Miracle-dz/UniFAR-FTUs huggingface dataset:
Graduates Competence Assessment on Employability Skills: Partial Least Square-Structural Equation Modeling

This article examines the necessary skills that students need as they graduate and enter the labor market, such as effective communication, relationship building and teamwork, leadership and management, and creative and critical thinking, among others. Some debate exists over the level at which college students develop their employability skills and the degree to which higher education institutions are effective at preparing their students to meet labor market requirements. Therefore, to offer additional insights into the debate, this study investigates the perceptions of College of Business Administration (CBA) students in the United States of their employability skills and how they developed those skills. Using a Likert-scale survey, 303 student-participants self-reported their skill levels in different employability skill competency areas. Confirmatory factor analysis was one of the structural equation modeling techniques used to explore and measure the skills that undergraduate students needed for employment. In the United States, higher education has become more expensive and students are graduating with considerable debt. Therefore, higher education institutions must be willing to address the issue of employability after graduation and find new and improved ways to develop their students’ ES, such that abilities to conceptualize that had the strongest influence on employment.
An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index

In this paper, efficient and accurate linear and nonlinear models are proposed for indicating comprehensive health requirements of the transformer using health index (HI) concept. The models are established with 336 experimental datasets including oil characteristics and dissolved gas analysis (DGA) of various types of transformers placed in different areas. The significance of DGA parameters in transformer health condition is considered with the inclusive DGA factor (DGAF) parameter, which considers the weighting importance of seven dissolved gases. Nonlinear models used in this paper are artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), which represent the behavior of transformer insulation parameters. The nonlinear models are compared with multiple linear regression (MLR) which is a linear statistical model. The models are established with 80 percent of the experimental dataset. The other 20 percent of data are utilized for the efficiency assessment of the models. The results demonstrate that the models provide an assessment of the health condition of the transformers comparable to existing models with high accuracy. The contributions of this paper are: 1) Evaluating the overall HI of the transformer employing a complete set of 15 input parameters of transformer oil-paper insulation system. 2) Adding DGAF, %WaterPaper, IFT parameters and showing the importance of these parameters. 3) Regarding the condition of solid insulation of the transformer particularly. 4) Applying a diverse and large practical dataset composed of 336 different transformers located in different country areas. 5) Using the MLR method for three purposes. 6) Providing linear (MLR) and nonlinear (ANN, ANFIS) models for HI calculation of the dataset, simultaneously. 7) Verifying the applicability and efficiency of the ANFIS model for simulating HI value.
Cornus mas and Cornus officinalis—A Comparison of Antioxidant and Immunomodulatory Activities of Standardized Fruit Extracts in Human Neutrophils and Caco-2 Models

Fruits of Cornus mas and Cornus officinalis are representative plant materials traditionally used in Europe and Asia, respectively, in the treatment of diabetes and diabetes-related complications, which are often mediated by pathogenic inflammatory agents. Additionally, due to the fact of mutual infiltration of Asian and European medicines, the differentiation as well as standardization of traditional prescriptions seem to be crucial for ensuring the quality of traditional products. The objective of this study was a comparison of biological activity of extracts from fruits of C. mas and C. officinalis by an assessment of their effect on reactive oxygen species (ROS) generation in human neutrophils as well as cytokines secretion both in neutrophils (tumor necrosis factor α, TNF- α; interleukin 8, IL-8; interleukin 1β, IL-1β) and in human colon adenocarcinoma cell line Caco-2 (IL-8). To evaluate the phytochemical differences between the studied extracts as well as to provide a method for standardization procedures, a quantitative analysis of iridoids, such as loganin, sweroside, and loganic acid, found in extracts of Cornus fruits was performed with HPLC-DAD. All standardized extracts significantly inhibited ROS production, whereas the aqueous-alcoholic extracts were particularly active inhibitors of IL-8 secretion by neutrophils. The aqueous-methanolic extract of C. officinalis fruit, decreased IL-8 secretion by neutrophils to 54.64 ± 7.67%, 49.68 ± 6.55%, 50.29 ± 5.87% at concentrations of 5, 50, and 100 µg/mL, respectively, compared to LPS-stimulated control (100%). The aqueous extract of C. officinalis fruit significantly inhibited TNF-α release by neutrophils at concentrations of 50 and 100 µg/mL. On the other hand, the aqueous-ethanolic extract of C. mas fruit showed the propensity to increase TNF-α and IL-1β secretion. The modulatory activity of the Cornus extracts was noted in the case of secretion of IL-8 in Caco-2 cells. The effect was comparable with dexamethasone. The content of loganin in aqueous and aqueous-methanolic extract of C. officinalis fruit was higher than in the aqueous-ethanolic extract of C. mas fruit, which was characterized by a significant quantity of loganic acid. In conclusion, the immunomodulatory effect observed in vitro may partially confirm the traditional use of Cornus fruits through alleviation of the development of diabetes-derived inflammatory complications. Loganin and loganic acid are significant markers for standardization of C. mas and C. officinalis fruit extracts, respectively.
Fabrication and Biological Assessment of Antidiabetic α-Mangostin Loaded Nanosponges: In Vitro, In Vivo, and In Silico Studies

Type 2 diabetes mellitus has been a major health issue with increasing morbidity and mortality due to macrovascular and microvascular complications. The urgent need for improved methods to control hyperglycemic complications reiterates the development of innovative preventive and therapeutic treatment strategies. In this perspective, xanthone compounds in the pericarp of the mangosteen fruit, especially α-mangostin (MGN), have been recognized to restore damaged pancreatic β-cells for optimal insulin release. Therefore, taking advantage of the robust use of nanotechnology for targeted drug delivery, we herein report the preparation of MGN loaded nanosponges for anti-diabetic therapeutic applications. The nanosponges were prepared by quasi-emulsion solvent evaporation method. Physico-chemical characterization of formulated nanosponges with satisfactory outcomes was performed with Fourier transform infra-red (FTIR) spectroscopy, differential scanning calorimetry (DSC), and scanning electron microscopy (SEM). Zeta potential, hydrodynamic diameter, entrapment efficiency, drug release properties, and stability studies at stress conditions were also tested. Molecular docking analysis revealed significant interactions of α-glucosidase and MGN in a protein-ligand complex. The maximum inhibition by nanosponges against α-glucosidase was observed to be 0.9352 ± 0.0856 µM, 3.11-fold higher than acarbose. In vivo studies were conducted on diabetic rats and plasma glucose levels were estimated by HPLC. Collectively, our findings suggest that MGN-loaded nanosponges may be beneficial in the treatment of diabetes since they prolong the antidiabetic response in plasma and improve patient compliance by slowly releasing MGN and requiring less frequent doses, respectively.
Preliminary investigation of performance of variable-throat extended-plug-type nozzles over wide range of nozzle pressure ratios

As part of an overall program for the experimental investigation of large-scale jet nozzles, a preliminary evaluation of the internal performance characteristics of several variable-throat extended-plug-type nozzles was obtained over a range of nozzle pressure rations from 2 to 15 with nozzle throat variations as great as 2:1. The extended-plug nozzle attained peak thrust coefficients as high as those which have been attained with fixed-geometry convergent-divergent nozzles. The thrust coefficients of the extended-plug nozzles were relatively insensitive to both nozzle pressure ratio and throat area over the range investigated. The lack of sensitivity of thrust coefficient to nozzle pressure ratio and throat area was due primarily to the absence of severe decreases in thrust coefficient, which are normally observed at low nozzle pressure ratios for convergent-divergent nozzles because of overexpansion. For choked flow, the flow coefficients of the extended-plug nozzles varied from 0.95 to 0.997. A ratio of the outer-shell exit area to nozzle throat area was found to be an important variable affecting nozzle performance. Most of the configurations reported herein showed a small decrease in thrust coefficient at low nozzle pressure ratios; however, it is believed that this characteristic can nearly be eliminated by proper nozzle design.
Wind turbine vibration study: A data driven methodology

Recommended Citation Zhang, Zijun. "Wind turbine vibration study: a data driven methodology. ii To My Parents and Family iii Hope is a waking dream. Aristotle iv ACKNOWLEDGMENTS I would like to express my sincere gratitude to my advisor Professor Andrew Kusiak, for his devotion to this research. He has been the most instrumental person for my academic and research achievements. He provided the motivation, encouragement, guidance and advice which have prepared me for the challenges of future life. I was fortunately exposed to industrial applications while working in the Intelligent Systems Laboratory. This invaluable experience has allowed me to maintain a balance between theory and practice leading to realistic solutions. MidAmerican Energy Company. The energy experts from company and Iowa Energy Center have extended invaluable information for this research. I thank all the members of the Intelligent Systems Laboratory who have worked with me and provided advice, reviews and suggestions. Special thanks to my colleagues: Dr. Zhe Song, who worked with me to solve challenging problems in wind energy domain; Wenyan Li, who shared her research experience with me; Mingyang Li, who enhanced my research capability through frequent communication and collaboration; and Robert A. Hamel, who discussed with me in wind energy topics and provided access to industrial data. And finally, and most importantly, I would like to express my sincere gratitude to my parents, who solidly supported me in my academic pursuit. v ABSTRACT Vibrations of a wind turbine have a negative impact on its performance and therefore approaches to effectively control turbines are sought by wind industry. The body of previous research on wind turbine vibrations has focused on physics-based models. Such models come with limitations as some ideal assumptions do not reflect reality. In this Thesis a data-driven approach to analyze the wind turbine vibrations is introduced. Improvements in the data collection of information system allow collection of large volumes of industrial process data. Although the sufficient information is contained in collected data, they cannot be fully utilized to solve the challenging industrial modeling issues. Data-mining is a novel science offers platform to identify models or recognize patterns from large data set. Various successful applications of data mining proved its capability in extracting models accurately describing the processes of interest. The vibrations of a wind turbine originate at various sources. This Thesis focuses on mitigating vibrations with wind turbine control. Data mining algorithms are utilized to construct …
MicroRNA 132-3p Is Upregulated in Laron Syndrome Patients and Controls Longevity Gene Expression

The growth hormone (GH)–insulin-like growth factor-1 (IGF1) endocrine axis is a central player in normal growth and metabolism as well as in a number of pathologies, including cancer. The GH–IGF1 hormonal system, in addition, has emerged as a major determinant of lifespan and healthspan. Laron syndrome (LS), the best characterized entity under the spectrum of the congenital IGF1 deficiencies, results from mutation of the GH receptor (GHR) gene, leading to dwarfism, obesity and other defects. Consistent with the key role of IGF1 in cellular proliferation, epidemiological studies have shown that LS patients are protected from cancer development. While reduced expression of components of the GH-IGF1 axis is associated with enhanced longevity in animal models, it is still unknown whether LS is associated with an increased lifespan. MicroRNAs (miRs) are endogenous short non-coding RNAs that regulate the expression of complementary mRNAs. While a number of miRs involved in the regulation of IGF components have been identified, no previous studies have investigated the differential expression of miRs in congenital IGF1 deficiencies. The present study was aimed at identifying miRs that are differentially expressed in LS and that might account for the phenotypic features of LS patients, including longevity. Our genomic analyses provide evidence that miR-132-3p was highly expressed in LS. In addition, we identified SIRT1, a member of the sirtuin family of histone deacetylases, as a target for negative regulation by miR-132-3p. The data was consistent with the notion that low concentrations of IGF1 in LS lead to elevated miR-132-3p levels, with ensuing reduction in SIRT1 gene expression. The impact of the IGF1-miR-132-3p-SIRT1 loop on aging merits further investigation.
ON THE SPEED-FLOW RELATIONSHIPS IN ROAD TRAFFIC: A MODEL OF DRIVER BEHAVIOUR

Focusing on the driver, his purposes, and the perceptual and cognitive determinants of his behaviour, a model explaining the typically observed speed-flow relations is developed. First, a simple Basic model of queue-driving is presented, in which the desired distance-gap is determined by a minimal buffer-distance and a minimal reaction time incremented with a constant factor on speed. Second, the model is extended considerably. Basically, the real values of speed, distance, necessary time-gap and costs are replaced by the perceived and subjective values, and a rather large number of factors determining the driver's perception and subjective estimates are introduced. Of general importance is the dynamically varying sources- and adaptation of perceived speed. At low traffic densities (free-flow) the driver adjusts his speed to optimize the subjective utility of driving, and major factors are his appreciation of time vs. costs of driving, risks of penalties, breakdown, and accidents. In dense and congested traffic, speed is limited by the vehicles ahead, focusing the factors influencing perceived speed, distances, and desired time-gaps. Third, the model of individual drivers is tentatively extended to reflect some effects of driver-interaction (i.e.: Start-delays, stop-and go driving, platooning, multilane driving, and anticipation of bottlenecks). Predicted maximal flow-rates at given speeds envelopes some published data rather well. While a linear speed-time gap relation is implied in the Basic model of queue-driving, the net result of the Extended model can approximate a linear speed-distance gap relation.
Application of Bayesian Optimization and Regression Analysis to Ferromagnetic Materials Development

Bayesian optimization (BO) is a well-developed machine learning (ML) field for black-box function optimization. In BO, a surrogate predictive model, here a Gaussian process, is used to approximate the black-box function. The estimated mean and uncertainty of the surrogate model are paired with an acquisition function to decide where to sample next. In this study, we applied this technique to known ferromagnetic thin-film materials such as ferromagnetic (Fe<sub>100−<italic>y</italic></sub>Ga<bold><italic>y</italic></bold>)<sub>1−<italic>x</italic></sub>B<sub><italic>x</italic></sub> (<italic>x</italic> = 0−21 and <italic>y</italic> = 9−17) to demonstrate optimization of structure–property relationships, specifically the dopant concentration or stoichiometry effect on magnetostriction and ferromagnetic resonance linewidth. Our results demonstrated that BO can be deployed to optimize structure–property relationships in FeGaB and FeGaC thin films. We have shown through simulation that using BO methods to guide experiments reduced the number of samples required to statistically determine the maximum or minimum by 50% compared to traditional methods. Our results suggest that BO can be used to save time and resources to optimize ferromagnetic films. This method is transferrable to other ferromagnetic material structure–property relationships, providing an accessible implementation of ML to magnetic materials development.
Sildenafil Citrate Downregulates PDE5A mRNA Expression in Women with Recurrent Pregnancy Loss without Altering Angiogenic Factors—A Preliminary Study

In our previous study, we showed that sildenafil citrate (SC), a selective PDE5A blocker, modulated NK cell activity in patients with recurrent pregnancy loss, which correlated with positive pregnancy outcomes. It was found that NK cells had a pivotal role in decidualization, angiogenesis, spiral artery remodeling, and the regulation of trophoblast invasion. Thus, in the current study, we determined the effects of SC on angiogenic factor expression and production, as well as idNK cell activity in the presence of nitric synthase blocker L-NMMA. Methods: NK cells (CD56+) were isolated from the peripheral blood of 15 patients and 15 fertile women on MACS columns and cultured in transformation media containing IL-15, TGF-β, and AZA—a methylation agent—for 7 days in hypoxia (94% N2, 1% O2, 5% CO2). Cultures were set up in four variants: (1) with SC, (2) without SC, (3) with NO, a synthase blocker, and (4) with SC and NO synthase blocker. NK cell activity was determined after 7 days of culturing as CD107a expression after an additional 4h of stimulation with K562 erythroleukemia cells. The expression of the PDE5A, VEGF-A, PIGF, IL-8, and RENBP genes was determined with quantitative real-time PCR (qRT-PCR) using TaqMan probes and ELISA was used to measure the concentrations of VEGF-A, PLGF, IL-8, Ang-I, Ang-II, IFN–γ proteins in culture supernatants after SC supplementation. Results: SC downregulated PDE5A expression and had no effect on other studied angiogenic factors. VEGF-A expression was increased in RPL patients compared with fertile women. Similarly, VEGF production was enhanced in RPL patients’ supernatants and SC increased the concentration of PIGF in culture supernatants. SC did not affect the expression or concentration of other studied factors, nor idNK cell activity, regardless of NO synthase blockade.
Comparison between Different Channel Coding Techniques for IEEE 802.11be within Factory Automation Scenarios

This paper presents improvements in the physical layer reliability of the IEEE 802.11be standard. Most wireless system proposals do not fulfill the stringent requirements of Factory Automation use cases. The harsh propagation features of industrial environments usually require time retransmission techniques to guarantee link reliability. At the same time, retransmissions compromise latency. IEEE 802.11be, the upcoming WLAN standard, is being considered for Factory Automation (FA) communications. 802.11be addresses specifically latency and reliability difficulties, typical in the previous 802.11 standards. This paper evaluates different channel coding techniques potentially applicable in IEEE 802.11be. The methods suggested here are the following: WLAN LDPC, WLAN Convolutional Codes (CC), New Radio (NR) Polar, and Long Term Evolution (LTE)-based Turbo Codes. The tests consider an IEEE 802.11be prototype under the Additive White Gaussian Noise (AWGN) channel and industrial channel models. The results suggest that the best performing codes in factory automation cases are the WLAN LDPCs and New Radio Polar Codes.
Electrical Characterization of Germanium Nanowires Using a Symmetric Hall Bar Configuration: Size and Shape Dependence

The fabrication of individual nanowire-based devices and their comprehensive electrical characterization remains a major challenge. Here, we present a symmetric Hall bar configuration for highly p-type germanium nanowires (GeNWs), fabricated by a top-down approach using electron beam lithography and inductively coupled plasma reactive ion etching. The configuration allows two equivalent measurement sets to check the homogeneity of GeNWs in terms of resistivity and the Hall coefficient. The highest Hall mobility and carrier concentration of GeNWs at 5 K were in the order of 100 cm2/(Vs) and 4×1019cm−3, respectively. With a decreasing nanowire width, the resistivity increases and the carrier concentration decreases, which is attributed to carrier scattering in the region near the surface. By comparing the measured data with simulations, one can conclude the existence of a depletion region, which decreases the effective cross-section of GeNWs. Moreover, the resistivity of thin GeNWs is strongly influenced by the cross-sectional shape.
Study and implementation of new DBA for WDM-PON

Proposta i avaluacio d'algorismes de gestio dinamica de recursos (com repartir les frequencies i els slots temporals ) en una nova xarxa d'acces passiva Optica de fibra fins a la llar multiportadora (WDM). Els algorismes i la seva avaluacio es realitzaran sobre el simulador OPNET.
Spectrum sensing in cognitive radio: A deep learning based model

Spectrum sensing is an efficient technology for addressing the shortage of spectrum resources. Widely used methods usually employ model‐based features as the test statistics, such as energies and eigenvalues, ignoring the temporal correlation aspect. Deep learning based methods have the potential to focus on various aspects, including temporal correlation. However, the existing ones are not good at capturing the temporal correlation features from spectrum data as traditional convolutional neural network (CNN) and long short‐term memory network (LSTM) are used for feature extraction. Traditional CNNs were not designed to capture the global temporal correlations from time series data. Standard LSTM captures the temporal correlations based on the data collected from previous time slots only and cannot emphasize some important parts of a time series. This article proposes a data‐driven deep learning based model to classify the received raw signals automatically, where the received signal data is considered time‐series data. The proposed deep neural network (DNN) model is mainly featured with 1‐dimensional convolutional neural network (1D CNN), bidirectional long short‐term memory network (BiLSTM), and self‐attention (SA). The 1D CNN and BiLSTM are responsible for extracting the local features and global correlations from the time series data, and BiLSTM could extract sufficient features in opposite directions. The SA layer enables the classifier network to emphasize those important features obtained by BiLSTM. The simulation results demonstrate that our model performs better than a number of existing DNN models in terms of the probabilities of missed detection and false alarm, especially when the signal to noise ratio is low. Moreover, the impacts of the modulation scheme and sample length on the detection performance are studied.