Miracle-dz/UniFAR-FTUs huggingface dataset:
Exponential stability for a multi-particle system with piecewise interaction function and stochastic disturbance

In this paper, a generalized Motsch-Tadmor model with piecewise interaction function is investigated, which can be viewed as a generalization of the model proposed in [ 9 ]. Our analysis bases on the connectedness of the underlying graph of the system. Some sufficient conditions are presented to guarantee the system to achieve flocking. Besides, we add a stochastic disturbance to the system and consider the flocking in the sense of expectation. As results, some criterions to the flocking solution with exponential convergent rate are established by the standard differential equations analysis.
Our initial experience with the three layers with three-port approach for laparoscopic radical cystectomy

Introduction Radical cystectomy (RC) remains the gold standard for the treatment of recurrent high-risk non-muscle-infiltrating bladder cancer (BC) and muscle-infiltrating BC. Currently, there is no uniform standardized procedure for laparoscopic radical cystectomy (LRC). Aim To share our initial experience with the three layers with three-port approach for laparoscopic radical cystectomy (TLTPA-LRC) and to investigate its safety and effectiveness. Material and methods Between April 2017 and March 2020, 32 patients with bladder tumors underwent TLTPA-LRC, pelvic lymph node dissection, and extracorporeal construction of the Studer neobladder. The basic characteristics of the patients, clinical pathology, and perioperative and follow-up data were analyzed. We also describe our step-by-step surgical technique for TLTPA-LRC. Results The median operation time was 278.5 min (range: 221–346 min), and the mean estimated blood loss was 233.4 ml (102–445 ml). The rates of intraoperative blood transfusion and postoperative transportation to the intensive care unit after surgery were 12.5% and 100%, respectively. Postoperative pathology showed 7 cases of T1, 20 cases of T2, and 5 cases of T3. Lymph node dissection and surgical margins were both negative. During a median follow-up of 13.5 months, 4 patients had early complications (< 30 days) and no patients had major complications (grade ≥ 3). The patients are now alive without local metastasis and with satisfactory urinary control ability day and night. Conclusions Although the TLTPA-LRC approach requires a certain level of surgical proficiency, it is feasible and serves as a minimally invasive method for selected patients.
The Effect of Sprint Training on Vertical Jump Height of Female Youth Volleyball Players

The aim of this study was to investigate the effect of the 60-m sprint training program on the vertical jump height of female youth volleyball players. In this quasi-experimental study, a one-group pretest/posttest design was utilized. Participants were 10 female youth volleyball players (aged 12-15) who were registered in a 12-session sprint training program at a junior sports organization in Yogyakarta, Indonesia. The training program was carried out on a 100-m flat track at the Athletic Field, Faculty of Sport Sciences of Yogyakarta State University. The stand and reach method was used to measure the vertical jump height. To compare the pretest and posttest scores, a paired samples t-test was used. The results indicated that there was a significant increase in vertical jump height from pretest to posttest with a high effect size. Thus, it can be concluded that a 4-week sprint training program can effectively increase the vertical jump height of young volleyball players. It is recommended that volleyball coaches and trainers implement a sprint training program as a step towards developing the vertical jump of their young athletes.
Electrochemical synthesis of nanosized hydroxyapatite/graphene composite powder

Abstract Electrochemical synthesis was employed to prepare a novel hydroxyapatite/graphene (HAP/Gr) composite powder suitable for medical applications as a hard tissue implant (scaffold). The synthesis was performed in a homogeneous dispersion containing Na 2 H 2 EDTA·2H 2 O, NaH 2 PO 4 and CaCl 2 with a Ca/EDTA/PO 43− concentration ratio of 0.25/0.25/0.15M, along with 0.01 wt% added graphene nanosheets, at a current density of 137 mA cm −2 and pH value of 9.0. The field emission scanning electron microscopy and transmission electron micros-copy observations of the composite HAP/Gr powder indicated that nanosized hydroxyapa-tite particles were uniformly placed in the graphene overlay. Raman spectroscopy, Fourier transform infrared spectroscopy and X-ray diffraction confirmed graphene incorporation in the HAP/Gr powder. The electrochemically prepared HAP/Gr composite powder exhibited slight antibacterial effect against the growth of the bacterial strain Staphylococcus aureus . Key words:
Tinkertoy: Build Your Own Operating Systems for IoT Devices

The Internet of Things (IoT) makes it possible for tiny devices with sensing and communication capabilities to be interconnected and interact with the cyber–physical world. However, these tiny devices have limited computing power and memory, so they often cannot run commodity operating systems, such as Windows and Linux. IoT devices are deployed everywhere, from smart home appliances to self-driving vehicles, and their applications impose ever-increasing and more heterogeneous demands on software architecture. There are many special-purpose and embedded operating systems built to satisfy these wildly different requirements, from early sensor network operating systems, such as TinyOS and Contiki, to more modern robot and real-time control systems, such as FreeRTOS and Zephyr. However, the rapid evolution and heterogeneity of IoT applications calls for a different solution. Specifically, this work introduces Tinkertoy, a collection of standard operating system modules from which developers can easily assemble customized operating systems. A customized operating system provides precisely the functionality needed by an application and consumes up to four times less memory than other IoT operating systems without sacrificing performance.
Identifying the spin transition in Fe2+-rich MgSiO3 perovskite from X-ray diffraction and vibrational spectroscopy

Abstract Based on numerical results from density-functional perturbation theory calculations, we show that the magnetic spin transition in Fe2+-rich MgSiO3 perovskite can be identified as changes in the powder X-ray diffraction (XRD) pattern and the vibrational spectra. In particular theory predicts how the symmetry breaking and the volume reduction associated with the spin transition affects both structural and vibrational properties. The XRD measurements of (Mg0.5Fe0.5)SiO3 perovskite indeed demonstrated that the new diffraction peaks and the peak broadening formed during the spin transition can be explained by the associated symmetry breaking. We also show computationally that certain vibrational peaks exhibit a shift at the transition; the Grüneisen parameters of certain modes are affected by the transition, thus bearing on the thermodynamical properties. Raman and/or infrared measurements before and after the spin transition could identify these changes.
Current-Fed Isolated Three-Phase Matrix-Type Grid Inverter With Soft-Switching Capability

The grid inverters play an important role in renewable energy systems, transferring the dc power to the ac grid. Galvanic isolation is usually needed between the dc source and the grid due to safety reasons and grounding leakage current mitigation. In this article, a soft-switching isolated grid inverter is proposed based on the current-fed matrix-type configuration. The topology of this converter is composed of a current-fed H-bridge on the side of dc source and a direct matrix converter on the grid side, which are linked by a high-frequency transformer. The current-fed H-bridge on the primary-side can smooth the dc-source current and control the dc-source current directly. The avoidance of dc-link energy-storage element in the single-stage matrix converter can prolong the system's life. A space-vector-modulation-based soft-switching scheme is proposed for the matrix converter on the grid side and a model-based commutation method is adopted in the current-fed H-bridge on the dc side. With the proposed modulation methods, the soft switching can be realized for all devices in the converter. Finally, a laboratory prototype of the proposed converter has been built to verify the effectiveness of the proposed method experimentally.
Brain Structural Covariance Network in Asperger Syndrome Differs From Those in Autism Spectrum Disorder and Healthy Controls

Introduction: Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD). Method: We compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age=14–50, IQ=92–132), 16 AS (age=7–58, IQ=93–133) and 28 HC (age=9–39, IQ=95–144). Result: The one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is altered in ASD. Conclusion: This changed structural covariance could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.
Research and Implementation of Breast Cancer Intelligent Recognition Algorithm Based on Deep Convolutional Neural Network

Breast cancer is one of the most dangerous cancers with a particularly high mortality rate for women. In the face of thousands of female pathological pictures, how to use computer related control algorithms to quickly and efficiently intelligently idently female breast cancer images has become an urgent problem in the world. With the development of computer vision and deep convolutional neural networks, this paper applies the convolutional neural network (CNN) algorithm to solve the classification and recognition computer tasks of histological images for breast cancer, aiming to learn from tens of thousands of histological images features for breast cancer, and to assist doctors in diagnosing patients’ disease was found by computer control algorithm. There are many well-known neural network computer algorithms, such as AlexNet, Inception-Net, ResNet. Image recognition for breast is a binary classification problem, and it is not appropriate to use a ResNet network with a large amount of parameters. Therefore, this paper borrowed Alexnet algorithm and redesigned the algorithm, then used it for breast cancer experiments. We compared with the capsule algorithm used by Anupama authors. Our experiment improves the accuracy rate by 2%, and reduces the amount of neural network parameters, which greatly speeds up the training time.
Preparation of One-Dimensional Titanate Nanomaterials Using Different Titania Sources

One-dimensional titanate nanomaterials were synthesized by a hydrothermal reaction using different titania sources.The morphology and crystal structure of the one-dimensional titanate nanomaterials were greatly affected by the primary particle size and crystal structure of the starting materials.The smaller initial particle size of the reactant led to a faster phase transformation of the products.The pure anatase titania favored the formation of titanate nanotubes,while the mixture of anatase titania and a small amount of rutile titania as a starting material favored the further transformation of nanotubes to nanowires or nanoribbons and promoted the phase transformation.
Adaptive Spatial Scattering Modulation

In this paper, two novel adaptive spatial scattering modulation (ASSM) algorithms, namely unequal transmission probability-based ASSM (UTP-ASSM) and equal transmission probability-based ASSM (ETP-ASSM), are proposed to pursue a better tradeoff between the computational complexity and spectral efficiency. According to available channel state information, the proposed ASSM algorithms are conducted to compute the optimal numbers of scatters and the optimal modulation orders constrained by the maximal tolerable symbol error probability (SEP). In addition, the minimal tolerate value of the minimal tolerant signal-to-noise ratio and SEP threshold are computed with a given data rate requirement by the bisection algorithm. At the receiver, a new type of optimal maximum likelihood detector is proposed to enhance the SEP performance. Furthermore, the union upper bound on the SEP is derived and analyzed. In addition, the computational complexity and system performance of the ASSM schemes are analyzed. Simulation results demonstrate that the proposed ASSM algorithms yield a lower SEP than the conventional spatial scattering modulation at the same value of average data rate.
Autonomous Manufacturing Using Machine Learning: A Computational Case Study With a Limited Manufacturing Budget

This paper studies the concept of manufacturing systems that autonomously learn how to build parts to a user-specified performance. To perform such a function, these manufacturing systems need to be adaptable to continually change their process or design parameters based on new data, have inline performance sensing to generate data, and have a cognition element to learn the correct process or design parameters to achieve the specified performance. Here, we study the cognition element, investigating a panel of supervised and reinforcement learning machine learning algorithms on a computational emulation of a manufacturing process, focusing on machine learning algorithms that perform well under a limited manufacturing, thus data generation, budget. The case manufacturing study is for the manufacture of an acoustic metamaterial and performance is defined by a metric of conformity with a desired acoustic transmission spectra. We find that offline supervised learning algorithms, which dominate the machine learning community, require an infeasible number of manufacturing observations to suitably optimize the manufacturing process. Online algorithms, which continually modify the parameter search space to focus in on favorable parameter sets, show the potential to optimize a manufacturing process under a considerably smaller manufacturing budget.
REGULATION OF ANDROGEN ACTION BY SEX HORMONE-BINDING GLOBULIN

ii Preface iv Table of contents v List of tables ix List of figures x List of abbreviations xii Acknowledgements xv 1. Literature review 1 1.1 Overview 1 1.2 Sex steroid hormones 2 1.2. 1 Biosynthesis of sex steroid hormones 2 1.2. 2 Genomic versus nongenomic effects 3 1.2. 3 Bioavailability of sex steroids 5 1.3 Sex hormone-binding globulin 6 1.3. 1 Sex hormone-binding globulin/androgen binding protein 6 1.3. 2 SHBG gene structure and organization 7 1.3. 3 Tissue specific expression of SHBG 9 1.3. 4 Regulation of plasma SHBG levels 12 1.4 Biochemical and molecular properties of SHBG 14 1.4. 1 SHBG protein structure 14 1.4. 2 Steroid-binding site 15 1.4. 3 Post-translational modification 16 1.5 Functions of SHBG 17 1.5. 1 Plasma steroid transport 17 1.5. 2 Presence of SHBG within tissues 18 1.5. 3 Presence of SHBG inside cells 18 1.5. 4 Putative SHBG receptor 19 1.5. 5 Endocytosis of SHBG 20 1.5. 6 Interactions between SHBG and other proteins 21 1.6 Androgen regulated genes 22
Time effects of bacterial vaginosis on infant morbidities in Kenya assessed using modified skewed generalized estimating equations

Infant morbidity and mortality are indicators used globally as measures of a country’s health status. Among the 8 millennium development goals (MDGs), this study aimed to address goal four (MDG 4) on the reduction of child mortality and six (MDG 6) on combating HIV and other diseases. We assessed different health conditions caused by bacterial vaginosis (BV) that could have life-long effects among infants. We aimed to address the time effects of BV on the long-term cause of infants' morbidities when asymmetry is assumed. We analyzed infant data from HIV-positive mothers with known BV status from a randomized controlled trial study conducted in Nairobi, Kenya. We aimed to investigate the effect of BV on infant morbidity with time from birth up to the age of 6 months. We derived a score for morbidity incidences depending on illnesses reported in the register during scheduled visits only. By adjusting for the mother’s BV status, child’s HIV status, sex, feeding status, and weight for age, we used two approaches for analysis. We considered and fitted the traditional generalized estimating (GEE) equations and our proposed skewed generalized estimating equations (SGEE). Overall, we included information on 327 infants. One thousand nine hundred sixty-two repeated measurements were available for analysis. Among the 327 mothers, 148 (45%) tested positive for BV, while 179 (55%) tested negative. We found that BV, gender, and time were associated with multiple health conditions in infants. Infants of women who tested positive for BV, at month 1, had 4.46 higher odds of various health conditions compared to infants of mothers who tested negative. The effects of BV tended to decrease with time, and at 5 months of age, children in the BV group had 1.10 times the odds of experiencing morbidity incidence. In the SGEE model, BV was statistically significant at the 0.05 level with a positive coefficient, indicating that children in the BV group had a higher probability of experiencing multiple morbidities. BV is a significant predictor of infant morbidity because its effects on exposed infants could persist over time. In contrast, the traditional GEE results showed an insignificant positive coefficient. The results indicate the need to factor in the skewness during analysis in case of data transformation, especially when converting from continuous to binary data for parsimony and straightforward interpretation of the effects of covariates. Maternal BV status was positively associated with morbidity incidences, which highlights the need for early intervention for infected women. Accelerated programs promoting access to BV treatment with proper infant handling practices that better deal with emerging multiple health conditions in infants may prove useful in reducing the incidence of infant morbidity in Kenya. Emphasis on care to promote better health for infants during growth is necessary to achieve the MDGs.
Conceptual Surface Electrochemistry and New Redox Descriptors

This paper aims at enlightening the link between surface properties, conceptual density functional theory (DFT), and electrochemistry. The energy of a metallic charged surface is expanded into classical terms such as electrochemical potential or chemical hardness and into new terms such as electromechanical coupling vector, electrochemical Hessian matrix change, etc. These quantities are shown to be crucial parameters to understand interface electrochemistry and in particular the surface zero-charge potential, the capacitance and its derivatives, and the surface structural polarization at the atomic scale. Furthermore, the Fukui function initially introduced to rationalize molecular reactivity is here shown to directly probe the electrochemical activity of the surface. First, it gives the spatial repartition on the surface of the charge added during an electrochemical step, and second it quantifies the electrochemical parameters of the system: the electrochemical potential and the electromechanical coupli.
Furosemide Solvates: Can They Serve As Precursors to Different Polymorphs of Furosemide?

In the present work, four solvates of furosemide with tetrahydrofuran S(THF), 1,4-dioxane S(DIOX), N,N-dimethylformamide S(DMF), and dimethylsulfoxide S(DMSO) were crystallized, the crystal structures were solved for S(THF), S(DIOX), and S(DMF). The existence of S(THF) and S(DMSO) was not reported before; for the previously known S(DIOX) and S(DMF), the crystal structures remained unsolved. The detailed structural analysis of furosemide containing crystal structures showed that the molecule of furosemide has a high conformational lability because of the rotations of the sulfamoyl and furanylmethylamino fragments. Some of the furosemide conformations were shown to be stabilized by the intramolecular N–H···Cl hydrogen bond. Desolvation of the four solvates was studied by TG and X-ray diffraction and was shown to give different products depending on the precursor and particle size: the desolvation of powder of S(THF) and the large crystals of S(THF), S(DIOX), and S(DMF) gives Form-III of furosemide, whereas .
Weak form market efficiency: A case study of Asia-Pacific markets

This study aims to test the weak form market efficiency for five developed markets, nine emerging markets and three frontier markets in the Asia-Pacific region. The tools applied in the test of this form of market efficiency are serial correlation test, runs test and unit root test. The analysis is performed by using logarithm return for the period of 2008 to 2018. For all markets in our research, the results strongly reject the weak form efficiency when the unit root tests are carried out, while the results from the Durbin-Watson test are in complete contrast. However, in the runs test and variance ratio test, the results provide mixed evidences of weak form efficiency of the markets