Miracle-dz/UniFAR-FTUs huggingface dataset:
Homogeneous and inhomogeneous material effect in gamma index evaluation of IMRT technique based on fan beam and Cone Beam CT patient images

Patient-specific Quality Assurance (QA) technique in lung case Intensity-Modulated Radiation Therapy (IMRT) is traditionally limited to homogeneous material, although the fact that the planning is carried out with inhomogeneous material present. Moreover, the chest area has many of inhomogeneous material, such as lung, soft tissue, and bone, which inhomogeneous material requires special attention to avoid inaccuracies in dose calculation in the Treatment Planning System (TPS). Recent preliminary studies shown that the role of Cone Beam CT (CBCT) can be used not only to position the patient at the time prior to irradiation but also to serve as planning modality. Our study presented the influence of a homogeneous and inhomogeneous materials using Fan Beam CT and Cone Beam CT modalities in IMRT technique on the Gamma Index (GI) value. We used a variation of the segment and Calculation Grid Resolution (CGR). The results showed the deviation of averaged GI value to be between CGR 0.2 cm and 0.4 cm with homogeneous material ranging from −0.44% to 1.46%. For inhomogeneous material, the value was range from −1.74% to 0.98%. In performing patient-specific IMRT QA techniques for lung cancer, homogeneous material can be implemented in evaluating the gamma index.
A framework for closed-loop flow control using the parabolized stability equations

We develop a reduced-order-model framework using the parabolized stability equations and identiﬁcation techniques for the closed-loop control of unsteady ﬂuctuations along ﬂuidic systems. These models had been successfully applied to a turbulent jet as estimation techniques and to an incompressible shear-layer for the development of closed-loop control laws. Through this paper, we propose a further investigation of the PSE-based transfer functions, exploring its ﬂexibility to educe diﬀerent control schemes and to determine the most eﬀective sensor/actuator positions. Emphasis is be given to the feedforward and feedback conﬁgurations for ﬂow control, and diﬀerences are understood in terms of causality. A study of the robustness to uncertainties in Reynolds and mean ﬂow velocity, along with external perturbations is also presented. These topics allow deeper insight into the active closed-loop ﬂow control problem and therefore may lead to more eﬀective schemes, particularly on what concerns the experimental implementation of closed-loop control.
Improving Cancer Detection Classification Performance Using GANs in Breast Cancer Data

Breast cancer is one of the most prevalent cancers in women. In recent years, many studies have been conducted in the breast cancer domain. Previous studies have confirmed that timely and accurate breast cancer detection allows patients to undergo early treatment. Recently, Generative Adversarial Networks have been applied in the medical domain to synthetically generate image and non-image data for diagnosis. However, the development of an effective classification model in healthcare is difficult owing to the limited datasets. To address this challenge, we propose a novel K-CGAN method trained in different settings to generate synthetic data. This study applied five classification methods and feature selection to non-image Wisconsin Breast Cancer data of 357 malignant and 212 benign cases for evaluation. Moreover, we used recall, precision, accuracy, and F1 Score on the synthetic data generated by the K-CGAN model to verify the classification performance of our proposed K-CGAN. The empirical study shows that K-CGAN performed well with the highest stability compared to the other GAN variants. Hence, our findings indicate that the synthetic data generated by K-CGAN accurately represent the original data.
Weakly Nonlinear Waves in Nonideal Fluids

We study the propagation of weakly nonlinear waves in nonideal fluids, which exhibit mixed nonlinearity. A method of multiple scales is used to obtain a transport equation from the Navier–Stokes equations, supplemented by the equation of state for a van der Waals fluid. Effects of van der Waals parameters on the wave evolution, governed by the transport equation, are investigated.
THE INFLUENCE OF USING SHORT STORY ANIMATION ON STUDENTS’ VOCABULARY MASTERY

This research purpose was to determine the influence of short story narrative text on students’ vocabulary mastery at the 10th grade of SMAN 1 PETIR. This research was done by true-experimental with random sampling. The population of this research was all of the first-grade high school students in Banten province, consisting of 350 students. The researcher used two classes, X MIPA 4 as the experimental and X MIPA 5 as the control class. Both classes consisted of 35 students. In obtaining the data, the researcher used multiple-choice questions as instruments. Try out was conducted as the first step to get the data. After conducting the tryout, the researcher gave a pre-test in both classes. The researcher gave the treatment in the experimental class using Short Story Animation as a media, and in the control class, the discussion method used a module. The result of the calculation by Independent T-test found that (0,002 < 0,005), which means the hypothesis alternative (Ha) is accepted. The effectiveness of using Short Story Animation Narrative Text can be seen in the ability obtained after the treatment.
Performance prediction of adaptive filters for EEG signal

A methodology for predicting the performance of algorithm in terms of fidelity parameters is introduced. Algorithm performance is measured in terms of space and time complexities required to solve a particular problem. The proposed approach correlates the performance with input data and output results. Initial theory of performance model generation is based on polynomial regressing and curve fitting of least mean square (LMS) algorithm for adaptive filtering. Testing and validation of the generated model has been done through the coefficient of determination parameters calculated over interpolation and extrapolation results. Other measures like adjusted R 2, sum of square error and mean square error are also reported. Overall study is summarised in form of algorithm, and is tested with LMS and recursive LMS algorithm of adaptive filtering applied over electroencephalography/event related potentials (EEG/ERP) noise removal to build up relation between input signal-to-noise ratio (SNR) and output fidelity parameters (like output SNR and correlation (r)).
An optical flow-based method for velocity field of fluid flow estimation

The aim of this paper is to present a method for estimating flow-velocity vector fields using the Lucas-Kanade algorithm. The optical flow measurements are based on the Particle Image Velocimetry (PIV) technique, which is commonly used in fluid mechanics laboratories in both research institutes and industry. Common approaches for an optical characterization of velocity fields base on computation of partial derivatives of the image intensity using finite differences. Nevertheless, the accuracy of velocity field computations is low due to the fact that an exact estimation of spatial derivatives is very difficult in presence of rapid intensity changes in the PIV images, caused by particles having small diameters. The method discussed in this paper solves this problem by interpolating the PIV images using Gaussian radial basis functions. This provides a significant improvement in the accuracy of the velocity estimation but, more importantly, allows for the evaluation of the derivatives in intermediate points between pixels. Numerical analysis proves that the method is able to estimate even a separate vector for each particle with a 5× 5 px2 window, whereas a classical correlation-based method needs at least 4 particle images. With the use of a specialized multi-step hybrid approach to data analysis the method improves the estimation of the particle displacement far above 1 px.
The Mediating Effect of Perceived Organizational Support Between Talent Culture and Employee Engagement among employees of Malaysian GLCs

Objective: The aim of this study is to examine how perceived organizational support mediated the relationship between talent culture and employee engagement among employees in the Malaysian governments-linked companies (GLCs). Theoretical framework: The dependent variable for this study is employee engagement while talent culture constitute the independent variable and perceived organizational support is the mediating in this study. Method: The survey distributed received responses from 164 employees currently employed at the 47 GLCs. The study utilised partial least square structural equation modelling (PLS-SEM) method to analyse the data and obtain results for the hypotheses generated. Results and conclusion: The results indicated that none of the mediated relationship were significant. Implications of the research: The findings will be able to provide guidance to the organizations’ top management to streamline their operations, business framework and values with the concepts of talent management practices, perceived organization support and employee engagement. Originality/value: In general, this study will be beneficial for GLCs to understand the importance implications of talent management practices in order to attract and retain critical talent and reduce employee turnover in their business entity.
Reducing the index of differential-algebraic equations by exploiting underlying structures

Differential-algebraic equations arise from the equation based modelling of physical systems, such as those found for example in engineering or physics. This thesis is concerned with square, sufficiently smooth, potentially non-linear differential-algebraic equations. Differential-algebraic equations can be classified by their index. This is a measure of how far a differential-algebraic equation is from an equivalent ordinary differential equation. To solve a differential-algebraic equation one usually transforms the problem to an ordinary differential equation, or something close to one, via an index reduction algorithm. This thesis examines how the index reduction (using dummy derivatives) of differential-algebraic equations can be improved via structural analysis, specifically the Signature Matrix method. Improved and alternative algorithms for finding dummy derivatives are presented and then a new algorithm for finding globally valid universal dummy derivatives is presented. It is also shown that the structural index of a differential-algebraic equation is invariant under order reduction.
Static behavior of fine particle clouds in fine particle (dusty) plasmas under gravity

Static behavior of fine particle cloud in fine particle (dusty) plasmas under gravity is analyzed numerically on the basis of the drift-diffusion equations. The system is assumed to have one-dimensional structure and the effect of the ion drag force is taken into account. The results confirm the analytically obtained prediction that we have an enhanced charge neutrality in fine particle clouds under gravity as well as under microgravity. They are also compared with a simple model based on the enhanced charge neutrality in clouds to show its applicability.
The Relation of Mother's Knowledge, Parenting Patterns and Household Food Security Status with Stunting Case in Children Aged 6-23 Months (Regional Study of Landasan Ulin Health Center, Banjarbaru City)

Stunting is a condition of failure to thrive due to malnutrition for a long time so that children are shorter than normal children their age. Stunting is not only caused by one factor but by many factors. The prevalence of stunting at the Landasan Ulin Health Center continued to increase from 2018 (16.0%), 2019 (21.89%) and 2020 (22.26%). Objective: To determine the relationship between maternal knowledge, parenting and household food security status with the incidence of stunting in children aged 6-23 months in the working area of the Landasan Ulin Health Center, Banjarbaru City. Methods: This type of research is an observational analytic study with a Cohort Retrospective design. The population of this study was all children aged 6-23 months in the working area of ? the Landasan Ulin Health Center and a sample of 92 children using the purposive sampling technique. Results: The knowledge of the mothers of children under two years was in the less category (47.8%), the parenting pattern for the children was in the sufficient category (39.1%), the household food security status was mostly classified as food insecure (42.4%) and the children who experienced stunting were less (40.2%) . Conclusions: There is a relationship between mother's knowledge, parenting, and household food security status with the incidence of stunting in children aged 6-23 months in the working area of ? the Landasan Ulin Health Center.
Generic Compact Modeling of Emerging Memories With Recurrent NARX Network

A generic compact modeling approach for emerging memories is developed in this work. State transitions, as well as the electric properties of each state, usually require separate efforts from a physics perspective. To accelerate memory modeling, a data-driven approach is explored with the recurrent Non-linear Auto Regressive with Exogenous inputs (NARX) network. The recurrent module captures the state transition dynamics, while a feedforward module is responsible for the non-linear states, such as high and low resistance states. The generic NARX-based model is demonstrated with high accuracy for emerging memories of different physics, such as resistive random-access-memory (ReRAM) and ferroelectric tunnel junction (FTJ). It is extendable to statistical modeling which is useful to cover memory variations. Circuit simulations are demonstrated with successful convergence, attributed to the capability of the NARX network and its compatibility with simulators.
Exploration-exploitation-based trajectory tracking of mobile robots using Gaussian processes and model predictive control

Abstract Mobile robots are a key component for the automation of many tasks that either require high precision or are deemed too hazardous for human personnel. One of the typical duties for mobile robots in the industrial sector is to perform trajectory tracking, which involves pursuing a specific path through both space and time. In this paper, an iterative learning-based procedure for highly accurate tracking is proposed. This contribution shows how data-based techniques, namely Gaussian process regression, can be used to tailor a motion model to a specific reoccurring reference. The procedure is capable of explorative behavior meaning that the robot automatically explores states around the prescribed trajectory, enriching the data set for learning and increasing the robustness and practical training accuracy. The trade-off between highly accurate tracking and exploration is done automatically by an optimization-based reference generator using a suitable cost function minimizing the posterior variance of the underlying Gaussian process model. While this study focuses on omnidirectional mobile robots, the scheme can be applied to a wide range of mobile robots. The effectiveness of this approach is validated in meaningful real-world experiments on a custom-built omnidirectional mobile robot where it is shown that explorative behavior can outperform purely exploitative approaches.
AN ANALYSIS OF ENTREPRENEURIAL ORIENTATION AMONG WOMAN IN BANGALORE

This study investigates the impact of social support, knowledge, and psychological support on entrepreneurial orientation among women in Bangalore. The sample consists of 257 women entrepreneurs and responses were generated using convenience sampling method through structured questionnaire. The findings revealed significant positive impact of social support orientation (β = .877, p < .001), knowledge (β = .714, p < .001) and psychological support on entrepreneurial orientation. This suggests that women with higher level of support from family, friends entrepreneurial communities will show greater interest to start business and individual with entrepreneurial skills, market insights and industry knowledge will demonstrate entrepreneurial tendencies. and indicating that higher levels of support from family, friends, mentors, and entrepreneurial communities contribute to womens entrepreneurial tendencies. Further, study also confirms that that women with greater self-confidence, self-efficacy, and resilience are more likely to pursue entrepreneurial endeavors. Overall, this study emphasizes the significance of social support, knowledge, and psychological well-being in shaping entrepreneurial orientation among women in Bangalore. KEY WORDS: Women entrepreneurship orientation, social support, knowledge, financial support and psychological support.
Insomnia Symptom Independently Predict Depression in the Patients With Panic Disorder

Objective: This study aimed to examine the association between insomnia symptoms, sub-symptoms of panic disorder, and depressive symptoms in patients with panic disorder.Methods: A retrospective cross-sectional study was conducted with 192 patients diagnosed with panic disorder. Data on panic disorder severity, anxiety sensitivity, agoraphobia, insomnia severity, and depressive symptoms were collected using psychological instruments. Correlation analysis and logistic regression were employed to analyze the relationships between the variables.Results: Patients with panic disorder accompanied by significant depressive symptoms had a higher prevalence of insomnia, younger age, and more severe panic symptoms. Logistic regression analysis revealed an independent association between insomnia symptoms and the presence of depressive symptoms in panic disorder patients. Specific panic disorder sub-symptoms, such as fear of publicly observable anxiety reactions and fear of cognitive dyscontrol, exhibited stronger associations with depressive symptoms than overall panic severity.Conclusion: The findings suggest a significant association between insomnia, specific panic disorder sub-symptoms, and depressive symptoms in patients with panic disorder. Addressing insomnia and cognitive symptoms may be crucial in managing panic disorder and preventing depressive symptoms. Psychological interventions, particularly cognitive behavioral therapy, targeted at these factors could improve outcomes for panic disorder patients.
CAR-Modified Vγ9Vδ2 T Cells Propagated Using a Novel Bisphosphonate Prodrug for Allogeneic Adoptive Immunotherapy

The benefits of CAR-T therapy could be expanded to the treatment of solid tumors through the use of derived autologous αβ T cell, but clinical trials of CAR-T therapy for patients with solid tumors have so far been disappointing. CAR-T therapy also faces hurdles due to the time and cost intensive preparation of CAR-T cell products derived from patients as such CAR-T cells are often poor in quality and low in quantity. These inadequacies may be mitigated through the use of third-party donor derived CAR-T cell products which have a potent anti-tumor function but a constrained GVHD property. Vγ9Vδ2 TCR have been shown to exhibit potent antitumor activity but not alloreactivity. Therefore, in this study, CAR-T cells were prepared from Vγ9Vδ2 T (CAR-γδ T) cells which were expanded by using a novel prodrug PTA. CAR-γδ T cells suppressed tumor growth in an antigen specific manner but only during a limited time window. Provision of GITR co-stimulation enhanced anti-tumor function of CAR-γδ T cells. Our present results indicate that, while further optimization of CAR-γδ T cells is necessary, the present results demonstrate that Vγ9Vδ2 T cells are potential source of ‘off-the-shelf’ CAR-T cell products for successful allogeneic adoptive immunotherapy.
A Prochlorperazine-Induced Decrease in Autonomous Muscle Activity during Hindlimb Unloading Is Accompanied by Preserved Slow Myosin mRNA Expression

Skeletal muscle disuse leads to pathological muscle activity as well as to slow-to-fast fiber-type transformation. Fast-type fibers are more fatigable than slow-type, so this transformation leads to a decline in muscle function. Prochlorperazine injections previously were shown to attenuate autonomous rat soleus muscle electrical activity under unloading conditions. In this study, we found that prochlorperazine blocks slow-to-fast fiber-type transformation in disused skeletal muscles of rats, possibly through affecting calcium and ROS-related signaling.
Computational Strong-Field Quantum Dynamics: Intense Light-Matter Interactions

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