Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Lateral SBD Temperature Sensor of a 4H-SiC VDMOS for Real-Time Temperature Monitoring

The novel integrated temperature sensor of a power 4H-SiC MOSFET for precise real-time temperature monitoring is proposed in this article, in which a lateral Schottky barrier diode (SBD) is embedded. A physical model considering the influence of the lateral-distribution effect and interface states of the lateral SiC SBD is presented to explain the mechanism and direct the design of the sensor. A high degree of linearity (<inline-formula> <tex-math notation="LaTeX">${R}^{{2}}{)}$ </tex-math></inline-formula> is achieved, and the proposed sensor obtains high design and fabrication tolerance and integration flexibility, namely, a much smaller size is viable. Specifically, the <inline-formula> <tex-math notation="LaTeX">${R}^{{2}}$ </tex-math></inline-formula> of the fabricated sensor is 0.9999 in the range of 15°–200°. Under the protection of the p-well, the crosstalk value (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {cross}}{)}$ </tex-math></inline-formula> between the SiC vertical double-diffused metal-oxide-semiconductor field-effect transistor (VDMOS) and sensor is greatly suppressed, and <inline-formula> <tex-math notation="LaTeX">${V}_{\text {cross}}$ </tex-math></inline-formula> is less than ±1.87° when the VDMOS is switching. Self-heating tests are employed for comparison with the temperature-sensitive electrical parameter (TSEP) method and <italic>RC</italic> thermal resistance model method. The proposed sensor has the potential for integration into SiC modules and integrated circuits (ICs) to realize real-time temperature estimation with high precision and low noise.
Study of linearity indices in analog/RF circuits using EKV model and comparing the results in three different CMOS processes

The paper reports on a reliable design methodology for linearity of analog/RF circuits based on EKV model and its main parameter, the inversion coefficient (IC). In this procedure, the linearity value is studied in all regions from WI to SI. Two important linearity indices, AldB and AIP3, are investigated for single-stage and two stage differential amplifiers and finally the optimum value of inversion coefficient is calculated for biasing of circuit devices.
Influence of Perceived Credibility of Online Reviews on Consumers’ Purchase Intentions in Travel E-Commerce Platforms

In the context of Internet purchase, online reviews become an important reference information for consumers to make travel arrangements. And perceived credibility is the determinant of whether online reviews are adopted or not. Based on SMCR theory, this paper introduces a mediator variable - perceived credibility into tourism products. The maximum likelihood estimation method and spss process are used to empiricately reveal the influence mechanism of perceived credibility of online reviews on the online purchase intention of tourism consumers. The research shows：(1)The vast majority of travel consumers trust online reviews;(2)Four influencing factors of credibility to be verified are extracted from the four elements of communication process: source, message, channel and receiver, the influence degree of these factors is clarified. ;(3)The information source, information quality, emotional tendency and platform trust of online reviews have important effects on purchase intention, perceived credibility plays a mediating role in the influencing mechanism; (4)Among them, information quality and platform trust have the largest impact on online purchase intention, and emotional tendency has the smallest impact, which needs to stimulate consumers 'purchase desire through the mediator variable of perceived credibility; (5)Emotional tendency and platform trust are the most important dimensions affecting perceived credibility, moreover, platform trust has the greatest impact on perceived credibility.
The Intelligent Development of Smart Transmitter Estimation in Mobile Ad Hoc Network

The Smart Transmitter (ST) parameter estimate presents a particular problem in Mobile Ad Hoc Networks (MANETs). Nodes in these networks must develop the ability to adapt to their dynamic environment, which includes elements like the number, distribution, and topology of nodes. As a result, a distributed approach must be used to estimate the ST parameters. Distributed estimate of ST parameters is a difficult task, particularly in dynamic mobile ad hoc networks. This study suggests an intelligent design for estimating mobile ad hoc network-wise transmitters. This is because the ST parameters must be changed to reflect the nodes' constant need to adapt to the environment's constant change. However, the ST parameters in these networks can be precisely estimated with the appropriate methods and instruments.
Noise-Disentangled Single-Pixel Imaging Under Photon-Limited Conditions

Single pixel imaging (SPI) is a well-established computational imaging modality that renders as a practical and innovative tool for sensing across almost the entire spectrum range. Given the specific measurement mode (e.g., bucket in junction with differential), SPI arguably allows for promising signal-to-noise ratio that underpins an economical alternative to high-sensitivity pixelated detectors. However, this common interpretation could be totally collapsed due to the inevitable time-varying interference, making the image fidelity fall far below those using classical detectors. In this work, we present a novel learning-based SPI protocol that maintains outstanding robustness to noise under extremely low-light condition that leads to 10 photons per pixel or even less. Specifically, a noise disentanglement paradigm is implemented across the data and image domains in an unsupervised framework. In experiments, two photon-limited scenarios that have been typically found in fluorescence imaging were investigated. The results validated the compelling superiorities of our strategy over other conventional SPI algorithms. This work substantially improves the reliability and validity of SPI with downstream applications in the field of biomedicine and other scenarios that suffer from limited number of photons.
Real Time Map Matching for Low Frequency GPS Data Based on Machine Learning Technology

GPS cannot perform well in urban area because of multipath problems that make GPS location contain low accuracy. So, the map matching method is the method to adjust GPS location to reduce GPS location error. However, the current map matching method requires GPS high frequency data for adjustment and it does not perform well in GPS low frequency data. So, the study explores the idea to improve the map matching method based on a kind of machine learning technology by using the classification method to adjust the low frequency GPS location. This research uses features such as speed, heading and location of the previous GPS point for a training machine learning the model and adjust points of the GPS location. The objective of this study is to develop accuracy of the map matching method and the speed of map matching method for low frequency GPS data. After processing data, we found that Random Forest is the machine learning model that contains highest precision, recall and F1-Score. Moreover, this model is a low sensitivity model when frequency of data changes, but Decision Tree is the fastest model in prediction time and it takes lower time for training than Random Forest model.
High-Throughput Communications Using Constant-Modulus Waveforms With Mitigation of Range-Sidelobe Modulation

In a previous paper, we introduced a flexible methodology for the design of dual-use waveform alphabets which are suitable for simultaneous use in radar and wireless communications. Here we extend this work to accommodate waveforms of constant modulus, and explain how pulse compression constraints may be leveraged to significantly mitigate the significant issue of range-sidelobe modulation. We report results of a field test which validated performance expectations.
Intrusiveness of Display Ads versus Video Ads of Instagram Feeds Promotional Content of a Coffee Retail Brand

Several major changes have occurred which have encouraged many business players globally to start using digital platform channels to maximize business output, which in recent times has been affected by limitations due to the COVID-19 outbreak. The purpose of this research is to investigate whether there are significant differences in intrusiveness levels as a result of different Instagram promotional content formats, i.e., display ads and video ads, that are used by coffee retail brands. To find the answer, this research uses a quantitative survey method with an experimental approach among 176 Indonesian coffee consumers that are part of the Instagram community members of a major Indonesian coffee retail brand. The results of the study show that there is a significant difference in terms of intrusiveness output between the two types of ad formats being compared. Further, the video ads type is perceived as more intrusive than the display one. The findings of this study can contribute to the coffee retail industry by giving insight into how to utilize different content and formats of social media ads to get the most effective result considering the rich options for creative content to be utilized as a promotional tool.
Machine Learning based Case-Based Reasoning Techniques using in Medical Diagnosis Systems

Like rule-based reasoning, neural networks, and genetic algorithms, Case-Based Reasoning (CBR) is an AI technique. When compared to other machine learning approaches, CBR techniques using dynamic knowledge discovery from prior experiences and advanced filtering of factors was the best predictor. Within the scope of this research, we introduce a CBR-based adaptation of the Neurology Diagnosis System (NDS). When qualified doctors are unavailable, this approach helps health care aides. The compositional adaptation method used in this study involves selecting and combining useful health information components from the solution components of numerous comparable historical situations to produce a new individualized health information package. In this paper discussed about any diagnosis of disease to seed in CBR technology maintain the relevency in future.
An SBR/image approach for radio wave propagation in indoor environments with metallic furniture

In this paper, we propose a deterministic approach to model the radio wave propagation channels in complex indoor environments. This technique applies the modified shooting-and-bouncing-ray (SBR) method to find the equivalent sources (images) for each launched ray tube. In addition, the first-order wedge diffraction from furniture is included and the diffracted rays also can be attributed to the corresponding images. By summing the contributions of all these images coherently, we can obtain the total received field at a receiver. Besides, the vector-effective height (VEH) of an antenna is introduced to consider the polarization coupling effect resulting from multiple reflection inside the rooms. We verify this approach by comparing the numerical results in three canonical examples where closed-form solutions exist. The good agreement indicates that our method can provide a good approximation of high-frequency radio propagation inside rooms where multiple reflection is dominant. Work reported in this paper has shown that the propagation loss in indoor environments varies considerably according to furniture and polarizations.
Performance Evaluation of Data Mining and Neural Network Based Models For Diabetes Prediction

Diabetes, often known as diabetes mellitus, is a disease that disrupts the body’s normal response to blood sugar. The pancreas releases insulin, which aids in the uptake of glucose from meals into cells to be used as fuel. Hyper-glycemia,or high blood sugar, is a typical result of uncontrolled diabetes and is associated with several significant health complications, most notably those affecting the nerves and blood vessels. Statistics indicate that in 2014, adults 18 and above had diabetes, and in 2019, diabetes was responsible for 1.5 million fatalities worldwide. Machine learning and deep learning predictive models have seen tremendous development throughout industries, including health care, making early diagnosis of diabetes a breeze. Many potentially fatal diseases, such as cancer, diabetes, heart disease, thyroid disease, etc., may be predicted or diagnosed with the use of machine learning classifiers. The treatment of chronic diabetes, one of the world’smost prevalent illnesses, might benefit greatly from improved diagnostic efficiency.Here, we examine the relative merits among several ML and DL approaches to the problem of early diabetic illness prediction. The fundamental purpose of this research is to organize and conduct out diabetes prognosis with several ML learning approaches and then analyze the results of these methods to determine which one is the most accurate classifier. In this work, we take a multifaceted approach to diabetes and its prediction by investigating a wide range of disease-related characteristics. We use the classic Dataset Based on PIDD, and we apply several Machine Learning and Deep learning classifiers to it, including Random Forest (RF), Logisticregression (LR), Support Vector Machine, Artificial Neural Network (ANN), Multilayer Perceptron (MLP), and Decision Tree, Gradient Boost (GB), XGBoost (GB), Adaboost (GB), CATBOOST (GB), and LightGBM (LGBM). There is a wide range of precision amongst the models used here. A technology that can precisely predict diabetes is shown in this research. The findings of this research indicate that one of the Data mining models, random forest (RF), and the ANN Model from the category of neural network models have superior accuracy in making diabetes forecasts.
Advancing Network Monitoring and Operation with In-band Network Telemetry and Data Plane Programmability

Modern communication networks operate under high expectations on performance and resilience (e.g., latency, bandwidth, availability) mainly due to the continuous proliferation of non-elastic highly-distributed applications. In this context, closely monitoring the state, behavior, and performance of networking devices and their traffic as well as quickly troubleshooting problems as they arise is essential for the operation of network infrastructures. Data Plane Programmability (DPP) along with In-band Network Telemetry (INT), backed by the recent advances in Software-Defined Networking, emerge in this context as promising platforms to meet these monitoring demands. In this thesis we make several contributions that advance the discipline of network monitoring and operation. We introduce and formalize the In-band Network Telemetry Orchestration (INTO) problem, which consists in assigning subsets of traffic to carry out INT and provide full monitoring coverage while minimizing the overhead. We prove this problem to be NP-Complete and propose polynomial computing time heuristic to solve it. In our evaluation using real wide-area network topologies, we observe that the heuristics produce solutions close to optimal to any network in under one second. Continuing our work, we investigate DPP capabilities further and design IntSight, a system for highly accurate and fine-grained detection and diagnosis of SLO violations. Our evaluation using real networks also shows that IntSight generates up to two orders of magnitude less monitoring traffic than state-of-the-art approaches. As a final step in this thesis, we shift our focus to quick reaction and propose Felix, a system for failure recovery that reroutes around failures at data-plane timescales while still using the shortest available paths. Our evaluation shows that our approach can recover from failures up to four orders of magnitude faster than existing SDN approaches while making sensible use of data-plane resources.
Star formation in galaxies and star clusters

This thesis is devoted to star formation from galaxy-scales (≈ 10 000 pc) to sub-star cluster-scales (≈ 0.1 pc). I develop and test a new method to derive star formation histories of Galaxies from their star cluster content, in particular the most massive star clusters. This is followed by an application of this method to the Large Magellanic Cloud, where the results of this new method are confirmed with results obtained from counting individual stars. Stars form in a grouped way, in the basic unit of a star cluster, so that both the mass function of stars formed within a star cluster and the mass function of star clusters in a galaxy need to be known for a quantitative description of galaxy-wide star formation. These mass functions have a power-law functional form, for which I develop statistical tools to estimate the parameters and to perform goodness-of-fit tests, with a particular emphasis on the upper mass end. The statistical methods are applied to the star clusters in M51 to investigate the shape of the initial star cluster mass function and the early evolution of it. I also investigate the upper mass end of the stellar initial mass function, both in observations and theory. The analysis of a numerical simulation of star cluster formation (time-evolution of substructure, mass segregation, stellar mass function) concludes this thesis. A new method to derive star formation histories of galaxies from their star cluster distributions. Maximum stellar mass versus cluster membership number revisited. Estimators for the exponent and upper limit, and goodness-of-fit tests for (truncated) power-law distributions. The star formation history of the Large Magellanic Cloud as seen by star clusters and stars.
Determinants of Renal Micro-Perfusion as Assessed with Contrast-Enhanced Ultrasound in Healthy Males and Females

(1) Background: The renal microcirculation is essential to maintain the renal function, but its determinants in humans have been poorly studied. Contrast-enhanced ultrasound (CEUS) allows the non-invasive quantification of the cortical micro-perfusion at the bedside using the perfusion index (PI). The aims of this study were to assess whether differences exist in PI between healthy males and females and to identify clinical determinants associated with cortical micro-perfusion. (2) Methods: Healthy, normotensive volunteers (eGFR > 60 mL/min/1.73 m2, no albuminuria) underwent CEUS under standardized conditions with the destruction–reperfusion (DR) technique. The mean PI of four DR sequences was reported as the primary outcome measure (3) Results: A total of 115 subjects (77 females and 38 males) completed the study; the mean ± SD age was, respectively, 37.1 ± 12.2 and 37.1 ± 12.7 years in females and males, and the mean eGFR was 105.9 ± 15.1 and 91.0 ± 17.4 mL/min/1.73 m2. The PI (median) was higher in females than in males, i.e., 2705 (IQR 1641–3777) vs. 1965 (IQR 1294–3346) arbitrary units (a.u), p = 0.02). A correlation analysis showed positive associations between PI and eGFR, female sex, heart rate, plasma renin activity (PRA) and plasma aldosterone concentrations (PAC), negative associations with potassium, bicarbonate and systolic blood pressure, and no associations with age, body mass index and renal resistive index (RRI). In a multivariate linear regression analysis, only PRA remained significantly associated with PI. (4) Conclusions: Although the PI was higher among females, this association was no longer significant after adjustment for covariates. There was no difference in females tested during the follicular or the luteal phases. In conclusion, the PI was only weakly influenced by classic clinical variables, but was positively associated with PRA, suggesting that the renin–angiotensin system plays a role in the regulation of the cortical micro-perfusion in humans. Identifying which other factors contribute to the large variations in micro-perfusion across individuals needs further study.
Influence of Students Leaders’ Selection Criteria on Management of Student Discipline in Public Secondary Schools in Tigania West Sub-County, Kenya

The success of the teaching-learning process in school is dependent upon the quality of students’ discipline. However, indiscipline among students is on the rise in public secondary schools. The issue of student strikes, fear of examinations, leading to cheating and the burning of school property has become one of the serious problems being faced by the country’s school principals. In light of this, the study sought to investigate selection criteria for the management of student discipline in public secondary schools in Tigania West Sub-County, Kenya. The study is based on Douglas Murray McGregor’s Theory X and Theory Y. The research employed a descriptive survey study. The population of the study consisted of 10 principals, 44 teachers and 80 student leaders to a total of 134 respondents, stratified random sampling was used to categorise schools into four: mixed day schools, boarding boys, boarding girls, and mixed boarding schools. Then 30% of principals were selected, while other categories were 10% each. The main research instrument was a questionnaire. The researcher piloted the questionnaires in one public secondary school, and the test-retest technique was used to assess the reliability of the research instrument. Quantitative data collected was analysed with the aid of the statistical package for social sciences version 21 to get the percentages and generate tables, charts, and figures for interpretation. The study established that the selection is mainly based on the academic performance and discipline of the individual, and this allows the selection of disciplined individuals with academic merit and good personality as leaders. The study concluded that the selection of student leaders by involving both teachers and students could enhance the ability of student leaders to promote student discipline. The study recommends that the selection of student leaders should involve both teachers and students in a democratic manner to avoid any disruption of academic work due to students feeling short-changed.
The Effectiveness of Jung’s Self-Knowledge on the Adaptability and Marital Satisfaction of Mothers with Hearing-Impaired Children

Background and Aim: Hearing-impairment and hearing loss are humans’ most common neurological and sensory injuries. Having a child with hearing loss, in addition to affecting the amount of stress on the family and parents, can also affect the marital satisfaction of the couple. This study aimed to evaluate the effectiveness of Jung’s self-knowledge on the adaptability and marital satisfaction of mothers with hearing-impaired children. Methods: This research was quasi-experimental with a pretest-post-test design with a control group. The study’s statistical population includes all mothers with hearing-impaired children living in Yazd City, Iran. A total of 30 women were selected using the purposive sampling method and were randomly divided into the control (15 people) and intervention groups (15 people). The intervention group received Jung’s self-knowledge training in eight 90-minute sessions once a week, while the control group received no training. Data were collected through a marital satisfaction questionnaire and a family adaptation questionnaire. The data were analyzed at the level of descriptive and inferential statistics. Results: The results of the data analysis showed a significant difference between the variables of compatibility and marital satisfaction in the post-test of the experimental and control groups. Conclusion: Jung’s self-knowledge affects the adaptability and marital satisfaction of mothers with hearing-impaired children. Therefore, self-knowledge with Jung’s method can be useful for improving the adaptability and marital satisfaction of mothers with hearing-impaired children. Keywords: Self-knowledge; Jung’s method; adaptability; marital satisfaction; hearing- impaired children