Miracle-dz/UniFAR-FTUs huggingface dataset:
A Transistor Clamped H-Bridge Inverter Based STATCOM For Reactive Power Compensation In Three-Phase Transmission System

This paper presents a transistor clamped h-bridge inverter-based STATCOM for reactive power compensation in a three-phase transmission system using MATLAB/Simulink. STATCOM is effectively taking up the challenges encountered in the field of power systems.It compensates for reactive power, active power, provides voltage support. In this paper STATCOM model, mathematical expressions, and MATLAB/SIMULINK model have been analyzed. Sine PWM (level shifted carrier) modulation technique has been employed to generate pulses for a five-level transistor clamped h-bridge inverter. By employing a PI controller a sinusoidal reference voltage is generated. Here reactive power demand of load is provided by STATCOM and reactive power supplied from grid becomes zero and thus the load on the grid is effectively lowered.
Optimization and Analysis of Wireless Powered Multi-Antenna Two-Way Relaying Systems

We consider a wireless powered two-way relaying system consisting of two energy constrained single antenna sources and one multi-antenna relay with constant power supply. The time division protocol is adopted, where the relay first acts as the energy source and employs energy beamforming to charge the two sources, and then switches its role as a relay to help forward the information to the sources. To maintain user fairness, we aim to maximize the minimum rate of two sources, by jointly optimizing the energy beamforming vector, time splitting factor and relay transformation matrix. To further reduce the complexity of the optimal algorithm, we propose an alternating optimization method, where closed-form expressions for the energy beamforming and time splitting factor are obtained. To gain more insights, we propose a simple suboptimal design and analyze the outage probability and the average rate when the relay applies simple energy beamforming and transformation matrix. The analysis shows that the system can achieve a diversity order of <inline-formula> <tex-math notation="LaTeX">$\frac {N}{3}$ </tex-math></inline-formula> for a system with a relay of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> antennas. Numerical results show that the performance of the proposed low-complexity alternating optimization method approaches the optimal algorithm over the entire SNR range, but has a significantly low complexity, and the proposed suboptimal design can also achieves a decent performance especially in small <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> regime.
Corporate Governance, Political Connections and Firm Performance: The Role of Political Regimes and Size

This article empirically examines the relationship between corporate governance and firm financial performance, and the interplay of political connections of 150 non-financial listed Pakistani firms ranging from 2001 to 2014. Generally, and consistent with the prior researchers, we reported that corporate governance is an essential predictor of firm financial performance in Pakistan. Moreover, the results indicate that political connections substitute non-executive directors (NXD), executive directors (EXD) and board meetings (BM) in terms of firm performance measure return on asset (ROA), whereas NXD and EXD complement in terms of Tobin’s Q. We also found some variations in these effects, when moving from large to small size sampled firms and dictator to democratic regimes. Theoretically, our results support the Agency, Resource Dependency and Stewardship theories.
Autonomous Assistance-as-Needed Control of a Lower Limb Exoskeleton With Guaranteed Stability

The use of exoskeletons for clinical lower-limb stroke rehabilitation offers the potential of improved and customized rehabilitation that reduces the requirements and demands placed on multiple staff members. Initial research with lower-limb exoskeletons show potential to alleviate this problem. Conventional assistance-based exoskeleton devices simply enforce the desired gait trajectory for the patient in order to ensure safety and stability. Unfortunately, if the end-user does not have to work to contribute to successful motion, rehabilitation often does not occur. Recent evidence has suggested that assistance-as-needed control prevents users from slacking, facilitating functional motor recovery. Assistance-as-needed control turns off assistive torques during periods when the patient is able to execute a desired gait pattern but if the patients gait deviates sufficiently from a desired trajectory then assistive torques are generated to compensate for the patients loss of strength. This strategy encourages the patient to contribute effort while still enabling the exoskeleton to guide movements. Assistance-as-needed control inherently leads to aperiodic gait patterns and has accordingly been difficult to employ in lower-limb exoskeletons due to the need to ensure stability. This work demonstrates how virtual constraint control—a method with robust stability properties used in prostheses and assistive exoskeletons control—can be combined with a velocity-modulated deadzone to ensure stability. Simulations suggest that the method can accommodate a large deadzone while remaining stable across a range of unanticipated gait pathologies, as demonstrated using Lorenz mappings that can accommodate the aperiodic nature of the resulting gait.
Covert Communications in D2D Underlaying Cellular Networks With Power Domain NOMA

A device-to-device (D2D) communication link is vulnerable because it is relatively easy to be compromised by adversaries due to the fact that D2D terminals are power limited nodes. This article proposes a covert communication scheme that allows D2D communication links to transmit covert signals (e.g., privacy sensitive data) to ensure a low probability of detection. We allocate transmit powers of cellular and D2D noncovert signals following a fading D2D channel to add the uncertainty in adversary's background noises. With the help of base station, cooperative power domain nonorthogonal multiple access and successive interference cancellation are used to decode covert signal. Depending on whether a D2D transmitter has the knowledge of adversary's detection threshold, we derive minimum error probability to measure the covertness performance. Furthermore, we evaluate covert throughput, which is defined as a maximum average transmission rate of covert signal, subject to a given covertness performance.
Sign Language Hand Gesture Recognition Using Autoencoder and Support Vector Machine Classifiers

– We propose a novel 4 stage hand gesture recognition algorithm. The algorithm uses combinations of features to implement vision–based hand gesture recognition. These include Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), fingertip finder, pixel segmentation, eccentricity, elongation, and rotation. Feature fusion is adopted by combining all extracted features, which can detect hand gesture with high accuracy. In addition, score fusion method is also used to combine results achieving high accuracy rate. The Automatic sign language system requires quick and precise methods to distinguish static signs or an arrangement of signs delivered to help translate their right significance. Deep Learning algorithm and Multiclass Support Vector Machine algorithm are used to distinguish 37 hand signs of American numbers and alphabets correctly that help the Human Computer Interaction (HCI) system. The proposed system was learned and tested using 1850 image for 37 gestures of hand and reaches a 98.64% correct classification in real time environment.
Assessment of Preoperative Anxiety among Patients Undergoing Elective Surgery

Introduction: Although patients undergoing elective surgery are assumed to have a lower level of preoperative anxiety, studies reported a high level of anxiety among those patients. This study aimed at assessing the level of preoperative anxiety and its predictors in adult patients undergoing elective surgery. Methods: This study is a cross-sectional design conducted among patients admitted for elective surgery. A total of 278 patients with non-life-threatening surgeries were included and those who had mental or psychological problems were excluded from this study. Sample type and sample size. The data collected through a questionnaire about demographics, preoperative knowledge and anxiety in the preoperative room 24 hours before the operation. The frequencies, percentages, mean and SD were calculated, and t-test was used to detect significant associations. Results: The overall preoperative anxiety score had a mean ±SD of 18.2±5.8 and the questions regarding knowledge component reported higher scores than those about anxiety component. The components of anxiety about surgery reported higher scores than those of anxiety about anesthesia. Age, gender, availability of family support and type of surgery were found as significant predictors for preoperative anxiety prior to elective surgery. However, the association between preoperative anxiety and marital status and previous experience of surgery were found statistically non-significant. Conclusion: This study concluded that patients underwent elective surgery had high preoperative anxiety. The patients with younger age, female gender, patients under general anesthesia, or those who had no family support showed a higher level of preoperative anxiety.
Data Science in Strategy: Machine Learning and Text Analysis in the Study of Firm Growth

This study examines the applicability of modern Data Science techniques in the domain of Strategy. We apply novel techniques from the field of machine learning and text analysis. WE proceed in two steps. First, we compare different machine learning techniques to traditional regression methods in terms of their goodness-of-fit, using a dataset with 168,055 firms, only including basic demographic and financial information. The novel methods fare to three to four times better, with the random forest technique achieving the best goodness-of-fit. Second, based on 8,163 informative websites of Dutch SMEs, we construct four additional proxies for personality and strategy variables. Including our four text-analyzed variables adds about 2.5 per cent to the R2. Together, our pair of contributions provide evidence for the large potential of applying modern Data Science techniques in Strategy research. We reflect on the potential contribution of modern Data Science techniques from the perspective of the common critique that machine learning offers increased predictive accuracy at the expense of explanatory insight. Particularly, we will argue and illustrate why and how machine learning can be a productive element in the abductive theory-building cycle.
Ciliogenesis Control Mechanisms in Cerebellar Neuron Progenitors

Cerebellar granule neuron progenitors (GNPs) require the primary cilium to proliferate in response to Sonic Hedgehog (SHH) during cerebellar development. As aberrant proliferation of GNPs may lead to SHH-type medulloblastoma (SHH-MB), a pediatric brain tumor, understanding which mechanisms control ciliogenesis in GNPs represents a major interest in the field. Here, we show that the proneural bHLH transcription factor Atoh1 controls the presence of primary cilia in GNPs both in vitro and in vivo, thus maintaining GNPs responsive to the mitogenic effects of SHH. Indeed, loss of primary cilia induced via knockdown of specific ciliary components (e.g. Kif3a and Ift88) abolishes the ability of Atoh1 to keep GNPs in proliferation in vivo. Mechanistically, Atoh1 controls ciliogenesis by regulating the proper peri-centrosomal clustering of centriolar satellites (CS), large multiprotein complexes working as essential machineries for ciliogenesis. Knockdown of Atoh1 in GNPs perturbs CS subcellular distribution, leading to impairment of ciliogenesis. Luciferase reporter assays and chromatin immunoprecipitation experiments indicate that Atoh1 can directly regulate the expression of Cep131, a key CS core component. Importantly, ectopic expression of Cep131 in GNPs depleted of Atoh1, is sufficient to restore proper CS localization and consequent primary cilia formation, indicating that the Atoh1-Cep131-CS axis is responsible for ciliogenesis in GNPs.In addition, we further demonstrated that these functions of Atoh1 are conserved in the context of SHH-MB, where Atoh1 is typically overexpressed and acts as a lineage-dependent transcription factor.These data reveal a mechanism whereby ciliogenesis is regulated in neuron progenitors providing novel insights into cerebellar neurogenesis and pathogenesis of SHH-MB.
The PSI Framework and Theory of Design

Problem space, social space, institutional space (PSI) is a framework and a theory of design for modeling design contexts, whose roots are in the reflection on our projects and studies over last decades; it took shape in the last seven years and has been evaluated extensively. This article traces the drivers for its development into a framework that provides the dimensions of describing design contexts in their variety and can be used to integrate other models and theories at different levels of granularity to explain intricate design situations. We have tested the framework on more than 170 cases to model, explain, and inform a variety of design situations. We describe several of them. The collected evidence supports the potential utility of PSI. Future work is required to continue develop the framework and form patterns of use that will help characterize and improve design processes.
Protein interaction mapping reveals widespread targeting of development-related host transcription factors by phytoplasma effectors

Phytoplasmas are pathogenic bacteria that reprogram plant host development for their own benefit. Previous studies have characterized a few different phytoplasma effector proteins that destabilize specific plant transcription factors. However, these are only a small fraction of the potential effectors used by phytoplasmas; therefore, the molecular mechanisms through which phytoplasmas modulate their hosts require further investigation. To obtain further insights into the phytoplasma infection mechanisms, we generated a protein-protein interaction network between a broad set of phytoplasma effectors and a large, unbiased collection of Arabidopsis thaliana transcription factors and transcriptional regulators. We found widespread, but specific, interactions between phytoplasma effectors and host transcription factors, especially those related to host developmental processes. In particular, many unrelated effectors target specific sets of TCP transcription factors, which regulate plant development and immunity. Comparison with other host-pathogen protein interaction networks shows that phytoplasma effectors have unusual targets, indicating that phytoplasmas have evolved a unique and unusual infection strategy. This study contributes a rich and solid data source that guides further investigations of the functions of individual effectors, as demonstrated for some herein. Moreover, the dataset provides insights into the underlying molecular mechanisms of phytoplasma infection.
Effect of Nutrition on ﬂaxseed Supplementation to Lactating Goats on Physiochemical and Nutritional Properties of Domiati Cheese in Halayeb, Shalateen and Abu Ramad Triangle of Egypt

The purpose of this research was to evaluate the effect of feeding lactating goats with flaxseed on the physicochemical, sensory properties, yield, texture and fatty acids profile of Domiati cheese. Goat's milk resulted from goat feed different flaxseed levels (0, 7 and 14%) were used in the preparation of goat's Domiati cheese which was stored at 4±1oC for 60 days. Results showed that supplementing flaxseed at level 7and 14% had significantly (P < 0.05) increased cheese protein, total solids and lactose, while no effect was observed on cheese fat. Cheese yield was significantly increased (P < 0.5) for cheese supplemented with 7 and 14%flaxseed than control cheese. Results also, showed in significant differences (P<0.05) on short(SCFA) and medium-chain fatty acids (MCFA) . While a significant increase of mono -unsaturated (MUFA) and poly-unsaturated fatty acids (PUFA) in cheese fat were observed. Moreover, concentrations of conjugated linoleic acid (CLA) isomers and linolenic acid (C18:3) in cheese fat were increased linearly (P < 0.05) with the increase of flaxseed level. The concentration of CLA and C18:3 in cheese with7 and 14 % flaxseed concentrates significant (P < 0.05) increased. It was concluded that feeding flaxseed to lactating goats improved fatty acids profile in cheese as well as cheese yield. The texture profile parameters were significant (P < 0.05) decreased. Sensory properties scores were increased during storage period. Generally, resultant goat's Domiati cheese supplemented with 7 and 14% flaxseed were considered highly acceptable and it has many health benefits than the control cheese.
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension

Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especiallyon problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. C3 is available at https://dataset.org/c3/.
Medical education units: A necessity for quality assurance in health professions education in Nigeria

In recent years, curricula and pedagogical methods in medical education have undergone rapid and unprecedented changes globally. The emphasis has shifted from traditional, teacher-centred learning, characterised by the accumulation of non-integrated volumes of knowledge, to active, student-centred learning. The Medical and Dental Council of Nigeria (MDCN) reports that there are 31 fully accredited and 6 partially accredited medical schools in Nigeria. The majority of these medical schools still offer undergraduate medical training based on a curriculum characterised by a distinct separation of preclinical and clinical training, with minimal or no integration. This approach is coupled with low-quality teaching by medical educators, as many medical colleges in Nigeria presently use specialists as lecturers who have little or no training in higher education practices; their only exposure to teaching is that obtained during their postgraduate specialty training. Similarly, very few medical schools in Nigeria have established medical education units (MEUs), as recommended by the World Health Organization and the World Federation for Medical Education. We discuss the shortcomings of the present medical education system in Nigeria and suggest ways to improve the quality of pedagogy among Nigerian medical educators, such as the establishment of clinical-skills centres and MEUs at Nigerian medical schools. In addition, this review highlights the role and importance of MEUs in facilitating quality assurance in health professions education, and the urgent need for more medical schools in Nigeria to establish MEUs to promote, co-ordinate and evaluate medical education reforms based on needs assessments and within the confines of MDCN standards.
Lightening the load: tackling overweight and obesity: a toolkit for developing local strategies to tackle overweight and obesity in children and adults

The toolkit, Lightening the load: tackling overweight and obesity, is a cornucopia of useful statistics, practical initiatives, evidence of effectiveness, checklists, frameworks and examples of good practice. Published jointly by the National Heart Forum and Faculty of Public Health, the new toolkit incorporates all the latest guidance from NICE (the National Institute for Health & Clinical Excellence) and other relevant organisations. It has been fully endorsed by NICE, the Food Standards Agency, the Royal College of General Practitioners, the Royal College of Nursing and the Royal College of Physicians; and is backed by the Department of Health.
Highly Efficient OFDM Applying Symbol-Edges Truncating Transmission Technique

Wireless technology that can conFigure wide-area IoT systems with high data rates is required toward 6G. To further expand area coverage, it is necessary to improve the data rate of the conventional machine type communications (MTC) such as LTE-M and mMTC at the edge of the coverage area. In this paper, we propose an orthogonal frequency division multiplexing (OFDM) with symbol-edges truncating transmission (STTOFDM) for data rate enhancement. In the proposed STT-OFDM scheme, only a part of the cyclic prefix (CP)-OFDM symbols, which is the legacy communication scheme adopted to 4G and 5G, is transmitted. On the receiver side, zeros are inserted where the no transmission took place at the transmitter side before normal OFDM demodulation is performed. The energy per one-symbol transmission is expected to be reduced by the proposed STTOFDM. We evaluate the achievable throughput and show the usefulness of the proposed STT-OFDM scheme. The proposed STT-OFDM can improve the throughput of the CP-OFDM by about 1.5 times even in low SNR environments.