Miracle-dz/UniFAR-FTUs huggingface dataset:
Cryptanalysis of Some Electronic Checkbook Schemes

: Paper-based check is the second mostly used payment method. Accordingly, efforts are underway to improve electronic checkbook (shortly, e-checkbook) systems which mimics the paper-based checkbook mechanism, in line with social needs. Considering the cost of paper check procedures and the amount of money transferred using checks, we believe that there should be a properly designed and provably secure e-checkbook scheme. Analyzing the vulnerabilities of the existing systems, and ﬁguring out where they originate is the ﬁrst step towards a secure e-checkbook mechanism. In this study, we show that the e-checkbook schemes denoted as PEEC , CYLL , CCL , CWL and CCW fail to achieve their claimed security and susceptible to various types of attacks including e-check forgery and manipulation. Particularly, we show that Pasupathinathan et al. ’s PEEC scheme does not satisfy the correctness, anonymous identity and payment unlinkability; Chen et al. ’s CYLL scheme is not secure against e-check manipulation and e-check forgery attacks; Chang et al. ’s CCL scheme, Chen et al. ’s CWL scheme and Chang et al. ’s CCW scheme are susceptible to e-check manipulation attack.
Anatomical-MRI Correlations in Adults and Children with Hypertrophic Cardiomyopathy

Hypertrophic Cardiomyopathy (HCM) is the most frequent hereditary cardiovascular disease and the leading cause of sudden cardiac death in young individuals. Advancements in CMR imaging have allowed for earlier identification and more accurate prognosis of HCM. Interventions aimed at slowing or stopping the disease’s natural course may be developed in the future. CMR has been validated as a technique with high sensitivity and specificity, very few contraindications, a low risk of side effects, and is overall a good tool to be employed in the management of HCM patients. The goal of this review is to evaluate the magnetic resonance features of HCM, starting with distinct phenotypic variants of the disease and progressing to differential diagnoses of athlete’s heart, hypertension, and infiltrative cardiomyopathies. HCM in children has its own section in this review, with possible risk factors that are distinct from those in adults; delayed enhancement in children may play a role in risk stratification in HCM. Finally, a number of teaching points for general cardiologists who recommend CMR for patients with HCM will be presented.
Fracturas intertrocantéricas en adultos mayores: análisis de los factores de riesgo asociados con falla en la osteosíntesis con clavo cefalomedular

Objetivos: Las fracturas intertrocantéricas son cada vez más frecuentes debido al envejecimiento de la población. La osteosíntesis con clavo cefalomedular se utiliza ampliamente para tratar estas fracturas. El objetivo de este estudio fue analizar la tasa de fallas en la osteosíntesis con clavo cefalomedular y sus factores de riesgo. Materiales y Métodos: Se evaluó retrospectivamente a pacientes >75 años con fracturas intertrocantéricas tratados con clavo cefalomedular, en nuestro Centro, entre enero de 2016 y diciembre de 2019. Se analizó el ángulo cervicodiafisario de la cadera operada en el posoperatorio inmediato, la distancia puntaápice y la posición del tornillo deslizante en la cabeza femoral. Resultados: Se incluyó a 66 pacientes. Hubo 8 casos de fallas en la osteosíntesis (12,12%). Se comprobó que ciertos factores ya establecidos (distancia punta-ápice >25 mm y reducción en varo) también fueron factores de riesgo significativos para falla en la osteosíntesis, en nuestra cohorte. La posición inadecuada del tornillo deslizante fue un factor de riesgo con significación estadística en el análisis univariado, pero no en el multivariado. Conclusiones: Al tratar las fracturas intertrocantéricas con un clavo cefalomedular, la reducción en neutro o ligero valgo, apuntando a una distancia punta-ápice ≤25 mm redujo significativamente el riesgo de falla en la osteosíntesis. Hallamos que una localización superior o posterior del tornillo deslizante aumenta el riesgo de falla en la fijación, si bien la localización del tornillo no fue un factor de riesgo significativo en el análisis multivariado.
Surface Water Salinity Evaluation and Identification for Using Remote Sensing Data and Machine Learning Approach

Knowledge of the spatio-temporal distribution of salinity provides valuable information for understanding different processes between biota and environment, especially in hypersaline lakes. Remote sensing techniques have been used for monitoring different components of the environment. Currently, one of the biggest challenges is the spatio-temporal monitoring of the salinity level in water bodies. Due to some limitations, such as the inability to be located there permanently, it is difficult to obtain these data directly. In this study, machine learning techniques were used to evaluate the salinity level in hypersaline East Sivash Bay. In total, 93 in situ data samples and 6 Sentinel-2 datasets were used, according to field measurements. Using linear regression, random forest and AdaBoost models, eight water salinity evaluation models were built (six with simple, one with random forest and one with AdaBoost). The accuracy of the best-fitted simple linear regression model was 0.8797; for random forest, it was equal, at 0.808, and for AdaBoost, it was −0.72. Furthermore, it was found that with an increase in salinity, the absorbing light shifts from the ultraviolet part of the spectrum to the infrared and short-wave infrared parts, which makes it possible to produce continuous monitoring of hypersaline water bodies using remote sensing data.
Design and implementation of 15-level cascaded multi-level voltage source inverter with harmonics elimination pulse-width modulation using differential evolution method

This study describes the application of a soft computing method - known as differential evolution (DE) to implement the harmonics elimination pulse-width modulation (HEPWM) for a 15-level, multi-level voltage source inverter (MVSI). The main feature of this technique is the wide range of fundamental output voltage that can be achieved, while maintaining a very low total harmonics distortion (below 6%). The fundamental voltage can be controlled very precisely (very small step size) and the trajectories of the HEPWM angles are continuous over the whole range of modulation index - making it very suitable for utility and variable speed drives application. The study details the procedures to obtain the HEPWM angles trajectories, which include the formulation of objective functions, appropriate selection of the DE evolutionary parameters and stopping criterions. The viability of the proposed method is simulated using MATLAB/Simulink and verified by a three-phase, cascaded-type MVSI test rig. Experimentally, The HEPWM waveforms are implemented using the Altera field programmable gate array, whereas the 15-level MVSI is built using metal-oxide semiconductor field effect transistor H-bridge. Selected results are presented; the simulation and hardware results are found to be in very close agreement with the theoretical predictions.
Natural Enantiomers: Occurrence, Biogenesis and Biological Properties

The knowledge that natural products (NPs) are potent and selective modulators of important biomacromolecules (e.g., DNA and proteins) has inspired some of the world’s most successful pharmaceuticals and agrochemicals. Notwithstanding these successes and despite a growing number of reports on naturally occurring pairs of enantiomers, this area of NP science still remains largely unexplored, consistent with the adage “If you don’t seek, you don’t find”. Statistically, a rapidly growing number of enantiomeric NPs have been reported in the last several years. The current review provides a comprehensive overview of recent records on natural enantiomers, with the aim of advancing awareness and providing a better understanding of the chemical diversity and biogenetic context, as well as the biological properties and therapeutic (drug discovery) potential, of enantiomeric NPs.
Influence of Phosphorous Auto-Doping on the Characteristics of SiO2/SiC Gate Dielectrics

We present the influence of phosphorous auto-doping on the characteristics of the oxide interface in 4H-SiC following high temperature gate oxide annealing. IV characteristics show no evidence of direct tunnelling breakdown; however Fowler Nordheim (F-N) conduction is observed in high electric field with the oxides able to sustain >10MV/cm. Capacitance Voltage data show DIT <1x1012 eV-1cm-2 close to the conduction band edge after POA, with undoped samples demonstrating DIT below 5x1011 eV-1cm-2. Photo CV data indicates smaller flat band voltage shifts of 0.6V at midpoint for the undoped samples, in comparison to 0.9V for the phosphorous doped devices. Temperature and bias stress tests at 200°C showed marginal hysteresis (0.3V) in both wafers. Reliability of time-dependent constant current and constant voltage characteristics revealed higher TDDB lifetimes in the undoped wafer. We conclude that the unintentional incorporation of phosphorous into the gate stack as a result of high temperature POA of the doped field oxide leads to a variation in flat band shift, higher DIT, and lower dielectric reliability.
Initial rotor position detection for surface mounted PMSM based on rotating high frequency signal injection

The estimation result of the initial rotor position for the permanent magnet synchronous machine(PMSM) maybe reverse with the high frequency signal injection method.A method was proposed to distinguish N/S polarity and detect initial rotor position according to the non-linear magnetizing characteristics of stator core.The rotor saliency detection result was extracted from the high frequency negative sequence current and N/S polarity was identified by analyzing the range information of current vector.Initial rotor position angle is gotten by use of the two results.Experimental results verify the effectiveness of the proposed approach.
Phenotyping Cardiopulmonary Exercise Limitations in Chronic Obstructive Pulmonary Disease

Background Exercise limitation in chronic obstructive pulmonary disease (COPD) is commonly attributed to abnormal ventilatory mechanics and/or skeletal muscle function, while cardiovascular contributions remain relatively understudied. To date, the integrative exercise responses associated with different cardiopulmonary exercise limitation phenotypes in COPD have not been explored but may provide novel therapeutic utility. This study determined the ventilatory, cardiovascular, and metabolic responses to incremental exercise in patients with COPD with different exercise limitation phenotypes. Methods Patients with COPD (n = 95, FEV1:23–113%pred) performed a pulmonary function test and incremental cardiopulmonary exercise test. Exercise limitation phenotypes were classified as: ventilatory [peak ventilation (VEpeak)/maximal ventilatory capacity (MVC) ≥ 85% or MVC-VEpeak ≤ 11 L/min, and peak heart rate (HRpeak) < 90%pred], cardiovascular (VEpeak/MVC < 85% or MVC-VEpeak > 11 L/min, and HRpeak ≥ 90%pred), or combined (VEpeak/MVC ≥ 85% or MVC-VEpeak ≤ 11 L/min, and HRpeak ≥ 90%pred). Results FEV1 varied within phenotype: ventilatory (23–75%pred), combined (28–90%pred), and cardiovascular (68–113%pred). The cardiovascular phenotype had less static hyperinflation, a lower end-expiratory lung volume and larger tidal volume at peak exercise compared to both other phenotypes (p < 0.01 for all). The cardiovascular phenotype reached a higher VEpeak (60.8 ± 11.5 L/min vs. 45.3 ± 15.5 L/min, p = 0.002), cardiopulmonary fitness (VO2peak: 20.6 ± 4.0 ml/kg/min vs. 15.2 ± 3.3 ml/kg/min, p < 0.001), and maximum workload (103 ± 34 W vs. 72 ± 27 W, p < 0.01) vs. the ventilatory phenotype, but was similar to the combined phenotype. Conclusion Distinct exercise limitation phenotypes were identified in COPD that were not solely dependent upon airflow limitation severity. Approximately 50% of patients reached maximal heart rate, indicating that peak cardiac output and convective O2 delivery contributed to exercise limitation. Categorizing patients with COPD phenotypically may aid in optimizing exercise prescription for rehabilitative purposes.
UAV-Enabled Mobile-Edge Computing for AI Applications: Joint Model Decision, Resource Allocation, and Trajectory Optimization

Due to the flexible mobility and agility, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future air–ground-integrated wireless networks, providing temporary and controllable coverage and additional computation capabilities for ground Internet of Things (IoT) devices with or without infrastructure support. Meanwhile, with the breakthrough of artificial intelligence (AI), more and more AI applications relying on AI methods such as deep neural networks (DNNs) are expected to be applied in various fields, such as smart homes, smart factories, and smart cities, to improve our lifestyles and efficiency dramatically. However, AI applications are generally computation intensive, latency sensitive, and energy consuming, making resource-constrained IoT devices unable to benefit from AI anytime and anywhere. In this article, we study mobile-edge computing (MEC) for AI applications in air–ground-integrated wireless networks. Our goal is to minimize the service latency while ensuring the learning accuracy requirements and energy consumption. To achieve that, we take DNN as the typical AI application and formulate an optimization problem that optimizes the DNN model decision, computation and communication resource allocation, and UAV trajectory control, subject to the energy consumption, latency, computation, and communication resource constraints. Considering the formulated problem is nonconvex, we decompose it into multiple convex subproblems and then alternately solve them till they converge to the desired solution. Simulation results show that the proposed algorithm significantly improves the system performance for AI applications.
Seropositivity among Blood Samples Drawn from Suspected Dengue Cases at a Tertiary Care Centre of Nepal: A Descriptive Cross-sectional Study

ABSTRACT Introduction: The cases of dengue fever have been reported more frequently in Nepal these days. The aim of this study was to find the prevalence of seropositivity among blood samples drawn from suspected dengue cases at a tertiary care centre of Nepal. Methods: A descriptive cross-sectional study was conducted at a tertiary care hospital from 1st June 2017 to 31st October 2018 after getting approval from the Institutional Review Committee (Reference number: 23/2016). A total of 537 suspected dengue patients were selected for the study using convenience sampling. These dengue positive sera were assayed for their reactivity with Immunoglobulin M and Immunoglobulin G present in sera and synthetic peptides of dengue virus by enzyme linked immunosorbent assay. Data was entered and analysed in Microsoft Excel 2016. Point estimate at 95% Confidence Interval was calculated along with frequency, percentage, mean and standard deviation. Results: Among 537 suspected dengue cases, the seropositivity for dengue was found in 124 (23.09%) (19.52-26.65 at 95% Confidence Interval) of the serum. Conclusions: The present study revealed that dengue was more prevalent in our setting compared to similar studies. All the synthetic peptides showed reactivity with dengue-positive sera with maximum reactivity shown by RR2 peptide. In dengue-positive sera, RR2 peptide of dengue virus identified more Immunoglobulin M than Immunoglobulin G.
Classificação dos Códigos de NCM Usando Processamento de Linguagem Natural

Esse artigo tem como objetivo desenvolver um processo para classificar as descrições dos produtos presentes nas Notas Ficais eletrônicas (NF-e). Essa classificação é feita sobre os Capítulos (primeiros dois dígitos) da Nomenclatura Comum do Mercosul (NCM). A classificação foi realizada utilizando o algorítimo de Máquina de vetores de suporte (SVM), com uma base de dados de 340.000 produtos distintos, que foram tratados usando as técnicas de Processamento natural de linguagem. Obteve-se um acurácia de 87% para um total de 50 classes.
AeroForce - Thrust/Drag Bookkeeping and Aerodynamic Force Breakdown over Components

Aeroforce is an aerodynamic data postprocessing tool that permits a more detailed analysis of the location of aerodynamic forces. For complex configurations the force evaluation of an arbitrary split-up of the entire aerodynamic surface into components like body, wing, pylon or engine is supported. Aeroforce calculates forces and aerodynamic coefficients for these components directly from the pressure and friction distributions.
Quality of hire: expanding the multi-level fit employee selection using machine learning

Purpose Organizational psychologists and human resource management (HRM) practitioners often have to select the “right fit” candidate by manually scouting data from various sources including job portals and social media. Given the constant pressure to lower the recruitment costs and the time taken to extend an offer to the right talent, the HR function has to inevitably adopt data analytics and machine learning for employee selection. This paper aims to propose the “Quality of Hire” concept for employee selection using the person-environment (P-E) fit theory and machine learning. Design/methodology/approach The authors demonstrate the aforementioned concept using a clustering algorithm, namely, partition around mediod (PAM). Based on a curated data set published by the IBM, the authors examine the dimensions of different P-E fits and determine how these dimensions can lead to selection of the “right fit” candidate by evaluating the outcome of PAM. Findings The authors propose a multi-level fit model rooted in the P-E theory, which can improve the quality of hire for an organization. Research limitations/implications Theoretically, the authors contribute in the domain of quality of hire using a multi-level fit approach based on the P-E theory. Methodologically, the authors contribute in expanding the HR analytics landscape by implementing PAM algorithm in employee selection. Originality/value The proposed work is expected to present a useful case on the application of machine learning for practitioners in organizational psychology, HRM and data science.
Emerging and frontier markets

The dramatic reversal of emerging market portfolio flows following the global spread of coronavirus (COVID-19) highlights the challenges of managing volatile portfolio flows and risks they may pose to financial stability. A prolonged period of low interest rates had encouraged both borrowers and lenders to take on more risk. Surges of portfolio inflows into riskier asset markets contributed to the buildup of debt and, in some cases, resulted in stretched valuations. This chapter quantifies the sensitivities of different types of portfolio flows and the associated cost of funding to global and domestic factors during “normal” times as well as during periods of weak or strong flows. Analysis suggests that both bond and equity flows are much more sensitive to global financial conditions during periods of extreme flows than in normal times, while domestic fundamentals may matter incrementally more for equities and local currency bond flows. Furthermore, greater foreign investor participation in local currency bond markets that lack adequate depth can greatly increase the volatility of bond yields. Dealing with immediate capital outflow pressures calls for using reserves to reduce excessive volatility, deploying capital flow management measures, and preparing for long-term external funding disruptions.
Economic and Production-Related Implications for Industrial Energy Efficiency: A Logistic Regression Analysis on Cross-Cutting Technologies

Increased industrial energy efficiency (EE) has become one of the main environmental actions to mitigate carbon dioxide (CO2) emissions, contributing also to industrial competitiveness, with several implications on the production system and cost management. Unfortunately, literature is currently lacking empirical evidence on the impact of energy efficiency solutions on production. Thus, this work primarily aims at investigating the economic and production-related influence on the reduction in industrial energy consumption, considering the cross-cutting technologies HVAC, motors, lighting systems and air compressor systems. The analysis is performed using data from previous studies that characterized the main EE measures for the cross-cutting technologies. Four logistic models were built to understand how costs and production influence energy efficiency across such cross-cutting technologies. In this way, motivating industries to implement measures to reduce electrical consumption, offering an economic cost–benefit analysis and optimizing industry processes so that the reduction in electricity consumption adds to industrial energy efficiency were the aims of this study. The results of this work show through the adjusted indicators that senior management is mainly responsible for energy savings. The operational measures of each piece of equipment can be oriented in the industry towards a specific maintenance process for each technology, becoming an active procedure in industrial productions to obtain EE. Additionally, maintenance planning and control is essential to the reliability of the reduced energy consumption of cross-cutting technologies. This article concludes with managerial implications and suggestions for future research in this field.
Conservation and Divergence of SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) Gene Family between Wheat and Rice

The SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) gene family affects plant architecture, panicle structure, and grain development, representing key genes for crop improvements. The objective of the present study is to utilize the well characterized SPLs’ functions in rice to facilitate the functional genomics of TaSPL genes. To achieve these goals, we combined several approaches, including genome-wide analysis of TaSPLs, comparative genomic analysis, expression profiling, and functional study of TaSPL3 in rice. We established the orthologous relationships of 56 TaSPL genes with the corresponding OsSPLs, laying a foundation for the comparison of known SPL functions between wheat and rice. Some TaSPLs exhibited different spatial–temporal expression patterns when compared to their rice orthologs, thus implicating functional divergence. TaSPL2/6/8/10 were identified to respond to different abiotic stresses through the combination of RNA-seq and qPCR expression analysis. Additionally, ectopic expression of TaSPL3 in rice promotes heading dates, affects leaf and stem development, and leads to smaller panicles and decreased yields per panicle. In conclusion, our work provides useful information toward cataloging of the functions of TaSPLs, emphasized the conservation and divergence between TaSPLs and OsSPLs, and identified the important SPL genes for wheat improvement.
The neural basis of word production

Traditionally, psycholinguistic models and models of speech motor control have approached the word production process from different angles and have remained, to a large extent, separate from one another. Ultimately, however, the process entails the entire pathway from semantic processing to articulation: producing a word from meaning requires activating a concept, retrieving the word, selecting its segments, ordering those segments into the right sequence, and finally articulating them with the correct timing. This chapter reviews the neural basis of word production from the complementary perspectives of the psycholinguistic literature and the speech motor control literature.