Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential of satellite-derived hydro - meteorological information for landslide hazard assessment thresholds in Rwanda

Satellite and hydrological model-based technologies provide estimates of rainfall and soil moisture over larger spatial scales and now cover multiple decades, sufficient to explore their value for the development of landslide early warning system in data scarce regions. In this study, we used statistical metrics to compare gauge-based to satellite-based precipitation products and assess their performance in landslide hazard assessment and warning in Rwanda. Similarly, the value of high 15 resolution satellite and hydrological model-derived soil moisture was compared to in situ soil moisture observations at Rwanda weather station sites. Based on statistical indicators, the NASA GPM-based IMERG rainfall product showed the highest skill to reproduce the main spatiotemporal precipitation patterns at the studies sites in Rwanda. Similarly, the satellite and model-derived soil moisture time series broadly reproduce the most important trends of in situ soil moisture observations. We evaluated two categories of landslide meteorological triggering conditions from IMERG satellite precipitation. First, the 20 maximum rainfall amount during a multiple day rainfall event. Second, the cumulative rainfall over the past few day(s). For each category, the antecedent soil moisture recorded at three levels of soil depth, top 5 cm by satellite-based technologies as well as top 50 cm and 2 m through modelling approaches, was included in the statistical models to assess its potential for landslide hazard assessment and warning capabilities. The results reveal the cumulative 3 day rainfall R D3 as the most effective predictor for landslide triggering. This was indicated not only by its highest discriminatory power to distinguish landslide from 25 no landslide conditions (AUC ~0.72) but also the resulting true positive alarms TPR of ~80 %. The modelled antecedent soil moisture in the 50 cm root zone Se root(t-3) was the most informative hydrological variable for landslide hazard assessment (AUC ~0.74 and TPR of 84 %). The hydro - meteorological threshold models that incorporate the Se root(t-3) and R D3 following the cause – trigger concept in a bilinear framework reveal promising results with improved landslide warning capabilities in terms of reduced rate of false alarms by ~20 % at the expense of a minor reduction of true alarms by ~8 %.
9 Exploration of the Water-Energy-Food Nexus for Policy Making and Implementation

The research aims to evaluate the concept and policy framework of the Water-Energy-Food (WEF) Nexus in the Korean and international context for achieving water security and make suggestions for relevant policy-making and implementation. The study introduces practices of the WEF Nexus in Saudi Arabia, Ethiopia, USA and South Korea. The WEF Nexus can be defined as ‘a way of thinking about the interdependencies, tensions and trade-offs between water, energy and food security in the wider context of environmental change with a focus on the impact on social systems (Houses of Parliament, 2016)’. There are three essential elements for the nexus approach, the interdependencies, trade-offs and synergies. Saudi Arabia has managed to export livestock and dairy products by consuming water produced through desalination plants. Such energy-intensive practices need to be avoided. Ethiopia has striven to achieve socio-economic development through the nexus approach which can be found from its Growth and Transformation Plan. The state of California, US, has coped with serious droughts via the introduction of brackish desalination plants. Floating solar power plants in South Korea indicates another attempt to apply the nexus approach to the existent infrastructure for efficiency and environmental conservation. Nexus-based policy-making and implementation should reflect the urgency to minimize trade-offs and optimize synergies between water, energy and food, which serves as a backbone to support the enhancement of water security. Mobilizing public and private funding will be necessary, and the socio-economic and environmental systems should be revised and adjusted to reflect nexus-based policies and frameworks.
Classification and Interchange of Informal and Formal English Text

The last two decades have seen a growing trend towards machine learning. Document Categorization, the assignment of a document to one or more groups or categories, is one of the data mining problems that cause many discussions. However, as a linguistic problem and sub-category of Document Categorization, formal and informal text classification has received rather little attention despite the fact that a real need exits to resolve the problem. In this paper, we work with machine learning techniques to deliver a model that can distinguish formal and informal texts with JavaScript implement since it needs to run on web browser. The explored classification estimators are decision tree, random forest and logistic regression. The accuracy and proficiency of the estimators are measured to find out which features are crucial in text formality classification. In the end, we discuss how to transform informal text to formal text and vice versa.
Optimizing the NIST Post Quantum Candidate SPHINCS+ using AVX-512

Today’s cryptography is not strong enough to ensure the security risks we might face in the future. One of the threats to cryptography is quantum computers, and for that reason, NIST created the NIST Post Quantum Cryptography Standardization (PQC), a standardization process for post-quantum schemes. SPHINCS+ is one of the schemes in the alternative candidates in the third round of this project. The main disadvantage of SPHINCS+ is its speed performance. This thesis analyzes ways to improve its performance using parallelism, more precisely using Intel’s AVX-512. Using this approach, we can achieve 1.8× or 1.45× speed up for key generation and signing (for the s and the f options of the scheme) and a near 1.3x time improvement for verification in the case of SHA256, compared to the AVX2 version, which is submitted to the NIST Post Quantum Cryptography Standardization. In the case of SHAKE256, we get 2.7× speed up performance for key generation and signing and 1.9× improvement for verification, comparing to the AVX2 version, which is submitted to the NIST Post Quantum Cryptography Standardization. We also present a comparison to the other NIST candidates who are still part of the third round.
Keratinocyte Proliferative and Wound Healing Effects of Edible Bird’s Nest Extract on Human Skin

Edible bird’s nest is a delicacy of Chinese cuisine. Its major components include mucin and sialic acid in addition to epidermal growth factor-like substances. However, its effects on skin cells and skin barrier function currently remain unknown. Therefore, we herein examined the effects of edible bird’s nest extract (EBN) on skin cell proliferation, wound healing, and tight junction proteins as well as human skin barrier function. The results obtained showed that EBN (0.1%) enhanced the proliferation of keratinocytes and fibroblasts as well as the migration and tight junction protein expression by keratinocytes. The oral inges tion of EBN (70 mg/day) standardized by sialic acid for 1 month reduced transepidermal water loss, the shallow wrinkle area, and dermal thickness. These results suggest that EBN exerts epidermal protective and smoothing effects through keratinocytes. Thus, EBN is a valuable ingredient for maintaining healthy skin. ( Int J Biomed Sci 2020; 16 (4): 43-51)
Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria)

Abstract. The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected, task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian Basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for the pre-industrial climate, while for the future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated for by much drier soils (−45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties in the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties in landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.
Efficacy and Feasibility of the Minimal Therapist-Guided Four-Week Online Audio-Based Mindfulness Program ‘Mindful Senses’ for Burnout and Stress Reduction in Medical Personnel: A Randomized Controlled Trial

Previous online mindfulness-based interventions for burnout and stress reduction in medical personnel had limited effect size and high dropout rate, so we developed a new online mindfulness program ‘Mindful Senses (MS)’ with aims to increase effect size and lower dropout rate. To test its efficacy and feasibility, ninety medical personnel with moderate or high levels of burnout and stress from across Thailand were recruited and randomly allocated into Group A and Group B equally. Group A read psychological self-help articles (PSA) and attended MS program through smartphone application during weeks 1–4. Group B read PSA during weeks 1–4 and attended MS program during weeks 9–12. Burnout, stress, anxiety, depression, mindfulness, and quality of life were measured at baseline, week 4, and week 8 for both groups, and at weeks 12 and 16 for Group B. Group x time interaction was analyzed by repeated-measures ANOVA. The results showed that, compared to PSA only, MS + PSA had statistically significant improvement of burnout, stress, anxiety, depression, mindfulness, and quality of life with moderate-to-very large effect sizes at week 4 (d = 1.33, 1.42, 1.04, 1.14, 0.70, and 1.03, respectively) and moderate-to-large effect sizes at week 8 (d = 0.84, 0.98, 0.73, 0.73, 0.66, and 0.94, respectively). The dropout rate was 4.4%. In conclusion, the MS program has potential to be an alternative intervention for medical personnel suffering from burnout and stress.
Learning Geometric Constraints for Safe Robot Interactions

The safety of robot is becoming increasingly important, especially in human-robot interaction workspace. The issue of robot collisions is also a hot topic in the field of robot safety. Many methods are now available for collision avoidance control through reactive controller. For Reinforcement Learning (RL), some approaches have achieved collision avoidance by establishing geometric constraints during exploration. However, there is no method that can accurately represent the distance field or geometric constraints for both static and dynamic objects throughout the workspace. In this work, we propose a novel type of Signed Distance Field (SDF): Regularized Deep Signed Distance Field (ReDSDF), which is a single neural implicit function and can compute smooth distance field and define dynamic geometric constraints at any scale. Nowadays, there are also many methods that compute distance fields by using neural networks, but these methods can only estimate the area near the object and fail for long distance. Moreover, there is no method to calculate the distance field for dynamical articulated objects. Therefore, the author proposes the approach of data generation, network architecture and training methods to remedy these shortcomings. The ReDSDF can be applied not only as repulsive potential function in reactive motion generation, but also as constraints in RL. In the reactive controller, the ReDSDF of each object can be directly used as an Artificial Potential Field (APF) to control the robot to avoid collisions. And the gradient of the APF can be calculated by deriving the neural network. In RL, ReDSDF can be applied as implicit equations to define the constraints through determining the safe distance between the robot and the object. The application of ReDSDF in robotics is not limited to this. It can be employed by any constrained planner, both as a differentiable constraint and as an energy function. To sum up, ReDSDF can achieve great efficacy in the field of robot safety.
A semiparametric shock model for a pair of event-related dependent censored failure times

In this paper, we propose a semiparametric shock model for two dependent failure times where the risk indicator of one failure time plays the role of a time-varying covariate for the other one. Following Hougaard (2000), the dependence between the two failure times is therefore of event-related type. To derive our results, we define a joint distribution for the pair of failure times by means of a pair of parameters and a pair of baseline hazard rates. We then construct the associated counting processes and their compensators: in particular, we exhibit a special form of the risk indicators. We then propose a maximal partial likelihood estimator for the pair of parameters and Breslow estimators for the pair of baseline cumulative hazard rates. We establish the large sample properties of our estimators, following Andersen et al. (1992) and we illustrate our results by a short simulation study and an application to a real data set in demography
Channels Selection for Pattern Recognition of Five Fingers Motor Imagery Electroencephalography Signals

Research about pattern recognition on electroencephalography (EEG) signal of finger motor imagery (MI) plays a critical role in Brain-Computer Interfaces (BCI) based hand prosthetics development. However, the previous research still used irrelevant channels to finger MI. This work proposed optimal EEG channels combination for five-finger MI. It is achieved by subject-dependence channel selection using One versus Rest Common Spatial Pattern (CSP-OVR) combined with sequential searching algorithms due to specific neural activation areas of MI. Optimal channels combinations are of great importance to reduce channels number. It supports the development of practical BCI-based hand prosthetics that can help hand handicapped to do daily activities easier. Experimental results show 4 out of 19 channels are relevant to five-finger MI with 0,6% accuracy degradation compared with EEG-MI pattern recognition using 19 channels. This result is better than the Principal Component Analysis (PCA) channel selection method that only selects 11 out of 19 channels with 1 % accuracy degradation.
The performance of the Optimisation and Regenerative Braking systems by using PI controlling technique for Electric Vehicle (EV)

Electric automobiles have lower fuel costs because electric motors are more efficient than internal combustion engines. These automobiles can only drive a certain distance because to insufficient batteries and the lack of charging stations. EVs, on the other hand, lack a short driving distance and require frequent charging and discharging of power batteries. EVs will benefit from the development of energy-saving technologies that will expand their reach and prevent battery damage caused by frequent charging and discharging. The goal is to improve the energy efficiency of braking system and its motor efficiency to generate current, as well as to improve regenerative braking (RB) efficiency in EV’s. Using a Brushless DC (BLDC) motor by adding an LC filter is the method that will be applied. A significant addition of current and less ripple and spike in the torque and a quick dynamic interference is the desired result. The performance of the system optimisation and RB circuit systems by using PI controlling technique. The output from torque produced by a 2200 Rpm is 3 Nm and demonstrates that the BLDC motor is efficient when driven by an LC filter circuit. Finally, using the Matlab/Simulink tool, this research presents a thorough simulation of a BLDC motor’s operation as a motor and a generator with RB to create more current in EVs.
Real Time Retinal Optic Disc Segmentation via Guided filter and Discrete Wavelet Transform

In the world, glaucoma is the one of the main causes for loosing vision. Optical Coherence Tomography or fundus camera is utilized to capture the optic disc and optic cup images. For the detection of glaucoma and afterwards monitoring the patients, investigation of the head of optic nerve or cup-to-disc ratio (CDR) is an important factor. For computing the CDR value, segmentation of optic disc and optic cup are utilized for the isolation of the relevant parts of the fundus image. Even though ophthalmologists are computing the CDR value physically, however, it limits the identification of glaucoma at the early stage. The accurate value of CDR is hard to find out if the optic cup and optic disc are not well defined properly. Thus, this paper has suggested a combined model of Guided filter and Discrete Wave Transform (DWT) for the enhancement and OD segmentation. Hysteresis thresholding is utilized for the optic cup segmentation. A set of 50 images of 25 patients are obtained from Visakha Eye Hospital, Visakhapatnam, India, is used to verify the performance of the suggested model. The recommended approach is also verified on the glaucoma subset of High-Resolution Fundus (HRF) database. The experimental results of both the databases demonstrate that the CDR value is computed accurately with low computational time.
Modelling of an Electric Vehicle

Electric Vehicles (EVs) are considered to be as an essential aspect in the immediate term to reduce global emissions and to improve local air quality. The automobile industry will have a substantial transformation in the upcoming five to ten years than it has in the last few decades. The development of technologies promoted a better eco-friendly version of EVs. Electric cars in short are the thought of batteries on wheels and are the future of motoring. Growing concerns over the limited supply of fossil fuels led to the invention of electric cars which truly rely upon the use of an electric drive system rather upon the Internal Combustion Engines. In addition, electric cars diminished the pressure of massive carbon emissions, urban pollution, greenhouse gases, and city noises. This paper proposes an investigative study on electric vehicle modelling, simulation, and step-by-step control design for an effective environment. For the purpose of modelling and simulation of an electric vehicle MATLAB/SIMULINK software is used.
Slice-to-voxel reconstruction of porous media using style-based generative adversarial networks

Prediction of microstructure properties is of great significance for porous media, and the use of limited two-dimensional data to synthesize three-dimensional (3D) structural samples is an important support for this task. Although generative adversarial network technology has been successfully migrated to this scenario, its applicability is limited by the development of deep network technology. In this work, we use a more advanced StyleGAN architecture to accomplish this task and show that this method can achieve or even exceed the reconstruction effect of the previous voxel-to-voxel method.
White Blood Cells Detection Using Saturation Level

Blood is made up of three main components; erythrocytes, leukocytes and thrombocytes. Each of these blood components all have their own roles in the human body. Leukocytes, which can be divided into five types; Basophil, Neutrophil, Eosinophil, Lymphocytes and Monocytes are all part of the body’s defence mechanism to fight against pathogens that could harm the body. Identifying the presence of these blood cells is one of the fundamental ways to diagnose a disease. Hence, blood tests are always being run by physicians in clinical practice. Manually identifying leukocytes is a tedious and time-consuming process, and does not guarantee standardised results as it depends fully on the operator’s skills. Therefore, many works have been done to develop an automated method of leukocyte identification, which aims to reduce the processing time, cost-effective and is efficient in producing standardised results. The proposed method uses the technique of segmenting the nucleus and cytoplasm of leukocytes by extracting it from the Saturation level of the image.
A new rejuvenation heat treatment of crept Ni-based superalloy single crystals

Ni-based superalloys are widely used in harsh environments due to their exceptional high-temperature mechanical properties, and their microstructures are inevitably subjected to degradation, including accumulation of strain/stress, generation of high density of dislocations, and rafting of γ′-particles, under creep damage. To prolong their service life, rejuvenation heat treatment is proposed to restore their microstructure. In this study, a Ni-based superalloy single crystal specimen with 0.15% creep strain is taken as a surrogate of partially served components. The microstructure, including the dislocation density and distribution as well as the size and morphology of γ′-particles, is investigated. Then two comparative heat treatment protocols are applied to the crept superalloy single crystal. Because super-solvus homogenization treatment triggers recrystallization, a recovery pre-annealing step at sub-solvus temperature is designed to relieve the stored deformation energy. After homogenization and aging heat treatment, the single crystalline structure is preserved, and in the meanwhile the γ′-particles are restored to uniform cuboidal morphology and the dislocations are fully eliminated. Our findings provide a route to rejuvenate crept superalloy single crystals.
Information-theoretic temporal Bell inequality and quantum computation (5 pages)

An information-theoretic temporal Bell inequality is formulated to contrast classical and quantum computations. Any classical algorithm satisfies the inequality, while quantum ones can violate it. Therefore, the violation of the inequality is an immediate consequence of the quantumness in the computation. Furthermore, this approach suggests a notion of temporal nonlocality in quantum computation.
Negatywne reguły asocjacyjne - wyznaczanie, miary i obszary zastosowania

Streszczenie. Artykul opisuje pozytywne oraz negatywne reguly asocjacyjne. Przyblizone zostaly najwazniejsze miary dla regul asocjacyjnych. Przykladowa analiza przeprowadzona zostala za pomocą środowiska R. Opisana zostala klasyfikacja bazująca na pozytywnych i negatywnych regulach asocjacyjnych.
Series Arc Fault Detection Sensor Based on an ABS Rogowski Coil in Medium Voltage

Three types of Rogowski coils were designed and constructed by utilising 3D printing technology: RC1, RC2, and RC3, with the sensors having the same geometrical dimensions but varying number of turns: 20, 50, and 100 respectively. To verify the feasibility and effectiveness of the proposed sensors, a series arc fault generator was set up and high voltage of up to 3-kV was injected. The analysis of the Fast Fourier Transform (FFT) was done by using MATLAB to determine frequency and voltage amplitude. From the analysis, it was determined that the higher the number of turns, the greater the sensitivity. The resonance frequency (MHz) dropped significantly as the number of turns increased. Furthermore, sensor features such as frequency bandwidth and sensitivity are affected by the number of turns, which was thoroughly investigated in this study. The Rogowski coil was created to detect the high frequency component of the series arc fault signal. All the designed sensors reliably detect the series arc fault signal, according to the experimental results.