Miracle-dz/UniFAR-FTUs huggingface dataset:
An integrated framework of global sensitivity analysis and calibration for spatially explicit agent‐based models

Calibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak.
Development and Validation of a Random Forest Risk Prediction Pneumothorax Model in Percutaneous Transthoracic Needle Biopsy

Background Computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) is an effective means for diagnosing various thoracic diseases. Pneumothorax is the most common complication, and when it becomes life-threatening, urgent medical intervention is required. The purpose of this study was to develop and validate a model that can be used to predict postoperative pneumothorax following CT-guided PTNB. Material/Methods We enrolled 245 patients who completed CT-guided PTNB to develop the model. A random forest (RF) model was built using 15 risk factors (15-RFs). The 7 most critical risk factors (7-RFs) were extracted by feature selection and used to build a new model. The independent external validation data contained 97 patients. Logistic regression (LR), support vector machine (SVM), and decision tree (DT) models were also developed using both 15-RFs and 7-RFs, and their performance was compared with the RF models. Results The length of the aerated lung traversed was identified as the most important risk factor for developing pneumothorax, followed by angle of pleural puncture, lesion depth, lesion size, age, procedure time, and sex. The RF model demonstrated better performance in the development and validation datasets when compared with the LR, SVM, and DT based on 15-RFs and 7-RFs. According to DeLong’s test for difference in ROC curves, the RF models based on the 15-RFs and 7-RFs achieved similar classification performance (P>0.05). Conclusions This study demonstrated the feasibility of using the 7-RFs RF model for predicting postoperative pneumothorax before patients undergo CT-guided PTNB.
USE OF TECHNOLOGY IN TEACHING ENGLISH

In language coaching and gaining knowledge of, we've got loads to select from the sector of generation: audio, TV, CD Rom, Computers, C.A. L.L. , the Internet, Electronic Dictionary, Email, Blogs and Audio Cassettes, Power Point, Videos, DVD’s or VCD’s. Technology has modified the dynamics of diverse industries, and has additionally encouraged the the manner human beings engage and paintings withinside the society. This speedy growing and improvement of statistics era has supplied a higher sample to discover the brand new coaching version. As a end result era performs a totally vital position in English coaching. Using multimedia to create a context to train English has its particular advantages. The instructors can have interaction the freshmen to grow to be professional at English Language through the usage of the revolutionary ELT strategies like English songs, movie, clippings, dramatics, advertisements, sports activities commentaries and plenty of extra. This paper stresses at the want to make English language training smooth and exciting thru revolutionary ELT techniquesby using a scope review study methodology, which is a literature review process between new theories and ideas, so this paper talk about definition and development of technology, use of generation in coaching English, the boom of elt via generation, and advantages of the use of generation in the classroom.
COMPARISON BETWEEN THE EFFECT OF TITANIUM DIOXIDE NANOPARTICLES AND IVERMECTIN ON HEAD LICE

Head lice cause social problems for patients as embarrassment, inflammation, skin irritationand secondary bacterial infection. Treating head lice is difficult because of development of resistanceto drug. In this study, our aim was to evaluate the effect of titanium dioxide, Ivermectinand Ivermectin-loaded on Titanium dioxide nanoparticles on head lice in vitro. This study wasconducted on 50 patients infected with head lice from Beni Suef Governorate. These patientswere of all age groups and both sexes.Patients were 13 males and 37 females, with ages ranged from 5 to72years (26.84±19.3). Theywere 34 rural residents, and 16 urban ones, 37 were at a low socioeconomic level and 13 at amoderate socioeconomic one. Number of family members ranged from (3 to 11) with an averageof (6.42±1.9). The results showed that mortality rate for topical Ivermectin (0.2% & 0.5%) was 86% & 94%,respectively, against adult head lice, while 88% and 94%, respectively, against nymphs.Regarding the effect of Titanium dioxide, the percentage mortality effects of Titanium dioxidewere 34, 58, & 100% at (2, 6, & 10 mg/L), respectively, against adult head lice, while Titaniumdioxide mortality rate was 40, 68 & 96% at (2, 6 &10 mg/L), respectively, against nymphs.
Experimental Analysis of Temperature Influence on Waste Tire Pyrolysis

Pyrolysis is an optimal thermochemical process for obtaining valuable products (char, oil, and gas) from waste tires. The preliminary research was done on the three groups of samples acquired by cutting the same waste tire of a passenger vehicle into cylindrical granules with a base diameter of 3, 7, and 11 mm. Each batch weighed 10 g. The heating rate was 14 °C/min, and the final pyrolysis temperature was 750 °C, with 90 s residence time. After the pyrolysis product yields were determined for all of the three sample groups, further research was performed only on 3 mm granules, with the same heating rate, but with altered final pyrolytic temperatures (400, 450, 500, 550, 600, 650, 700, and 750 °C). The results of this study show that thermochemical decomposition of the waste tire sample takes place in the temperature range of 200–500 °C, with three distinct phases of degradation. The highest yield of the pyrolytic oil was achieved at a temperature of 500 °C, but further heating of volatile matters reduced the oil yield, and simultaneously increased the yield of gas, due to the existence of secondary cracking reactions. The analysis of pyrolytic oil and char showed that these products can be used as fuel.
Advances in Genetics and Molecular Breeding of Broccoli

Broccoli (Brassica oleracea L. var. italica) is one of the most important vegetable crops cultivated worldwide. The market demand for broccoli is still increasing due to its richness in vitamins, anthocyanins, mineral substances, fiber, secondary metabolites and other nutrients. The famous secondary metabolites, glucosinolates, sulforaphane and selenium have protective effects against cancer. Significant progress has been made in fine-mapping and cloning genes that are responsible for important traits; this progress provides a foundation for marker-assisted selection (MAS) in broccoli breeding. Genetic engineering by the well-developed Agrobacterium tumefaciens-mediated transformation in broccoli has contributed to the improvement of quality; postharvest life; glucosinolate and sulforaphane content; and resistance to insects, pathogens and abiotic stresses. Here, we review recent progress in the genetics and molecular breeding of broccoli. Future perspectives for improving broccoli are also briefly discussed.
Do employment opportunities decrease for older workers?

ABSTRACT Increasing the labour market participation rates of older workers is a means to secure the sustainability of public finances. However, questions about the effects of job loss of unemployed older workers and their employment prospects remain. This paper investigates why workers, aged 50 and over, have less employment opportunities. Using a competing risks model on British panel data, we examine the chances of re-employment after unemployment spells for older individuals. We find that human capital characteristics and economic incentives play an important role in their chances of getting back to work. We show that the probability of returning to employment after an unemployment spell decreases as workers get older. A decomposition analysis supports the role of age in the unemployment duration gap between ‘older’ and ‘younger’ individuals. The duration of leaving unemployment to employment of older workers would be lower if they will be treated in the same way as the younger ones, which is consistent with elderly employment barriers.
Inhalations in Speech: Acoustic and Physiological Characteristics

This paper examines the acoustic properties of breath noises in speech pauses in relation to similar speech segments and with regard to their inhalation speed. We measured intensity, center of gravity, and formants, as well as kinematic data (via Respiratory Inductance Plethysmography) for inhalations, aspirations of stops, glottal fricatives, and schwa vowels. We ﬁnd that inhalations within speech are louder than those initiating speech, share spectral properties (center of gravity) with the aspiration phase of /k/-realizations, and generally involve a more open vocal tract (higher F1) than schwa-realizations. Intensity, center of gravity, and F1 are found to be positively correlated to inhalation speed. Overall, we conclude that jaw openness and inhalation speed are major contributors to inhalation noises in speech pauses.
Noise-Tolerant Self-Supervised Learning for Audio-Visual Voice Activity Detection

Recent audio-visual voice activity detectors based on supervised learning require large amounts of labeled training data with manual mouth-region cropping in videos, and the performance is sensitive to a mismatch between the training and testing noise conditions. This paper introduces contrastive self-supervised learning for audio-visual voice activity detection as a possible solution to such problems. In addition, a novel self-supervised learning framework is proposed to improve overall training efficiency and testing performance on noise-corrupted datasets, as in real-world scenarios. This framework includes a branched audio encoder and a noise-tolerant loss function to cope with the uncertainty of speech and noise feature separation in a self-supervised manner. Experimental results, particularly under mismatched noise conditions, demonstrate the improved performance compared with a self-supervised learning baseline and a supervised learning framework.
Emotional Prosody Control for Speech Generation

Machine-generated speech is characterized by its limited or unnatural emotional variation. Current text to speech systems generates speech with either a flat emotion, emotion selected from a predefined set, average variation learned from prosody sequences in training data or transferred from a source style. We propose a text to speech(TTS) system, where a user can choose the emotion of generated speech from a continuous and meaningful emotion space (Arousal-Valence space). The proposed TTS system can generate speech from the text in any speaker's style, with fine control of emotion. We show that the system works on emotion unseen during training and can scale to previously unseen speakers given his/her speech sample. Our work expands the horizon of the state-of-the-art FastSpeech2 backbone to a multi-speaker setting and gives it much-coveted continuous (and interpretable) affective control, without any observable degradation in the quality of the synthesized speech.
Voltage Control of Flexible-Joint Robot Manipulators using Singular Perturbation Technique for Model Order Reduction

Background and Objectives: Robot manipulator with flexible-joints is very complex nonlinear system whose control is one of the most challenging issues in the control word. Therefore, the design of voltage-based controller in addition, using of order reduction methods can reduce the complexity of the control law in such systems. Methods: This paper proposes a voltage controller for flexible-joint robot manipulators based on singular perturbation method. The presented control approach has all three advantages of the singular perturbation method for model order reduction, the proper structure of Port-Hamiltonian systems, and voltage control strategy (VCS). In this approach, the robot manipulator model is divided into three sub-systems, slow, medium and fast sub-systems. Each of the sub-systems is controlled using separate controller. In addition, the stability of these sub-systems and ultimately the whole system are proved. Unlike other related works, in this work the tracking error system is considered from the beginning, and by singular perturbation method, a controller is designed to stabilize the tracking system. Moreover, in the suggested voltage-based controller unlike torque control strategy, the electrical model of actuators is used. Results: The main advantages of proposed approach are simple structure, using only velocity of motors, the position of the joints as a control signal and considering the electrical model of the actuators. So, practical implementation of this controller will be with much less effort, compared to the methods like feedback linearization or other controllers in related works. Moreover, using the Lyapunov-based method, the ultimate bounded stability of the closed loop system is proved. Then, some simulations are provided for tracking, regulation, robustness and response speed purposes. Conclusion: Since, the controllers for every sub-system are designed separately, also, the control signal parameters such as joints position, motors velocity, and motors current can be simply measured, therefore, the structure of designed controller is very simple and practically implementable. As the simulation results confirm, the performance of this controller is appropriate, even when external disturbances are present or the frequency of reference signal increases. At last, by comparison and analysis of the simulation results between the presented approach and a related work, the suitable tracking performance of the suggested controller is shown. ======================================================================================================Copyrights©2021 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers. ======================================================================================================
Des théories psychosociales aux modèles de succès des systèmes d’information : Proposition d’un cadre théorique du comportement d’utilisation.

Le succes de l’utilisation au niveau individuel est l’un des principaux courants de recherche sur la performance des systemes d’information. Cette reussite avec l’utilisateur permet a ces systemes de bien jouer leur role d’interface entre les organes de pilotage et d’execution au sein de l’entreprise. De ce fait, une bonne mesure de cette composante s’avere indispensable afin de juger du rendement global des SI. Ce travail se veut une contribution a la mise en place d’un cadre de mesure integrateur du succes des systemes d’information au niveau individuel a travers le rapprochement entre les theories psychosociales et les modeles de succes precedents.
Performance-Vesting Share Award Outcomes and CEO Incentives

I investigate the incentives provided by performance-vested stock awards (PVSAs) and the accuracy of ex ante PVSA valuations. In PVSAs, the number of shares awarded to the CEO varies based on how firm performance compares to target performance levels. The incentives provided by PVSAs and their valuations depend on the difficulty of the performance targets, which cannot be easily determined using ex ante disclosures. Using a hand-collected dataset of PVSA outcomes, I find that target difficulty is such that CEOs have incentives to earn additional shares from improved firm performance in 75 percent of PVSAs. In contrast, findings in prior studies using methodologies that rely on comparisons of PVSA targets to similarly named financial statement metrics suggest only 12 percent of awards provide such incentives. I further find that, on average, PVSAs pay out 115 percent of shares disclosed in ex ante valuations, suggesting the valuations of awards are understated. Data Availability: Data are available from sources cited in the text. PVSA outcome data are available upon request from the author. JEL Classifications: G34; J33; M12.
Applicability of organ-on-chip systems in toxicology and pharmacology

Abstract Organ-on-chip (OoC) systems are microfabricated cell culture devices designed to model functional units of human organs by harboring an in vitro generated organ surrogate. In the present study, we reviewed issues and opportunities related to the application of OoC in the safety and efficacy assessment of chemicals and pharmaceuticals, as well as the steps needed to achieve this goal. The relative complexity of OoC over simple in vitro assays provides advantages and disadvantages in the context of compound testing. The broader biological domain of OoC potentially enhances their predictive value, whereas their complexity present issues with throughput, standardization and transferability. Using OoCs for regulatory purposes requires detailed and standardized protocols, providing reproducible results in an interlaboratory setting. The extent to which interlaboratory standardization of OoC is feasible and necessary for regulatory application is a matter of debate. The focus of applying OoCs in safety assessment is currently directed to characterization (the biology represented in the test) and qualification (the performance of the test). To this aim, OoCs are evaluated on a limited scale, especially in the pharmaceutical industry, with restricted sets of reference substances. Given the low throughput of OoC, it is questionable whether formal validation, in which many reference substances are extensively tested in different laboratories, is feasible for OoCs. Rather, initiatives such as open technology platforms, and collaboration between OoC developers and risk assessors may prove an expedient strategy to build confidence in OoCs for application in safety and efficacy assessment.
European Option Pricing Formula in Risk-Aversive Markets

In this study, using the method of discounting the terminal expectation value into its initial value, the pricing formulas for European options are obtained under the assumptions that the financial market is risk-aversive, the risk measure is standard deviation, and the price process of underlying asset follows a geometric Brownian motion. In particular, assuming the option writer does not need the risk compensation in a risk-neutral market, then the obtained results are degenerated into the famous Black–Scholes model (1973); furthermore, the obtained results need much weaker conditions than those of the Black–Scholes model. As a by-product, the obtained results show that the value of European option depends on the drift coefficient μ of its underlying asset, which does not display in the Black–Scholes model only because μ = r in a risk-neutral market according to the no-arbitrage opportunity principle. At last, empirical analyses on Shanghai 50 ETF options and S&P 500 options show that the fitting effect of obtained pricing formulas is superior to that of the Black–Scholes model.
Crack detection using fusion features‐based broad learning system and image processing

Deep learning has been widely applied to vision‐based structural damage detection, but its computational demand is high. To avoid this computational burden, a novel crack detection system, namely, fusion features‐based broad learning system (FF‐BLS), is proposed for efficient training without GPU acceleration. In FF‐BLS, a convolution module with fixed weights is used to extract the fusion features of images. Feature nodes and enhancement nodes randomly generated by fusion features are used to estimate the output of the network. Meanwhile, the proposed FF‐BLS is a dynamical system, which achieves incremental learning by adding nodes. Thus, the trained FF‐BLS model can be updated efficiently with additional data, and this substantially reduces the training cost. Finally, FF‐BLS was applied to crack detection. Compared with some well‐known deep convolutional neural networks (VGG16, ResNet50, InceptionV3, Xception, and EfficientNet), the FF‐BLS achieved a similar level of recognition accuracy, but the training speed was increased by more than 20 times.
A Machine Learning Pipeline for Measurement of Arterial Stiffness in A-Mode Ultrasound

Arterial stiffness (AS) of the carotid artery is an early marker of stratifying cardiovascular disease risk. This article aims to improve the performance of ARTSENS, a noninvasive A-mode ultrasound-based device for measuring AS. The primary objective of ARTSENS is to enable the measurement of elastic modulus using A-Mode ultrasound and blood pressure. As this device is image-free, there is a need to automate: 1) carotid detection; 2) wall localization; and 3) inner lumen diameter measurement. This has been performed using conventional signal processing methods in some of the earlier works in this domain. In this article, deep neural network (DNN) models are employed to perform the above three tasks. The DNNs were trained over data acquired from 82 subjects at two different medical centers. Ground-truth labeling was performed by a trained operator using corresponding measurements from the state-of-the-art Aloka e-Tracking system. All three DNN models had significantly lower errors compared to earlier signal processing methods and could perform their measurements using a single A-Mode frame. Using the DNNs, two different machine learning pipelines have been proposed here to measure the elastic modulus; the best among them could achieve an error of 9.3% with the Pearson correlation coefficient of 0.94 ( ${p} < 0.001$ ). The models were tested on Raspberry Pi and Jetson Nano single board computers to demonstrate real-time processing on low computational resources.