, EGF, TNF, TGF B1, IFNG, TP73, TP73L, BAXva, CDC2, CDC2va, CCND3, CCNB1, BAX and IL12. RESULTS: VEGF A, VEGF B, VEGF C, EGF, TNF, TGF B1, IFNG, TP73, TP73L, BAXva, CDC2, CDC2va, CCND3, CCNB1 were significantly overexpressed in women with CE compared to controls, while BAX and IL12 had similar expression between groups. In women with CE, we found an altered endometrial expression of genes involved in inflammatory, cell proliferation, and apoptosis processes. The dominance of proliferative and anti-apoptotic activity in CE may potentially promote the development of polyps and hyperplastic lesions.
Histologic Analysis of Clinically Healthy Human Gingiva in Patients with Altered Passive Eruption
Authors: Pilloni Andrea , Marini Lorenzo , Zeza Blerina , Ferlosio Amedeo , Aghazada Rustam
Published: March 2021

The purpose of this study was to histologically examine the clinically healthy gingiva of patients with altered passive eruption (APE). Five patients with type 1 APE were enrolled. They underwent scaling and polishing and received oral hygiene instructions. After 6 months of supervised plaque control and uninterrupted gingival clinical health (Gingival Index (GI) = 0 and no Bleeding on Probing (BoP)), upper anterior teeth were surgically treated. During the surgical procedure, the excised gingival margin was collected to be histologically examined. In four out of five patients, signs of inflammation including spongiosis and neutrophil exocytosis could be found in the epithelium of the gingival sulcus. Ulceration with exposure of the lamina propria and inflammatory granulation tissue were evident in the most severe cases. Normal density and orientation of collagen fibers could be seen within the superficial and the deep portions of connective tissue, with an increase in size and number of the deep collagen fibers and a reduced laxity of the superficial ones. In conclusion, the clinically healthy gingiva of APE patients showed features compatible with persistent inflammation, possibly due to recurrent traumatisms caused by an incisally placed gingival margin.
From Kerr to Heisenberg
Authors: Tartaglia Angelo , Ruggiero Matteo Luca
Published: March 2021

In this paper, we consider the space-time of a charged mass endowed with an angular momentum. The geometry is described by the exact Kerr–Newman solution of the Einstein equations. The peculiar symmetry, though exact, is usually described in terms of the gravito-magnetic field originated by the angular momentum of the source. A typical product of this geometry is represented by the generalized Sagnac effect. We write down the explicit form for the right/left asymmetry of the times of flight of two counter-rotating light beams along a circular trajectory. Letting the circle shrink to the origin the asymmetry stays finite. Furthermore it becomes independent both from the charge of the source (then its electromagnetic field) and from Newton’s constant: it is then associated only to the symmetry produced by the gravitomagnetic field. When introducing, for the source, the spin of a Fermion, the lowest limit of the Heisenberg uncertainty formula for energy and time appears.
A Note on Causation versus Correlation in an Extreme Situation
Authors: Liang X. San , Yang Xiu-Qun
Published: March 2021

Recently, it has been shown that the information flow and causality between two time series can be inferred in a rigorous and quantitative sense, and, besides, the resulting causality can be normalized. A corollary that follows is, in the linear limit, causation implies correlation, while correlation does not imply causation. Now suppose there is an event A taking a harmonic form (sine/cosine), and it generates through some process another event B so that B always lags A by a phase of π/2. Here the causality is obviously seen, while by computation the correlation is, however, zero. This apparent contradiction is rooted in the fact that a harmonic system always leaves a single point on the Poincaré section; it does not add information. That is to say, though the absolute information flow from A to B is zero, i.e., TA→B=0, the total information increase of B is also zero, so the normalized TA→B, denoted as τA→B, takes the form of 00. By slightly perturbing the system with some noise, solving a stochastic differential equation, and letting the perturbation go to zero, it can be shown that τA→B approaches 100%, just as one would have expected.
Bayesian Analysis of Finite Populations under Simple Random Sampling
Authors: Mendoza Manuel , Contreras-Cristán Alberto , Gutiérrez-Peña Eduardo
Published: March 2021

Statistical methods to produce inferences based on samples from finite populations have been available for at least 70 years. Topics such as Survey Sampling and Sampling Theory have become part of the mainstream of the statistical methodology. A wide variety of sampling schemes as well as estimators are now part of the statistical folklore. On the other hand, while the Bayesian approach is now a well-established paradigm with implications in almost every field of the statistical arena, there does not seem to exist a conventional procedure—able to deal with both continuous and discrete variables—that can be used as a kind of default for Bayesian survey sampling, even in the simple random sampling case. In this paper, the Bayesian analysis of samples from finite populations is discussed, its relationship with the notion of superpopulation is reviewed, and a nonparametric approach is proposed. Our proposal can produce inferences for population quantiles and similar quantities of interest in the same way as for population means and totals. Moreover, it can provide results relatively quickly, which may prove crucial in certain contexts such as the analysis of quick counts in electoral settings.
Performance Evaluation of Construction Companies Using Integrated Entropy–Fuzzy VIKOR Model
Authors: Lam Weng Siew , Lam Weng Hoe , Jaaman Saiful Hafizah , Liew Kah Fai
Published: March 2021

The construction sector plays an important role in a country’s economic development. The financial performance of a company is a good indicator of its financial health and status. In Malaysia, the government encourages the construction industry to develop an advanced infrastructure related to health, transport, education and housing. In view of the COVID-19 pandemic, the operations and financial performance of construction sector companies have been affected recently. Additionally, uncertainty plays a vital role in the multi-criteria decision-making (MCDM) process. Based on previous studies, there has been no comprehensive study conducted on the evaluation of the financial performance of construction companies by integrating entropy and fuzzy VIKOR models. Therefore, this paper aims to propose an MCDM model to evaluate and compare the financial performance of construction companies with an integrated entropy–fuzzy VIKOR model. A case study is carried out by evaluating the listed construction companies in Malaysia with the proposed model. The findings of this paper indicate that the company ECONBHD achieves the best financial performance over the study period. The significance of this paper is to determine the priority of the financial ratios and ranking of the construction companies with the proposed entropy–fuzzy VIKOR model.
Integrate Candidate Answer Extraction with Re-Ranking for Chinese Machine Reading Comprehension
Authors: Zeng Junjie , Sun Xiaoya , Zhang Qi , Li Xinmeng
Published: March 2021

Machine Reading Comprehension (MRC) research concerns how to endow machines with the ability to understand given passages and answer questions, which is a challenging problem in the field of natural language processing. To solve the Chinese MRC task efficiently, this paper proposes an Improved Extraction-based Reading Comprehension method with Answer Re-ranking (IERC-AR), consisting of a candidate answer extraction module and a re-ranking module. The candidate answer extraction module uses an improved pre-training language model, RoBERTa-WWM, to generate precise word representations, which can solve the problem of polysemy and is good for capturing Chinese word-level features. The re-ranking module re-evaluates candidate answers based on a self-attention mechanism, which can improve the accuracy of predicting answers. Traditional machine-reading methods generally integrate different modules into a pipeline system, which leads to re-encoding problems and inconsistent data distribution between the training and testing phases; therefore, this paper proposes an end-to-end model architecture for IERC-AR to reasonably integrate the candidate answer extraction and re-ranking modules. The experimental results on the Les MMRC dataset show that IERC-AR outperforms state-of-the-art MRC approaches.
Some Lessons Learned in the Use of Distance Learning with Students with Special Educational Needs during COVID-19 Outbreak
Authors: Petretto Donatella Rita , Carta Stefano Mariano , Cataudella Stefania , Masala Ilaria , Mascia Maria Lidia , Penna Maria Pietronilla , Piras Paola , Pistis Ilenia , Masala Carmelo
Published: March 2021

At the end of 2019, COVID-19 (an infective respiratory disease caused by SARSCoV-2) was discovered in Wuhan, China [...]
Palliation of Four-Wave Mixing in Optical Fibers Using Improved DSP Receiver
Authors: Muhammad Fazal , Ali Farman , Abbas Ghulam , Abbas Ziaul Haq , Haider Shahab , Bilal Muhammad , Piran Md. Jalil , Suh Doug Young
Published: March 2021

A long haul optical communication system (LHOCS) is one of the key resources to fulfill the higher capacity requirements in future communication networks. To launch LHOCS, the system mainly faces high order nonlinear effects. The four-wave mixing (FWM) is one of the major nonlinear effects, which limits the transmission distance. Therefore, in this paper, an advanced duo-binary (DB) modulation scheme-based system is evaluated by employing an improved digital signal processing (IDSP) approach at the receiver side to suppress the FWM effect. In addition, an analytical analysis is also performed for the proposed system. To observe the difference between the IDSP and conventional digital signal processing (DSP), the various performance metrics such as bit error rate (BER), Q-factor, and optical signal-to-noise ratio (OSNR) parameters are evaluated. Variable channel spacing along with polarization mode dispersion (PMD) are analyzed at several ranges of input powers and fiber lengths. The analytical and simulation-based calculations exhibit the effectiveness of the proposed model and hence, FWM effect are compensated to achieve 500 km optical fiber propagation range with a BER below 10−6.
Efficient Design Strategy for Optimizing the Settling Time in Three-Stage Amplifiers Including Small- and Large-Signal Behavior
Authors: Giustolisi Gianluca , Palumbo Gaetano
Published: March 2021

An analytical criterion for the optimization of the small-signal settling time in three-stage amplifiers is carried out. The criterion is based on making equal the two exponential decays of the step response. Including slew-rate effects, a useful design strategy for the design of three-stage operational transconductance amplifier is provided. Extensive time-domain simulations on a transistor-level design in a 65-nm CMOS process confirm the validity of the proposed approach.
Computer-Human Interaction and Collaboration: Challenges and Prospects
Authors: Ortega Manuel
Published: March 2021

Through a series of projects carried out by the Computer–Human Interaction and COllaboration (CHICO) group of the University of Castilla-La Mancha, some proposals are presented to improve the current e-Learning systems by making use of different paradigms of human-computer interaction. Synchronous and asynchronous collaborative systems, ubiquitous computing, and augmented reality can improve the current learning environments. The use of artificial intelligence mechanisms for both learner support and assessment complements these techniques. Emphasis is also placed on the use of automatic application generation techniques using models.
New Time-Frequency Transient Features for Nonintrusive Load Monitoring
Authors: Drouaz Mahfoud , Colicchio Bruno , Moukadem Ali , Dieterlen Alain , Ould-Abdeslam Djafar
Published: March 2021

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.
How Much Polish Consumers Know about Alternative Fuel Vehicles? Impact of Knowledge on the Willingness to Buy
Authors: Kowalska-Pyzalska Anna , Kott Marek , Kott Joanna
Published: March 2021

Limited consumer knowledge reduces the chances of the spread of alternative fuel vehicles (AFVs), and hence slows down AFV market spread. In our empirical survey conducted in the first quarter of 2020 among 1002 Poles planning to buy a car in the next 12 months or who have just bought one, we examine what socio-economic and attitudinal factors influence their willingness to buy an AFV. In particular, we are interested in exploring how AFV knowledge related to understanding of the differences between hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), as well as brand recognition associate with the willingness to buy. To the best our knowledge, this is a unique study among consumers in Central and Eastern Europe, characterized by lower exposure to AFVs and lower purchasing power. Our results indicate that males with pro-environmental beliefs and behaviors who are interested in modern automotive technologies and have good AFV brand recognition are predominated to be willing to buy an AFV in the near future.
Locational Marginal Pricing and Daily Operation Scheduling of a Hydro-Thermal-Wind-Photovoltaic Power System Using BESS to Reduce Wind Power Curtailment
Authors: Menezes Roberto Felipe Andrade , Soriano Guilherme Delgado , de Aquino Ronaldo Ribeiro Barbosa
Published: March 2021

The Daily Operation Scheduling (DOS) gets new challenges while a large-scale of renewable energy is inserted into the power system. In addition to the operation, the power variability of these sources also causes a problem in the hourly pricing, represented here by Locational Marginal Pricing (LMP). Therefore, new applications, such as energy shifting, offer greater efficiency to the system, minimizing the negative effects caused by wind power curtailment (WPC). This paper shows the LMP formation in the DOS of the hydro-thermal-wind-photovoltaic power system with a battery energy storage system and the reduction of WPC. Here, the wind and photovoltaic power plants are designed to be dispatched, not mandatory, to be able to cut the generation, and the insertion of Distributed Generation is considered. Moreover, to solve the DOS problem, the interior-point method is used. Additionally, the DC optimal power flow, used to represent the DOS in addition to the representation of the electric grid, is modeled with an iterative approach. The analysis is made in an IEEE 24-bus system with data from Brazil. Lastly, the results of simulations are presented and discussed, demonstrating the effectiveness of the optimization to reduce the WPC, the total operation cost, and to provide the LMP curve.
Designing Tools for Energy System Scenario Making in Municipal Energy Planning
Authors: Johannsen Rasmus Magni , Østergaard Poul Alberg , Maya-Drysdale David , Krog Elmegaard Mouritsen Louise
Published: March 2021

Energy planning increasingly revolves around the use of tools for energy system modelling and analysis with a view to generating scenarios to show implications and possibilities for decision makers. Municipalities engage in the transition to renewable energy systems through the formulation of strategies and goals at a local level despite often lacking appropriate tools and resources to conduct the needed complex analyses. Tools for energy system analyses have traditionally been designed either with the scope of national energy systems or detailed project-specific analysis in mind, leaving municipal planners in a state of flux. This study aims to identify important specifications and critical design principles for future energy system modelling tools designed for municipal planners. Through a qualitative case-oriented approach, this study investigates the planning practices of four municipalities. It is found that future tools for municipal planning purposes need to combine the need for systematic analyses with concrete and implementable initiatives while balancing analytical complexity with operational simplicity.
Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques
Authors: Poncela-Blanco Marta , Poncela Pilar
Published: March 2021

Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system. As the relationship between wind and the generated power is highly nonlinear and time-varying, and given the increasing number of available forecasting techniques, it is possible to use alternative models to obtain more than one prediction for the same hour and forecast horizon. To increase forecast accuracy, it is possible to combine the different predictions to obtain a better one or to dynamically select the best one in each time period. Hybrid alternatives based on combining a few selected forecasts can be considered when the number of models is large. One of the most popular ways to combine forecasts is to estimate the coefficients of each prediction model based on its past forecast errors. As an alternative, we propose using multivariate reduction techniques and Markov chain models to combine forecasts. The combination is thus not directly based on the forecast errors. We show that the proposed combination strategies based on dimension reduction techniques provide competitive forecasting results in terms of the Mean Square Error.
An Optimized and Decentralized Energy Provision System for Smart Cities
Authors: Swain Ayusee , Salkuti Surender Reddy , Swain Kaliprasanna
Published: March 2021

Energy efficiency and data security of smart grids are one of the major concerns in the context of implementing modern approaches in smart cities. For the intelligent management of energy systems, wireless sensor networks and advanced metering infrastructures have played an essential role in the transformation of traditional cities into smart communities. In this paper, a smart city energy model is proposed in which prosumer communities were built by interconnecting energy self-sufficient households to generate, consume and share clean energy on a decentralized trading platform by integrating blockchain technology with a smart microgrid. The efficiency and stability of the grid network were improved by using several wireless sensor nodes that manage a massive amount of data in the network. However, long communication distances between sensor nodes and the base station can greatly consume the energy of sensors and decrease the network lifespan. Therefore, bio-inspired algorithm approaches were proposed to improve routing by obtaining the shortest path for traversing the entire network and increasing the system performance in terms of the efficient selection of cluster heads, reduced energy consumption, and extended network lifetime. This was carried out by studying the properties and mechanisms of biological systems and applying them in the communication systems in order to obtain the best results for a specific problem. In this comprehensive model, particle swarm optimization and a genetic algorithm are used to search for the optimal solution in any problem space in less processing time.
Fast Computation of Multi-Parametric Electromagnetic Fields in Synchronous Machines by Using PGD-Based Fully Separated Representations
Authors: Sancarlos Abel , Ghnatios Chady , Duval Jean-Louis , Zerbib Nicolas , Cueto Elias , Chinesta Francisco
Published: March 2021

A novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric solutions for electromagnetic fields in synchronous machines. Specifically, the intrusive version of the Proper Generalized Decomposition (PGD) is employed to simulate a Permanent-Magnet Synchronous Motor (PMSM). The result is a virtual chart allowing real-time evaluation of the magnetic vector potential as a function of the operation point of the motor, or even as a function of constructive parameters, such as the remanent flux in permanent magnets. Currently, these solutions are highly demanded by the industry, especially with the recent developments in the Electric Vehicle (EV). In this framework, standard discretization techniques require highly time-consuming simulations when analyzing, for instance, the noise and vibration in electric motors. The proposed approach is able to construct a virtual chart within a few minutes of off-line simulation, thanks to the use of a fully separated representation in which the solution is written from a series of functions of the space and parameters coordinates, with full space separation made possible by the use of an adapted geometrical mapping. Finally, excellent performances are reported when comparing the reduced-order model with the more standard and computationally costly Finite Element solutions.
Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand
Authors: Boonluk Panyawoot , Khunkitti Sirote , Fuangfoo Pradit , Siritaratiwat Apirat
Published: March 2021

The optimal siting and sizing of battery energy storage system (BESS) is proposed in this study to improve the performance of the seventh feeder at Nakhon Phanom substation, which is a distribution network with the connected photovoltaic (PV) in Thailand. The considered objective function aims to improve the distribution network performance by minimizing costs incurred in the distribution network within a day, comprising of voltage regulation cost, real power loss cost, and peak demand cost. Particle swarm optimization (PSO) is applied to solve the optimization problem. It is found that the optimal siting and sizing of the BESS installation could improve the performance of the distribution network in terms of cost minimization, voltage profile, real power loss, and peak demand. The results are investigated from three cases where case 1 is without PV and BESS installation, case 2 is with only PV installation, and case 3 is with PV and BESS installations. The comparison results show that case 3 provided the best costs, voltage deviation, real power loss, and peak demand compared to those of cases 1 and 2; system costs provided by cases 1, 2 and 3 are USD 4598, USD 5418, and USD 1467, respectively.
An Efficiency Analysis of 27 Level Single-Phase Asymmetric Inverter without Regeneration
Authors: Espinosa Eduardo , Melín Pedro , Baier Carlos , Espinoza José , Garcés Hugo
Published: March 2021

For medium voltage applications, multilevel inverters are used. One of its classic topologies is the Cascaded H-Bridge, which requires isolated DC voltages to work. Depending on the DC voltage ratio used in the Cascaded H-bridge can be classified into symmetric and asymmetric. In comparison between symmetric and asymmetric inverters, the latter can generate an AC output voltage with more output voltage levels. DC voltage ratio most documented are binary and trinary. The last can generate an AC voltage of 3n = 27 levels is obtained, using n = 3 inverters in cascade and NLM modulation, which generates a flow power of the load to the inverters (regeneration). This work analyzes the semiconductor losses (switching and conduction) and the THD of the AC output voltage in function of index modulation, considering a non-regenerative modulation technique for a 27-level single-phase asymmetric inverter. To confirm the theoretical analyzes, simulation and experimental results are shown.
Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics
Authors: Liu Jinming , Zeng Changhao , Wang Na , Shi Jianfei , Zhang Bo , Liu Changyu , Sun Yong
Published: March 2021

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.
Application of System Dynamic Modelling for Evaluation of Carbon Mitigation Strategies in Cement Industries: A Comparative Overview of the Current State of the Art
Authors: Kunche Akhil , Mielczarek Bożena
Published: March 2021

Cement is the key ingredient in concrete, which is the most consumed resource on the planet after water. As an energy-intensive industry, cement production is one of the largest sources of greenhouse emissions in the world today. The demand for cement is synonymous with the growth in infrastructure demand and per-capita gross domestic product in the world, calling the need for mitigation measures within the industry in order to contribute to the global climate change efforts. System dynamics (SD) is a simulation approach that is used for studying the nonlinear behaviours in complex systems over time, often used in industrial domains for emission forecasts as well as policy experimentation. With the adoption rates of mitigation strategies in the cement industry being inadequate, there is a need for improvisation in policymaking through better decision-support tools. In this paper, a comparative overview of the studies that specifically utilise the SD approach for evaluation of carbon mitigation strategies in the cement industry is presented on the basis of their scope, model description, scenarios tested, and featured mitigation methods. Additionally, the potential for improvements in future studies is discussed.
Development and Validation of a Machine Learned Turbulence Model
Authors: Bhushan Shanti , Burgreen Greg W. , Brewer Wesley , Dettwiller Ian D.
Published: March 2021

A stand-alone machine learned turbulence model is developed and applied for the solution of steady and unsteady boundary layer equations, and issues and constraints associated with the model are investigated. The results demonstrate that an accurately trained machine learned model can provide grid convergent, smooth solutions, work in extrapolation mode, and converge to a correct solution from ill-posed flow conditions. The accuracy of the machine learned response surface depends on the choice of flow variables, and training approach to minimize the overlap in the datasets. For the former, grouping flow variables into a problem relevant parameter for input features is desirable. For the latter, incorporation of physics-based constraints during training is helpful. Data clustering is also identified to be a useful tool as it avoids skewness of the model towards a dominant flow feature.
Fault Sealing Evaluation of a Strike-Slip Fault Based on Normal Stress: A Case Study from Eastern Junggar Basin, NW China
Authors: Ji Jie , Wu Kongyou , Pei Yangwen , Guo Wenjian , Liu Yin , Li Tianran
Published: March 2021

The sealing of a fault zone has been a focus for geological studies in the past decades. The majority of previous studies have focused on the extensional regimes, where the displacement pressure difference between fault rock and reservoir was used to evaluate the fault-sealing property from the basic principle of fault sealing. When considering the displacement pressure difference, the impact of gravity on the fault rock was considered, whereas the impact of horizontal stress was ignored. In this study, we utilize the displacement pressure difference as an index to evaluate the sealing capacity of strike-slip faults, in which both the impacts of gravity and horizontal stress on the fault rocks are all integrated. By calculating the values of σH/σV and σh/σV in the vicinity of fault planes, the coefficient K of compaction impacts on fault rocks between normal stress to fault planes and gravity was then determined. By revealing the quantitative relationship between the displacement pressure of rocks, burial depth and clay content, the displacement pressure difference between fault rocks and reservoirs were calculated. The results suggest that the sealing capacity of a strike-slip fault is not only related to the magnitude of normal stress to the fault plane, but also to the stress regime. The clay content is also an important factor controlling the sealing capacity of strike-slip faults.
Antibiotics and Non-Targeted Metabolite Residues Detection as a Comprehensive Approach toward Food Safety in Raw Milk
Authors: Chiesa Luca Maria , Di Cesare Federica , Nobile Maria , Villa Roberto , Decastelli Lucia , Martucci Francesca , Fontana Mauro , Pavlovic Radmila , Arioli Francesco , Panseri Sara
Published: March 2021

Antibiotic residues in milk are a serious health and technological problem in dairy processes. This study aims to verify the absence of administered antimicrobials after therapeutic treatments, taking into consideration the withdrawal period, and to evaluate the reliability of screening tests under field conditions after confirmatory HPLC-HRMS (High Performance Liquid Chromatography-High-Resolution Mass Spectrometry) Orbitrap analysis. Moreover, the presence of expected or non-targeted metabolites was investigated using the new Compound Discoverer approach. The presence of antimicrobial drugs was shown in 29% of the samples, and also sometimes their metabolites (for enrofloxacin and lincomycin), despite the fact that samples were collected at the seventh milking. Moreover, in 9% of the samples, undeclared treatments were revealed due to the presence of both parent drugs and metabolites. Lastly, the putative identification of two new enrofloxacin metabolites, ENRO-N-methylacetamide and ENRO-ornithine, was proposed. In the light of this evidence, it must be borne in mind that metabolites, some of which are pharmacologically active, may also pose a risk to consumers and for the entire processing of milk in the cheese industries.