Miracle-dz/UniFAR-FTUs huggingface dataset:
NOVEL INTRUSION DETECTION METHODS FOR SECURITY OF WIRELESS SENSOR NETWORK

Wireless Sensor Network (WSN) has to face many threats as it consists of sensor nodes and needs installation in the open area. Intrusion Detection System (IDS) is an essential safety method of handling vulnerabilities and threats for WSN. This study is a relative assessment of the best performed IDS methods of WSNs, the analysis of this method is technically represented in detail. Threats to WSN are categorized into the criteria. Customized dataset is prepared by KDD dataset with five stages to normalize it. Normal class has four types of attacks which are much related attributes and used for classification routine. This study is applied to methods (e.g. CfsSubsetEval and BestFirst) for selection of attributes procedure to remove irrelevant attributes. Experimental work reports the algorithm which provides high detection rate. Finally, in conclusion which has satisfactory statements and rules for future research works to implement IDS in WSNs. Many recommendations are mentioned as well as future directions regarding this study.
Retrospective analysis of complications in anterior cervical discectomy and fusion (ACDF) with use of plate at medium-term follow up

Background: Anterior cervical discectomy and fusion with plate implementation represents one of the most used procedure for the treatment of single- and multilevel cervical degenerative disc disease. Since few information are reported about clinical complication related to the use of plate, the aim of this retrospective study is to evaluate qualitatively and quantitatively the intra-operative and post-operative complications. Methods: Fifty-five (55) patients (23 males and 32 females, mean age 49.8 years/old) who underwent cervical fusion by intervertebral TiPEEK cage, and 3 cervical plate were retrospectively reviewed for clinical and radiological outcomes. Results: Cervical spinal fusion was performed at one level in 24 patients (43.6%), at two levels in 26 patients (47.3%), and at three levels in 5 patients (9.1%). The computed tomography scan of two patients included in this retrospective analysis was not available after one year from surgery (3.6%). The remaining 53 patients (96.4%) had a complete proven fusion. At a mean follow-up of 38.6 months, the clinical outcomes were “excellent”, “very good” or “good” for 47.3%, 27.3% and 16.4% of patients respectively. Only 9.1% reported a “poor” outcome. No adverse effects were observed after the use of cervical Mecta-C plates. Conclusions: This retrospective study showed no clinical complication or adverse event after anterior cervical discectomy and fusion with plate implementation demonstrating the safety of Mecta-C cervical plate as an effective support of the TiPEEK cage.
Generalised semi‐analytical method for excitability curves calculation and numerical modal amplitude analysis for Lamb waves

The paper presents an efficient and exact method for the calculation of excitability curves and for the analysis of numerical models for guided waves. The proposed procedure makes use of the wave equation and through‐thickness‐only discretisation of anisotropic, layered plates to obtain the Lamb wave amplitude characteristics. Thereby, the method can be used for any arbitrarily selected anisotropic (layered structures, such as composites) material in a straightforward manner. A general framework for the proposed analysis is given, along with application examples. For this purpose, orthogonality conditions based on the reciprocity theorem are derived for the numerical model. Although the developed mathematical framework is verified and application examples are given based on the local interaction simulation approach for elastic waves propagation, the proposed methodology can easily be adopted for other numerical methods (eg, finite elements and cellular automata for elastodynamics). The method can also be used to study the influence of discretisation parameters on excitability curves estimates.
BREXIT: P REDICTING THE B REXIT UK E LECTION R ESULTS BY C ONSTITUENCY USING T WITTER L OCATION BASED S ENTIMENT AND M ACHINE L EARNING

revised version of the Agreement’, UK Prime Minister Boris Johnson called a snap general election in October 2019 to capitalise on his growing support to ‘Get Brexit Done’. Johnson’s belief was that he had enough support countrywide to gain a majority to push his Brexit mandate through parliament based on a parliamentary seat majority strategy. The increased availability of large-scale Twitter data provides rich information for the study of constituency dynamics. In Twitter, the location of tweets can be identified by the GPS and the location field. This provides a mechanism for location-based sentiment analysis which is the use of natural language processing or machine learning algorithms to extract, identify, or distinguish the sentiment content of a tweet (in our case), according to the location of origin of said tweet. This paper examines location-based Twitter sentiment for UK constituencies per country and aims to understand if location-based Twitter sentiment majorities per UK constituencies could determine the outcome of the UK Brexit election. Tweets are gathered from the whisperings of the UK Brexit election on September 4th 2019 until polling day, 12th December 2019. A Naive Bayes classification algorithm is applied to assess political public Twitter sentiment. We identify the sentiment of Twitter users per constituency per country towards the political parties’ mandate on Brexit and plot our findings for visualisation. We compare the grouping of location-based sentiment per constituency for each of the four UK countries to the final Brexit election first party results per constituency to determine the accuracy of location-based sentiment in determining the Brexit election result. Our results indicate that location-based sentiment had the single biggest effect on constituency result predictions in Northern Ireland and Scotland and a marginal effect on Wales base constituencies whilst there was no significant prediction accuracy to England’s constituencies. Decision tree, neural network, and Naïve Bayes machine learning algorithms are then created to forecast the election results per constituency using location-based sentiment and constituency-based data from the UK electorate at national level. The predictive accuracy of the machine learning models was compared comprehensively to a computed-baseline model. The comparison results show that the machine learning models outperformed the baseline model predicting Brexit Election constituency results at national level showing an accuracy rate of 97.87%, 95.74 and 93.62% respectively. The results indicate that location-based sentiment is a useful variable in predicting elections.
Effects of ﬁnite chain extensibility on the stress ﬁelds near the tip of a mode III crack

We carried out a detailed analysis of the asymptotic stress and deformation ﬁelds at the tip of a mode III crack in a hyperelastic solid described by Gent’s model. This model accounts for ﬁnite chain extensibility so that the deformation everywhere in the solid, including the crack tip, is bounded. We also present an exact solution for the ‘small-scale yielding’ problem where the region of large deformation is small compared with specimen dimensions. Our result shows that the crack tip stress ﬁeld is non-separable. In addition, an inﬁnite number of parameters are needed to completely specify the angular variation of crack tip stress ﬁeld.
Identity fusion and its influence in processes of remembrance

Purpose: From the assumption that identity fusion will influence the processes of remembrance, this works propose that individuals classified as fused will remember more in detail certain events of their countries, and the influence of these events will be greater in their identities than the influence in individuals classified as not fused. Method: In a sample of 450 Spanish subjects, with representativeness close to gender parity, a correlational study is proposed through an online survey. The main objective is to test the possible influence of "person-country" fusion in his/her remembrance processes, for unpleasant experiences in that country, as well as, in his /her identity conformation . Findings 1 : The two hypotheses proposed have been refused, due to there is not a significant difference between means in both groups, fused and not fused with their country, for remembrance clarity and remembrance importance, as variables in this study, for the individual’s identity conformation in the experimental condition unpleasant event .
VCAM-1 targeted magnetic resonance imaging enables detection of brain micrometastases from different primary tumours

Purpose: A major issue for the effective treatment of brain metastasis is the late stage of diagnosis with existing clinical tools. The aim of this study was to evaluate the potential of vascular cell adhesion molecule-1 (VCAM-1) targeted magnetic resonance imaging (MRI) for early detection of brain micrometastases in mouse models across multiple primary tumour types. Experimental Design: Xenograft models of brain micrometastasis for human breast carcinoma (MDA231Br-GFP), lung adenocarcinoma (SEBTA-001) and melanoma (H1_DL2) were established via intracardiac injection in mice. Animals (n=5-6/group) were injected intravenously with VCAM-1 targeted microparticles of iron oxide (VCAM-MPIO) and, subsequently, underwent T 2 *-weighted MRI. Control groups of na(cid:239)ve mice injected with VCAM-MPIO and tumour-bearing mice injected with non-targeting IgG-MPIO were included. Results: All models showed disseminated micrometastases in the brain, together with endothelial VCAM-1 upregulation across the time-course. T 2 *-weighted MRI of all tumour-bearing mice injected with VCAM-MPIO showed significantly more signal hypointensities (p<0.001; two-sided) than control cohorts, despite a lack of blood-brain barrier impairment. Specific MPIO binding to VCAM-1 positive tumour-associated vessels was confirmed histologically. VCAM-1 expression was demonstrated in human brain metastasis samples, across all three primary tumour types. Conclusions: VCAM-1-targeted MRI enables detection of brain micrometastases from the three primary tumour types known to cause the majority of clinical cases. These findings represent an important step forward in the development of a broadly applicable and clinically relevant imaging technique for early diagnosis of brain metastasis, with significant implications for improved patient survival.
LSU Digital Commons LSU Digital Commons Baby signs: caregiver perceptions of their use and benefit to Baby signs: caregiver perceptions of their use and benefit to children children

Gesture use is a precursor to spoken language, and children who exhibit frequent use of gesture have higher rates of speech comprehension and production when tested between 13 and 15 months of age (Bates et al, 1989). The purpose of this study was to learn more about caregivers’ perceptions of the use and benefits of sign to their children. Fifty-three caregivers were recruited for the study, and 33 (62%) of these caregivers reported that they had used or were currently using baby signs with one of their children. Six (18%) of these caregivers also reported that their children presented developmental delays, whereas the others reported that their children were developing language typically. Information about these caregivers’ perceptions of signs was collected through a survey. Results indicated that no significant demographic differences existed between those who used baby signs and those who did not. The child’s gender and childcare setting also did not influence a caregiver’s use of sign. Results also showed that caregivers began introducing baby signs to their children around 8 months of age, and the average number of different signs produced by these children was 17. The most common signs reported by the caregivers were “more,” “eat,” “milk,” “thank you” and “all done. ” Perceived benefits of sign use included the facilitation of their children’s communication skills (40%), reduction of child frustration (32%), caregiver-child enjoyment (15%), and caregiver-child bonding (13%). The clinical status of the children minimally influenced the results; however, caregivers of children with developmental delays reported that they used signs more frequently and for a longer period of time than caregivers of children without developmental delay.
A Novel Active Gate Driver for Improving Switching Performance of High-Power SiC MOSFET Modules

Featuring higher switching speed and lower losses, the silicon carbide mosfets (SiC mosfets) are widely used in higher power density and higher efficiency power electronic applications as a new solution. However, the increase of the switching speed induces oscillations, overshoots, electromagnetic interference (EMI), and even additional losses. In this paper, a novel active gate driver (AGD) for high-power SiC mosfets is presented to fully utilize its potential of high-speed characteristic under different operation temperatures and load currents. The principle of the AGD is based on drive voltage decrement during the voltage and current slopes since high dV/dt and dI/dt are the source of the overshoots, oscillations, and EMI problems. In addition, the optimal drive voltage switching delay time has been analyzed and calculated considering a tradeoff between switching losses and switching stresses. Compared to conventional gate driver with fixed drive voltage, the proposed AGD has the capability of suppressing the overshoots, oscillations, and reducing losses without compromising the EMI. Finally, the switching performance of the AGD was experimentally verified on 1.2 kV/300 A and 1.7 kV/300 A SiC mosfets in double pulse test under different operation temperatures and load currents. In addition, an EMI discussion and cost analysis were realized for AGD.
Effect of Rotor Bars Shape on the Single-Phase Induction Motors Performance: An Analysis toward Their Efﬁciency Improvement

: Mandatory regulations are published worldwide for the efﬁciency of line-operated electric motors. The small-sized single-phase induction motors (SPIMs) will not be off the hook in terms of efﬁciency, since new regulations are scheduled to be introduced regarding them no later than July 2023. By doing so, the efﬁciency of capacitor-run SPIMs will be forced to exceed the (currently) typical ratings and comply with the requirements of the IE3 (i.e., premium) efﬁciency class. Since this task is challenging, the already published research works investigated several design, control, and manufacturing aspects. Nevertheless, less attention has been devoted to the study of the rotor bar’s shape impact, both on the SPIMs’ efﬁciency and starting capability. This gap is ﬁlled in this work by examining rotor squirrel-cage conﬁgurations with eight different bar shapes for the case of a four-pole/1.0 HP capacitor-run SPIM. A sensitivity analysis, which involves the simultaneous variation of the bar’s cross-sectional area, run-capacitor value, and auxiliary to main winding turns ratio, is performed. The motor’s electromagnetic behavior is estimated through ﬁnite element analysis. Through the acquired results, useful directions toward the SPIMs’ efﬁciency enhancement are provided, while simultaneously conclusions—not found elsewhere—are drawn concerning performance quantities, such as the motor’s starting current, currents shift angle, particular losses, breakdown torque, etc.
Plasma Lipid Profile of Healthy Saudi Population in Makkah Region

Five Hundred healthy Saudi subjects aged 18-80 years (48.65 + SEM) (400 males and J 00 females) were randomly included in this pilot study from Makkah area for the estimation of plasma lipids. Thes subjects were subclassified into four age groups with a gap of J 5 years. The sample included representative groups of University students, office and executive workers, Religious leaders and the general population. Standard exclusion criteria were laid down. S.bilirubin, SGOT SGPT were also estimated along with the lipid profile in the fasting state. density lipoprotein (HDL cholesterol) higher in the younger age group. revealed that 28% of the subjects had Total Cholesterol ~ 210 mg/dl (5.4 mmoVL), 16% had HDL cholesterol < 35 mg/dl (0.9 mmoVL) and 35% were active smokers.
MULTIMAT: AN API FOR MANAGING MULTIMATERIAL SIMULATION DATA

Multimaterial simulation codes model the ﬂow of materials with di ↵ ering physical properties over a computational domain. Due to the intrinsically complicated access and traversal patterns on the underlying material-based ﬁeld data deﬁned over its mesh cells, such codes require implementations to strike a careful balance between competing demands of usability and performance. For a successful multimaterial simulation code, the designs of space e � - cient data structures, performant implementations, and ﬂexible, developer-friendly representations that can adapt to varying traversal patterns and computer architectures must all satisfy this balance. Towards this aim, we introduce MultiMat , an open source library designed for e � cient interaction with multimaterial mesh data and clear, ﬂexible expression of multimaterial algorithms. MultiMat provides an intuitive API for operating on multimaterial data, several concrete data structures for representing this data, and functions to easily convert between di ↵ erent representations. We include code-to-code comparisons against explicit implementations of several representative physics kernels. Our results indicate that MultiMat simpliﬁes data access and increases code readability while achieving comparable performance.
Model Selection and Adaptive Markov Chain Monte Carlo for Bayesian Cointegrated VAR Models.

This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian CVAR models, involving griddy Gibbs, with an automated eﬃcient alternative, based on the Adaptive Metropolis algorithm of Roberts and Rosenthal (2009). Developing the adaptive MCMC framework for Bayesian CVAR models allows for eﬃcient estimation of posterior parameters in signiﬁcantly higher dimensional CVAR series than previously possible with existing griddy Gibbs samplers. For a n-dimensional CVAR series, the matrix-variate posterior is in dimension 3 n 2 + n , with signiﬁcant correlation present between the blocks of matrix random variables. Hence, utilizing a griddy Gibbs sampler for large n becomes computationally impractical as it involves approximating an n × n full conditional posterior using a spline over a high dimensional n × n grid. The adaptive MCMC approach is demonstrated to be ideally suited to learning on-line a proposal to reﬂect the posterior correlation structure, therefore improving the computational eﬃciency of the sampler. We also treat the rank of the CVAR model as a random variable and perform joint inference on the rank and model parameters. This is achieved with a Bayesian posterior distribution deﬁned over both the rank and the CVAR model parameters, and inference is made via Bayes Factor analysis of rank. rank as a random quantity we can ensure our resulting trading models are able to adjust to potentially time varying market conditions in a coherent statistical framework.
Trabajo en equipo de enfermería: circunscrito a la profesión o colaboración interprofesional

OBJETIVO Compreender as concepcoes dos profissionais de enfermagem sobre trabalho em equipe e seus elementos constituintes. METODO Pesquisa qualitativa, realizada em hospital oncologico, por meio de entrevista semiestruturada com 21 profissionais de enfermagem. RESULTADOS Duas concepcoes emergiram dos relatos, trabalho em equipe circunscrito a enfermagem e trabalho em equipe com colaboracao interprofissional, com destaque para dimensao interativa: comunicacao, confianca e vinculo, respeito mutuo e reconhecimento do trabalho do outro, colaboracao e conflito. Esta ultima subcategoria foi apontada como obstaculo para o trabalho em equipe. CONCLUSAO A enfermagem concebe majoritariamente o trabalho em equipe como acao interprofissional, e isto decorre da qualidade da interacao entre os profissionais das diferentes areas e o reconhecimento e manejo de conflitos.
Predicting Metabolic Syndrome by Visceral Adiposity Index, Body Roundness Index and a Body Shape Index in Adults: A Cross-Sectional Study from the Iranian RaNCD Cohort Data Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy

Background: The use of anthropometric indices is one of the new and low-cost diagnostic methods of metabolic syndrome (MetS). The present study aimed to determine optimal cutoff points for the visceral adiposity index (VAI), body roundness index (BRI), and a body shape index (ABSI) in the prediction of MetS. Methods: This cross-sectional study was performed on 10,000 individuals aged from 35 to 65 years, recruited in Ravansar Non-Communicable Diseases (RaNCD) cohort study, in the west region of Iran, in 2019. MetS was de ﬁ ned according to International Diabetes Federation (IDF) criteria. The receiver operating characteristic (ROC) curve analysis was used to assess predictive anthropometric indices and determine optimal cutoff values. Results: The optimal cutoff points for VAI were 4.11 (AUC: 0.82; 95% CI: 0.81 – 0.84) in men and 4.28 (AUC: 0.86; 95% CI: 0.85 – 0.87) in women to prediction of MetS. The optimal cutoff points for BRI were 4.75 (AUC: 0.75; 95% CI: 0.74 – 0.77) in men and 6.17 (AUC: 0.62; 95% CI: 0.61 – 0.64) in women to prediction of MetS. The optimal cutoff points for ABSI were 0.12 (AUC: 0.49; 95% CI: 0.47 – 0.51) in men and 0.13 (AUC: 0.49; 95% CI: 0.47 – 0.51) in women to prediction of MetS. The risk of MetS in men and women with a VAI higher than the optimal cutoff point was, respectively, 9.82 and 11.44 times higher than that in those with a VAI lower than the cutoff point. Conclusion: Although VAI might not be very cost-bene ﬁ cial compared to IDF, our study showed VAI is a better predictor of MetS than BRI in adults. ABSI was not a suitable predictor for MetS.
Inference for high-dimensional varying-coefficient quantile regression

Quantile regression has been successfully used to study heterogeneous and heavy-tailed data. Varying-coefficient models are frequently used to capture changes in the effect of input variables on the response as a function of an index or time. In this work, we study high-dimensional varying-coefficient quantile regression models and develop new tools for statistical inference. We focus on development of valid confidence intervals and honest tests for nonparametric coefficients at a fixed time point and quantile, while allowing for a high-dimensional setting where the number of input variables exceeds the sample size. Performing statistical inference in this regime is challenging due to the usage of model selection techniques in estimation. Nevertheless, we can develop valid inferential tools that are applicable to a wide range of data generating processes and do not suffer from biases introduced by model selection. We performed numerical simulations to demonstrate the finite sample performance of our method, and we also illustrated the application with a real data example. MSC2020 subject classifications: Primary 62G08.