Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer processes in turbulent pipe flow described by the ERSR model

A model is presented for a quantitative prediction of the transfer rates of momentum, heat and mass in turbulent pipe flow. In this so-called extended random surface renewal (ERSR) model, the tube wall is assumed to be covered by a mosaic of fluid elements of random age and laminar flow with unsteady profiles of axial velocity, temperature or concentration. Both the age distribution and the mean age of the fluid elements at the tube wall, predicted by the ERSR model, quantitatively agree with experimental results obtained from velocity signals measured with a laser-Doppler anemometer at 5·103⩽Re⩽43·103. The time-averaged radial profiles of the axial velocity, the temperature and the concentration in the wall region, and the heat- and mass-transfer coefficients derived with the ERSR model agree with empirical results presented in literature. Furthermore, the ERSR model provides a basis for explaining the Chilton-Colburn analogy.
Phase Transitions in Monolayers

When we began this review, we intended to write a chapter entitled, "Phase transitions in two-dimensional systems. " It soon became evident, however, that it was impossible to cover so broad a topic within the imposed size limitation. Even with the restriction to monolayers, it is difficult to provide a complete overview of this lively and growing field. Fortunately, recent reviews by M6hwald (1) and McConnell (2) dealt with Langmuir monolayers. Because they did not focus on phase transitions, our chapter complements, rather than duplicates, this work. New experimental tech­ niques that have been applied to the study of monolayers have also been described recently (3); two Annual Review of Materials Science articles are also relevant (4, 5). And, Wortis (6) has evaluated the state of the art (as of 1990) in the study of surface films of small molecules in a brief and entertaining review. We have divided this chapter into four main sections. The first deals with structure and is primarily a review of experiments. Recent Monte Carlo (MC) and molecular dynamics (MD) simulations are discussed next. The kinetics of monolayer phase transitions are then described, and we
Structure redetermination of dextromethorphan hydrobromide monohydrate, C18H28BrNO2 – localization of hydrogen atoms

Abstract C18H28BrNO2, orthorhombic, P212121 (no. 19), a = 7.0417(4) Å, b = 9.1635(5) Å, c = 27.3371(15) Å, V = 1763.97(17) Å3, Z = 4, Rgt(F) = 0.0275, wRref(F2) = 0.0596, T = 200 K.
Cu-Cu diffusion bonding enhancement at low temperature by surface passivation using self-assembled monolayer of alkane-thiol

Self-assembled monolayer (SAM) of 1-hexanethiol is applied on copper (Cu) surface to retard surface oxidation during exposure in the ambient. This SAM layer can be desorbed effectively with an annealing step in inert N2 ambient to provide a clean Cu surface. Using this passivation method with SAM, wafers covered with thin Cu layer are passivated, stored, desorbed, and bonded at 250 °C. The bonded Cu layer presents clear evidence of substantial interdiffusion and grain growth despite prolonged exposure in the ambient. This method of passivation is proven to be effective and can be further optimized to enable high quality Cu–Cu direct bonding at low temperature for application in three-dimensional integration.
DOSing Distributed Ledger Technology: IOTA

With advancements in connected technology, the number of ambitious applications involving Internet of Things (IoT) are drastically growing. This increases concerns related to security, scalability, and interoperability of IoT. As the network of connected devices grows, decentralized technologies become inevitable. Within this trend towards decentralization, distributed ledger technology (for instance IOTA) will be a significant driving force. IOTA is an innovative distributed ledger technology targeted towards low power devices, where energy efficiency is a high priority. Public research regarding security threats against IOTA especially denial-of-service (DoS) is essentially non-existent. In this paper we focus on exploring a DoS attack against IOTA. The proposed attack methodology takes advantage of the lack of fees along with the ability to transfer minuscule amounts. By sending many conflicting transactions as it results in a high number of re-attachments. The high number of re-attachments threatens IOTA's suitability for the IoT sphere. The implications of such attack, as well as the future of this issue in terms of the planned removal of the centralized coordinator are discussed.
FS-MPC Based Thermal Stress Balancing and Reliability Analysis for NPC Converters

Active thermal control has been introduced to regulate the steady state and reduce the transient thermal-mechanical stress in power electronic modules. Specifically, it can equally distribute the temperature among the devices, thereby better distributing the stress among a set of devices and reducing the failure probability in the most thermally-stressed devices. This is of great importance for multilevel topologies and in particular for the Neutral Point Clamped (NPC) topologies, which have an inherent thermal unbalance among devices of the same phase. In hybrid structures, whereby Si and SiC devices are mixed to achieve a better trade-off between efficiency and cost, this problem is even worse due to technology differences. This paper investigates the advantages introduced by using Finite-Set Model Predictive Control (FS-MPC) algorithms designed for achieving a balanced device junction temperature in hybrid NPC and Active-NPC converters. Moreover, a novel setup composed by a hybrid ANPC based power modules is used to experimentally validate the presented FS-MPC algorithm. A lifetime estimation is performed for the two topologies to highlight the long-term benefits of FS-MPC algorithm in these hybrid topologies for UPS applications.
Automated Improved Detection of Parkinson’s Disease using Ensemble Modeling

A person suffering from Parkinson’s disease (PD) mostly has speech impairments. Speech impairment in PD refers to the disorder of speech due to motor and non-motor deficits. Many articles have been reported in developing PD detection model by using standard speech datasets. But still there is further scope of improvement in the detection potentiality of PD. In this paper, this issue has been addressed and a novel ensemble method by combining the advantages of two popular classifiers such as Support Vector Machine (SVM) and Random Forest (RF) has been proposed. The contribution of the classifiers in the final prediction is determined by assigning weights which are optimized by Particle Swarm Optimization (PSO) algorithm. Further the performance of the proposed model is evaluated using a popular and standard UCI Oxford Parkinson’s Disease Detection Dataset and it is demonstrated that the proposed ensemble models achieves 93.7% and 100% accuracy by using 10-fold cross validation scheme and 30% testing set respectively. Thus the suggested ensemble approach outperforms the existing PD prediction models.
Biopolímeros, Carbono e Enxofre Totais Associados à Atividade Bacteriana dos Sedimentos Superficiais do Delta do Paraíba do Sul, RJ-Brasil

The Paraiba do Sul River basin is located on the Rio de Janeiro-Sao Paulo axis, an area of great demographic density and intense industrialization. This work aimed to perform a quali-quantitative analysis of the organic matter (biopolymers, total organic carbon and sulfur) and the bacterial metabolic state in the estuarine-deltaic superficial sediments of the Paraiba do Sul River, relating them to their granulometric distribution. On average, 65.5% of sediments were classified as sandy and poor in carbon and sulfur, with average concentrations of 0.71% and 0.04%, respectively. The C:S element ratio indicates that the environment is predominantly oxidizing, which is coherent with the strong fluvial influence of such a middle-sized river over the estuary. The carbohydrate (average of 2075.93 I¼g C/g), protein (average of 25.70 I¼g C/g) and lipid (average of 363.10 I¼g C/g) biopolymers also presented concentrations lower than those found in other environments. Based on these low concentrations, the environment was classified as meso-oligotrophic. The CARBOHYDRATE:TOC ratio evidenced the impact from anthropic activities, more specifically from the input of domestic effluents along the entire river's course and from the sugar industry, strongly present on its lower course. The bacterial community responsible for organic matter hydrolysis is predominantly anaerobic and facultatively anaerobic, implying in loss of nitrogen to the atmosphere (denitrification) and production of sulfides (sulfate reduction), and the low PROTEIN:CARBOHYDRATE ratios reflect this loss of nitrogen (present in the protein compounds), considering the occurrence of denitrifying bacteria. The bacterial carbon values found are high (3.29 I¼g C/g) and they are also probably due to the impacts above described
Analysis and numerical simulation of eloctrokinetic models for the evolution of charged droplets

The purpose of this thesis is to develop tools to study some problems in the intersection between microfluidics and electrokinetics. In this introduction we provide an overview of this areas and introduce the specific problem that we will be dealing with: the evolution of charged drops of ionic solutions. 1.1 Motivation Electrified droplets are present in thunderstorms clouds [20] as well as in technological applications such as electrospraying [8] (used, for instance, to produce small samples of ionic solutions suitable for spectrometry, an application that deserved the 2002 Nobel Prize in Chemistry by J. Fenn), microencapsulation [48], ink-jet printing [53], Field Emission Electric Propulsion (FEEP) thrusters [68], Electronic paper [40], microfluidic chips [21], [18], [29], [65], etc.In order to translate concepts into practical devices, we must learn to control fluids with great precision and speed at, sometimes, ultra small scales. About charged fluid droplets, it is known that they become unstable when charged beyond a number called the Rayleigh limit, and then they develop singularities (cf [6], [7] and [20] for experimental results and [26],
Off-Line Complexation/Supercritical Fluid Extraction and Gas Chromatography with Atomic Emission Detection for the Determination and Speciation of Organotin Compounds in Soils and Sediments

As part of an ongoing evaluation of new sample preparation methods conducted by the U.S. Environmental Protection Agency (EPA) in Las Vegas, especially those that minimize waste solvent generation, we have developed and evaluated an off-line complexation/supercritical fluid extraction (SFE) method for 13 organotin compounds. Soil or sediment samples, amended with a complexing agent (diethylammonium diethyldithiocarhamate), were extracted with supercritical carbon dioxide modified with 5% methanol at 450 atm/60 o C, 20 min static, followed by 30 min dynamic
AG+‐HPLC of conjugated linoleic acids on a silica‐based stationary phase. Part IV: A reference stationary phase and retention mechanisms

In the previous paper on the Ag+-HPLC of conjugated linoleic acids (CLAs) on a silica-based stationary phase, we examined the relative retentions of the CLAs, their methyl esters (CLAMEs), and stearic acid. The retention of the CLAs far exceeds the sum of the retention of the CLAMEs and stearic acid. In this paper we isolate the interactions at the silica surface by determining the retentions of the same compounds on the NH4+ cation exchanger that is the precursor to the Ag+ column in the manufacturing process. On the NH4+ column, the CLAMEs are very weakly retained, but the sum of the retentions of the CLAMEs plus stearic acid is equal to that of the CLAs. It was determined that stearic acid is retained to the same degree on both the NH4+ and Ag+ stationary phases. By implication, the CLA carboxylic acid functionality does not interact with Ag+ and thus three-centre complexes of the Ag+ with the CLA π bonds and carboxyl group do not occur. (Independent supporting evidence is also presented.) A model for CLA retention on the Ag+ stationary phase that explains the synergy of retention is presented. It involves a random arrangement of collapsed Ag+ alkyl sulphonate bonded phase on the silica surface. The nature of retention on the NH4+ stationary phase is also discussed. Partial separation of the CLAs on the NH4+ stationary phase shows that π bond interactions with the silica surface also contribute to the separations of the CLAs, the CLAMEs, and other unsaturated analytes. As was found on the Ag+ column in the absence acetonitrile, normal phase elution occurs and log k′ versus log (% strong solvent) plots for all analytes are linear on the NH4+ column. (Where the specific displacement mechanism of unsaturates from the Ag+ by ACN was present, the log-linear plots were straight lines.)
PUF Based Secure Framework for Hardware and Software Security of Drones

Unmanned Aerial Vehicles (UAVs) augmented with sensors like cameras, GPS and LiDARs, shows tremendous potential in delivering Internet of Things (IoT) services from great heights. Current security challenges in the drone deployment for such applications are: software and hardware breaches, identification of rogue drones and link hijack avoidance. In this work, we extend the secure framework for not only software, but also for hardware breaches and identification of compromised drones using a Trusted Execution Environment (TEE) created by ARM's TrustZone technology and Physically Unclonable Function (PUF). The PUF based unique fingerprints of onboard sensors, flight controller and companion computer of the drone are implemented in a Field Programmable Gate Array (FPGA). The PUF based drone authentication algorithm has been implemented and demonstrated in a proof of concept system and can be incorporated in drone deployments.
A Review on Comparison of Different Techniques of Image Dehazing

Due to the environmental factor like unbalanced air light, darkness, contrast, saturation and attenuation causes hazy image. Many techniques are used to produce good quality image that retain color, illumination and better edges which suffer from weak edges and halo artifacts. In this paper we will be discussing previously proposed method which is adopted to remove haze from image. Several methods have been adopted to remove haze such as image dehazing is classified into two types such as single image dehazing and multi image dehazing. Different techniques of Image dehazing have been discussed in this review and also their advantage and drawback.
Cryptoanalysis of an Authenticated Data Structure Scheme With Public Privacy-Preserving Auditing

In this letter, we point out that the privacy-preserving adaptive trapdoor hash authentication tree scheme (published in IEEE TIFS, doi: 10.1109/TIFS.2020.2986879) can be invalidated by an adversarial cloud server: if the outsourced data is arbitrarily modified, the cloud server still can pass the third-party auditor’s auditing.
A Sub-threshold Low Noise Amplifier Design with an Emphasis on PVT Analysis

In this article, an amplifier has been proposed for biomedical applications. The performance of all the key parameters has been investigated about process, voltage, and temperature variations. The circuit has been simulated for five process corners (FF, SS SF, FS, and TT), temperature variations from −25°C to + 140°C, and voltage variations. The maximum gain of 79.7 dB is obtained for the FF process corner whereas the lowest power consumption is observed for FS corner simulation. The lowest input-referred noise is achieved Asat 1KHz, for SS corner simulation. The process corner simulations also show significant results for CMRR and phase margin. The effect of temperature variations on the static power consumption is observed to evaluate the robustness of the designed amplifier. The overall PVT analysis provides valuable data regarding the effect of variability on different design parameters.
An Artificial Intelligence approach to Intelligent Vehicle Control and Monitoring System

At present, day's rapid growth in our technological world and infrastructure has made our lifestyle the easiest on the existing system. The features of Engineering and Technology have also increased the traffic-related prone hazards analyzing using Traffic Management System under control of Artificial Intelligence (AI) with the help of the IoT environment and avoid the road collisions and accidents happen, which quickly causes speak to colossal loss of our lives. Furthermore, property because of the most decreased emergency workplaces. The vehicle discovery System will give distinguished and an ideal answer for this disadvantage to defeat on the existing condition. An accelerometer is utilizing in a vehicle recognition application, so perilous tasks on driving can be distinguished, it might be used as a mishap or rollover pointer of the vehicle in the midst of and after a mishap. With signs from an ultrasonic sensor, an extreme mishap, because of a snag can be perceived. When a vehicle meets with an incident, or if a vehicle moves over, the accelerometer and ultrasonic sensor normally distinguish the banner getting ready and instantly sends it to the microcontroller for a fast response. The current paper is toward utilizing the remote development intended used for the vehicle circumstances via the GSM Modem new in transfer SMS if nearby have to emerge an event of theft implication.The principle center of this paper is to stop the motor of a vehicle and consequently identify the areas through the Internet of Things applications. The direct way is given to the microcontroller from the first to the last boundary AI identified, which establishes an exchange driver or user in the direction of a journey the hand-off to withdraw the beginning of the vehicle, realizing and stopping. The microcontroller sends the distinguished caution message through required the GSM module including, the zone to existing Government or Private bodies controls room or rescue teams a rescue gathering. So the emergency help gathering can speedily finish the region, the GPS module, in the wake of tolerating the information from existing resources. The required territory can be seen through Google maps and ISRO BHUVAN. In the wake of adjusting the territory, an imperative move is made through indispensable bodies. If the individual meets with a little disaster or if there is no veritable hazard to anyone's life, by then the alert message can be finished by the driver by a switch gave to refuse to waste the beneficial time of the restorative rescue gathering, at whatever point required movement is essential by then advised through PDA contraption on presented applications. We proposed this work is valuable to recognize Traffic load breaking down and vehicle following and distinguishing when the mishap exactly happened by methods for both accelerometer and ultrasonic sensor join working amid AI Transportation utilizing Internet of Things applications with monitoring and surveillance through Mobile App.
End-to-End 6DoF Pose Estimation From Monocular RGB Images

We present a conceptually simple framework for 6DoF object pose estimation, especially for autonomous driving scenarios. Our approach can efficiently detect the traffic participants from a monocular RGB image while simultaneously regressing their 3D translation and rotation vectors. The proposed method 6D-VNet, extends the Mask R-CNN by adding customised heads for predicting vehicle’s finer class, rotation and translation. It is trained end-to-end compared to previous methods. Furthermore, we show that the inclusion of translational regression in the joint losses is crucial for the 6DoF pose estimation task, where object translation distance along longitudinal axis varies significantly, e.g., in autonomous driving scenarios. Additionally, we incorporate the mutual information between traffic participants via a modified non-local block to capture the spatial dependencies among the detected objects. As opposed to the original non-local block implementation, the proposed weighting modification takes the spatial neighbouring information into consideration whilst counteracting the effect of extreme gradient values. We evaluate our method on the challenging real-world Pascal3D+ dataset and our 6D-VNet reaches the 1st place in ApolloScape challenge 3D Car Instance task (Apolloscape, 2018), (Huang et al., 2018).
Sentiment Analysis on COVID-19 Twitter Data

Corona Virus or COVID-19 first appeared in December, 2019 in Wuhan, China. People tweeted aggressively on twitter at that time. This paper analysed the tweets regarding COVID-19 from November, 2019 to May, 2020 in India and its affect. All tweets are categorized into 3 categories(Positive, Negative and Neutral). Multiple datasets are created State-wise, Month-wise, combined of all states to analyze the people's reactions towards Lockdown in June, 2020 and about everything related to COVID-19. Most people started having Negative tweets but with increasing time people shifted towards positive and neutral comments. In April, 2020 most comments were Positive and about winning against Corona virus.
A Review of Cyber Securities in Smart Grid Technology

A Smart Grid (SG) is an electrical infrastructure much like legacy power grid with scalable and pervasive two-way communications, timely control capabilities, large scale integration of distributed resources and efficient use of resources. The SG provides the features of pervasive smart monitoring technologies, automatic equipment fault sensing and self-healing. The features like ‘Wireless Automatic Meter Reading’ (WAMR), monitoring of power system stability, distributed energy resources optimization and applications of Demand Response system makes it intelligent infrastructure. Given some of these salient features, SGs without any doubt are the future power infrastructure of energy world. A smart grid has ability to connect millions of consumers and devices in a network which demand it to be highly robust, reliable and secure. Security is considered as one of the major challenges in the present-day smart grid systems because of its long-range communication on open networks. Cybercriminals, hackers, terrorists are trying to attack this national infrastructure due to their malicious intensions or to get control on the automated energy monitoring and remote controlling for personal gains. This review paper offers a comprehensive survey of understanding of Smart Grids, architecture of SGs, methodologies used, the communication protocols but the focus is mainly on the cyber-attacks carried out and solutions recommended on the smart grids. We finally discuss the various challenges in cyber security, the issues that still exist in the literature and current solution space with future research gap.
A Feature Subset Selection Method Based on the Combination of PCA and Improved GA

Nowadays, intrusion detection data has redundant features, which leads to unsatisfactory detection results. This paper proposes a feature selection method based on the combination of principal component analysis (PCA) and improved Genetic algorithm(GA). First, PCA is used to reduce the dimensionality of the intrusion data, and the cosine similarity is used to calculate angles between the principal component and the original feature; the feature with high cosine similarity to the principal component is selected and the genetic algorithm is input, and the improved inter-group crossover strategy is adopted to reduce the probability of entering the local optimum. The fitness function of the genetic algorithm is the detection rate, and the optimal feature subset is selected as the intrusion detection data feature. Experimental results show that this method can improve the efficiency of intrusion detection and improve the classification ability of the classifier.