Miracle-dz/UniFAR-FTUs huggingface dataset:
Steep-Slope Devices: Prospects and Challenges

In this work, an overview is given on the prospects and challenges of two novel device concepts,namely the Tunnel FET (TFET) and the Superlattice FET (SL-FET). The optimization effort ofhomo- and hetero-junction TFETs carried out so far shows that these devices can provide an advantageover CMOS FETs only for very-low power and low-performance niche applications, so long asthe supply voltage is scaled below 300 mV. The required materials for homojunction TFETs are lowbandgap semiconductors, such as InAs and InGaAs; for heterojunction TFETs the best semiconductorpair appears to be (Al)GaSb-InAs. Several technological problems are still unsolved: poor qualityof the oxide interface with III-V materials and device variability are probably the most important. The SL-FET represents in principle a better device concept, as it provides outstanding performance and meets nearly all targets of the high performance (HP), low operating power (LOP) and low standby power (LSTP) of the ITRS at VDD = 0.4V. A suitably-designedInGaAs-InAlAs SL-FET has turned out to provide the best simulation results. However, the fabricationprocess of SL-FETs is much more complex, as it requires molecular epitaxy to deposit multiplelayers with a very strict control of their nanometric thickness. Besides, vertical devices can poseunexpected problems as far as layout organization and parasitics are concerned.
Inflation Targeting

We evaluate the case for perfect price (inflation) stabilization in a NNS model that includes capital accumulation, a variety of shocks, a monetary and an imperfect competition distortion. In such a model, the flexible price equilibrium is inefficient, which may provide the monetary authorities with an opportunity to improve upon it via the suitable cyclical manipulation of real marginal costs. We find that such an opportunity is of limited value. With only the imperfect competition distortion present (in the ”cashless” version of the model), inflation variability is costly independent of the level of capital adjustment costs, the size of markups, the degree of risk aversion and the type of the shock. The presence also of a non-negligible monetary friction may make a modest amount of inflation variability desirable when prices are fairly flexible and capital adjustment costs low. JEL class: E32 E52
Biological Effects of Non-ionizing Electromagnetic Fields at 27 GHz on Sperm Quality of Mytilus galloprovincialis

Recently, a rising use of wireless internet technologies has been demonstrated. The devices 18 which use these technologies emit in new spectral regions an electromagnetic radiation (EMFs) 19 which could interact with the male reproductive system. The aim of this study was to investigate in 20 vitro influence of electromagnetic fields at 27 GHz on sperm quality in Mytilus galloprovincialis. 21 Sperm samples, were collected from sexually mature males of M. galloprovincialis and placed in sea- 22 water. Once evaluated the number and quality of spermatozoa, sperm cells were exposed to elec- 23 tromagnetic fields radiated by a pyramidal horn antenna. The effect of exposure was evaluated after 24 10, 20, 30,40 and 60 minutes with light microscope and using Eosin test. Ten replications were per- 25 formed and statistical analysis was carried out by one-way ANOVA test. A significative decrease 26 in sperm motility was observed after 10 minutes of exposure and after 30 minutes most of sperms 27 were immobile and not vital. This study provides useful data on potential ecological impact of the 28 5G high-band on animal fertility, whose effect is currently under investigation.
Video augmentation technique for human action recognition using genetic algorithm

Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi‐Uncut, iXMAS, and IAVID‐1 datasets.
Privacy preserving scheme for document similarity detection

: The problem of detecting similar documents plays an essential role for many real-world applications, such as copyright protection and plagiarism detection. To protect data privacy, the new version of such a problem becomes more challenging, where the matched documents are distributed among two or more parties and their privacy should be preserved. In this paper, we propose new privacy-preserving document similarity detection schemes by utilizing the locality-sensitive hashing technique, which can handle the misspelled mistakes. Furthermore, the keywords’ occurrences of a given document are integrated into its underlying representation to support a better ranking for the returned results. We introduced a new security definition, which hides the exact similarity scores towards the querying party. Extensive experiments on real-world data illustrate that our proposed schemes are eﬀicient and accurate.
Pinning control of an array of the globally coupled FitzHugh–Nagumo oscillators via a single damaged unit

A combined control method for stabilizing the steady states of the globally coupled oscillators is described. It includes two techniques, the pinning control and damaging a single oscillator. Two versions of damaging the pinned oscillator are considered: (1) the oscillator is short-circuited with an external adjustable voltage source, (2) the oscillator is short-circuited with an external large capacitor. Analytical, numerical and experimental investigations have been carried out using an array of the mean-field coupled FitzHugh–Nagumo oscillators.
Satellite Characterization via Restoration-Free Imaging: A Novel Machine Learning Paradigm for SSA

In this paper, we investigate several state-of-the-art deep convolutional neural network architectures using a lightweight Bayesian neural architecture search strategy to classify images of satellites affected by varying levels of simulated atmospheric distortion. This paper provides a comprehensive study of the state-of-the-art for deep learning in SSA. Over the course of 392 trained models, we obtain a ﬁnal, repeatable model with over 90% accuracy on unseen images with varying amounts of atmospheric distortion. Our approach demonstrates that sufﬁciently deep convolutional neural networks implicitly learn to ignore or remove atmospheric distortion when tasked with classiﬁcation alone, mitigating the necessity for image restoration techniques for satellite characterization.
Design and physico-mechanical evaluation of fast-dissolving valsartan polymeric drug delivery system by electrospinning method

Objective(s): Chronic hypertension is a pervasive morbidity and the leading risk factor for cardiovascular diseases. Valsartan, as an antihypertensive drug, has low solubility and bioavailability. The application of orodispersible films of valsartan is suggested to improve its bioavailability. With this dosage form, the drug dissolves rapidly in saliva and is absorbed readily without the need for water. Materials and Methods: For this purpose, valsartan with polyvinylpyrrolidone (PVPK90) polymer were exposed to the electrospinning technique to construct orodispersible nanofilms. The optimum obtained nanofiber, selected by Design-Expert software, was evaluated in terms of mechanical strength for evaluation of the flexibility and fragility of the nanofibers. The drug content, wettability, and disintegration tests, as well as the release assessment of the nanofibers, were performed followed by DSC, FTIR, and XRD assays. Results: The uniform nanofibers’ diameter increased with the increase of the polymer concentration. The tensile test verified a stress reduction at the yield point as the polymer concentration increased. Then, the 492 nm nanofiber with above 90% drug encapsulation, containing 8% polymer and 18% valsartan made below 9 kV, was selected. The wetting time was less than 30 sec and over 90% of the drug was released in less than 2 min. The XRD and DSC studies also confirmed higher valsartan solubility due to the construction alternations in nanofibers. The FTIR examination indicated the chemical bonding between the drug and the polymer. Conclusion: The selected nanofibers of valsartan present the essential drug feature and acceptable drug release for further investigations.
Determination of Median Lethal Dose of Zinc chloride in Wistar Rat

| Zinc is one of the most abounded trace elements in the body following iron. Zinc when supplemented in a trace amount participates as a catalytic cofactor of enzymes and regulates various metabolic functions in the body. Excess supplementation of any trace element disturbs the metabolic functions of organs and leads to toxicity. The aim of the work was taken to find out the median lethal dose of zinc chloride (ZnCl2) to albino rats via intraperitoneal (IP) route. A single dose of ZnCl2 was dissolved in distilled water (Milli-Q) and administered intraperitoneally at the concentrations of 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 mg/ kg body weight of the experimental animals. Experimental units were observed every 3 hrs before dose administration and later after 6 hrs, 12hrs, 24 hrs and 48hrs for the development of any abnormal behaviors and toxicity symptoms. After 48 hrs, mortalities were estimated in each group in correlation to the total susceptible population. The obtained results were evaluated by the Statistical Probit Analysis Method. The results revealed that the lethal dose 50 (LD50) value within 48 hrs in Albino rats was 57.348 mg/kg b.w. The identification and evaluation of the LD50 against zinc chloride were essential for understanding zinc toxicity because it has been commercially utilized in the form of inorganic zinc salt as a therapeutic agent, dry cell batteries, and for household purpose, thus information about the toxic impacts on ZnCl2 is useful for experimental or toxicological approaches. The in-vitro LD50 evaluations of target chemicals in Wister rats were highly associated with zinc toxicity-related physiological disorders perceptive and therapy.
ENCRYPTION USING CHAOTIC MAPS

: The use of chaotic systems for secure or private communications has been an active area of research in the past few years. It is based on the facts that chaotic signals are usually noise-like and chaotic systems are very sensitive to initial conditions. In recent years, external key was introduced to chaotic cryptography by Pareek et al. and find its application in several discrete chaotic cryptosystems. In 2013 an improved symmetric encryption scheme using one dimensional chaotic map in order to obtain chaotic sequences with better cryptographic features was proposed by Nisha Khushwah. We propose a modified version of the chaotic cryptographic method based on iterating a skew tent map and logistic map.
University of Birmingham Adiabatic compressed air energy storage with packed bed thermal energy storage

(cid:2) The paper presents a thermodynamic analysis of A-CAES using packed bed regenerators. (cid:2) The packed beds are used to store the compression heat. (cid:2) A numerical model is developed, validated and used to simulate system operation. (cid:2) The simulated efﬁciencies are between 70.5% and 71.1% for continuous operation. (cid:2) Heat build-up in the beds reduces continuous cycle efﬁciency slightly.
Food and Restaurant Recommendation System Using Hybrid Filtering Mechanism

The application of machine learning and Artificial Intelligence system in the food industry is not more profound than the other sectors. Those available systems do not fully answer the expectations of customers, lacks user personalization’s. This paper presents the recommendation system for restaurants and food using a hybrid filtering mechanism. Multiple filtering mechanisms were applied on datasets to recommend food and restaurants for customers. The dataset was collected from recognized machine learning repositories of the U.S.A. government. Feature extraction and sampling are done on the datasets to test the performance of the system. This paper will try to answer user personalization preferences by applying Hybrid mechanisms. The recommendation was based on different customer preference like ratings, top sale, discount, weather condition etc. This paper combines content-based and collaborative based filtering mechanisms to provide the user with full functionalities of the recommender systems. It will adopt a hybrid system from the two mechanisms for effective implementation of the recommendation. For the development of this paper, data collection was done from known repositories, and feature extraction was done to filter out the unnecessary data. Sampling techniques were also applied to the dataset to distinguish between train and test data. 70% of the total dataset were allocated for training the system and 30% for test purpose. Sampling was also conducted in the model test stage by assigning 30% for evaluation purpose. To evaluate the performance of the proposed system, machine learning algorithms such as random forest, gradient boosting, decision tree, linear regression and K-Nearest neighbor were applied. The final performance of the model is so promising that it achieved an 83.5% success rate. Model loss and accuracy were also conducted, and the best fitting algorithms were selected. Based on the final result, the random forest algorithm shows significant performance with 0.859 accuracies and 0.1193 loss
Research Themes on Warnings in Information Systems Crisis Management Literature

Early Warning Systems (EWS) are crucial to mitigating and reducing disaster impacts. Furthermore, technology and information systems (IS) are key to the success of EWSs. This systematic literature review investigates the research topics and themes from the past six years of Information Systems for Crisis Response and Management (ISCRAM) conference proceedings and seeks to identify the research developments and directions for EWSs to steer a discourse to advance the research in this field. Findings from a sample size of 60 papers show that there are technical, social, and topical considerations to using and advancing technology for EWSs. While technology has advanced EWSs to new levels, it is important to consider the influence of technology in the successful operation of EWSs. The results are based on the ISCRAM proceedings literature and may be broader or have different prioritization if a wider disciplinary body of literature was explored. This will be considered in the future.
Percutaneous K-wires Fixation of Pediatric Both Bone Forearm Fractures: An updated Overview

Forearm fractures are the most common fractures in children, representing 40 to 50 percent of all childhood fractures. In one large series, forearm shaft fractures of the radius ranked as the third most common fracture after distal radial fractures and supracondylar humeral fractures. In addition, midshaft forearm fractures are the most common sites for refracture in children and among the most common sites of pediatric open fractures.
GI-2021-21 Reply to Reviewers

The manuscript by Nevalainen et al. introduces the Field Observatory Network and its methodology which combines data from different sources (offline and near real time in situ data, satellite imagery and climatic data) with ecosystem modelling to support the monitoring and verification of soil carbon sequestration in agricultural soils. The network currently consists of 20 pilot farm sites in Finland with the involvement of different actors from researchers to farmers with the common goal of optimizing agricultural practices in order to preserve and possibly enhance soil carbon stocks. Both data and simulations results are disseminated publicly through a web graphical user interface. Such a system and intended as a decision support for the farmers who are able to monitor the effects of agricultural practices in relation to crop productivity and carbon sequestration is innovative, however the limits of the decision-making based on the information currently provided by the FiON are lacking in the discussion section. In addition, some aspects of CO2 flux data processing have to be clarified to make the current methodology scalable in view of an expansion of the sites network.
Title: Visible and socially-just pedagogy: Implications for mathematics teacher education

In this paper I consider the notion of “visible pedagogy” in refining a conceptualisation of “socially-just pedagogy” in the mathematics classroom. I explore reasons why the recent focus on promoting formative assessment and metacognition, embodied in Hattie’s “visible learning”, has failed to bring about the fundamental shift in pedagogic approaches that was hoped for. I draw on three sociocultural theories, Brousseau’s “didactic contract”, Bourdieu’s theory of “reproduction” and Bernstein’s theory of “pedagogic discourse”, to explain the continued predominance of teacher-centred pedagogies in mathematics education. I highlight concerns raised by some equity-minded researchers that a “socially-just pedagogy”, which incorporates collaborative, discursive, problem-solving and inquiry-oriented teaching approaches, might further disadvantage marginalised students due to its relative lack of structure. I argue for an alternative interpretation of Bernstein’s “visible pedagogy” that rejects teacher-centred approaches, as a way of making the instructional and regulative discourse more explicit, in favour of a “socially-just pedagogy” in which the teacher’s “pedagogic rationale” is made more explicit to learners. I highlight the potential for such an approach, and exemplify what it might look like in practice, by drawing on my extensive experience as a teacher educator in analysing a vignette of typical classroom practice from sociocultural
Capturing Variants of Transliterated Arabic Names in English Text

Transliteration is the process of representing words of one language into another using corresponding equivalent phonemes. For example, “Mohammed”, “Mohammad”, or “Muhammed” are three valid transliterations to the Arabic proper noun “ ﺪﻤﺤﻣ ”. Transliteration from Arabic to English usually results in several different versions for the same Arabic name causing some names to have more than 40 different versions. Finding transliterated names is a problem in most languages. In English, this problem has been studied by researchers and many techniques have been developed to find transliterated names referring to the same foreign name. Techniques such as string matching and phonetic matching have been used to find similar names. However, some of these techniques were designed to find similar names of English origin and not a specific transliterated names. In this paper we review current techniques used to find variants of the same name and introduce a new technique we specifically developed to find transliterated Arabic names in English text. We developed a data set of more than 25,000 transliterated Arabic names and tested the effectiveness of current and the new technique on finding 115 names within that list. Our results show that our technique is superior to all other techniques. We also present an online system that we developed to find transliterated Arabic names on the web using our technique.
Incremental open-set recognition for semantic segmentation

Semantic segmentation is an important task of computer vision with many interesting applications. Currently, the state of the art in the field is achieved by discriminative convolutional models. Unfortunately, discriminative models are prone to unwarranted optimism. Consequently, outliers are often misclassified with very small uncertainty. This thesis attempts to solve the problem by incremental learning with respect to a noisy negative set that approximates distribution of the entire visual world.
The Importance of Capital Investment and Debt Financing in Deriving Shareholders’ Value; A Literature Survey

Shareholders’ value is the most important goal and an integral part of the companies’ strategic decision-making process. When a corporate performs well and creates value for its shareholders, it benefits the whole economy. The past studies concluded that efficient decision making in the areas of capital investments and debt financing can ensure high financial performance and shareholders’ value creation. This paper thoroughly reviews the literature on impact of capital investment and debt financing decisions on shareholders’ value. Capital investment is a very important managerial decision because it increases company's economic profit. However, past studies have found that not every time the capital investment results in increasing the value as it may vary with the level of investment. Moreover, debt financing lowers the free cash flows due to the payment of fixed interest payments, thus lowering shareholders' return and value. Therefore, this paper recommends the need of further research to better understand the effect of capital investment and debt financing decisions on shareholders’ value.
Fault-tolerant rotor position and velocity estimation using binary hall-effect sensors for low-cost vector control drives

High-resolution position and velocity estimation algorithms based on binary Hall-effect sensors for low-cost vector control drives have been the subject of significant research in recent years. While different estimation algorithms have been proposed and analyzed, no contribution so far has dealt with Hall-effect sensor faults and their repercussion on the drive. Here, single and multiple Hall-effect sensor faults are analyzed, and a method to detect, identify, and mitigate such faults is investigated. The method is general, and it ensures proper operation of the drive and is not dependent on the particular estimation algorithm that is used; by way of example, here it will be applied to a vector-tracking observer. Limitations on the performances of the faulty system are discussed, and experimental results are reported to confirm the theoretical analysis.
The problem of control of rod heating process with nonseparated conditions at intermediate moments of time

The problem of control of rod heating process by changing the temperature along the rod whose ends are thermally insulated is considered. It is assumed that, along with the classical boundary conditions, nonseparated multipoint intermediate conditions are also given. Using the method of separation of variables and methods of the theory of control of finite-dimensional systems with multipoint intermediate conditions, a constructive approach is proposed to build the sought function of temperature control action. A necessary and sufficient condition is obtained, which the function of the distribution of the rod temperature must satisfy, so that under any feasible initial, nonseparated intermediate, and final conditions, the problem is completely controllable. As an application of the proposed approach, control action with given nonseparated conditions on the values of the rod temperature distribution function at the two intermediate moments of time is constructed.