Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Docking Study on Some Isonicotinoyl Hydrazide Derivatives as Potential Inhibitors of COVID-19

Coronavirus (COVID-19) is more than a health disaster;it is the greatest challenge that the world confrontsnowadays. There is a race to slow the spread of this disease. Searching for an antiviral agent to stop COVID-19 is an essential demand since there is no approved drug for COVID-19 till now. Molecular docking is a powerful tool in predicting new drugs. In this study, Favpiravir (Avigan), Hydroxychloroquine, and a series of biologically active compounds derived from iso-nicotinoyl hydrazide have been chosen for molecular docking study. Molecular docking was carried out by theMolegro virtual docker program on proteaseenzyme of COVID-19.The results showed that all the studied molecules are located in the active sites of protease after molecular docking. The tested nicotinoyl hydrazide derivatives showed a higher ranking docking score than Favpiravir (Avigan). According to the docking score ranking rearrangement, Hydroxychloroquine comes the third, and Favpiravir comes the last among the tested compounds. N(2-iso-nicotinoyl hydrazine-carbonthioyl)benzamide(2) and the enol form of (E)-N-(1-phenylethylidene)-nicotinohydrazide(7) have shown the highest docking score (123.23 and -123.12 kcal/mol respectively) among the tested compounds. Ligands (2) and (7) are expected to be potential inhibitors of the main protease enzyme of coronavirus.
Autonomous tracked vehicles effectiveness estimation

Effectiveness of application of autonomous tracked vehicles depends on many factors. For the manned car, the driver decides how to choose the optimal speed. The speed of autonomous vehicles, especially over highly rough terrain, is significantly lower and is caused by the operation of an autonomous control system. In tracked chassis, one of the agility components is the turnability, which characterizes the control ability of the vehicle under the given conditions. Controllability and ride comfort define how the power-weight ratio influence the average vehicle speed. At the same time, the behavior of an autonomous vehicle during rotation depends not only on the power-weight ratio, but also on the dimensions of the tracks support surfaces; pressure on the ground; steering radius provided by steering drive; traction properties, as well as on road conditions (path curve, traction and steering resistance coefficients). The report presents the analysis of the dependence of tracks slipping on the speed of the tracked autonomous vehicle chassis. The parameters that determine the state of the chassis of an autonomous tracked vehicle are listed. The results of mathematical simulation of the tracked mobile robot movement for different types of soils and with different speeds are presented.
Ponds

SUMMARY he present study investigated the effect of rearing the meagre, Argyrosomus regius in brackish-water pond aquaculture without using supplementary feeding in the Mediterranean area near Damietta City during June, 2017 to June, 2018, Fingerlings of mean length 13.0cm and 40.0 g weight were stocked in brackish-water pond measuring 1 feddan and 1.25 m deep at a rate of 4000 fish/ feddan in a monoculture system on natural occurring prey (natural spawned tilapia) and small shrimp. Temperature varied from 21.0 – 30.8°C; pH, 7.4– 8.9; Dissolved Oxygen (DO), 4.0 – 5.0 mg /L and salinity, 16.0 – 18.8 ppt during the study. Meagre, attained average final length 40 cm/ fish and average final weight 1500g / fish at harvest with an increment of 1460.0g/ fish and a daily gain of 4.0g/ fish. Average survival rate was 80% and net production was 4640.0kg/ feddan. The resultsrevealed that the cost of feeds accounted for the largest proportion (68.55%) of the total cost of fish production,then fingerlings (8.56%), rent (3.35%) and labor (12.60%). This is followed by cost of fixed input and variable coast (12%). This experiment demonstrated the possibility of cultivation of A. regius as well as the higher commercial value where better net return (54.48 LE /feddan) was recorded with investment return of 23.33 LE / return LE cost. The results of the study indicated meagre as a promising candidate for the brackish water pond aquaculture.
Ozone Dissolved in Water: An Innovative Tool for the Production of Young Plants in Grapevine Nurseries?

ABSTRACT Grapevine trunk diseases can infect nursery plants at different stages of propagation. Here, we evaluated the efficiency of aqueous ozone against two fungi associated with trunk diseases (Phaeoacremonium minimum and Phaeomoniella chlamydospora). The effect of ozone on the plant growth is also studied. Aqueous ozone completely suppressed spore germination in vitro. In planta fungal development was reduced six weeks post-inoculation. In the nursery, ozone treatment was comparable to the usual chemical treatment. Irrigation with aqueous ozone had no negative effects on plant growth. These anti-fungal properties and the absence of phytotoxicity make ozone a promising alternative for controlling microbial infection in nurseries.
Nomogram-Based Prediction of Post-Traumatic Occult Shock: Development and Evaluation of Novel Predictive Scores

Background Shock is a common severe complication of trauma and can result in multiple organ dysfunction and even death. To improve prognosis, it is crucial that clinicians intervene and treat occult shock, which shows no obvious changes in vital signs. The purpose of this study was to develop a new, nomogram-based shock prediction score model that is independent of the trauma injury severity score, abbreviated injury scale, and Glasgow coma scale. Our shock prediction score should help clinicians diagnose post-traumatic occult shock (PTOS) early and intervene promptly to improve patient outcomes. Patients and methods This study recruited 1005 injured patients (431 with shock and 574 without shock) from the emergency department of the Affiliated Hospital of Guizhou Medical University. Based on least absolute shrinkage and selection operator technology, 31 commonly used indicators were screened for influence on traumatic shock. We used multivariate logistic regression analysis to screen each independent influencing factor (p < 0.05), construct a nomogram-based prediction model, and formulate a PTOS score. We also estimated the predictive power of the model using C-index and the area under the receiver operating characteristic curve (AUC) of PTOS. The stability and net benefit range of this model were evaluated using a calibration diagram, internal grouping data verification, and decision curve analysis. Results Six individual influencing factors were selected using multivariate logistic regression to construct the nomogram-based model (systolic pressure, diastolic pressure, white blood cell count, absolute basophil count, red blood cell count, and hematocrit). The incidence of traumatic shock increased with an increase in the PTOS score. The C-index of the PTOS score in the training dataset was 0.92 (95% confidence interval [CI]: 0.90– 0.94), and the AUC was 0.92. The C-index of the validation dataset was 0.90 (95% CI: 0.86–0.94), and the AUC value was 0.90. The calibration chart showed that our model had excellent prediction. Decision curve analysis showed that the shock nomogram predictive score was clinically useful when the intervention was decided at a shock probability threshold more significant than 2%. Conclusion Using clinical electronic medical record system data, we developed and validated a new PTOS score with high accuracy, stability, and calibration to predict the risk of shock in patients with trauma.
Modelling Anti-Phishing Authentication Ceremonies

This dissertation explores the use of security ceremonies as a way of extending the reach of current methods for social, technological and contextual analysis of web authentication schemes and the reasons they do not protect effectively against phishing. A security ceremony extends the concept of security protocol to include both user interface and human-protocol interaction. This thesis enhances the understanding of security ceremonies and their importance in the analysis of web authentication schemes. The modelling of the human elements of a ceremony is an active research area. Our focus is the modelling of human communication processing in authentication ceremonies. Phishing is a form of electronic fraud that tries to steal users’ authentication credentials by spoofing the login page of a legitimate, trusted website. Various antiphishing authentication schemes are being proposed to help users resist attacks. The threat analysis and security proofs of these schemes rarely specifically take into account the human user limitations as a potential threat. When authentication protocols are implemented and used by humans, the underlying assumptions about human-protocol interactions are often susceptible to social engineering attacks such as phishing. Incorrect human trust decisions, i.e. human factors, play a big role in phishing. In this thesis we present the Human Factors in Anti-Phishing Authentication Ceremonies (HF-APAC) Framework as a way to analyse the communication pro-
Purification and heterologous expression of a novel antifungal protein from Bacillus subtilis strain Z-14

Background: Wheat sheath blight, a soil borne fungal disease caused by Rhizoctonia cerealis, is considered as one of the most serious threats to wheat worldwide. Bacillus subtilis Z-14 was isolated from soil sampled from a wheat rhizosphere and has been confirmed to have strong antifungal activity against R. cerealis. Results: An antifungal protein, termed F2, was isolated from the culture supernatant of Z-14 strain using precipitation with ammonium sulfate, anion exchange chromatography, and reverse phase chromatography. Purified F2 had a molecular mass of approximately 9 kDa, as assessed using sodium dodecyl sulfate polyacrylamide gel electrophoresis. Edman degradation was used to determine the amino acid sequence of the N-terminus, which was NH2-ASGGTVGIYGANMRS. This sequence is identical to a hypothetical protein RBAM_004680 (YP_001420098.1) synthesized by B. amyloliquefaciens FZB42. The recombinant F2 protein (rF2) was heterologously expressed in the yeast host Pichia pastoris, purified using Ni‑affinity column, and demonstrated significant antifungal activity against R. cerealis. The purified rF2 was thermostable, retaining 91.5% of its activity when incubated for 30 min at 100 °C. rF2 maintained its activity under treatment by proteinase K and trypsin.Conclusions: A novel antifungal protein F2 was purified from biocontrol Bacillus subtilis Z-14 strain fermentation supernatant and heterologously expressed in Pichia pastoris to certificate the antifungal activity against R. cerealis and the validity of gene sequence of protein F2. Considering its significant antifungal activity and stable characteristics, protein F2 presents an alternative compound to deal with fungal infections caused by R. cerealis.
Accounting Information Systems (AIS) in SMEs

Small-medium enterprises (SMEs) have always been considered to be the backbone of Brunei's economy. As published by the OECD in 2006, SMEs constitute a high percentage of businesses, where they account for 98% of all active business enterprises, and contribute about 92% of the employment in the private sector, and at least 66% of the GDP within the non-oil sector of the economy. Since SMEs are considered to be significant for the economy, they are encouraged to improve their business performance in order to sustain their business development by enhancing their accounting information system. This article will focus on the current accounting practices in which Brunei SMEs have been implemented by MMA Cube Stores as a case study. As cube shop businesses have been growing all over the country, it is significant to carry out this research as it may apply to the other similar business in Brunei. Reasonable recommendations are included in this article for the augmentation of the accounting system among Brunei SMEs.
Building an on-ball screen dataset using supervised and unsupervised learning

Applications of statistics and machine learning in sports analytics have made significant advances since the early days of Bill James’ publication, Baseball Analyst. In the publication, analysts and researchers would investigate matters such as how often a batter would reach first base to determine which players were the most optimal to acquire. This type of data that recorded when players reached bases was relatively simple and cheap to acquire. Over time, more complex data became available to different professional sporting leagues. In the National Basketball Association(NBA), cameras were installed in arenas to track player and ball movements. Movement tracking data enabled analysts and researchers to explore locations of players during in-game events, instead of providing insights from box score summaries and play-by-play data. The new tracking data has enabled teams to create insights from focus points that teams find valuable, such as on-ball screens. Important, detailed annotations like on-ball screens are not recorded in standard data sets. In order to capture the time stamps of the different events that are not recorded in standard data, teams have to employ analysts to record these time stamps and provide additional labels to the data. As available performance data expanded, the scientific methods and tools used to analyze the data expanded as well. Recent advances in machine learning have shown different applications of neural network layers to be able to create abstract
Obstacle detection and tracking method for autonomous vehicle based on three-dimensional LiDAR

The movement state of obstacle including position, velocity, and yaw angle in the real traffic scenarios has a great impact on the path planning and decision-making of autonomous vehicle. Aiming at how to get the obstacle’s movement state in the real traffic scenarios, an approach is proposed to detect and track obstacle based on three-dimensional Light Detection And Ranging (LiDAR). Firstly, the point-cloud data produced by three-dimensional LiDAR after the road segmentation is rasterized, and the reuse of useful non-obstacle cells is carried out on the basis of the rasterized point-cloud data. The proposed eight-neighbor cells clustering algorithm is used to cluster the obstacle. Based on the clustering result, static obstacle detection of multi-frame fusion is worked out by combining real-time kinematic global positioning system data and inertial navigation system data of autonomous vehicle. And we further use the static obstacle detection result to detect moving obstacle located in the travelable area. After that, an improved dynamic tracking point model and Kalman filter are applied to track moving obstacle stably, and we finally get the moving obstacle’s stable movement state. A large amount of experiments on the autonomous vehicle developed by us show that the method has a high degree of reliability.
Study on temperature (τ ) variation for SimCLR-based activity recognition

Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. Unsupervised contrastive learning has been excellent, while the contrastive loss mechanism is less studied. In this paper, we provide a temperature (τ) variance study affecting the loss of SimCLR model and ultimately full HAR evaluation results. We focus on understanding the implications of unsupervised contrastive loss in context of HAR data. In this work, also regulation of the temperature(τ) coefficient is incorporated for improving the HAR feature qualities and overall performance for downstream tasks in healthcare setting. Performance boost of 1.3% is observed in experimentation.
An Intelligent Microcontroller based System for Driver and Passenger Safety

The number of vehicles is increasing rapidly and so is the number of accidents. The root cause of majority of accidents can be traced back to the behavior of the one who drives the vehicle. So it is important to develop cost-effective technological solutions that can accurately identify the behavior of drivers and to assist them. Our project will provide an optimum solution to these drawbacks by performing alcohol detection, sleep detection, call detection, person level identification and accident detection using PIC16F887 microcontroller and various sensors. The ignition is activated only if the driver is confirmed rid of alcohol. When the vehicle is running, continuous monitoring is carried out to detect accidents, incoming calls to the driver and whether the driver is sleeping . A mechanism to detect human presence inside a locked car is also implemented. Each method is used to rectify the carelessness of the driver and immediate intimation of accident is provided by the use of GSM technology. The system will send the accident location acquired from GPS along with the time. This will help the rescue service to reach in time and save the valuable human life. The simulation has been carried out in Proteus and the prototype is devised. Keywords— GSM, GPS, MQ-3,PIR, PIC16F887 microcontroller, Collision Detection and Accident Avoidance
Global Responses of Gravity Waves and Zonal Mean Winds to the Madden‐Julian Oscillation and the Latitudinal Dependence of Their Relations Using MERRA‐2

Using 17 years of Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2) data, significant responses of gravity wave (GW) variances, zonal winds and parameterized GW drag to the Madden‐Julian Oscillation (MJO) are identified globally during boreal winter, and their relations are examined. The relative anomalies of GW variances range from −4% (phase 7) to 8% (phase 4) in tropics, and −20% (phase 1) to 20% (phase 5) in the northern polar region (NPR). The anomalies of zonal winds are from −3–3 m/s and −4–8 m/s in tropics and NPR, respectively. The vertical and latitudinal structures of MJO signals in GW, wind and GW drag show coherent patterns. Further analysis implies that in the NPR, the eastward wind leads to westward momentum flux carried by the GWs. This flux leads to westward drag, which drives that of zonal winds and imprint the MJO signal in GWs to the wind.
A Double Adaptive Approach to Tackle Malicious Users in Cognitive Radio Networks

Cognitive radio (CR) is being considered as a vital technology to provide solution to spectrum scarcity in next generation network, by efficiently utilizing the vacant spectrum of the licensed users. Cooperative spectrum sensing in cognitive radio network has a promising performance compared to the individual sensing. However, the existence of the malicious users’ attack highly degrades the performance of the cognitive radio networks by sending falsified data also known as spectrum sensing data falsification (SSDF) to the fusion center. In this paper, we propose a double adaptive thresholding technique in order to differentiate legitimate users from doubtful and malicious users. Prior to the double adaptive approach, the maximal ratio combining (MRC) scheme is utilized to assign weight to each user such that the legitimate users experience higher weights than the malicious users. Double adaptive threshold is applied to give a fair chance to the doubtful users to ensure their credibility. A doubtful user that fails the double adaptive threshold test is declared as a malicious user. The results of the legitimate users are combined at the fusion center by utilizing Dempster-Shafer (DS) evidence theory. Effectiveness of the proposed scheme is proved through simulations by comparing with the existing schemes.
Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats

ii Problem illustration . iii Acknowledgment . iv Thesis synopsis . vi Thesis organization . viii Table of Content . ix List of Tables . xii List of Figures . xiii Chapter I: Introduction . 1 1. Motivation . 1 2. Problem definition and scope. 2 Chapter II: Contemporary cyber threat detection approaches . 4 1. Intrusion Detection System (IDS) a brief overview . 5 2. Types of Intrusion Detection Systems (IDS) . 6 2.1 Host Intrusion Detection System (HIDS) . 6 2.2 Network Intrusion Detection System (NIDS) . 8 2.2.1 Signature based network intrusion detection . 8 2.2.2 Anomaly based network intrusion detection. 9 3. Current literature on anomaly based network intrusion detection in cyber-security . 11 Chapter III: Cyber threat landscape and cyber kill chain . 13 1. Cyber threat landscape . 14 1.1 Cyber threats a statistical glance . 14 1.2 Cyber threats a brief discussion . 16 1.2.1 Malware . 16 1.2.2 Botnets . 16 1.2. 3 Web based attacks . 16 1.2. 4 Denial of Service (DoS) attacks . 16 1.2. 5 Spam and phishing attacks . 17
TEDUSAR White Book - State of the Art in Search and Rescue Robots

The aim of this paper is to document the state of the art in disaster robotics and provide an overview of technology already available or currently under development. Over the last years, robotics technologies for disaster response continued to develop and a wide range of robotics solutions for first responders are available or in development. The idea is to provide first responder with information about technology that can be immediately applied as well as to provide information on ongoing research to allow responder to identify future promising application areas.
Towards a graph model application for automatic text processing in data management

Based on the two models, “bag-of-words” and graph model, the paper deals with the development of methods for automated text analysis with the purpose to classify natural language texts and randomly generated documents. Within “bag-of-words” model, the authors have found that the primary Zipf’s law, which states that given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table, does not hold continuously true. Modifications to this law have been proposed that enable us to classify texts more efficiently. Using the graph model of the text, which takes into account the occurrence of two random words in a sentence, and the median degree of the vertices of the graph, the authors demonstrate that it can be applied to differentiate meaningless texts from meaningful ones even though the word lists of the two texts are identical.
Incentive Compatibility and the Consequences When It Is Missing: Experiments with Water Quality Credits Purchase

This article implemented four treatments to elicit preferences for a nonmarket good, including (1) a hypothetical referendum, (2) a real referendum lacking incentive compatibility, (3) a real choice with incentive compatibility, and (4) a hybrid approach based on (2) and (3). We develop a method to estimate the probability that observed choices do not identify the highest-utility alternative in a choice question. We find that in the hypothetical referendum, the estimated percentage of individuals choosing the alternative that gives the highest utility is the lowest among the treatments. Adding policy consequentiality or payment consequentiality increases the percentage of truthful responses.