Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence of Russian Regions: Different Patterns for Poor, Middle and Rich

The Strategy of Spatial Development of the Russian Federation until 2025 aims at the economic growth acceleration and reduction of the intra-regional socio-economic differences. Therefore, the factors affecting the economic growth of regions, convergence of regions, spillover effects from the neighbouring regions are of importance. Russian regions are very different and do not converge to a unique equilibrium path. 80 Russian regions were divided into the groups of poor, middle and rich regions. Three main hypotheses were considered, based on the differences in the 1) convergence speed, 2) influence of the same factors, 3) different mutual influence of regions. They were tested using a modified spatially autoregressive model for the three groups using the Russian regional data for 2000–2017. Beta-convergence was found only for the middle and rich regions, the rate of convergence was higher in the rich regions. The poor regions did not grow faster than the other regions, confirming the relevance of the Strategy of Spatial Development. The similarities and differences were identified in the factors ensuring the economic growth of regions belonging to the three groups. The growth in all regions is stimulated by the regional economy openness. The growth of rich regions can be achieved by increasing the investment and reducing the investment risk. However, the investments in the poor and middle regions are not effective. The poor and middle regions receive positive spillovers from the growth of the neighbouring regions. It is possible to expect reduced differences in the living standards between the poor and rich regions.
Crisis and arbitrage opportunities: The role of causation, effectuation and entrepreneurial learning

This article examines the process by which entrepreneurs identify and work with an arbitrage opportunity emerging from an episodic crisis. Although prior research has investigated the role of entrepreneurial characteristics and context on opportunity development, the specific manner in which these factors emerge in the course of opportunity development during a crisis remain underexplored. By adopting a qualitative approach grounded in case studies of eight entrepreneurs in the US distillery industry, this article addresses that gap by examining the process of arbitrage opportunity development during COVID-19. Our study reveals the primacy of both causation and effectuation-based entrepreneurial decision logics and the role of double-loop learning, as entrepreneurs interact with the time-compressed duration of the arbitrage opportunity. Implications and insights for entrepreneurs, researchers and policymakers are discussed.
A novel current based faulted phase selection and fault detection technique for EHV transmission systems with some penetration of wind generation

In recent years, the capacity of electrical power systems (EPS) has been growing in order to match an increasing demand for electrical power. The expanding power source especially the penetration of the renewable energy makes the systems more difficult to manage and operate. Thus the task of protecting these systems especially for the extra-high-voltage (EHV) transmission line can no longer be handled using the traditional protection schemes, which were designed for simple power system configurations and therefore are not suitable for modern power systems. Also, the protection of EHV transmission line should take into account the increasing penetration of renewable energy generation such as wind farms and the effects of such generations on protection schemes. This thesis describes a novel phase selection and fault detection scheme using current signal data from one end only of a typical UK 400kV transmission system. Firstly, the measured current signals are decomposed using the wavelet transform to obtain the necessary frequency details and then the spectral energy for a chosen number of wavelet coefficients are calculated using a moving short time window; this forms the feature extraction stage, which in turn, defines the inputs for the artificial neural network which is used for classifying the types of fault. After the fault type is identified, the proposed scheme selects the specific neural network of the fault type to distinguish between internal and external faults by utilizing the same patterns features extracted from the previous stage. The input features comprise both the high and low frequency components to enhance the performance of the scheme. An extensive series of studies for a whole variety of different system and fault conditions clearly show that the performance of the scheme both for phase selection and detection is accurate and robust. For testing the robustness of the scheme and also as this research project is a UK-China jointly funded EPSRC project, this designed scheme is also applied to a typical 500kV Chinese transmission system with only traditional power generations and with both traditional and renewable generations (wind farms). The effect of the penetration of wind farms on the performance of the proposed protection scheme is thus investigated. For both systems, promising results from the new protection scheme for the phase selection and fault detection are achieved.
Feature Description with Feature Point Registration Error Using Local and Global Point Cloud Encoders

SUMMARY A typical approach to reconstructing a 3D environment model is scanning the environment with a depth sensor and ﬁtting the accumulated point cloud to 3D models. In this kind of scenario, a general 3D environment reconstruction application assumes temporally continuous scanning. However in some practical uses, this assumption is unacceptable. Thus, a point cloud matching method for stitching several non-continuous 3D scans is required. Point cloud matching often includes errors in the feature point detection because a point cloud is basically a sparse sampling of the real environment, and it may include quantization errors that cannot be ignored. Moreover, depth sensors tend to have errors due to the reﬂec-tive properties of the observed surface. We therefore make the assumption that feature point pairs between two point clouds will include errors. In this work, we propose a feature description method robust to the feature point registration error described above. To achieve this goal, we designed a deep learning based feature description model that consists of a local feature description around the feature points and a global feature description of the entire point cloud. To obtain a feature description robust to feature point registration error, we input feature point pairs with errors and train the models with metric learning. Experimental results show that our feature description model can correctly estimate whether the feature point pair is close enough to be considered a match or not even when the feature point registration errors are large, and our model can estimate with higher accuracy in comparison to methods such as FPFH or 3DMatch. In addition, we conducted experiments for combinations of input point clouds, including local or global point clouds, both types of point cloud, and encoders.
Patterns of Internal Communication in the Implementation of Total Quality Management (TQM) at Manufacturing Organisations

Organisations need to perform, productivity needs to be improved and organisations’ activities need to be more effective, especially after the world economic crisis due to the Covid-19 pandemic. Therefore, the need for quality improvements is crucial in both manufacturing and service sectors. The main purpose of this paper is to present an analysis of internal communication patterns in TQM implementation in Malaysia's manufacturing organisations. This is a multiple case study done using qualitative in-depth interview technique of data collections with 14 informants representing five large manufacturing organisations. Open-ended questions in semi-structured interviews enabled informants to describe their answers based on themes. The study has been carried out in manufacturing organisations originating from three different continents; two Malaysian local organisations, two eastern and one western organizations, and all of them located in Malaysia. In selecting the informants, the study adopts a judgment type of purposive sampling techniques. Results show that the most preferable and reliable communication medium between superiors and subordinates is still face-to-face meetings, although we are in the era of technology and IR4.0. Meanwhile, telephones are popular among the administration staff. The preferable form of communication between internal-operational is formal, without denying that informal form ofl communication is also important. Keywords: Internal communication, organizational communication, multiple case study, systematic process analysis, TQM.
Proteolytic Profiling of Streptococcal Pyrogenic Exotoxin B (SpeB) by Complementary HPLC-MS Approaches

Streptococcal pyrogenic exotoxin B (SpeB) is a cysteine protease expressed during group A streptococcal infection that represents a major virulence factor. Although subject to several studies, its role during infection is still under debate, and its proteolytic properties remain insufficiently characterized. Here, we revisited this protease through a set of complementary approaches relying on state of-the-art HPLC-MS methods. After conceiving an efficient protocol to recombinantly express SpeB, the zymogen of the protease and its activation were characterized. Employing proteome-derived peptide libraries, a strong preference for hydrophobic and aromatic residues at P2 alongside negatively charged amino acids at P3′ to P6′ was revealed. To identify relevant in vivo substrates, native proteins were obtained from monocytic secretome and plasma to assess their cleavage under physiological conditions. Besides corroborating our findings concerning specificity, more than 200 cleaved proteins were identified, including proteins of the extracellular matrix, proteins of the immune system, and proteins involved in inflammation. Finally, the cleavage of IgG subclasses was studied in detail. This study precisely depicts the proteolytic properties of SpeB and provides a library of potential host substrates, including their exact cleavage positions, as a valuable source for further research to unravel the role of SpeB during streptococcal infection.
Finding simplicity in the complex systemic arterial system: Basis of increased pulse pressure

EXTRAMURAL GRANTS  Preclinical evaluation of the adjustable systemic-pulmonary artery shunt. NIH R21 Exploratory/Developmental Research Grant. $403,000 (TC). M.P.I. : M.W. Mohiuddin. 2011-2013.  Evaluation of the adjustable pediatric systemic-to-pulmonary artery shunt. NIH Small Business Innovative Research (SBIR). $450,000 (TC). P.I. : M.W. Mohiuddin. 2011-2013 (relinquished to accept the NIH-R21).  Preclinical development of an adjustable pediatric systemic-pulmonary artery shunt. American Heart Association Post-doctoral Award (TC). $78,000. P.I. : M. W. Mohiuddin. 2008-2010.
E-Gadget to Detect Food Freshness using IoT and ML

Abstract: The food we consume plays an important role in our daily life. It provides us energy which is needed to work, grow, be active, and to learn and think. The healthy food is essential for good health and nutrition. Light, oxygen, heat, humidity, temperature and spoilage bacteria can all affect both safety and quality of perishable foods. Food kept at room temperature undergoes some chemical reactions after certain period of time, which affects the taste, texture and smell of a food. Consuming spoiled food is harmful for consumers as it can lead to foodborne diseases. This project aims at detecting spoiled food using appropriate sensors and monitoring gases released by the particular food item. Sensors will measure the different parameters of food such as pH, ammonia gas, oxygen level, moisture, etc. The microcontroller takes the readings from sensors and these readings then given as an input to a machine learning model which can decide whether the food is spoilt or not based on training data set. Also, we plan to implement a machine learning model which can calculate the lifespan of that food item. Index Terms: Arduino Uno, Food spoilage, IoT, Machine Learning, Sensors.
Random and Diblock Thermoresponsive Oligo(ethylene glycol)-Based Copolymers Synthesized via Photo-Induced RAFT Polymerization

Amphiphilic random and diblock thermoresponsive oligo(ethylene glycol)-based (co)polymers were synthesized via photoiniferter polymerization under visible light using trithiocarbonate as a chain transfer agent. The effect of solvent, light intensity and wavelength on the rate of the process was investigated. It was shown that blue and green LED light could initiate RAFT polymerization of macromonomers without an exogenous initiator at room temperature, giving bottlebrush polymers with low dispersity at sufficiently high conversions achieved in 1–2 h. The pseudo-living mechanism of polymerization and high chain-end fidelity were confirmed by successful chain extension. Thermoresponsive properties of the copolymers in aqueous solutions were studied via turbidimetry and laser light scattering. Random copolymers of methoxy- and alkoxy oligo(ethylene glycol) methacrylates of a specified length formed unimolecular micelles in water with a hydrophobic core consisting of a polymer backbone and alkyl groups and a hydrophilic oligo(ethylene glycol) shell. In contrast, the diblock copolymer formed huge multimolecular micelles.
Multi-label text classification with an ensemble feature space

Multi-label text classification aims at assigning more than one class to a given text document, which makes the task more ambiguous and challenging at the same time. The ambiguities come from the fact that often several labels in the prescribed label set are semantically close to each other, making clear demarcation between them difficult. As a consequence, any Machine Learning based approach for developing multi-label classification scheme needs to define its feature space by choosing features beyond linguistic or semi-linguistic features, so that the semantic closeness between the labels is also taken into account. The present work describes a scheme of feature extraction where the training document set and the prescribed label set are intertwined in a novel way to capture the ambiguity in a meaningful way. In particular, experiments were conducted using Topic Modeling and Fuzzy C-means clustering which aim at measuring the underlying uncertainty using probability and membership based measures, respectively. Several Nonparametric hypothesis tests establish the effectiveness of the features obtained through Fuzzy C-Means clustering in multi-label classification. A new algorithm has been proposed for training the system for multi-label classification using the above set of features.
Role Of C-Reactive Protein in Diagnosis of Acute Appendicitis

Background: Appendicitis is a common surgical emergency and diagnostic dilemma. Making the correct diagnosis is often difficult as the clinical presentation varies according to the age of the patient and the position of appendix. The objective of this study was to identify clinical applicability of C- reactive protein, as a diagnostic test for appendicitis. Methods: This prospective study was carried out in Federal government Polyclinic hospital, Islamabad from January to July 2019, 114 patients underwent appendectomy for clinically diagnosed acute appendicitis. The decision to operate the patient was given by senior registrar. The blood samples for C-reactive protein were drawn before taking the patient to the operating theatre. Removed appendices were sent for histopathological confirmation of diagnosis. The C-reactive protein was then compared with the results of histopathology to determine its validity. The data was entered and analysed in SPSS 23. Results: The sensitivity, specificity, positive and negative predictive values of C-reactive protein in patients with clinical diagnosis of acute appendicitis were found to be 94%, 78%, 93% and 74 % respectively. Conclusion: CRP is helpful in making diagnosis of acute appendicitis. It is highly sensitive but has a relatively low specificity.
Nonlinear Analysis of an Asynchronous Vibration in the Flexible Rotor Supported by the Full-Circular Journal Bearing under the Short Bearing Model and Half Sommerfeld Boundary Condition

This study investigates the asynchronous vibration of the flexible rotor supported by the full-circular journal bearing. The numerical method called shooting method is used, and both the stable and the unstable solutions of the asynchronous vibration are obtained. The dynamical characteristics of asynchronous vibration are clarified and explained. Particularly, the rotational speed range in which the rotor state may enter to the large amplitude asynchronous vibration by impact even if the rotational speed is lower than the limit of stability of the linearized system is shown and explained. Moreover, it is shown that the unstable solution acts as the threshold whether the state enters to the large amplitude asynchronous vibration or stays in the static equilibrium condition with zero amplitude. Also, the continuous transition of the frequency of the asynchronous vibration from the oil whirl to the oil whip after the saddle-node bifurcation from the unstable solution blanch is explained
Generation of Heterogeneous Semantic Annotations of XML Pages: A Multi-viewpoints Approach

The semantic annotations presented in current research are based on consensual descriptions of domain knowledge which are used to generate a consensual interpretation of the resource content. Sometimes specialists in a domain want to enrich this interpretation with specific interpretations based on their specialties, consistent with the interpretations of other specialists. However, extant work cannot enrich consensual annotations with heterogeneous annotations. We propose an approach that constructs a consensual (global) annotation and then enriches the consensual annotation with heterogeneous (local) annotations, each of which is a specification of the global annotation. The heterogeneous annotations are consistent with the global interpretations and with one another. What is unique in our approach is that one element in a resource can be annotated with different specific concepts, based on different viewpoints. Our approach utilizes a multi-viewpoints ontology [1], with heterogeneity at the local level and consensus at the global level.
Coronary Flow Velocity Reserve Using Dobutamine Test for Noninvasive Functional Assessment of Myocardial Bridging

Background: It has been shown that coronary flow velocity reserve (CFVR) measurement by transthoracic Doppler echocardiography (TTDE) during dobutamine (DOB) provocation provides a more accurate functional evaluation of myocardial bridging (MB) compared to adenosine. However; the cut-off value of CFVR during DOB for identification of MB associated with myocardial ischemia has not been fully clarified. Purpose: This prospective study aimed to determine the cut-off value of TTDE-CFVR during DOB in patients with isolated-MB, as compared with stress-induced wall motion abnormalities (VMA) during exercise stress-echocardiography (SE) as reference. Methods: Eighty-one symptomatic patients (55 males [68%], mean age 56 ± 10 years; range: 27–74 years) with the existence of isolated-MB on the left anterior descending artery (LAD) and systolic MB-compression ≥50% diameter stenosis (DS) were eligible to participate in the study. Each patient underwent treadmill exercise-SE, invasive coronary angiography, and TTDE-CFVR measurements in the distal segment of LAD during DOB infusion (DOB: 10–40 μg/kg/min). Using quantitative coronary angiography, both minimal luminal diameter (MLD) and percent DS at MB-site at end-systole and end-diastole were determined. Results: Stress-induced myocardial ischemia with the occurrence of WMA was found in 23 patients (28%). CFVR during peak DOB was significantly lower in the SE-positive group compared with the SE-negative group (1.94 ± 0.16 vs. 2.78 ± 0.53; p < 0.001). ROC analyses identified the optimal CFVR cut-off value ≤ 2.1 obtained during high-dose dobutamine (>20 µg/kg/min) for the identification of MB associated with stress-induced WMA, with a sensitivity, specificity, positive and negative predictive value of 96%, 95%, 88%, and 98%, respectively (AUC 0.986; 95% CI: 0.967–1.000; p < 0.001). Multivariate logistic regression analysis revealed that MLD and percent DS, both at end-diastole, were the only independent predictors of ischemic CFVR values ≤2.1 (OR: 0.023; 95% CI: 0.001–0.534; p = 0.019; OR: 1.147; 95% CI: 1.042–1.263; p = 0.005; respectively). Conclusions: Noninvasive CFVR during dobutamine provocation appears to be an additional and important noninvasive tool to determine the functional severity of isolated-MB. A transthoracic CFVR cut-off ≤2.1 measured at a high-dobutamine dose may be adequate for detecting myocardial ischemia in patients with isolated-MB.
The International Monetary Fund’s intervention in education systems and its impact on children’s chances of completing school

Enabling children to acquire an education is one of the most effective means to reduce inequality, poverty, and ill-health globally. While in normal times a government controls its educational policies, during times of macroeconomic instability, that control may shift to supporting international organizations, such as the International Monetary Fund (IMF). While much research has focused on which sectors has been affected by IMF policies, scholars have devoted little attention to the policy content of IMF interventions affecting the education sector and children’s education outcomes: denoted IMF-education policies. This article evaluates the extent which IMF-education policies exist in all programs and how these policies and IMF programs affect children’s likelihood of completing schools. While IMF-education policies have a small adverse effect yet statistically insignificant on children’s probability of completing school, these policies moderate effect heterogeneity for IMF programs. The effect of IMF programs (joint set of policies) adversely effect children’s chances of completing school by six percentage points. By analyzing how IMF-education policies but also how IMF programs affect the education sector in low- and middle-income countries, scholars will gain a deeper understanding of how such policies will likely affect downstream outcomes.