bluuebunny/crossref_metadata_2025 huggingface dataset:
NLFEA of Sulfate-Damaged Circular CFT Steel Columns Confined with CFRP Composites and Subjected to Axial and Cyclic Lateral Loads
Authors: Al-Rousan Rajai , Nusier Osama , Abdalla Khairedin , Alhassan Mohammad , Lagaros Nikos D.
Published: March 2022

It is rather costly and difficult to experimentally evaluate the performance of concrete-filled tubular (CFT) circular steel columns exposed to combined axial and cyclic lateral loads. This research paper uses the nonlinear finite element (NLFEA) technique to assess the influence of using carbon-fiber-reinforced polymer (CFRP) laminates on the structural response and failure mode of damaged-by-sulfate CFT circular steel columns. At the beginning, twenty-one CFT circular steel column models were devised and checked for soundness using the findings of previously conducted research. Next, the models were broadened to investigate how the models’ behavior was influenced by the CFRP number of layers and the level of sulfate damage. For experimental purposes, the numbers of CFRP layers were set to be zero, five, six, seven, eight, nine, and ten, while there were three levels of sulfate damage, namely: level 0 (undamaged), level 1 (73 days), and level 2 (123 days). Some of the models were left unconfined with CFRP wraps for comparison. The CFRP confinement was at the end of the models due to its importance regarding the models’ capacity of lateral load. The columns’ ends were confined to prevent the models from outward local buckling, which led to higher strength, bigger net drift, and enhanced energy dissipation. The NLFEA models were then appropriately modified and adjusted in accordance with credible previously conducted experimental research; after which, a parametric study was performed to investigate how the models’ behavior was affected by the number of CFRP layers and the level of axial load. The study found that the CFT circular steel column models’ performance significantly enhanced when the models were wrapped with five to ten CFRP layers. It must be mentioned, though, that using eight, nine, and ten CFRP layers gave almost similar results. In addition, the NLFEA results revealed that when the damaged-by-sulfate models were repaired externally with CFRP wraps, there was an improvement in the models’ cyclic behavior, as they showed a raise in the load capacity, an enhancement in the horizontal displacement, a greater displacement ductility, better energy dissipation, and little deterioration in secant stiffness. The study found that using wraps of CFRP proved a great efficiency with the change in the sulfate damage level.
Monocarboxylate Transporters Are Involved in Extracellular Matrix Remodelling in Pancreatic Ductal Adenocarcinoma
Authors: Ufuk Ayşe , Garner Terence , Stevens Adam , Latif Ayşe
Published: March 2022

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a five-year survival rate of <8%. PDAC is characterised by desmoplasia with an abundant extracellular matrix (ECM) rendering current therapies ineffective. Monocarboxylate transporters (MCTs) are key regulators of cellular metabolism and are upregulated in different cancers; however, their role in PDAC desmoplasia is little understood. Here, we investigated MCT and ECM gene expression in primary PDAC patient biopsies using RNA-sequencing data obtained from Gene Expression Omnibus. We generated a hypernetwork model from these data to investigate whether a causal relationship exists between MCTs and ECMs. Our analysis of stromal and epithelial tissues (n = 189) revealed nine differentially expressed MCTs, including the upregulation of SLC16A2/6/10 and the non-coding SLC16A1-AS1, and 502 ECMs, including collagens, laminins, and ECM remodelling enzymes (false discovery rate < 0.05). A causal hypernetwork analysis demonstrated a bidirectional relationship between MCTs and ECMs; four MCT and 255 ECM-related transcripts correlated with 90% of the differentially expressed ECMs (n = 376) and MCTs (n = 7), respectively. The hypernetwork model was robust, established by iterated sampling, direct path analysis, validation by an independent dataset, and random forests. This transcriptomic analysis highlights the role of MCTs in PDAC desmoplasia via associations with ECMs, opening novel treatment pathways to improve patient survival.
Role of GD3 Synthase ST8Sia I in Cancers
Authors: Kasprowicz Angelina , Sophie Groux-Degroote , Lagadec Chann , Delannoy Philippe
Published: March 2022

GD3 synthase controls the biosynthesis of complex gangliosides, bearing two or more sialic acid residues. Disialylated gangliosides GD3 and GD2 are tumor-associated carbohydrate antigens (TACA) in neuro–ectoderm-derived cancers, and are directly involved in cell malignant properties, i.e., migration, invasion, stemness, and epithelial–mesenchymal transition. Since GD3 and GD2 levels are directly linked to GD3 synthase expression and activity, targeting GD3 synthase appears to be a promising strategy through which to interfere with ganglioside-associated malignant properties. We review here the current knowledge on GD3 synthase expression and regulation in cancers, and the consequences of complex ganglioside expression on cancer cell signaling and properties, highlighting the relationships between GD3 synthase expression and epithelial–mesenchymal transition and stemness. Different strategies were used to modulate GD3 synthase expression in cancer cells in vitro and in animal models, such as inhibitors or siRNA/lncRNA, which efficiently reduced cancer cell malignant properties and the proportion of GD2 positive cancer stem cells, which are associated with high metastatic properties, resistance to therapy, and cancer relapse. These data show the relevance of targeting GD3 synthase in association with conventional therapies, to decrease the number of cancer stem cells in tumors.
Comparison of Active Surveillance to Stereotactic Radiosurgery for the Management of Patients with an Incidental Frontobasal Meningioma—A Sub-Analysis of the IMPASSE Study
Authors: Islim Abdurrahman I. , Mantziaris Georgios , Pikis Stylianos , Chen Ching-Jen , Bunevicius Adomas , Peker Selçuk , Samanci Yavuz , Nabeel Ahmed M. , Reda Wael A. , Tawadros Sameh R. , El-Shehaby Amr M. N. , Abdelkarim Khaled , Emad Reem M. , Delabar Violaine , Mathieu David , Lee Cheng-Chia , Yang Huai-Che , Liscak Roman , May Jaromir , Alvarez Roberto Martinez , Moreno Nuria Martinez , Tripathi Manjul , Kondziolka Douglas , Speckter Herwin , Albert Camilo , Bowden Greg N. , Benveniste Ronald J. , Lunsford Lawrence Dade , Sheehan Jason P. , Jenkinson Michael D.
Published: March 2022

Meningioma is a common incidental finding, and clinical course varies based on anatomical location. The aim of this sub-analysis of the IMPASSE study was to compare the outcomes of patients with an incidental frontobasal meningioma who underwent active surveillance to those who underwent upfront stereotactic radiosurgery (SRS). Data were retrospectively collected from 14 centres. The active surveillance (n = 28) and SRS (n = 84) cohorts were compared unmatched and matched for age, sex, and duration of follow-up (n = 25 each). The study endpoints included tumor progression, new symptom development, and need for further intervention. Tumor progression occurred in 52.0% and 0% of the matched active surveillance and SRS cohorts, respectively (p < 0.001). Five patients (6.0%) treated with SRS developed treatment related symptoms compared to none in the active monitoring cohort (p = 0.329). No patients in the matched cohorts developed symptoms attributable to treatment. Three patients managed with active surveillance (10.7%, unmatched; 12.0%, matched) underwent an intervention for tumor growth with no persistent side effects after treatment. No patients subject to SRS underwent further treatment. Active monitoring and SRS confer a similarly low risk of symptom development. Upfront treatment with SRS improves imaging-defined tumor control. Active surveillance and SRS are acceptable treatment options for incidental frontobasal meningioma.
Comparing the Performance of Supported Ru Nanocatalysts Prepared by Chemical Reduction of RuCl3 and Thermal Decomposition of Ru3(CO)12 in the Sunlight-Powered Sabatier Reaction
Authors: Burova Daria , Rohlfs Jelle , Sastre Francesc , Molina Pau Martínez , Meulendijks Nicole , Verheijen Marcel A. , Kelchtermans An-Sofie , Elen Ken , Hardy An , Van Bael Marlies K. , Buskens Pascal
Published: March 2022

The preparation of Ru nanoparticles supported on γ-Al2O3 followed by chemical reduction using RuCl3 as a precursor is demonstrated, and their properties are compared to Ru nanoparticles supported on γ-Al2O3 prepared by impregnation of γ-Al2O3 with Ru3(CO)12 and subsequent thermal decomposition. The Ru nanoparticles resulting from chemical reduction of RuCl3 are slightly larger (1.2 vs. 0.8 nm). In addition, Ru nanoparticles were deposited on Stöber SiO2 using both deposition techniques. These particles were larger than the ones deposited on γ-Al2O3 (2.5 and 3.4 nm for chemical reduction and thermal decomposition, respectively). Taking into account the size differences between the Ru nanoparticles, all catalysts display similar activity (0.14–0.63 mol·gRu−1·h−1) and selectivity (≥99%) in the sunlight-powered Sabatier reaction. Ergo, the use of toxic and volatile Ru3(CO)12 can be avoided, since catalysts prepared by chemical reduction of RuCl3 display similar catalytic performance.
The Cell Membrane of a Novel Rhizobium phaseoli Strain Is the Crucial Target for Aluminium Toxicity and Tolerance
Authors: Wekesa Clabe , Muoma John , Reichelt Michael , Asudi George , Furch Alexandra , Oelmüller Ralf
Published: March 2022

Soils with low pH and high aluminium (Al) contamination restrict common bean production, mainly due to adverse effects on rhizobia. We isolated a novel rhizobium strain, B3, from Kenyan soil which is more tolerant to Al stress than the widely used commercial strain CIAT899. B3 was resistant to 50 µM Al and recovered from 100 µM Al stress, while CIAT899 did not. Calcein labeling showed that less Al binds to the B3 membranes and less ATP and mScarlet-1 protein, a cytoplasmic marker, leaked out of B3 than CIAT899 cells in Al-containing media. Expression profiles showed that the primary targets of Al are genes involved in membrane biogenesis, metal ions binding and transport, carbohydrate, and amino acid metabolism and transport. The identified differentially expressed genes suggested that the intracellular γ-aminobutyric acid (GABA), glutathione (GSH), and amino acid levels, as well as the amount of the extracellular exopolysaccharide (EPS), might change during Al stress. Altered EPS levels could also influence biofilm formation. Therefore, these parameters were investigated in more detail. The GABA levels, extracellular EPS production, and biofilm formation increased, while GSH and amino acid level decreased. In conclusion, our comparative analysis identified genes that respond to Al stress in R. phaseoli. It appears that a large portion of the identified genes code for proteins stabilizing the plasma membrane. These genes might be helpful for future studies investigating the molecular basis of Al tolerance and the characterization of candidate rhizobial isolates that perform better in Al-contaminated soils than commercial strains.
Tph2 Gene Expression Defines Ethanol Drinking Behavior in Mice
Authors: Zaniewska Magdalena , Mosienko Valentina , Bader Michael , Alenina Natalia
Published: March 2022

Indirect evidence supports a link between disrupted serotonin (5-hydroxytryptamine; 5-HT) signaling in the brain and addictive behaviors. However, the effects of hyposerotonergia on ethanol drinking behavior are contradictory. In this study, mice deficient in tryptophan hydroxylase 2 (Tph2−/−), the rate-limiting enzyme of 5-HT synthesis in the brain, were used to assess the role of central 5-HT in alcohol drinking behavior. Life-long 5-HT depletion in these mice led to an increased ethanol consumption in comparison to wild-type animals in a two-bottle choice test. Water consumption was increased in naïve 5-HT-depleted mice. However, exposure of Tph2−/− animals to ethanol resulted in the normalization of water intake to the level of wild-type mice. Tph2 deficiency in mice did not interfere with ethanol-evoked antidepressant response in the forced swim test. Gene expression analysis in wild-type animals revealed no change in Tph2 expression in the brain of mice consuming ethanol compared to control mice drinking water. However, within the alcohol-drinking group, inter-individual differences in chronic ethanol intake correlated with Tph2 transcript levels. Taken together, central 5-HT is an important modulator of drinking behavior in mice but is not required for the antidepressant effects of ethanol.
Zinc Finger E-Box Binding Homeobox 2 as a Prognostic Biomarker in Various Cancers and Its Correlation with Infiltrating Immune Cells in Ovarian Cancer
Authors: Kim Hye-Ran , Seo Choong Won , Han Sang Jun , Lee Jae-Ho , Kim Jongwan
Published: March 2022

This study investigated the expression of zinc finger E-box binding homeobox 2 (ZEB2), its prognostic significance in various cancers, and the correlation between ZEB2 and infiltrating immune cells and ZEB2-related proteins in ovarian cancer (OV). The Gene Expression Profiling Interactive Analysis tool was used to analyze RNA sequencing data and cancer survival rates, based on normal and tumor tissue data available in The Cancer Genome Atlas (TCGA) database. The Kaplan–Meier plotter and PrognoScan databases were used to analyze the prognostic value of ZEB2 in OV (n = 1144). The Tumor Immune Estimation Resource was used to investigate the correlation between ZEB2 and infiltrating immune cells in various cancers, including OV. High ZEB2 expression was associated with a poorer prognosis in OV. In OV, ZEB2 is positively correlated with CD8+T cells, neutrophils, macrophages, and dendritic cell invasion; and ZEB2 is negatively correlated with tumor-infiltrating B cells. The STRING database was used to investigate the correlations with ZEB2-related proteins. The results reveal that ZEB2 was positively correlated with SMAD1 and SMAD2 in OV. Our findings may serve as a potential prognostic biomarker, and provide novel insights into the tumor immunology in OV. Thus, ZEB2 may be a potential diagnostic and therapeutic target in OV.
Application of Machine Learning to Accelerate Gas Condensate Reservoir Simulation
Authors: Samnioti Anna , Anastasiadou Vassiliki , Gaganis Vassilis
Published: March 2022

According to the roadmap toward clean energy, natural gas has been pronounced as the perfect transition fuel. Unlike usual dry gas reservoirs, gas condensates yield liquid which remains trapped in reservoir pores due to high capillarity, leading to the loss of an economically valuable product. To compensate, the gas produced on the surface is stripped from its heavy components and reinjected back to the reservoir as dry gas thus causing revaporization of the trapped condensate. To optimize this gas recycling process compositional reservoir simulation is utilized, which, however, takes very long to complete due to the complexity of the governing differential equations implicated. The calculations determining the prevailing k-values at every grid block and at each time step account for a great part of total CPU time. In this work machine learning (ML) is employed to accelerate thermodynamic calculations by providing the prevailing k-values in a tiny fraction of the time required by conventional methods. Regression tools such as artificial neural networks (ANNs) are trained against k-values that have been obtained beforehand by running sample simulations on small domains. Subsequently, the trained regression tools are embedded in the simulators acting thus as proxy models. The prediction error achieved is shown to be negligible for the needs of a real-world gas condensate reservoir simulation. The CPU time gain is at least one order of magnitude, thus rendering the proposed approach as yet another successful step toward the implementation of ML in the clean energy field.
The Effects of Parental Intervention on Sleep Patterns and Electronic Media Exposure in Young Adolescents
Authors: Flint Bretler Ofra , Tzischinsky Orna , Asraf Kfir , Shochat Tamar
Published: March 2022

Objective: This study evaluated the effectiveness of a parent-focused intervention aimed at the promotion of healthy sleep patterns and controlled exposure to electronic media (EM) in young adolescents. Participants: The sample included 70 dyads of parents (68 mothers and 2 fathers) and adolescents. Intervention and control groups each consisted of 35 young adolescents with a mean age of 10.7 (0.9) years old. Methods: Three waves of data collection included baseline, post-intervention, and 3 month follow-up. In each wave, adolescents reported habitual electronic media exposure and sleep patterns for a week and wore an actigraph for five nights. Parents in the intervention group participated in a six-session interactive workshop, while parents in the control group received equivalent written information by mail. Results: The intervention led to earlier bedtimes (p < 0.001), increased sleep efficiency (p < 0.01), increased sleep duration (p < 0.001) and reduced video games exposure (p < 0.01). Benefits were maintained at the follow-up. Conclusion: Interventions tailored for parents can create lasting positive changes in sleep patterns and EM exposure in young adolescents.
Structure Formation of Diamond-Containing Coatings during Sintering of Specially-Shaped Grinding Wheels
Authors: Sokolov Evgeny , Ozolin Alexander , Golius Dmitriy
Published: March 2022

In this work, the structure formation of powder diamond-containing coatings with Sn-Cu-Co binders during sintering of specially-shaped grinding wheels has been studied. The kinetics of structure formation was studied using diamond-free metallic binders and diamond-containing samples. Powder components of the coatings were mixed with an organic plasticizer and applied on steel workpieces by rolling. Sintering was performed in vacuum at 700–820 °C. The microstructure, phase composition, and distribution of elements in metallic binders and interface layers between the binders and steel base were studied. The morphology of the surface and structure of fractures of the diamond-containing samples have been examined. Stages of structure formation of the coatings during heating and sintering have been found. At 700–780 °C, diffusion of tin into copper particles plays the key role in the structure formation of the coatings. Dissolution-reprecipitation of cobalt at 780–820 °C has a significant effect on formation of the coating structure and interface layers between the coating and steel base.
In Situ Analytical Methods for the Characterization of Mechanochemical Reactions
Authors: Weidenthaler Claudia
Published: March 2022

The interest in mechanochemical reactions and their fields of application have increased enormously in recent times. Mechanically activated reactions offer the advantage of cost-efficiency as well as environmentally friendly syntheses routes. In contrast to thermally induced processes, the energy transfer via the milling media takes place on a local scale. This leads to unique reaction pathways, which often also result in the formation of metastable phases. For the understanding of reaction pathways on a mechanistic level, it is very important to follow the processes taking place in the grinding jar during milling. Besides the measurement of pressure and temperature changes during a mechanochemical reaction, in situ high energy synchrotron X-ray powder diffraction and Raman spectroscopy experiments have been successfully implemented over the last 10 years. This review will highlight the developments which were achieved in the field of in situ monitoring of mechanochemical reactions and their input to the understanding of mechanochemistry.
Sequential Use of Carfilzomib and Pomalidomide in Relapsed Multiple Myeloma: A Report from the Canadian Myeloma Research Group (CMRG) Database
Authors: McCurdy Arleigh , Venner Christopher P. , Masih-Khan Esther , Louzada Martha , LeBlanc Richard , Sebag Michael , Song Kevin , Jimenez-Zepeda Victor H. , Kotb Rami , Kardjadj Moustafa , Mian Hira , White Darrell , Stakiw Julie , Aslam Muhammad , Reiman Anthony , Gul Engin , Reece Donna
Published: March 2022

The treatment of multiple myeloma has dramatically improved due to the availability of novel therapies that are highly effective and are quickly moving into first-line therapy. The Canadian Agency for Drugs and Technologies in Health (CADTH) recently recommended that patients who receive daratumumab should only be eligible to receive either carfilzomib or pomalidomide but not both, for relapsed MM. In order to assess the efficacy of these two agents in the relapsed setting, we utilized our national myeloma database. A total of 121 patients were reviewed, 49 patients received CAR- before POM-based (CAR-POM), and 73 patients received POM- before CAR-based (POM-CAR) therapy. In the groups selected, the median PFS was 4.93 months (95% CI, 2.76–7.07) and 5.36 months (95% CI, 3.75–6.94) for CAR-POM and POM-CAR, respectively. The median OS for patients treated with CAR-POM was 11.01 months (95% CI, 4.50–19.13), and for patients treated with POM-CAR the median OS was 10.98 months (95% CI, 8.98–19.17). In this real-world observational study, we demonstrated that both CAR- and POM-based therapies, irrespective of the order in which they were used, were effective treatment options for patients with advanced relapsed MM.
The OncoSim-Breast Cancer Microsimulation Model
Authors: Yong Jean H. E. , Nadeau Claude , Flanagan William M. , Coldman Andrew J. , Asakawa Keiko , Garner Rochelle , Fitzgerald Natalie , Yaffe Martin J. , Miller Anthony B.
Published: March 2022

Background: OncoSim-Breast is a Canadian breast cancer simulation model to evaluate breast cancer interventions. This paper aims to describe the OncoSim-Breast model and how well it reproduces observed breast cancer trends. Methods: The OncoSim-Breast model simulates the onset, growth, and spread of invasive and ductal carcinoma in situ tumours. It combines Canadian cancer incidence, mortality, screening program, and cost data to project population-level outcomes. Users can change the model input to answer specific questions. Here, we compared its projections with observed data. First, we compared the model’s projected breast cancer trends with the observed data in the Canadian Cancer Registry and from Vital Statistics. Next, we replicated a screening trial to compare the model’s projections with the trial’s observed screening effects. Results: OncoSim-Breast’s projected incidence, mortality, and stage distribution of breast cancer were close to the observed data in the Canadian Cancer Registry and from Vital Statistics. OncoSim-Breast also reproduced the breast cancer screening effects observed in the UK Age trial. Conclusions: OncoSim-Breast’s ability to reproduce the observed population-level breast cancer trends and the screening effects in a randomized trial increases the confidence of using its results to inform policy decisions related to early detection of breast cancer.
In Silico Assessment of Probe-Capturing Strategies and Effectiveness in the Spider Sub-Lineage Araneoidea (Order: Araneae)
Authors: Li Yi-Yen , Tsai Jer-Min , Wu Cheng-Yu , Chiu Yi-Fan , Li Han-Yun , Warrit Natapot , Wan Yu-Cen , Lin Yen-Po , Cheng Ren-Chung , Su Yong-Chao
Published: March 2022

Reduced-representation sequencing (RRS) has made it possible to identify hundreds to thousands of genetic markers for phylogenomic analysis for the testing of phylogenetic hypotheses in non-model taxa. The use of customized probes to capture genetic markers (i.e., ultraconserved element (UCE) approach) has further boosted the efficiency of collecting genetic markers. Three UCE probe sets pertaining to spiders (Araneae) have been published, including one for the suborder Mesothelae (an early diverged spider group), one for Araneae, and one for Arachnida. In the current study, we developed a probe set specifically for the superfamily Araneoidea in spiders. We then combined the three probe sets for Araneoidea, Araneae, and Arachnid into a fourth probe set. In testing the effectiveness of the 4 probe sets, we used the captured loci of the 15 spider genomes in silico (6 from Araneoidea). The combined probe set outperformed all other probe sets in terms of the number of captured loci. The Araneoidea probe set outperformed the Araneae and Arachnid probe sets in most of the included Araneoidea species. The reconstruction of phylogenomic trees using the loci captured from the four probe sets and the data matrices generated from 50% and 75% occupancies indicated that the node linked to the Stegodyphus + RTA (retrolateral tibial apophysis) clade has unstable nodal supports in the bootstrap values, gCFs, and sCFs. Our results strongly indicate that developing ad hoc probe sets for sub-lineages is important in the cases where the origins of a lineage are ancient (e.g., spiders ~380 MYA).
Design of Waste Management System Using Ensemble Neural Networks
Authors: Geetha Subbiah , Saha Jayit , Dasgupta Ishita , Bera Rahul , Lawal Isah A. , Kadry Seifedine
Published: March 2022

Waste management is an essential societal issue, and the classical and manual waste auditing methods are hazardous and time-consuming. In this paper, we introduce a novel method for waste detection and classification to address the challenges of waste management. The method uses a collection of deep neural networks to allow for accurate waste detection, classification, and waste size quantification. The trained neural network model is integrated into a mobile-based application for trash geotagging based on images captured by users on their smartphones. The tagged images are then connected to the cleaners’ database, and the nearest cleaners are notified of the waste. The experimental results using publicly available datasets show the effectiveness of the proposed method in terms of detection and classification accuracy. The proposed method achieved an accuracy of at least 90%, which surpasses that reported by other state-of-the-art methods on the same datasets.
Machine Learning Models for Predicting Mortality in 7472 Very Low Birth Weight Infants Using Data from a Nationwide Neonatal Network
Authors: Do Hyun Jeong , Moon Kyoung Min , Jin Hyun-Seung
Published: March 2022

Statistical and analytical methods using artificial intelligence approaches such as machine learning (ML) are increasingly being applied to the field of pediatrics, particularly to neonatology. This study compared the representative ML analysis and the logistic regression (LR), which is a traditional statistical analysis method, using them to predict mortality of very low birth weight infants (VLBWI). We included 7472 VLBWI data from a nationwide Korean neonatal network. Eleven predictor variables (neonatal factors: male sex, gestational age, 5 min Apgar scores, body temperature, and resuscitation at birth; maternal factors: diabetes mellitus, hypertension, chorioamnionitis, premature rupture of membranes, antenatal steroid, and cesarean delivery) were selected based on clinical impact and statistical analysis. We compared the predicted mortality between ML methods—such as artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—and LR with a randomly selected training set (80%) and a test set (20%). The model performances of area under the receiver operating curve (95% confidence interval) equaled LR 0.841 (0.811–0.872), ANN 0.845 (0.815–0.875), and RF 0.826 (0.795–0.858). The exception was SVM 0.631 (0.578–0.683). No statistically significant differences were observed between the performance of LR, ANN, and RF (i.e., p > 0.05). However, the SVM model was lower (p < 0.01). We suggest that VLBWI mortality prediction using ML methods would yield the same prediction rate as the traditional statistical LR method and may be suitable for predicting mortality. However, low prediction rates are observed in certain ML methods; hence, further research is needed on these limitations and selecting an appropriate method.
Human Hydration Indices: Spot Urine Sample Reference Values for Urine Concentration Markers in Athletic Populations
Authors: Wardenaar Floris C.
Published: March 2022

Background: Reference values and confidence intervals for the hydration indices of a large athletic population are currently lacking. Methods: Urine indices were gathered from an athletic population (n = 189) based on spot-urine samples. Results: High urine concentration was associated with a low volume and short void duration. When stratifying the data, differences for urine volume were seen for race and ethnicity and for athletic affiliation (p < 0.05), but no differences were found for urine concentration markers or volume for time of day of collection, thirst sensation, or age (p > 0.05). When classifying urine samples for a low vs. a high urine concentration by scoring urine color (Uc), the athletic population reported a slightly lower accuracy (4–7%) compared to investigators (p < 0.02). Subjects scored samples as lighter than the investigators, with a higher misclassification of the more concentrated urine samples. Conclusions: In this convenience sample of a predominantly young athletic population, urinary indices did not differ for subgroups within a large athletic population aside from some difference for race and ethnicity on urine volume. Although well-trained investigators reported better accuracy for Uc scoring, both athletes and investigators reported the highest accuracy for correctly classifying samples with a very low or a very high urine concentration.
Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series
Authors: Shi Zhigang , Bai Yuting , Jin Xuebo , Wang Xiaoyi , Su Tingli , Kong Jianlei
Published: March 2022

The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to predict. An improved deep prediction method is proposed herein based on the dual variational mode decomposition of a nonstationary time series. First, criteria were determined based on information entropy and frequency statistics to determine the quantity of components in the variational mode decomposition, including the number of subsequences and the conditions for dual decomposition. Second, a deep prediction model was built for the subsequences obtained after the dual decomposition. Third, a general framework was proposed to integrate the data decomposition and deep prediction models. The method was verified on practical time series data with some contrast methods. The results show that it performed better than single deep network and traditional decomposition methods. The proposed method can effectively extract the characteristics of a nonstationary time series and obtain reliable prediction results.
Quantifying and Interpreting Connection Strength in Macro- and Microscopic Systems: Lessons from Bell’s Approach
Authors: Gallus Christoph , Blasiak Pawel , Pothos Emmanuel M.
Published: March 2022

Bell inequalities were created with the goal of improving the understanding of foundational questions in quantum mechanics. To this end, they are typically applied to measurement results generated from entangled systems of particles. They can, however, also be used as a statistical tool for macroscopic systems, where they can describe the connection strength between two components of a system under a causal model. We show that, in principle, data from macroscopic observations analyzed with Bell’ s approach can invalidate certain causal models. To illustrate this use, we describe a macroscopic game setting, without a quantum mechanical measurement process, and analyze it using the framework of Bell experiments. In the macroscopic game, violations of the inequalities can be created by cheating with classically defined strategies. In the physical context, the meaning of violations is less clear and is still vigorously debated. We discuss two measures for optimal strategies to generate a given statistic that violates the inequalities. We show their mathematical equivalence and how they can be computed from CHSH-quantities alone, if non-signaling applies. As a macroscopic example from the financial world, we show how the unfair use of insider knowledge could be picked up using Bell statistics. Finally, in the discussion of realist interpretations of quantum mechanical Bell experiments, cheating strategies are often expressed through the ideas of free choice and locality. In this regard, violations of free choice and locality can be interpreted as two sides of the same coin, which underscores the view that the meaning these terms are given in Bell’s approach should not be confused with their everyday use. In general, we conclude that Bell’s approach also carries lessons for understanding macroscopic systems of which the connectedness conforms to different causal structures.
SG-ACCORD: Designing Virtual Agents for Soft Skills Training in the School Context
Authors: Dell’Aquila Elena , Vallone Federica , Zurlo Maria Clelia , Marocco Davide
Published: March 2022

This study focuses on the behavior of interactive agents and how to ground their implementation in psychology and cognitive modeling to capture fundamental psycho-pedagogical elements to deliver an effective competency-based training and assessment tool for teachers, trainers, and educators working in the school context. The study describes the design and implementation of a Serious Game, “Attain Cultural Integration through COnflict Resolution skill Development” (SG-ACCORD), to develop and assess soft skills that are pivotal in educational working contexts (i.e., negotiation, effective communication, intercultural conflict management). The SG-ACCORD has been developed by applying a tailored methodology, namely the Educational Technology-enhanced Role-Playing, addressing two main facets: psycho-pedagogical (theoretical frameworks) and technological (production of artificial worlds based on computer-simulated models). The game is a single-player virtual role-play offering a series of scenarios reflecting the real-life experiences of teachers in which the role of interactions and human factors is crucial. It focuses on the simulation of a dialogue between two characters (an avatar controlled by a human player and a BOT computer-controlled counterpart), enacting interethnic conflicts between teacher and students within realistic school context scenarios. The SG-ACCORD represents a free, easily manageable, and flexible educational tool that can be used as a stand-alone resource and envisages an automatic feedback and debriefing system.
Teacher Agency and Futures Thinking
Authors: Varpanen Jan , Laherto Antti , Hilppö Jaakko , Ukkonen-Mikkola Tuulikki
Published: March 2022

Problems encountered in top-down school reforms have repeatedly highlighted the significance of teachers’ agency in educational change. At the same time, temporality has been identified as a key element in teachers’ agency, with teachers’ beliefs about the future and experiences of the past shaping their agentic orientations. However, research on teachers’ future orientations is typically limited to short-term trajectories, as opposed to long-term visions of education. To address this, we draw on a futures studies perspective to give more explicit attention to teachers’ long-term visions of their work. We argue that the method of future narratives, already well-established in the field of futures studies, is a fruitful methodological framework for studying these long-term visions. In this paper, we first show that the futures studies approach is theoretically compatible with the ecological model of teacher agency. We then outline the method of future narratives to point out the possibilities it offers. Finally, we illustrate our approach with an exploratory analysis of a small set of future narratives where teachers imagine a future workday. Our analysis reveals that the narratives offer a rich view of teachers’ longer-term visions of education, including instances of reflecting on the role of education in relation to broader societal developments. Our study suggests that this novel approach can provide tools for research on teacher agency as well as practical development of teacher education, addressing long-term educational issues and policies.
Linking Character Strengths and Key Competencies in Education and the Arts: A Systematic Review
Authors: Vazquez-Marin Pedro , Cuadrado Francisco , Lopez-Cobo Isabel
Published: March 2022

Positive education, as a method for the positive development of students’ personality, embodies the 24 character strengths that Peterson and Seligman developed in their studies and that are necessary for new professional profiles. This new social and work landscape inspired supranational institutions, such as the European Union, to develop theories for new educational systems. These Key Competencies seek the comprehensive training of students, on not only the cognitive but the socioemotional plane, as occurs with arts education. With this literature review, we intend to demonstrate the relationship between the Key Competencies as catalysts for the development of character strengths in students through art education programmes. The results conclude that it is possible to define a relationship between the Key Competencies and character strengths and to outline the nature of these relationships, noting that certain patterns of combinations of strengths are repeated in the Key Competencies. Additionally, our work leads us to propose the need to increase the volume of research in this field and to design future studies that allow an empirical evaluation of the nature of these connections and whether they are efficient and enduring.
Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences
Authors: Meira Jorge , Carneiro João , Bolón-Canedo Verónica , Alonso-Betanzos Amparo , Novais Paulo , Marreiros Goreti
Published: March 2022

Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, and limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we sought to facilitate the information acquisition process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning methods to predict users’ ratings. We used Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. We then applied supervised methods such as Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Recurrent Neural Networks to determine whether a tourist likes/dislikes a given point of interest. We also used a distinctive approach in this field through unsupervised techniques for anomaly detection problems. The goal was to improve the supervised model in identifying only those tourists who truly like or dislike a particular point of interest, in which the main objective is not to identify everyone, but fundamentally not to fail those who are identified in those conditions. The experiments carried out showed that the developed models could predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users. Unsupervised method Local Outlier Factor improved the results, reducing Logistic Regression false positives with an associated cost of increasing false negatives.
Deep Learning for Activity Recognition Using Audio and Video
Authors: Reinolds Francisco , Neto Cristiana , Machado José
Published: March 2022

Neural networks have established themselves as powerhouses in what concerns several types of detection, ranging from human activities to their emotions. Several types of analysis exist, and the most popular and successful is video. However, there are other kinds of analysis, which, despite not being used as often, are still promising. In this article, a comparison between audio and video analysis is drawn in an attempt to classify violence detection in real-time streams. This study, which followed the CRISP-DM methodology, made use of several models available through PyTorch in order to test a diverse set of models and achieve robust results. The results obtained proved why video analysis has such prevalence, with the video classification handily outperforming its audio classification counterpart. Whilst the audio models attained on average 76% accuracy, video models secured average scores of 89%, showing a significant difference in performance. This study concluded that the applied methods are quite promising in detecting violence, using both audio and video.
A Conceptual Framework for Determining Quality Requirements for Mobile Learning Applications Using Delphi Method
Authors: Almaiah Mohammed , Hajjej Fahima , Lutfi Abdalwali , Al-Khasawneh Ahmad , Alkhdour Tayseer , Almomani Omar , Shehab Rami
Published: March 2022

The development of mobile learning apps might fail due to poor selection of the suitable technical requirements for mobile devices. This will affect the quality of mobile learning applications and, thus, will increase the development cost of mobile learning apps. Due to the above issues, we need to determine the most appropriate technical quality requirements for the development of mobile learning apps that meet user requirements. To achieve that, we propose a comprehensive framework to capture the most suitable technical quality requirements for mobile learning apps. A Delphi method was used to collect, evaluate, and analyze the data for this study. As a result of our Delphi study, we have identified nineteen technical quality requirements, divided into six quality dimensions, for the development of mobile learning applications. The proposed framework is expected to be a guideline for mobile apps designers and developers to successfully develop mobile learning apps.
Reduction of Online Fraudulent Activities in Freelancing Sites Using Blockchain and Biometric
Authors: Batool Amreen , Byun Yungcheol
Published: March 2022

Digital freelancing refers to hiring potential employees to work on specific projects on a contract basis by organizations, companies, and individuals. Existing freelancing systems are based on client-server models and managed in a centralized way. Centralized freelancing systems are originating several issues: lack of trust, payment delays, central control over all operations, less reliability, online frauds, and lack of fairness. Hence, we propose a decentralized freelancing system based on blockchain and biometrics. Blockchain in the freelancing system makes contracts decentralized, and no one can control the interaction and deals between buyers and sellers. Biometric signatures on smart contracts in the freelancing system enhance the authenticity and validity of agreements between buyers and sellers. Our decentralized freelancing system is an online web-based platform based on blockchain and biometrics and built on Python. It makes the interaction and deals between buyers and sellers via the innovative approach of smart contracts. The proposed work illustrates the development, implementation, and functionalities of the freelancing system, such as registration as buyer or seller, logging in to the system, posting a job, bidding on jobs, hiring process, finalizing deals with smart contracts having a biometric signature of both parties and distributing smart contracts among all stakeholders via blockchain. Our system proves the fairness, clarity, security, and reliability in all processes of posting and hiring without the involvement of any central management authority.
Microgrid Emergence, Integration, and Influence on the Future Energy Generation Equilibrium—A Review
Authors: Chartier Sabrina Lee , Venkiteswaran Vinod Kumar , Rangarajan Shriram S. , Collins Edward Randolph , Senjyu Tomonobu
Published: March 2022

Microgrids are emerging throughout the world as a means of integrating decentralized, renewable energy power generation. The flexibility of this customer-driven, behind the meter solution allows it to address unique challenges. This variability that drives microgrid adoption is the same thing that keeps them from being categorized and repeatable. This lack of specific modeling leads to a stalling in financing and wide-scale adoption. By analyzing the microgrid system development, evolution, architecture, integration zones, technological advances, and business models, a clearer picture of how these entities are intertwined emerges. Several case studies of deployed microgrids will showcase the cutting-edge solutions they apply. The future implications of this new energy revolution will be highlighted and shown to create an energy generation equilibrium and the significant role played by microgrids in this new energy revolution. Although many compilations of research work on microgrids have been previously presented by various reviewers, most of them are specific to an electrical or power quality-related issue, which addresses a discrete audience. This work only includes within its scope a general outlook of microgrids and the present-day challenges in its use of rural/urban renewable energy production and distribution. The results allowed for the researchers to conclude that microgrids have emerged as a great solution in situations where energy has to be transmitted from a decentralized system to a centralized system. Challenges will arise in the microgrid management and government laws and regulations if rectified microgrids can lead to an equilibrium between decentralized and centralized bulk energy networks.
Sensorless Control of Seven-Phase PMSM Drives Using NSV-SVPWM with Minimum Current Distortion
Authors: Saleh Kamel , Sumner Mark
Published: March 2022

This paper introduces three different algorithms to track the saliency in the seven-phase PMSM to obtain sensorless speed control. These algorithms are based on measuring the amount of change of the stator currents when different active vectors are implemented using the Near Six Vectors-Space Vector Pulse Width Modulation (NSV-SVPWM). Then, a comparison between these algorithms in terms of the Total Harmonics Distortion (THD) associated with implementing each algorithm is presented. The increase in the current distortion in these algorithms is related to the fact that narrow active vectors in these algorithms should be extended to tmin (8 us) for proper measurement in the current dynamics due to the oscillation in the currents. However, since the time duration of these active vectors in NSV-SVPWM will be 2.52 times longer than others, it is found that using these long active vectors will result in minimum current distortion. Simulation results are provided to investigate the effect of using the dynamic response of different active vectors on the THD of the current. Moreover, the results demonstrate the effectiveness of the proposed control technique to maintain the performance of the seven-phase PMSM motor drive post the failure in the speed sensor.