Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Protection of Nucleic Acids from Herbicide Attack: Self-Assembly of Betaines Based on Pillar[5]arene with Glyphosate and DNA

Herbicides are one of the main parts of pesticides used today. Due to the high efficiency and widespread use of glyphosate-based herbicides, the search for substances reducing their genotoxicity is an important interdisciplinary task. One possible approach for solving the problem of herbicide toxicity is to use compounds that can protect DNA from damage by glyphosate derivatives. For the first time, a method for developing DNA-protecting measures against glyphosate isopropylamine salt (GIS) damage was presented and realized, based on low-toxicity water-soluble pillar[5]arene derivatives. Two- and three-component systems based on pillar[5]arene derivatives, GIS, and model DNA from salmon sperm, as well as their cytotoxicity, were studied. The synthesized pillar[5]arene derivatives do not interact with GIS, while GIS is able to bind DNA from salmon sperm with lgKa = 4.92. The pillar[5]arene betaine derivative containing fragments of L-phenylalanine and the ester derivative with diglycine fragments bind DNA with lgKa = 5.24 and lgKa = 4.88, respectively. The study of the associates (pillar[5]arene-DNA) with GIS showed that the interaction of GIS with DNA is inhibited only by the betaine pillar[5]arene containing fragments of L-Phe (lgKa = 3.60). This study has shown a possible application of betaine pillar[5]arene derivatives for nucleic acid protection according to its competitive binding with biomacromolecules.
Privilege Escalation Attack Detection and Mitigation in Cloud Using Machine Learning

Because of the recent exponential rise in attack frequency and sophistication, the proliferation of smart things has created significant cybersecurity challenges. Even though the tremendous changes cloud computing has brought to the business world, its centralization makes it challenging to use distributed services like security systems. Valuable data breaches might occur due to the high volume of data that moves between businesses and cloud service suppliers, both accidental and malicious. The malicious insider becomes a crucial threat to the organization since they have more access and opportunity to produce significant damage. Unlike outsiders, insiders possess privileged and proper access to information and resources. In this work, a machine learning-based system for insider threat detection and classification is proposed and developed a systematic approach to identify various anomalous occurrences that may point to anomalies and security problems associated with privilege escalation. By combining many models, ensemble learning enhances machine learning outcomes and enables greater prediction performance. Multiple studies have been presented regarding detecting irregularities and vulnerabilities in network systems to find security flaws or threats involving privilege escalation. But these studies lack the proper identification of the attacks. This study proposes and evaluates ensembles of Machine learning (ML) techniques in this context. This paper implements machine learning algorithms for the classification of insider attacks. A customized dataset from multiple files of the CERT dataset is used. Four machine learning algorithms, i.e., Random Forest (RF), Adaboost, XGBoost, and LightGBM, are applied to that dataset and analyzed results. Overall, LightGBM performed best. However, some other algorithms, such as RF or AdaBoost, may perform better on some internal attacks (Behavioral Biometrics attacks) or other internal attacks. Therefore, there is room for incorporating more than one machine learning algorithm to obtain a stronger classification in multiple internal attacks. Among the proposed algorithms, the LightGBM algorithm provides the highest accuracy of 97%; the other accuracy values are RF at 86%, AdaBoost at 88%, and XGBoost at 88.27%.
Recommoning water: Crossing thresholds under citizen-driven remunicipalisation

Since 2008, the call to ‘remunicipalise’ water resources has become a key strategy for water movements across Europe. Remunicipalisation aimed at opposing the new wave of privatisation programmes and water commodification incentivised under austerity frameworks. However, the water movements’ lack of direct engagement with questions of re/commoning resulted in under-explored links, in practitioner and scholarly arenas, between demands for water remunicipalisation and practices of commoning. This article brings into dialogue the bodies of literature on commoning and remunicipalisation. It examines the conditions which enable crossing the paradigm threshold from municipal governance, towards more collective and situated models of water governance rooted in practices of commoning. The article operationalises the concept of recommoning water to capture this process, and proposes an analytical definition grounded in a case study of water remunicipalisation in Terrassa, Spain. In 2019, Terrassa achieved remunicipalisation to create a citizen water observatory. The empirical findings demonstrate that water activists in Terassa’s Observatory are reclaiming and reproducing the commons on a daily basis through a process of experimentation with institutional bricolage and (re)negotiation of power and autonomy. This citizen-led observatory is ensuring that resources are shared in common, are used for the common good and are reproducing the commons. The study concludes that water remunicipalisation can act as an important step for enabling processes of recommoning. Nevertheless, the institutionalisation of recommoning water under a public management regime is confronted with multifaceted tensions that merit attention from both activists and policymakers.
Optimizing the Learner’s Role in Feedback: Development of a Feedback-Preparedness Online Application for Medical Students in the Clinical Setting

Feedback is an essential component of medical education, especially during clinical rotations. There is growing interest in learner-related factors that can optimize feedback’s efficiency, including goal orientation, reflection, self-assessment, and emotional response. However, no mobile application or curriculum currently exists to specifically address those factors. This technical report describes the concept, design, and learner-based feedback of an innovative online application, available on mobile phones, developed to bridge this gap. Eighteen students in their third or fourth year of medical school provided comments on a pilot version of the application. The majority of learners deemed the module relevant, interesting, and helpful to guide reflection and self-assessment, therefore fostering better preparation before an upcoming feedback session. Minor improvements were suggested in terms of content and format. The learners’ initial positive response supports further efforts to engage in validity and evaluation research. Future steps include modifying the mobile application based on learners’ comments, evaluating its efficacy in a real clinical setting, and clarifying whether it is most beneficial for mid-rotation or end-of-rotation feedback sessions.
Quantitative Assessment of Upper Limb Movement in Post-Stroke Adults for Identification of Sensitive Measures in Reaching and Lifting Activities

Background: The assumption of this work is the achievement of objective results of the movement structure, which forms the basis for in-depth analysis and, consequently, for determining the upper limb movements that are most affected by stroke compared to healthy people. Methods: An analysis of relevant and systematically identified features of upper limb movement in post-stroke adults is presented based on scalable hypothesis tests. The basic features were calculated using movements defined by the x, y, and z coordinates (i.e., 3D trajectory time series) and compared to the results of post-stroke patients with healthy controls of similar age. Results: After automatic feature selection, out of the 1004 common features of upper limb movement, the most differentiated were the upper arm movements in reaching kinematics. In terms of movement type, movements in the frontal plane (shoulder abduction and adduction) were the most sensitive to changes. The largest number of discriminating features was determined on the basis of acceleration time series. Conclusions: In the 3D assessment of functional activities of the upper limb, the upper arm turned out to be the most differentiated body segment, especially during abduction and adduction movements. The results indicate a special need to pay attention to abduction and adduction movements to improve the activities of daily living of the upper limbs after a stroke.
Accounting Information System Quality and Organizational Performance: the Mediating Role of Accounting Information Quality

Purpose: The aim of this study is to examine the mediating role of accounting information quality in the relationship between accounting information system quality and organizational performance in rural banks in the Province of Bali-Indonesia. Theoretical framework: This research builds on the work of DeLone and McLean, regarding information systems success. Design/methodology/approach: The data for this study were collected using a questionnaire sent to the director of rural banks in the Province of Bali-Indonesia as many as 99 participated in this study. The research data were analyzed using the structural equation-partial least squares (SEM-PLS) model. Findings: Empirically the results of this study provide empirical evidence that the accounting information system quality has a significant positive effect on organizational performance and the accounting information quality. Meanwhile, the accounting information quality has a significant positive effect on organizational performance. In addition, the accounting information quality partially mediates the effect of the accounting information system quality on organizational performance. Research, Practical & Social implications: The study are expected to provide insight into the management of rural banks in the Province of Bali-Indonesia, regarding the important role of managing accounting information system quality and accounting information quality because they are able to improve the performance of the bank organizations they lead. Originality/value: The originality of this research is the role model of accounting information quality as a mediator of the influence of information system quality on organizational performance.
Bengali Speech Emotion Recognition: A hybrid approach using B-LSTM

Bengali Speech Emotion recognition is one of the major outstanding and novel pieces of work in Bengali signal processing and the field of Bengali Artificial Intelligence (AI). The audio of human speech consists of various tones from the cochlea and vocal cords. Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Gamma-Frequency Cepstral Coefficients (GFCC), and Mel Spectrogram are already state-of-the-art techniques to extract various features from human speech audio. In our study, MFCC, Chroma, and Mel-Spectrogram techniques are used to extract the features for each audio on a Bengali speech audio corpus, i.e., “SUST Bengali Emotional Speech Corpus (SUBESCO)” since no other study has used all 3 features together yet. Besides, a novel model using Bidirectional Long Short Term Memory (Bi-LSTM) is proposed using these extracted features to detect different emotions in the Bengali speech corpus. Finally, our proposed model shows an overall accuracy of 83.33% with better average precision and f1-score than the Support Vector Machine (SVM) classifier and Multi-Layer Perceptron (MLP) Classifier in detecting 7 different emotion classes.
Nonlinear Energy Harvesting by Piezoelectric Bionic ‘M’ Shape Generating Beam Featured in Reducing Stress Concentration

Inspired by the flapping wings of seagulls during flight, a new low-cost, magnet-free, bistable piezoelectric energy harvester is proposed to obtain energy from low-frequency vibration and convert it into electrical energy and reduce fatigue damage caused by stress concentration. In order to optimize the power generation efficiency of this energy harvesting, finite element analysis and experimental tests were carried out. The results of finite element analysis and experimental results are in good agreement, and the superior performance in improving stress concentration of the energy harvester compared to the previous parabolic (bow-shaped) one using bistable technology was quantitatively analyzed using finite element simulation, with a maximum stress reduction of 32.34%. The experimental results showed that under optimal operating conditions, the maximum open-circuit voltage of the harvester was 11.5 V, and the maximum output power was 73 μW. These results indicate that this is a promising strategy, which provides a reference for collecting vibrational energy in low-frequency environments.
Within, Between, and Beyond: A New Approach to Examining World Income Inequality

Abstract:World income inequality is comprised of uneven development between states and unequal distribution within states. Recent work shows that the "between-country" component still accounts for a majority of the total, but that attention is shifting to the "within-country" portion, which is growing in both absolute and relative terms. What is less appreciated, though, is that the way income is distributed within countries also plays an indirect role in how income differences are recognized between them. When a nation's income distribution is highly unequal, its mean income is substantially larger than the income of its average person, thereby masking a depreciation in living standards for those residing in the middle. The practical effect of this distortion is that poor, unequal countries seem wealthier than they really are when using mean incomes to represent country averages, as is typically done. I address this shortcoming in prior work by estimating between-country inequality using median incomes. My analysis covers the 1990–2017 period for 123 countries that represent over 90 percent of the world's population. According to Theil's T, I find that (a) inequality in median incomes is almost 15 percent higher than inequality in mean incomes, and that (b) median incomes are converging about 7.5 percent more slowly than mean incomes. This translates to a higher level of world income inequality, which is likewise converging at a slower rate. Overall, I find that the direct and indirect effects of national inequality are now responsible for about half of the world's income inequality.
Analysis of recurrence determination for epileptic brain electric signals

This paper used the method of order recurrence plot to analyze the determination(DET) of epileptic brain electrical signals.The results show that DET of epileptic scalp brain electric signals(EEG) is higher than the ones of healthy EEG.And the difference of DET is more obvious in some partial electrodes.DET of these partial electrodes could be used as features to auto-diagnose epilepsy.Then it conducted analysis of DET for electrocorticogram(ECoG) during a seizure and that during a seizure-free interval.The results show there is no significant difference for determination in the whole band.However,DET during a seizure is obviously higher than that during a seizure-free interval in beta band.Changes of DET in beta band could be used to predict epilepsy seizure.The research results provide a theoretical basis for auto-diagnosis of epilepsy and seizure prediction.
Planetarium software in the classroom

Students often find astronomy and astrophysics to be most interesting and exciting, but the Universe is difficult to access using only one’s eyes or simple equipment available at different educational settings. To open up the Universe and enhance learning astronomy and astrophysics different planetarium software can be used. In this article we discuss the usefulness of such simulation software and give four examples of how such software can be used for teaching and learning astronomy and astrophysics.
Synergetic effect of milling speed and duration on particle morphology and mechanical properties of nanocrystalline Al matrix containing SiC

ABSTRACT In the present study, the composite sheets were produced by powder rolling the ball-milled Al/SiC mixture. The effect of milling speed (350–550 rev min–1) and milling duration (8 and 12 h) on the Al particle morphology and subsequent mechanical properties were examined for composites with different volume fractions of SiC. From the microstructural investigation, SiC particles are evenly distributed and well dispersed in the Al matrix; those are prepared under higher milling speed (550 rev min–1) and higher milling time (12 h). Furthermore, the flattened-shaped particle morphology was obtained under lower milling conditions, whereas the reduced-granule shape was obtained under higher milling conditions (550 rev min–1, 12 h). Consequently, the Al/SiC composite sheets made from a granule-shaped Al/SiC mixture show higher density, microhardness, and tensile strength due to significant consolidation and SiC uniformity. In addition, the tensile strength of 430.26 MPa has been achieved for the Al/2vol. -%SiC sheet compared to pure Al (393.49 MPa) under similar processing conditions.
Choice of Antenna Array Geometry for Digital Spatial Filtering in Azimuth and Elevation

In the paper a comparative analysis of digital spatial filtering is carried out depending on the geometry of the antenna array, as well as the directivity of the antenna elements. It is assumed that the considered digital antenna arrays estimate the spatial coordinates of radio emission sources at the first stage. Then they form the radiation pattern. However, it has recently been found that the characteristics of digital spatial processing can be significantly improved. The paper considers spherical, cylindrical and planar antenna arrays with directional radiators. The characteristics of the direction-of-arrival estimation (DOA) algorithm are first researched depending on the azimuth and elevation scanning based on the antenna arrays. In particular, root mean square error (RMSE) is evaluated. In addition, the characteristics of digital beamforming are given, such as the signal-to-interference noise ratio (SINR), as well as a bit error rate (BER) at the output of the digital array of the considered geometries.
Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques

Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection.
Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training

Tactile attention tasks are used in the diagnosis and treatment of neurological and sensory processing disorders, while somatosensory event-related potentials (ERP) measured by electroencephalography (EEG) are used as neural correlates of attention processes. Brain-computer interface (BCI) technology provides an opportunity for the training of mental task execution via providing online feedback based on ERP measures. Our recent work introduced a novel electrotactile BCI for sensory training, based on somatosensory ERP; however, no previous studies have addressed specific somatosensory ERP morphological features as measures of sustained endogenous spatial tactile attention in the context of BCI control. Here we show the morphology of somatosensory ERP responses induced by a novel task introduced within our electrotactile BCI platform i.e., the sustained endogenous spatial electrotactile attention task. By applying pulsed electrical stimuli to the two proximal stimulation hotspots at the user’s forearm, stimulating sequentially the mixed branches of radial and median nerves with equal probability of stimuli occurrence, we successfully recorded somatosensory ERPs for both stimulation locations, in the attended and unattended conditions. Waveforms of somatosensory ERP responses for both mixed nerve branches showed similar morphology in line with previous reports on somatosensory ERP components obtained by stimulation of exclusively sensory nerves. Moreover, we found statistically significant increases in ERP amplitude on several components, at both stimulation hotspots, while sustained endogenous spatial electrotactile attention task is performed. Our results revealed the existence of general ERP windows of interest and signal features that can be used to detect sustained endogenous tactile attention and classify between spatial attention locations in 11 healthy subjects. The current results show that features of N140, P3a and P3b somatosensory ERP components are the most prominent global markers of sustained spatial electrotactile attention, over all subjects, within our novel electrotactile BCI task/paradigm, and this work proposes the features of those components as markers of sustained endogenous spatial tactile attention in online BCI control. Immediate implications of this work are the possible improvement of online BCI control within our novel electrotactile BCI system, while these finding can be used for other tactile BCI applications in the diagnosis and treatment of neurological disorders by employing mixed nerve somatosensory ERPs and sustained endogenous electrotactile attention task as control paradigms.
Evaluation of Techniques for Sim2Real Reinforcement Learning

Reinforcement learning (RL) has demonstrated promising results in transferring learned policies from simulation to real-world environments. However, inconsistencies and discrepancies between the two environments cause a negative transfer. The phenomenon is commonly known as the “reality gap. ” The reality gap prevents learned policies from generalizing to the physical environment. This paper aims to evaluate techniques to improve sim2real learning and bridge the reality gap using RL. For this research, a 3-DOF Stewart Platform was built virtually and physically. The goal of the platform was to guide and balance the marble towards the center of the Stewart platform. Custom API was created to induce noise, manipulate in-game physics, dynamics, and lighting conditions, and perform domain randomization to improve generalization. Two RL algorithms; Q-Learning and Actor-Critic were implemented to train the agent and to evaluate the performance in bridging the reality gap. This paper outlines the techniques utilized to create noise, domain randomization, perform training, results, and observations. Overall, the obtained results show the effectiveness of domain randomization and inducing noise during the agents' learning process. Additionally, the findings provide valuable insights into implementing sim2real RL algorithms to bridge the reality gap.