Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Monte Carlo Methods Eﬀiciency to Solve Radiative Energy Transfer in High Fidelity Unsteady 3D Simulations

The present work assesses different Monte Carlo methods in radiative heat transfer problems, in terms of accuracy and computational cost. Achieving a high scalability on numerous CPUs with the conventional forward Monte Carlo method is not straightforward. The Emission-based Reciprocity Monte Carlo Method (ERM) allows to treat each mesh point independently from the others with a local monitoring of the statistical error, becoming a perfect candidate for high-scalability. ERM is however penalized by a slow statistical convergence in cold absorbing regions. This limitation has been overcome by an Optimized ERM (OERM) using a frequency distribution function based on the emission distribution at the maximum temperature of the sys-tem. Another approach to enhance the convergence is the use of low-discrepancy sampling. The obtained Quasi-Monte Carlo method is combined with OERM. The efﬁciency of the considered Monte-Carlo methods are compared
A SURVEY ON SECURITY THREATS IN THE INTERNET OF MEDICAL THINGS (IoMT)

Recent developments in the Internet of Things (IoT) have led to the development of the Internet of Medical Things (IoMT). Data collection, analysis, and transmission are key elements of IoMT tools that are revolutionizing healthcare delivery. Nevertheless, researchers and industry practitioners have several challenges with IoMT, particularly in the area of data security. Security breaches in the healthcare industry have downsides such as patient's personal information breach, possibly of death incidence. As a such, IoMT requires high standards of security. In this paper, we provide a review of IoMT security challenges and their possible mitigations. By surveying multiple scientific research papers, we aimed to guide researches on latest trends in medical device security provide mitigation of threats against various IoMT devices. In addition, the current weaknesses of various e-Health domains and demonstrates the results of recent works to overcome these obstacles is also explored.
Effect of an of ﬁ ce ergonomic randomised controlled trial among workers with neck and upper extremity pain

Background Of ﬁ ce computer workers are at increased risk for neck/upper extremity (UE) musculoskeletal pain. Methods A seven-month of ﬁ ce ergonomic intervention study evaluated the effect of two engineering controls plus training on neck/UE pain and mechanical exposures in 113 computer workers, including a 3-month follow-up period. Participants were randomised into an intervention group, who received a keyboard/mouse tray (KBT), touch pad (TP) for the non-dominant hand and keyboard shortcuts, and a control group who received keyboard shortcuts. Participants continued to have available a mouse at the dominant hand. Outcomes were pain severity, computer rapid upper limb assessment (RULA), and hand activity level. Prevalence ratios (PRs) evaluated intervention effects using dichotomised pain and exposure scores. Results In the intervention group, the dominnt proximal UE pain PR=0.9, 95% CI 0.7 to 1.2 and the dominant distal UE PR=0.8, 95% CI 0.5 to 1.3, postintervention. The non-dominant
DESIGN OF MUTATION OPERATORS FOR TESTING USING PARALLEL GENETIC ALGORITHM FOR OPEN-SOURCE ENVIRONMENTS

Specification-based testing approaches create test data without having any prior knowledge of the program's structure. However, the quality of this test data isn't always reliable enough to catch errors when non-functional modifications are made to the software. We offer a novel technique that combines formal requirements and the evolutionary algorithm to successfully produce test data. In this technique, Parallel Genetic Algorithm (PGA) rewrites formal requirements in order to create input values that kill as many mutants of the target programmed as feasible. To explain how the approach works, two famous instances are offered. The results suggest that the proposed technique may successfully produce test cases to eliminate programmed mutants, resulting in improved software maintenance.
FORMING COMMANDS FOR VOICE CONTROL IN KALDI ENVIRONMENT BASED ON JSPEECH GRAMMAR FORMAT TECHNOLOGY

The work is devoted to the study of the features of voice control of a mobile robot and the processing of voice commands. For these purposes, standard IT technologies are used. An approach to solving such a problem based on various voice recognition systems is considered. Four main speech recognition systems are described. A system for recognizing voice commands for controlling a robot is considered. A distinctive feature of the proposed system is that the system does not require a permanent connection to the Internet, and the autonomous operation of such a system has the advantage of lower requirements for working with it
MIT Open Access Articles Wavelet-based Burst Event Detection and Localization in Water Distribution Systems

In this paper we present techniques for detecting and locating transient pipe burst events in water distribution systems. The proposed method uses multiscale wavelet analysis of high rate pressure data recorded to detect transient events. Both wavelet coefﬁcients and Lipschitz exponents provide additional information about the nature of the signal feature detected and can be used for feature classiﬁcation. A local search method is proposed to estimate accurately the arrival time of the pressure transient associated with a pipe burst event. We also propose a graph-based localization algorithm which uses the arrival times of the pressure transient at different measurement points within the water distribution system to determine the actual location (or source) of the pipe burst. The detection and localization performance of these algorithms is validated through leak-off experiments performed on the WaterWiSe@SG wireless sensor network test bed, deployed on the drinking water distribution system in Singapore. Based on these experiments, we also present a systematic analysis of the sources of localization error.
Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics

In eukaryotic cells, membrane proteins play a crucial role. They fall into three categories: intrinsic proteins, extrinsic proteins, and proteins that are essential to the human genome (30% of which is devoted to encoding them). Hydrophobic interactions inside the membrane serve to stabilize integral proteins, which span the lipid bilayer. This review investigates a number of computational and experimental methods used to study membrane proteins. It encompasses a variety of technologies, including electrophoresis, X-ray crystallography, cryogenic electron microscopy (cryo-EM), nuclear magnetic resonance spectroscopy (NMR), biophysical methods, computational methods, and artificial intelligence. The link between structure and function of membrane proteins has been better understood thanks to these approaches, which also hold great promise for future study in the field. The significance of fusing artificial intelligence with experimental data to improve our comprehension of membrane protein biology is also covered in this paper. This effort aims to shed light on the complexity of membrane protein biology by investigating a variety of experimental and computational methods. Overall, the goal of this review is to emphasize how crucial it is to understand the functions of membrane proteins in eukaryotic cells. It gives a general review of the numerous methods used to look into these crucial elements and highlights the demand for multidisciplinary approaches to advance our understanding.
and thalamic metabolites in obsessive-compulsive disorder.

Focal brain metabolic effects detected by proton magnetic resonance spectroscopy (MRS) in obsessive-compulsive disorder (OCD) represent prospective indices of clinical status and guides to treatment de-sign. Sampling bilateral pregenual anterior cingulate cortex (pACC), anterior middle cingulate cortex (aMCC), and thalamus in 40 adult patients and 16 healthy controls, we examined relationships of the neurometabolites glutamate þ glutamine (Glx), creatine þ phosphocreatine (Cr), and choline-compounds (Cho) with OCD diagnosis and multiple symptom types. The latter included OC core symptoms (Yale-Brown Obsessive-Compulsive Scale – YBOCS), depressive symptoms (Montgomery-Åsberg Depression Rating Scale – MADRS), and general functioning (Global Assessment Scale – GAS). pACC Glx was 9.7% higher in patients than controls. Within patients, Cr and Cho correlated negatively with YBOCS and MADRS, while Cr correlated positively with the GAS. In aMCC, Cr and Cho correlated negatively with MADRS, while Cr in thalamus correlated positively with GAS. These ﬁ ndings present moderate support for glutamatergic and cingulocentric perspectives on OCD. Based on our prior metabolic model of OCD, we offer one possible interpretation of these group and correlational effects as consequences of a corticothalamic state of elevated glutamatergic receptor activity alongside below-normal glutamatergic transporter activity.
PROPOSITION OF A TRADING BOT FOR CRYPTOCURRENCY MARKET

Blockchain technology opens up new business opportunities, and the market capitalization of the industries it connects has been growing quickly in recent years. As cryptocurrencies gain popularity, trading them are growing in importance. Due to the market's great volatility, trading carries a very high risk, and many new traders face financial losses. Furthermore, seasoned traders are unable to work nonstop and thus, lose out on some trading opportunities. The aim of this study is to design a trading bot for the cryptocurrency market. There are currently a lot of trading bots in use on the crypto exchange markets. They never pass up a trading opportunity that comes around in a day. Both new and seasoned traders can use automated trading bots, depending on their needs. However, most of these bots are not free to use and their free features are very limited. This study aims to fill this gap by designing a free trading bot for the cryptocurrency market. MACD and RSI indicators will be used while designing the trading bot. As a result, a simple bot which both new and seasoned traders can easily used will be available.
young’ Environmental determinants of healthy eating: in need of theory and evidence

In order to promote healthful nutrition, insight is needed into the determinants of nutrition behaviours. Behavioural determinant research and behavioural nutrition interventions have focused mostly on individual-level motivational factors. It has been argued that the individual’s socio-cultural and physical environments may be the main determinants of nutrition behaviours. However, the theoretical basis and empirical evidence for environmental determinants of nutrition behaviours are not strong. The present paper is a narrative review informed by a series of systematic reviews and recent original studies on associations between environmental factors and nutrition behaviours to provide an overview and discussion of the evidence for environmental correlates and predictors of nutrition behaviour. Although the number of studies on potential environmental determinants of nutrition behaviours has increased steeply over the last decades, they include only a few well-designed studies with validated measures and guided by sound theoretical frameworks. The preliminary evidence from the available systematic reviews indicates that socio-cultural environmental factors deﬁning what is socially acceptable, desirable and appropriate to eat may be more important for healthful eating than physical environments that deﬁne the availability and accessibility of foods. It is concluded that there is a lack of well-designed studies on environmental determinants of healthful eating behaviours. Preliminary evidence indicates that social environmental factors may be more important than physical environmental factors for healthful eating. Better-designed studies are needed to further build evidence-based theory on environmental determinants to guide the development of interventions to promote healthful eating.
A NOVEL HOMOMORPHIC AND MATRIX OPERATION FOR RANDOMIZATION ENCRYPTION SCHEMES FOR PRIVACY IN CLOUD COMPUTING ARCHITECTURE

Traditional outsourced methods are being replaced by the emerging cloud computing architecture, which offers adaptable services to clients in many regions through the Internet. Due to this, it becomes necessary for data categorization to be carried out by probably dangerous cloud servers. In this situation, clients can use a classification created by the server to categorize their unique cloud-based resource test results. The current study effort created a training approach that protects confidentiality using the Matrix Operation for Randomization and Encryption (MORE) scheme, allowing neural network model calculations. The neural unit of the model may retain certain critical data as a result of over-fitting during the deep learning training phase. It is possible for data privacy to be compromised whenever the intruder creates the appropriate assault scenario. In this research article, a Differential Privacy Subspace Approximation with Adjusted Bias (DPSaab) is proposed to train the Feed forward-designed Alexnet convolutional neural network (FF-ACNN). Initially, the data normalization is done by Min-Max normalization then the privacy-preserving training method based on A FF-Alexnet CNN model's calculations may be done immediately on floating point data. This is possible due the encryption technique MORE. In addition, the DPSaab method is used to maintain differentiated anonymity in FF - CNN's Alexnet. The efficacy of the suggested approach is tested on the MINST identification database using the DPSaab method, which meets the concept of differentiated secrecy.
CYBERBULLYING DETECTION: CURRENT TRENDS AND FUTURE DIRECTIONS

As we see the rapid growth of Web 2.0; online social networks-OSNs and online communications which provides platforms to connect each other all over the world and express the opinion and interests. Online users are generating big amount of data every day. As result, OSNs are providing opportunities for cybercrime and cyberbullying activities. Cyberbullying is online harassing, humiliating or insulting an online user through sending text messages of threatening or harassing using online tool of communication. This research paper provides the comprehensive overview of cyberbullying that occurs usually on OSNs websites and provides current approaches to tackle cyberbullying on OSNs. It also highlights the issues and challenges in cyberbullying detection system and outline the future direction for research in this area. The topic discussed in this paper start with introduction of OSNs, cyberbullying, types of cyberbullying, and data accessibility is reviewed. Lastly, issues and challenges concerning cyberbullying detection are highlighted.
Modelling catchment inflows into Lake Victoria: uncertainties in rainfall–runoff modelling for the Nzoia River

Abstract Climate and soil characteristics vary considerably around the Lake Victoria basin resulting in high spatial and temporal variability in catchment inflows. However, data for estimating the inflows are usually sparsely distributed and error-prone. Therefore, modelled estimates of the flows are highly uncertain, which could explain early difficulties in reproducing the lake water balance. The aim of this study was to improve the estimates of catchment flow to Lake Victoria. The WASMOD model was applied to the Nzoia River, one of the major tributaries to Lake Victoria. Uncertainty was assessed within the GLUE framework. During calibration, log-transformation was performed on both simulated and observed flows. The results showed that, despite its simple structure, WASMOD produces acceptable results for the basin. For a Nash-Sutcliffe efficiency (NS) threshold of 0.6, the percentage of observations bracketed by simulations (POBS) was 74%, the average relative interval length (ARIL) was 0.93, and the maximum NS value was 0.865. The residuals were shown to be homoscedastic, normally distributed and nearly independent. When the NS threshold was increased to 0.8, POBS decreased to 54% with an improvement of ARIL to 0.49, highlighting the effect of the subjective choice of likelihood threshold. Citation Kizza, M. , Rodhe, A. , Xu, C. -Y. & Ntale, H. K. (2011) Modelling catchment inflows into Lake Victoria: uncertainties in rainfall–runoff modelling for the Nzoia River. Hydrol. Sci. J. 56(7), 1210–1226.
INTENTION TO USE ANALYSIS ON TWITTER AS A BANK CUSTOMER CARE IN JABODETABEK

This study examines the interest of using Twitter as a consumer service media in local private banking companies. The purpose of this research is to find out the intention of using Twitter as a customer services media because Twitter considered as a good media for complain. This research is a quantitative study using DeLone & McLean, UTAUT dan TTF methodology that focuses on 3 local private banks which has verified status on their Twitter account and have customer data publications, which is BCA, Permata Bank and Panin Bank as research subjects. The results in this study concluded that the complaint itself is an individual's desire based on the customer services and is not influenced by the social environment. In addition, banking companies also need to build an image of "serving consumers closer" through better services so that customer are interested in making complaints and consulting through social media, especially Twitter.
Polyunsaturated fatty acid biosynthesis is required for phenylephrine-mediated calcium release in vascular smooth muscle cells

Stimulation of vascular smooth muscle (VSM) (cid:1) 1 -adrenoceptors induces myosin phosphorylation and vasoconstriction via mobilisation of intracellular calcium and production of specific eicosanoids. Polyunsaturated fatty acid (PUFA) biosynthesis in VSM cells is involved, although the precise mechanism is not known. To address this, we characterised PUFA biosynthesis in VSM cells and determined its role in intracellular calcium release and eicosanoid production. Murine VSM cells converted 18:2n-6 to longer chain PUFA including 22:5n-6. (cid:2) 6 (D6d) and (cid:2) 5 (D5d) desaturase, and elongase (Elovl) 5 were expressed. Elovl2 was not detected in
Cell Type-specic Study to Unveil the Relation Between DLBCL and GBM Through Blood-Brain-Barrier Disruption

Diﬀuse large B cell lymphoma is an aggressive and fast-growing form of non-Hodgkin’s lymphoma. DLBCL becomes fatal if it is not treated in the early stage. Despite its aggressive nature, 50-70% patients respond successfully, whereas 30-40% patients have an inadequate response to the currently available therapies. DLBCL spread to the brain through spinal ﬂuid. It is becoming a rapid emergency to detect the cell-speciﬁc changes of DLBCL metastasis that leads to central nervous system (CNS) disorder. As per our knowledge, the cell-speciﬁc studies of genes of DLBCL involved in CNS disorder are still limited. To bridge this gap, we proposed a novel analysis. Firstly, we have identiﬁed DLBCL cell-speciﬁc gene clusters and considered genes along with their associated pathways that have an impact on blood-brain-barrier disruption. In the second stage, we tried to establish the relation between Diﬀuse large B cell lymphoma and glioblastoma based on cell-speciﬁc pathways. Interestingly it has been observed that high semantic-valued pathways are the prime regulators of GBM. Finally, this study reveals how cell types are responsible for the pathway shifts and also provides a cell-speciﬁc channel of triggering the progression of GBM among DLBCL patients.
Secure Noise Sampling for DP in MPC with Finite Precision

Using secure multi-party computation (MPC) to generate noise and add this noise to a function output allows individuals to achieve formal differential privacy (DP) guarantees without needing to trust any third party or sacrifice the utility of the output. However, securely generating and adding this noise is a challenge considering real-world implementations on finite-precision computers, since many DP mechanisms guarantee privacy only when noise is sampled from continuous distributions requiring infinite precision. We introduce efficient MPC protocols that securely realize noise sampling for several plaintext DP mechanisms that are secure against existing precision-based attacks: the discrete Laplace and Gaussian mechanisms, the snapping mechanism, and the integer-scaling Laplace and Gaussian mechanisms. Due to their inherent trade-offs, the favorable mechanism for a specific application depends on the available computation resources, type of function evaluated, and desired ( ϵ, δ )-DP guarantee. The benchmarks of our protocols implemented in the state-of-the-art MPC framework MOTION (Braun et al., TOPS’22) demonstrate highly efficient online runtimes of less than 32 ms/query and down to about 1ms/query with batching in the two-party setting. Also the respective offline phases are practical, requiring only 51 ms to 5.6 seconds/query depending on the batch size.
MACHINE LEARNING-BASED AUGMENTED REALITY FOR IMPROVED TEXT GENERATION THROUGH RECURRENT NEURAL NETWORKS

Machine learning (ML) is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence (AI). The main focus of the field is learning from previous experiences. Classification in ML is a supervised learning method, in which the computer program learns from the data given to it and make new classifications. There are many different types of classification tasks in ML and dedicated approaches to modeling that may be used for each. For example, classification predictive modeling involves assigning a class label to input samples, binary classification refers to predicting one of two classes and multi-class classification involves predicting one of more than two categories. Recurrent Neural Networks (RNNs) are very powerful sequence models for classification problems. However, in this paper, we will use RNNs as generative models, which means they can learn the sequences of a problem and then generate entirely a new sequence for the problem domain, also, we propose an architecture of a learning application that benefits from the advantages of both machine learning and augmented reality to provide a better learning experience and help teachers build adaptive educational content using the proposed model.
Society Human Factors and Ergonomics Human Factors: The Journal of the

Objective: A novel model of measuring clutter in complex geospatial displays was compared with human ratings of subjective clutter as a measure of convergent validity. The new model is called the color-clustering clutter (C3) model. Background: Clutter is a known problem in displays of complex data and has been shown to affect target search performance. Previous clutter models are discussed and compared with the C3 model. Method: Two experiments were performed. In Experiment 1, participants performed subjective clutter ratings on six classes of information visualizations. Empirical results were used to set two free parameters in the model. In Experiment 2, participants performed subjective clutter ratings on aeronautical charts. Both experiments compared and correlated empirical data to model predictions. Results: The first experiment resulted in a . 76 correlation between ratings and C3. The second experiment resulted in a . 86 correlation, significantly better than results from a model developed by Rosenholtz et al. Outliers to our correlation suggest further improvements to C3. Conclusions: We suggest that (a) the C3 model is a good predictor of subjective impressions of clutter in geospatial displays, (b) geospatial clutter is a function of color density and saliency (primary C3 components), and (c) pattern analysis techniques could further improve C3. Application: The C3 model could be used to improve the design of electronic geospatial displays by suggesting when a display will be too clut-tered for its intended audience.