Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery of Ion Parameters from NPA Measurements on TCV

plasmas has been developed by modelling the measured energy spectra of the neutral deuterium fluxes varying the parameters of the T i profile. A Neutral Particle Analyser (NPA) [1,5] is used on the TCV tokamak [2] to measure the energy spectrum of hydrog en and deuterium neutrals escaping the plasma. A Kinetic Transport Algorithm, (KN1D) [3], was applied to obtain neutral density profiles in the bulk plasma. The response of Charge eXchange energy spectra to plasma parameters was examined with a model prediction. Ion temperature profiles, evaluated by this method, were compared with the classical logarithmic CX-spectrum slope evaluation. Results based on analysis of NPA data were also compared with measurements from charge exchange recombination spectroscopy (CXRS) [4]. TCV [2] is a medium-sized tokamak (R=0.88 m, a=0.25 m, Ip < 1 MA, BT < 1.54 T, plasma elongation 1-2.8) able to produce a plasma with the central electron density 0.5 -15×10 19 m -3 , an electron temperature over 10 keV with bulk central ion temp eratures 0.3-1 keV. A 5 -channel NPA, with electrostatic energy separation [1], measures the ion parameters [5-7]. The NPA voltage sweeps the energy channels to measure neutral particle energies in the range of (0.6 →6.5 keV), with a time resolution of ≥13 ms viewing the plasma along a vertical chord (fig.1). CX spectrum and ion temperature. The energy spectrum of passive atomic flux J(E) traversing the plasma surface and entering the external instrument (NPA) is the sum of fluxes in the plasma column along the view line of the analyser [8]
Social networking sites passive use and its effects on sad-happy mood

The social comparison theory explains some negative effect of social networking sites (SNSs) use. These Internet applications have made easier the online social comparison that in turn predicts depression and lower life satisfaction. Individuals prone to depression engage in greater levels of social comparison, particularly with others who are thought to be slightly better off, and experience a decrease in mood or self-esteem in the light of others? perceived happiness. The present study aimed at investigating the impact of the use of SNSs on the mood in an experimental design. In total, 120 university students were randomly assigned to one of two groups. In the experimental groups, the participants were instructed to access their Facebook and browse personal profiles for 20 minutes; while in the control, they read articles. For the participants with initial low mood using Facebook further lowered their mood whereas for the other participants did not have any effect. Furthermore, using Facebook lowered the mood of the participants not accustomed to use it frequently.
Gated Spatial Memory and Centroid-Aware Network for Building Instance Extraction

Automatic building extraction from high-resolution remote sensing images plays an important role in many application fields, such as the urban planning and photogrammetry. However, the complex background and large variety in building appearances in high-resolution remote sensing images make the building instance extraction challenging. In this study, we propose a novel two-stage instance segmentation network named gated spatial memory and centroid-aware network (GSMC) to handle these problems. Two new modules, including a gated spatial memory module (GSM) and a centroid-aware head (CH), are developed in our GSMC. The GSM is a top-down spatial structure and semantic information transmission module, where two gates including an input gate and a state gate are designed to strengthen the important features and replenish the lacking information. The CH is a new task head for regressing the geometric center of each instance, which can help to promote the accurate and complete recognition for irregularly shaped buildings. Experiments on the WHU Aerial data set, the WHU Satellite data set, and the Massachusetts Building data set demonstrate that the proposed GSMC can achieve consistently superior performances when compared with the recent state-of-the-art deep learning methods.
Modulating carboxymethylcellulose-based hydrogels with superior mechanical and rheological properties for future biomedical applications

Herein, a grafted copolymer composed of carboxymethylcellulose (CMC) and polyethylene-glycol-aminated (PEG-NH 2 ) was successfully synthesized via a facile ionic interaction and scalable route in the presence of EDC/NHS ( N -ethyl- N ′-(3-dimethylaminopropyl)carbodiimide/ N -hydroxysuccinimide) activators. From Fourier transform infrared (FT-IR) spectroscopy, the absorption peak at 1652 cm –1 corresponded to –NH groups of PEG-NH 2 . After grafting, the grafted CMC-PEG was characterized for surface morphology, crystallinity, functional groups determination, and thermal analysis. No cytotoxicity effect was observed in normal human dermal fibroblasts (NHDFs) cells following exposure to the grafted CMC-PEG up to 2 mg/ml. The rheological studies suggested that the optimized 10% (w/v) grafted CMC-PEG hydrogels crosslinked with 5% (w/v) citric acid (CA) exhibited better mechanical properties compared to the non-grafted CMC. This work highlights the characterizations of grafted CMC-PEG and demonstrates the potential of grafted CMC-PEG hydrogels crosslinked with CA for advanced 3D-bioprinting or as injectable hydrogels in various biomedical applications such as tissue engineering, wound dressing materials, and drug delivery systems.
On the Cumulants of the First Passage Time of the Inhomogeneous Geometric Brownian Motion

We consider the problem of the first passage time T of an inhomogeneous geometric Brownian motion through a constant threshold, for which only limited results are available in the literature. In the case of a strong positive drift, we get an approximation of the cumulants of T of any order using the algebra of formal power series applied to an asymptotic expansion of its Laplace transform. The interest in the cumulants is due to their connection with moments and the accounting of some statistical properties of the density of T like skewness and kurtosis. Some case studies coming from neuronal modeling with reversal potential and mean reversion models of financial markets show the goodness of the approximation of the first moment of T. However hints on the evaluation of higher order moments are also given, together with considerations on the numerical performance of the method.
Continuous-Domain Real-Time Distributed ADMM Algorithm for Aggregator Scheduling and Voltage Stability in Distribution Network

With an increase in the number of electric vehicles (EVs) and battery storage systems (BSSs), there are major challenges in the distribution grid to maintain a scaling control structure. Also, with the vehicle-to-grid (V2G) technology, EVs can now inject power into the grid for voltage regulation. This article proposes a new distributed real-time alternating direction method of multipliers (ADMM) technique to control EVs and BSSs for voltage regulation while maximizing their utility function. More specifically, a continuous-domain real-time optimization and control algorithm is developed in closed form, which exchanges relevant information among the neighboring nodes through the communication network and optimizes a combined convex objective of EVs and BSSs welfare and voltage regulation with power flow equations as constraints. Convergence analysis is provided using the Lyapunov direct approach, and simulation results are included to illustrate the effectiveness of the proposed scheme. Note to Practitioners—This article is motivated by the problems that an electric distribution system is facing today due to the penetration of electric vehicles and battery storage systems (BSSs). The presence of a large number of electric vehicles (EVs) and BSSs is causing a substantial degradation of the quality and reliability of the power grid. Despite the adverse effects, EVs and BSSs can be controlled and used as a power source to benefit the grid. Also, this control of EVs and BSS cannot be done by a centralized body since this would be nonscalable and would require huge communication bandwidth. To tackle this ever scaling problem, this article develops a continuous-domain multiagent distributed algorithm to control the EVs and BSSs and utilize them to maintain the grid voltage within the normal operating range while also satisfying the consumers by maximizing their welfare. The algorithm was developed in the continuous domain since, in most of the other algorithms that are iterative and in discrete time, the accuracy of the optimal solution greatly depends on the sampling time, and thus, it is not robust to changes that are prevalent with distributed energy resources such as EVs and BSSs.
A two-step process for growth of highly oriented Sb2Te3 using sputtering

A two-step growth method is proposed for the fabrication of highly-oriented Sb2Te3 and related superlattice films using sputtering. We report that the quality and grain size of Sb2Te3 as well as GeTe/Sb2Te3 superlattice films strongly depend on the thickness of the room-temperature deposited and subsequently by annealing at 523 K Sb2Te3 seed layer. This result may open up new possibilities for the fabrication of two-dimensional electronic devices using layered chalcogenides.
Enhanced Charge Efficiency in Capacitive Deionization Achieved by Surface-Treated Electrodes and by Means of a Third Electrode

In this paper, we report on attempts to improve the charge efficiency of electrochemical capacitive deionization (CDI) processes without limiting the range of applied potentials, by using surface-treated (oxidized or reduced) activated carbon fiber (ACF) electrodes, and by means of a third, auxiliary electrode. For oxidizing the ACF electrodes, we etched ACFs for different periods of time with a concentrated nitric acid solution. For reduction of the ACFs, several surface treatments were considered: reaction with hydrogen at high temperatures, removal of oxygen surface groups by heating under vacuum at high temperatures, reaction with a concentrated aqueous solution of a sodium borohydride solution, and reaction with a concentrated sodium borohydride solution after oxidation with a concentrated nitric acid solution. To examine the charge efficiency, we elaborated a special flow-through cell (where the solution flows through the ACF electrode) with a silver/silver chloride mesh reference electrode. The fea.
Preparation and CO2 Sorption/Desorption of N-(3-Aminopropyl)Aminoethyl Tributylphosphonium Amino Acid Salt Ionic Liquids Supported into Porous Silica Particles

To search for robust CO2 capture materials, several N-(3-aminopropyl)aminoethyl tributylphosphonium amino acid salts ([apaeP444][AA])-type task specific ionic liquids (TSILs) were synthesized and immobilized into porous silica support through a facile impregnation–vaporization method. The ILs and thus prepared sorbents, Sorb-AA, were well characterized, and their CO2 sorption and desorption behaviors under temperature- and vacuum-swing conditions were investigated. The ILs can be immobilized facilely into silica up to 1/1 IL/SiO2 weight ratio. After IL loading, the sorbents retain reasonably high specific surface area and porosity and therefore exhibit rapid sorption and desorption rates as well as excellent sorption capacity and selectivity and can be used repeatedly. Among them, Sorb-Lys has the highest CO2 sorption capacity. It can capture 1.54 mmol or 67.9 mg CO2 per gram sorbent from a simulated flue gas containing 14% CO2 in each cycle of sorption and desorption. Sorb-Gly has slightly less CO2 sorpt.
Adaptive Spectral Rotation via Joint Cluster and Pairwise Structure

Density structure and pairwise structure serve as two different but complementary perspectives for clustering. Either side of road is frequently visited and explored by multiple clustering methods. However, there are seldom approaches, which could mutually exploit both structures for clustering. To address this problem, in this paper, we develop a novel adaptive joint clustering algorithm, which combines unsupervised discrete orthogonal least squares discriminant analysis (DOLSDA) and discrete spectral clustering (DSC) with adaptive neighbors and side information into a unified model. First, we extend supervised OLSDA to a discrete kernel clustering problem. To further achieve a clear pairwise structure, a new similarity with adaptive neighbors is then derived to establish sparse Laplacian matrix. In addition, side information could be incorporated to formulate clearer graph by modifying the proposed similarity. Based on the constructed graph, DSC is embedded with the discrete kernel OLSDA (DKOLSDA) clustering to exploit both cluster and pairwise data structures. Equipped with the proposed framework regarding quadratic weighted optimization, adaptive weight can be obtained automatically to leverage both unsupervised DKOLSDA and DSC. Since the unified problem is still discrete, we develop an increment scheme to achieve the optimal spectral rotation for the approximate solution to the predicted indicator.
New Generation of Electrochemical Sensors for Nitric Oxide;Ruthenium/Carbon-Based Nanostructures and Colloids as Electrocatalytic Platforms

Nitric oxide (NO) is an important intercellular messenger that acts in many tissues to regulate a diverse range of physiological and pathological processes. The physiologically implications of NO function are far from being completely understood. The multifaceted reactivity of NO prompted the need for accurate determination of the concentration of this molecule. However, it is difficult to detect nitric oxide, particularly in biological media and near live cells due to its short half-life, a result of its reactivity and the low levels of NO produced in vivo. As a result, the accurate and reliable detection of NO under varying experimental conditions has always posed a challenging task. The main goal was to develop ultra-sensitive electrocatalytic sensors for accurate quantification of NO. We report the fabrication and characterization of improved NO sensors based on electrocatalytic platforms such as ruthenium (colloids, nanoparticles, and nanotubes) and carbon (pastes and nanotubes), acting as catalytic sites for NO oxidation. These sensors are characterized using various surface analytical tools. The electrocatalytic oxidation of NO is assessed by cyclic voltammetry and amperometry both in solution phase and gas phase. Excellent sensitivity and linearity are observed for
Increased Laser-Damage Resistance of Sol–Gel Silica Coating by Structure Modification

Sol–gel silica coatings prepared for high-power laser applications were optimized by a two-step structure modification through treatment of the sol with poly(ethylene glycol) (PEG), followed by treatment of the coating with ammonia. The effect of each treatment on the laser-induced damage threshold (LIDT) at 1064 nm wavelength was studied, mainly in terms of the change in the coating’s structure. Both of these treatments increased the LIDT, and the greatest LIDT was achieved by combining the two treatments. The LIDT increase can be attributed to the decrease in structural defects such as nodular defect and the variety of pore-distribution, resulting from PEG and ammonia treatments, respectively. The procedure of producing coated glass with high LIDT for 1064 nm laser irradiation was suggested, involving substrate etching, sol modification, and coating treatment.
Hydrothermal Synthesis of CuO Nanoparticles: Study on Effects of Operational Conditions on Yield, Purity, and Size of the Nanoparticles

Hydrothermal synthesis of CuO nanoparticles under near-critical and supercritical conditions was investigated from two different standpoints in the current study. The first standpoint was optimization of “yield”, “purity”, and “size of the nanoparticles” that were optimized at T = 500 °C, time = 2 h, [Cu(NO3)2] = 0.1 mol dm−3, and pH 3. This was achieved by undertaking an orthogonal experiment design methodology and performing different instrumental analyses, such as X-ray diffractometry, inductively coupled plasma spectrometry, and transmission electron microscopy, along with treatment of the data by analysis of variance (ANOVA). The second goal of the study was elucidation of the mechanisms of effects of operational conditions (e.g., temperature) on the above-mentioned target parameters, through application of the appropriate mechanisms of formation of nanoparticles. Nanoparticles are suggested to form initially in the liquid phase as Cu(OH)2, which are later transformed to Cu2(OH)3NO3, through which Cu.
Online Customer Shopping Behaviour: The Mediating Role of Online Perceived Risk

The studies of online purchase intention have been done extensively throughout the world by testing many predictors. Due to the rapid growth of technological advances affecting internet users suggest other new predictors considered as an online purchase intention element. This study suggests customer review, online store image, and perceived online risk (an element of a mediating factor) as three main variables that drive customers to purchase online. A simple convenience sampling method was used to analyse the responses from online users via the online questionnaire web surveys. Multiple regressions and Sobel tests were used in this study to test the hypotheses. The result shows that online customer review and online store image have a positive relationship with the perceived risk online as well as perceived risk itself also has a positive relationship with online purchase intention. Consequently, in terms of mediating factors, perceived risk online is seen as a mediator for both online customer review and online store image with online purchase intention. Having said that, the study hopes that the findings would help boost the online retail industry as a contributor to the country's economic development.
Use of Machine Learning in Stock Market Prediction

Objective: The objective of this study was to examine and determine future directions in regard to future machine learning techniques based on the review of the current literature. Methodology: A systematic review has been used to review the current trends from the peer-reviewed journal articles in the past twenty years. For this study, four categories have been categorized, the use of neural networks, support vector machines, the use of a genetic algorithm, and the combination of hybrid techniques. Studies in each of these categorize have been evaluated. Finding: Firstly, there is a strong link between machine learning methods and the prediction problems they are associated with. The second conclusion that we can conclude from this review is that past studies need to improve its generalizability results. Most of the studies that have been reviewed in this analysis has only used the machine learning systems through the use of one market or during only a one time period without taking into consideration whether the system would be adaptable in other situations and conditions. Limitations, future trends, as well as policy implications have been defined.
Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization

Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain—without guarantees—the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time. Note to Practitioners—This article is motivated by the emerging need for effective network reconfiguration approaches in modern power distribution systems, including microgrids. The proposed metaheuristic optimization strategy allows the decision maker (i.e., the distribution system operator) to determine in reasonable time the optimal network topology, minimizing the overall power losses and considering the system operational requirements. The proposed optimization framework is generic and flexible, as it can be applied to different architectures both of large distribution networks (DNs) and microgrids, considering various types of system objectives and technical constraints. The presented strategy can be implemented in any decision support system or engineering software for power grids, providing decision makers with an effective information and communication technology tool for the optimal planning of the energy efficiency and environmental sustainability of DNs.
Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines

Support Vector Machines (SVM) has been a naval research field in scientific research for forecasting. This study deals with the application of SVM in financial time series predicting. This paper suggests a model of stock market prediction based on SVMs with appropriate parameter values. A data set of daily closing prices of five selected companies such as Alhaj Textiles Limited, Apex Tannery Limited, Jamuna Bank Limited, Padma Oil Company, and Square Pharmaceuticals Limited of the Dhaka Stock Exchange (DSE) from 01 January 2017 to 13 August 2019 was selected and uses these data to train the model and checks the predictive power of the model. The obtained results show that all the companies closing stock prices are non-stationary. Also the number of support vectors and mean square error is decreasing pattern with the increase of kernel parameter. It is also found that original data and predicted data are very much identical. The result shows that in all the cases SVM model has some predictive power it can be used to forecast financial time series. Several methods, such as SVM, ARIMA, single exponential smoothing, and double exponential smoothing, were performed to predict Bangladesh's stock market. Amazingly, the outcome shows the most efficient method to be Support Vector Machine because of its lowest forecasting errors.