Miracle-dz/UniFAR-FTUs huggingface dataset:
Unit Commitment Framework to Assess Flexibility Resource Capability for High RE Penetration

Increasing the share of intermittent renewable energy (RE) will challenge the capability of existing power systems to cope with increased variability in net load. This may result in frequent RE and load curtailment in the absence of adequate flexibility. To maximize RE utilization and maintain supply reliability, system flexibility must be enhanced. However, the capability of various flexibility resources to facilitate RE penetration needs to be assessed for optimal system planning. Further, to capture the impact of RE's seasonal variability, a longterm assessment of chronological short-term system operations is required. In this context, this paper proposes a computational framework of the yearly unit commitment model. An attempt has been made to maximize RE penetration at different integration levels by increasing operational flexibility and energy storage integration. Results from various cases examined on the IEEE RTS 24-bus system show that although enhanced operational flexibility is a viable solution for smooth RE integration, a high RE share would require alternative capital-intensive solutions.
Flexible FDTD Simulation for the Wireless Earphone Exposure Evaluation

The electromagnetic field penetration into a human head is computed when a wireless earphone is dressed. The computation done by a home made FDTD code able to account for tissues permittivity and for a real feeding of the earphone antenna. We adopted a human head model developed on the Zubal Phantom downloaded from SourceForge. Permittivity values are assigned to each tissue accounting for the Cole-Cole frequency dependence.
Wideband High-Efficiency Circularly Polarized Phased Array Antenna with Low Profile for Ka-Band Satellite Communication

This paper presents a wideband high-efficiency circularly polarized (CP) phased array antenna with low profile for Ka-band satellite. The antenna introduces a common ground structure for both microstrip line and circular patch to reduce the profile and make the antenna easily integration. In addition, complementary metal grids and defective ground effectively reduce the coupling between units to improve the gain of the antenna. At the same time, the sequential rotation technique is applied in the antenna array to expand the axial ratio bandwidth. The profile of the antenna is 0. 076λ0 (λ0 is the wavelength in free space). The impedance bandwidth for S11<-10 dB is 15.8%, and the AR is below to 3dB in the whole operating band. The radiation efficiency can reach more 90% over the whole band.
Evolution of embryo implantation was enabled by the origin of decidual stromal cells in eutherian mammals.

Mammalian pregnancy evolved in the therian stem lineage, that is, before the common ancestor of marsupials and eutherian (placental) mammals. Ancestral therian pregnancy likely involved a brief phase of attachment between the fetal and maternal tissues followed by parturition-similar to the situation in most marsupials including the opossum. In all eutherians, however, embryo attachment is followed by implantation, allowing for a stable fetal-maternal interface and an extended gestation. Embryo attachment induces an attachment reaction in the uterus that is homologous to an inflammatory response. Here, we elucidate the evolutionary mechanism by which the ancestral inflammatory response was transformed into embryo implantation in the eutherian lineage. We performed a comparative uterine transcriptomic and immunohistochemical study of three eutherians, armadillo (Dasypus novemcinctus), hyrax (Procavia capensis), and rabbit (Oryctolagus cuniculus); and one marsupial, opossum (Monodelphis domestica). Our results suggest that in the eutherian lineage, the ancestral inflammatory response was domesticated by suppressing one of its modules detrimental to pregnancy, viz. neutrophil recruitment by cytokine IL17A. Further, we propose that this suppression was mediated by decidual stromal cells, a novel cell type in eutherian mammals. We tested a prediction of this model in vitro, and showed that decidual stromal cells can suppress the production of IL17A from helper T cells. Together, these results provide a mechanistic understanding of early stages in the evolution of eutherian pregnancy.
Position Sensorless BLDC Motor Drive for Single Stage PV Based Water Pumping

This paper deals with a water pumping system, characterized by photovoltaic (PV)-powered brushless DC (BLDC) motor. An intermediate DC-DC power converter, commonly used for the peak power tracking of PV array, is eliminated in this system. Thus, the proposed system is based on a single stage PV energy conversion. The water pump is driven by a position sensorless-BLDC motor drive. Together with eliminating the position sensors, the said system obviates the motor current sensing. The motor speed is adjusted as per the available maximum power. A three-phase inverter is used to control the motor. The maximum PV power generation is also obtained through the same inverter. The performance of single stage PV array-powered water pump with proposed position sensorless-drive is tested through the experimental results.
UAV Assisted Vehicular Communication with VLCusing NOMA

Visible light communication (VLC) technology has recently found its place in vehicular networks due to the increasing availability of LED lights in automobiles, license free spectrum, high capacity and channel security. In this work, we propose a UAV assisted vehicular network where the UAV provides downlink services to a group cars in the network using NOMA-OFDM-VLC. We demonstrate the feasibility of the proposed system through experiments and computer simulations. We further present optimization of the location of UAV to maximize the SNRs at the receivers and demonstrate improved bit-error-rate (BER) and throughput.
Pathological complete response to neoadjuvant trastuzumab and pertuzumab therapy is related to human epidermal growth factor receptor 2 (HER2) amplification level in HER2-amplified breast cancer

Abstract The human epidermal growth factor receptor 2 (HER2) is amplified in approximately 20% of breast cancers, and HER2 receptor targeting therapy is associated with a significant improvement in disease-free and overall survival. In several clinical trials, the pathologic complete response (pCR) rate was significantly increased with combined pertuzumab and trastuzumab treatment in HER2-amplified breast cancer. Although the efficacy and safety of anti-HER2 dual blockade therapy has been reported, the markers that predict the response are still unclear. This study aimed to investigate the relationship between the level of HER2 amplification and the pCR in trastuzumab and pertuzumab neoadjuvant therapy. Twenty-two HER2-amplified early breast cancer patients who had received neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab (TCHP) therapy were included in this study. HER2/CEP17 ratio and average HER2 copy number were measured by fluorescence in situ hybridization analysis. The relationship between level of HER2 amplification and tumor pCR status was investigated. The median age was 47.5 years (range, 36–62). 31.8% of the patients were hormone receptor (HR) positive and 68.2%% of the patients were HR negative. The pCR (ypN0/is ypN0) rate in the breast and axilla was 68.2%. The patients who experienced a pCR had a median HER2/CEP17 ratio of 7.08 (range, 3.16–10.40) and average HER2 copy number of 17.00 (range, 5.85–37.50). The patients who did not experience a pCR had a median ratio of 4.70 (range, 1.06–9.00) and median HER2 copy number of 12.00 (range, 5.85–20.95) (P = .030, P = .174), respectively. pCR was highly correlated with HER2/CEP17 ratio in neoadjuvant anti-HER2 dual blockade. This suggests that the HER2/CEP17 ratio can be used as a predictive marker for pCR in neoadjuvant trastuzumab and pertuzumab therapy.
Wideband Scattering Analysis Method Based on Macro Block-Characteristic Basis Function Method and Interpolation for Large-Scale Finite Periodic Arrays with Planar Unit Cells

An efficient algorithm based on the macro block-characteristic basis function (MB-CBF) method and the interpolation technique is proposed to analyze the wideband electromagnetic characteristics of the large-scale finite periodic arrays with planar unit cells. The MB-CBF method can greatly reduce the number of unknowns. It is the frequency-domain algorithm. When used for the frequency sweep, the impedance matrix needs to be calculated repeatedly at each frequency, which is very time consuming. To accelerate generation of impedance matrices, the interpolation technique is adopted. Simulation results show that the proposed algorithm is accurate and efficient.
AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.
Machine Learning Improvements to Human Motion Tracking with IMUs

Inertial Measurement Units (IMUs) have become a popular solution for tracking human motion. The main problem of using IMU data for deriving the position of different body segments throughout time is related to the accumulation of the errors in the inertial data. The solution to this problem is necessary to improve the use of IMUs for position tracking. In this work, we present several Machine Learning (ML) methods to improve the position tracking of various body segments when performing different movements. Firstly, classifiers were used to identify the periods in which the IMUs were stopped (zero-velocity detection). The models Random Forest, Support Vector Machine (SVM) and neural networks based on Long-Short-Term Memory (LSTM) layers were capable of identifying those periods independently of the motion and body segment with a substantially higher performance than the traditional fixed-threshold zero-velocity detectors. Afterwards, these techniques were combined with ML regression models based on LSTMs capable of estimating the displacement of the sensors during periods of movement. These models did not show significant improvements when compared with the more straightforward double integration of the linear acceleration data with drift removal for translational motion estimate. Finally, we present a model based on LSTMs that combined simultaneously zero-velocity detection with the translational motion of sensors estimate. This model revealed a lower average error for position tracking than the combination of the previously referred methodologies.
NGD Analysis of 10-Line Microstrip Structure Crosstalk

An analysis of coupling effect between "1" and "0" shape lines is studied in this paper. After the "10" structure description, the two-port equivalent circuit is introduced. The S-matrix model is presented. Then, the 10-structure group delay (GD) is expressed. The theoretical equation explains that because of coupling, the 10-structure generates a bandpass NGD function around an NGD center frequency depending on the length of the 0 line. The analytical model is validated with a 10-microstrip structure. The calculated, simulated and measured results are in good correlation. The 10 prototype behaves as bandpass NGD function with 2.7 GHz center frequency, -6 ns NGD value with 6 dB insertion loss.
GSK3β-Mediated Expression of CUG-Translated WT1 Is Critical for Tumor Progression

These findings demonstrate that CUG-translated WT1 plays an oncogenic role in vivo, and GSK3β-mediated phosphorylation of cugWT1 induces its ubiquitination and degradation in concert with FBXW8. The Wilms' tumor 1 (WT1) gene is well known as a chameleon gene. It plays a role as a tumor suppressor in Wilms' tumor but also acts as an oncogene in other cancers. Previously, our group reported that a canonical AUG starting site for the WT1 protein (augWT1) acts as a tumor suppressor, whereas a CUG starting site for the WT1 protein (cugWT1) functions as an oncogene. In this study, we report an oncogenic role of cugWT1 in the AOM/DSS-induced colon cancer mouse model and in a urethane-induced lung cancer model in mice lacking cugWT1. Development of chemically-induced tumors was significantly depressed in cugWT1-deficient mice. Moreover, glycogen synthase kinase 3β promoted phosphorylation of cugWT1 at S64, resulting in ubiquitination and degradation of the cugWT1 associated with the F-box−/− WD repeat-containing protein 8. Overall, our findings suggest that inhibition of cugWT1 expression provides a potential candidate target for therapy. Significance: These findings demonstrate that CUG-translated WT1 plays an oncogenic role in vivo, and GSK3β-mediated phosphorylation of cugWT1 induces its ubiquitination and degradation in concert with FBXW8.
Multi-level Predictive with Training Framework (MP with TF) for ranking machine learning algorithms

Machine learning algorithms differ from conventional ones in a way that they adjust themselves to perform better when exposed to surplus data. Huge volumes of data were pipelined from the grades scored by student over the last ‘n’ years. Among the data earned from these, ‘k’ years were populated as trained set and ‘(n-k)’ as test sets. For the proposed Multi-level Predictive with Training Framework (MP with TF), five AI based approaches such as $k$-nearest neighbors, neural networks, Logistic regression, Naive Bayes and Random forest were considered to predict the target variable set. The performance of these machine learning algorithms were implemented using the actual grades scored by the students in their End Examination through a data science toolkit. Furthermore, the performances of these algorithms were ranked based on the accuracy of predicting the grades scored by the students.
Understanding and Characterizing Changes in Bugs Priority: The Practitioners’ Perceptive

Assigning appropriate priority to bugs is critical for timely addressing important software maintenance issues. An underlying aspect is the effectiveness of assigning priorities: if the priorities of a fair number of bugs are changed, it indicates delays in fixing critical bugs. There has been little prior work on understanding the dynamics of changing bug priorities. In this paper, we performed an empirical study to observe and understand the changes in bugs’ priority to build a 3-W model on Why and When bug priorities change, and Who performs the change. We conducted interviews and a survey with practitioners as well as performed a quantitative analysis containing 225,000 bug reports, developers’ comments, and source code changes from 24 open-source systems. The interviews with 11 developers from industry aim to establish an initial model to characterize the changes in bugs priority. The survey with an additional 38 developers was to understand their experience in why and when bug priorities change, and who performs the change. Then, we conducted a manual inspection of the collected data on open-source projects to compare our final bugs priority change model with changes identified in practice. Our quantitative results confirmed the outcomes of our interviews and surveys. For instance, we observed frequent changes in bug priorities and their impact on delaying critical bug fixes especially just before shipping a new release. Our findings can enable 1) researchers to build automated tools for checking and validating requests for bug priority changes, 2) practitioners to use a standard format in documenting and approving bug priority changes, and 3) educators to teach the better management of bug priorities.
Metagenomic assessment of the global diversity and distribution of bacteria and fungi

Summary Bacteria and fungi are of uttermost importance in determining environmental and host functioning. Despite close interactions between animals, plants, their associated microbiomes, and the environment they inhabit, the distribution and role of bacteria and especially fungi across host and environments as well as the cross‐habitat determinants of their community compositions remain little investigated. Using a uniquely broad global dataset of 13 483 metagenomes, we analysed the microbiome structure and function of 25 host‐associated and environmental habitats, focusing on potential interactions between bacteria and fungi. We found that the metagenomic relative abundance ratio of bacteria‐to‐fungi is a distinctive microbial feature of habitats. Compared with fungi, the cross‐habitat distribution pattern of bacteria was more strongly driven by habitat type. Fungal diversity was depleted in host‐associated communities compared with those in the environment, particularly terrestrial habitats, whereas this diversity pattern was less pronounced for bacteria. The relative gene functional potential of bacteria or fungi reflected their diversity patterns and appeared to depend on a balance between substrate availability and biotic interactions. Alongside helping to identify hotspots and sources of microbial diversity, our study provides support for differences in assembly patterns and processes between bacterial and fungal communities across different habitats.
SMAP 2020 15th International Workshop on Semantic and Social Media Adaptation & Personalization

This volume constitutes the proceedings of the 15<sup>th</sup> International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2020), held as a Virtual Workshop in Zakynthos, Greece, on October 29<sup>th</sup> - 30<sup>th</sup>, 2020.
The 14‐day repeated‐dose toxicity study of a fixed‐dose combination, QXOH/levobupivacaine, via subcutaneous injection in beagle dogs

QXOH‐Levobupivacaine (LB) is a fixed‐dose combination of 35‐mM QXOH and 10‐mM LB. It was developed for perioperative analgesia because of its long‐acting analgesic effect. The purpose of this study was to evaluate the potential toxicity of QXOH‐LB in beagle dogs in accordance with the Guidance on the repeated‐dose toxicity published by the China Food and Drug Administration. Groups of five male and five female beagle dogs received normal saline, QXOH‐LB (2, 4, and 8 mg/kg, calculated as QXOH), QXOH (2, 4, and 8 mg/kg), or LB (2 mg/kg, equals the concentration of LB in 8‐mg/kg QXOH‐LB group) at the volume of 1 mL/kg once per day for 14 days through subcutaneous injection. No mortality was observed. Dogs in the control group as well as animals treated with 2‐mg/kg QXOH or QXOH‐LB exhibited normal behaviors. Clinical signs of toxicity in dogs treated with 4 and 8 mg/kg of QXOH or QXOH‐LB included decreased activity, unsteady gait, jerks, tremors, vocalization, emesis, ataxia, lateral/sternal recumbency, deep/rapid respiration, and gasping. Additionally, neurological function was found to be affected by QXOH and QXOH‐LB at the doses of 4 and 8 mg/kg. All clinical signs were recovered within 24 h. The no‐observed‐adverse‐effect level of QXOH and QXOH‐LB was considered to be 2 mg/kg. Toxicokinetic data showed that exposure to QXOH and LB increased as QXOH‐LB doses were increased from 4 to 8 mg/kg. There was no evidence of drug accumulation or any effect of gender.
Modifying the Windings of HFCT Restrain Magnetic Saturation and Harmonics Interference

The ferrite core design for HFCT sensor uses the small signal current sampling as the basis. The non-linear loads, such as system unbalance or interphase ground fault, produces considerable harmonic induction, thus leading to magnetic saturation of the ferrite core. The aliasing of high and low frequency signals distorts the extracted waveform or damages the HFCT. To address the above problem, this study used sensors to improve two sets of windings, whereby the forward winding and backward winding were connected in series in the coil position. As the low frequency signal neutralized the increase of induced potential through the backward winding loop path, the low frequency response hazard generated was reduced by a low frequency signal. The experimental results showed that the unmodified sensor correlation coefficient quantized value R=0.94137 was positively correlated, which validates the excellent ability to suppress magnetic saturation and harmonic interference.
Improving the Cognitive Levels of Automatic Generated Questions using Neuro-Fuzzy Approach in e-Assessment

Assessment is a fundamental activity to realize the objective of a course and to enhance the teaching learning process. Ensuring quality in the question papers for testing the different cognitive skills is important in the test or examination component of any academic or training domain. Bloom’s taxonomy, a popular framework has been used to assess the learning skills of students. This paper describes the methodology to auto-generate questions based on bloom’s cognitive levels. Various Natural Language Processing (NLP) techniques are incorporated to construct a textgraph using input statements from the web where native intelligence acquired from ConceptNet interrelates the nodes. The proposed work resolves the complexity of categorising autogenerated questions with similar action verbs. It follows a combinatorial formulation of fuzzy logic to generate cognitively fluent questions. A Fuzzy Restricted Boltzman machine coupled with Gaussian Markov based softmax is used in the proposed architecture. Experiments reveal the significance of the proposed system in generating cognitively fluent questions when the same action verb gets interlinked with different cognitive level of auto-generated questions.
Modeling of Product Life Cycle Stages Optimization Taking into Consideration Uncertainty of the Main Parametrical Variables

Modern trends in the development of military and civil aviation are aimed at the constant growth of cruising and limit flight speeds of promising aircraft. The largest aviation powers (Russia, the USA, the EU, China, Japan) have created supersonic passenger and military aircraft and are talking about creating hypersonic aircraft. The pace of development of industrial technologies requires a significant reduction in the development of aviation equipment, so the advanced military aircraft of the 5th generation (Su-57, F-22, J-20, F-35) were developed within 10-15 years, and the requirements to the terms of creation of advanced aircraft complexes currently is no more than 8 years. These facts require a significant optimization of the aircraft life cycle stages, in particular, a significant volume of the development stage. In this paper, we consider the process of multi-criteria optimization of aircraft parameters at the stage of development and initial coordination of the minimum aerodynamic drag head-end of the aircraft fuselage, and assess the impact of this optimization on the life cycle of the product.