Miracle-dz/UniFAR-FTUs huggingface dataset:
Parasitic Coupling in 3D Sequential Integration: The Example of a Two-Layer 3D Pixel

In this paper, we present a thorough analysis of parasitic coupling effects between different electrodes for a 3D Sequential Integration circuit example comprising stacked devices. More specifically, this study is performed for a Back-Side Illuminated, 4T–APS, 3D Sequential Integration pixel with both its photodiode and Transfer Gate at the bottom tier and the other parts of the circuit on the top tier. The effects of voltage bias and 3D inter-tier contacts are studied by using TCAD simulations. Coupling-induced electrical parameter variations are compared against variations due to temperature change, revealing that these two effects can cause similar levels of readout error for the top-tier readout circuit. On the bright side, we also demonstrate that in the case of a rolling shutter pixel readout, the coupling effect becomes nearly negligible. Therefore, we estimate that the presence of an inter-tier ground plane, normally used for electrical isolation, is not strictly mandatory for Monolithic 3D pixels.
Cloud-based Voice Home automation System Based on Internet of Things

The rapid development of automation industries and technologies has shown incredible prospects for transforming our homes into a smart home automation system, which are more secure than a simple home. This paper proposes a home application based on voice and text called the Automated Control and Monitoring System (ASCM). This application can be utilized by both normal and vision-impaired people by using with a mobile phone. The application allows users to send voice commands through Google Assistant installed on Android to control the appliances. They can also have complete monitoring by logging onto the ThingSpeak dashboard, which displays a device status indicator and sends alert messages in the event of danger. The NodeMCU development board based on the ESP8266 and a combination of sensors, modules and actuators were used to create a "control system" for home appliances based on the Internet of Things (IoT). This work mitigates the challenges that faced the design of smart homes based on (IoT). The control and monitoring system is using voice without the need to use programming languages. As well as the automation process is not only on / off but also querying the status of any device sensors. In addition, this application dispenses with IFTTT(If-This-Then-that) services to send alert messages because their number is limited during the month, compared to the alerts provided by the Blynk app, which is unlimited. Mobile phones and robotic homes will make life easier and more enjoyable for the elderly and differently abled people who are unable to manually turn the items ON/OFF.
Multi-Spectral Imaging for Fruits and Vegetables

In the field of agriculture, fruit grading and vegetable classification is an important and challenging task. The current fruit and vegetable classifications are done manually, which results in inconsistent performance. There is an influence of external surroundings on this manual classification. Sometimes getting an expert fruit or vegetable grader, is challenging and the performance of that person may become stagnant over some time. With the recent development in computer technology and multispectral camera system, it is possible to achieve an efficient fruit grading or vegetable classification system. In this manuscript, we summarize different automated fruit grading as well as vegetable classification systems, which are based on multi-spectral imaging. We have focused our analysis on four major areas such as varietal identification, fruit quality assessment, classification, and disease detection. From our analysis, we have found that the Partial Least Square Discriminant Analysis (PLS-DA) was most effective for identifying varieties of tomato seeds. For analyzing the quality of pomegranate fruits, the multiple linear regression model was the most optimal method. Multi-Layer Perceptron (MLP) classifier was the recommended method for classifying medicinal plant leaves. A Linear Discriminant Analysis (LDA) based classifier could accurately detect diseases in fruits and vegetables. Keywords—Multi-spectral imaging; fruit grading; vegetable classification; fruit quality; disease detection; fruit maturity
Postresidency Impact of Alcohol and Drug Screening and Intervention Training.

BACKGROUND AND OBJECTIVES Many residency programs provide alcohol and drug screening, brief intervention (BI), and referral to treatment (SBIRT) training, hoping to impact residents' future practice activities. Little is known about postresidency use of these skills. This study assesses postresidency impact of SBIRT training. METHODS Over 3 years, physicians who participated in SBIRT training in four residency programs were recruited for follow-up. Participants chose between a paper and online questionnaire 12-24 months after graduation; participants received $20 gift cards. We first analyzed postresidency responses only (n=74), then compared pre- and posttraining results of those completing both surveys (n=50). RESULTS Of 182 enrolled graduates, 74 (41%) completed questionnaires. In paired comparisons to their pretraining responses, graduates increased endorsement of statements that BIs can reduce risky use and reduced endorsement of statements that they do not have adequate training or time to address patients' alcohol use, or that discussing alcohol use with patients is uncomfortable. While most barriers to providing interventions were endorsed less frequently by SBIRT-trained clinicians in postresidency surveys, ongoing concerns included poor reimbursement, little time, low success rates, and some discomfort with interventions. Seventy percent of graduates felt motivational interviewing techniques created stronger doctor-patient relationships; 16% reported colleagues in their practices had increased SBIRT activities after they joined the practice. CONCLUSIONS SBIRT trainees reported high levels of SBIRT activity 12-24 months after graduation and increased SBIRT activities by their colleagues. While some barriers remain, residency training appears to be a promising approach for disseminating SBIRT into clinical practice.
Testing for Vitamin D in High-Risk COPD in Outpatient Clinics in Spain: A Cross-Sectional Analysis of the VITADEPOC Study

Background: Vitamin D deficiency has been associated with an accelerated deterioration in lung function and increased exacerbations in chronic obstructive pulmonary disease (COPD). 25(OH) vitamin D levels have been indicated as a potentially useful marker for adverse results related to COPD. Methods: VITADEPOC is a cross-sectional clinical study recruiting consecutive patients with high-risk COPD. The objective of our study was to investigate vitamin D determination frequency in patients with high-risk COPD in clinical practice at outpatient clinics in Spain and to describe the factors associated with vitamin D testing. We also aimed to determine the frequency of vitamin D deficiency in these patients. Results: Only 51 (44%) patients underwent vitamin D determination and 33 (28.4%) had received vitamin D supplements in clinical practice. The patients who underwent testing for vitamin D in clinical practice were more often women (58.8% vs. 26.2%, p < 0.001) with comorbidities such as osteoporosis (19.6% vs. 6.2%, p < 0.001) or chronic renal failure (7.8% vs. 0%, p < 0.001) and with exacerbator phenotype (55% vs. 32.3%, p = 0.015). A total of 63 (54.3%) patients had serum vitamin D levels <20 ng/mL at the inclusion visit. Of these, 29 (46%) had serum vitamin D levels <12 ng/mL (severe deficiency). Having a history of inhaled corticosteroids (OR 3.210, p < 0.016), being treated with a cycle of systemic corticosteroids (OR 2.149, p < 0.002), and having a lower physical activity level (OR 3.840, p < 0.004) showed a statistically significant positive association with vitamin D deficiency. Conclusion: The testing of vitamin D levels in patients with high-risk COPD treated at outpatient respiratory clinics in Spain is infrequent. However, when tested, a severe deficiency is detected in one in four patients. Efforts to optimize case detection in COPD are needed.
Illuminating the Role of Reflexivity Within Qualitative Pilot Studies: Experiences From a Scholarship of Teaching and Learning Project

Pilot studies within qualitative inquiry are crucial yet often hidden aspects of research design. In this article, we argue for pilots to have greater visibility. We explore the role of a pilot in providing a foundation for enhancing ethical reflexivity, drawing on a recent pilot study within a tertiary healthcare education setting. The Scholarship of Teaching and Learning (SoTL) presents a unique environment with complex stakeholder relationships. There is a lack of consensus nationally and internationally on whether all SoTL projects require consideration by institutional ethics review bodies. A pilot study offers an opportunity for ethical steerage of a research project, reflecting ethics in practice whilst augmenting any procedural ethics review requirements. We propose that a qualitative pilot study, as a design strategy, can enhance ethical conduct by researchers. Within SoTL specifically, the pilot can provide an opportunity for researchers to demonstrate a commitment to a pedagogy of care spanning the project’s duration, signifying a commitment to enduring teacher-student relationships within the broader learning environment. Beyond tertiary settings, we believe the pilot study, as a space for ethical reflexivity, has applicability to research settings where caring for and being seen to care for the wider participant community is a critical ethical consideration.
Machine Learning Methods-Based Modeling and Optimization of 3-D-Printed Dielectrics Around Monopole Antenna

In this article, we propose using new machine learning (ML)-based optimization methods as an alternative to traditional optimization methods, for complex antenna designs. This is an efficient methodology to tackle computational challenges, as it is capable of handling a large number of design parameters and is more efficient as well as informative. The proposed technique is applied for modeling the gain performance in the principal plane of a monopole antenna when its radiation properties are modified by placing spatially dependent dielectric material around it. Using the proposed methodology, the dielectric constant values are mapped to the gain pattern of the design. We use two ML techniques for this purpose, namely Gaussian process (GP) regression and artificial neural network (ANN). Once each of these models is obtained, they are further used for estimating the dielectric constant values that can suggest optimal directions to modify gain patterns for single-beam and multiple-beam patterns rather than the conventional omnidirectional pattern of a monopole antenna. The performance of this technique is compared with heuristic optimization techniques, such as genetic algorithms (GAs). The proposed method proves to be quite accurate in spite of being a high-dimensional nonlinear problem. A prototype of a monopole design with three-beam gain pattern is fabricated and tested. The measurement results agree well with the simulation results. The proposed methodology can provide useful and scalable optimization tools for computationally intensive antenna design problems.
An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis

By virtue of the prediction abilities of machine learning (ML) methods, the ML-assisted evolutionary algorithm has been treated as an efficient solution for antenna design automation. This article presents an efficient ML-based surrogate-assisted particle swarm optimization (SAPSO). The proposed algorithm closely combines the particle swarm optimization (PSO) with two ML-based approximation models. Then, a novel mixed prescreening (mixP) strategy is proposed to pick out promising individuals for full-wave electromagnetic (EM) simulations. As the optimization procedure progresses, the ML models are dynamically updated once new training data are obtained. Finally, the proposed algorithm is verified by three real-world antenna examples. The results show that the proposed SAPSO-mixP can find favorable results with a much smaller number of EM simulations than other methods.
Creating a measurement tool for entrepreneurship education: a participatory development approach

The aim of this paper is to illustrate and model the construction of a measurement tool for entrepreneurship education where the tool itself is targeted toward Finnish teachers working in primary and secondary education. This study represents participatory action research (Argyris 1993) as the research context has been facilitated and provided by the researchers, and where the study objects initiate, respond, and develop their activities, thereby reforming the context further. The presented case is an illustration of the building of a Measurement Tool for Entrepreneurship Education, prepared in an ESF-funded project. In this study we present multi-method, multi-investigator, multiple data, and multiple theory triangulation (Denzin 1988) settings. From the data, the phases of the measurement tool construction were identified. Our aim is to present the process in order to link the theory and practice of entrepreneurship education. Here, a broad and multilayered definition of entrepreneurship education is utilized, and by making these aspects explicit the tool itself has a role not only as a teacher’s self-evaluation kit but also as a steering system for developing schools and regions on a larger scale.
Penal Arts Interventions and Hope: Outcomes of Arts-Based Projects in Prisons and Community Settings*

The value of arts-based projects within the criminal justice system is well documented, as research has identified positive outcomes relating to inmates’ behavior and their relationships with others. This article examines the work of the Soft Touch Arts project at HMP Leicester, UK and identifies the importance of hope as a transformative outcome. Interviews with artists in prison and community settings demonstrated the value of engaging in creative and purposeful activity, generating hope which enabled artists to aspire to a better future. This occurred alongside ameliorating the harms of prison and helping artists manage their relationship with probation services.
License Plate Recognition Integrated with Android and Mobile Vision

License plate is an important thing on vehicle. License plate is an identity of vehicle. All of the information about type, year, tax, and owner of vehicle. The development of technology especially in digital image processing and mobile android technology. There are so many android applications that used digital image processing known as optical character recognition. The purpose of this paper is to make an android application that can recognize license plate with mobile vision library. The research method of this paper is waterfall, starts with integrating android with mobile vision library and doing test to find out the accuracy of mobile vision. Designing an application models with flowchart diagram and unified modelling language.
Genome wide expression analysis of circular RNAs in mammary epithelial cells of cattle revealed difference in milk synthesis

Milk is an excellent source of nutrients for humans. Therefore, in order to enhance the quality and production of milk in cattle, it is interesting to examine the underlying mechanisms. A number of new investigations and research have found that, circRNA; a specific class of non-coding RNAs, is linked with the development of mammary gland and lactation. In the present study, genome wide identification and expression of the circRNAs in mammary epithelial cells of two distinct cattle breeds viz Jersey and Kashmiri at peak lactation was conducted. We reported 1554 and 1286 circRNA in Jersey and Kashmiri cattle, respectively, with 21 circRNAs being differentially expressed in the two breeds. The developmental genes of the established differentially expressed circRNAs were found to be largely enriched in antioxidant activity, progesterone, estradiol, lipid, growth hormone, and drug response. Certain pathways like MAPK, IP3K and immune response pathways were found significantly enriched in KEGG analysis. These results add to our understanding of the controlling mechanisms connected with the lactation process, as well as the function of circRNAs in bovine milk synthesis. Additionally, the comparative analysis of differentially expressed circRNAs showed significant conservation across different species.
A correlation to predict the thermal conductivity of MXene‐silicone oil based nano‐fluids and data driven modeling using artificial neural networks

In this article, MXene nano‐material is used with silicone oil to improve the thermal conductivity of nano‐fluids and it further increases the heat transfer. MXene nano‐material‐silicone oil‐based nano‐fluids have the capacity to be employed as heat transfer fluids in various thermal applications since it has excellent thermal properties, higher heat resistance and silicone oil have the ability to operate upto a temperature of 400°C. This paper investigates the thermal conductivity values for different concentrations and temperature of MXene nano‐fluids. The thermal conductivity of the prepared MXene nano‐fluids is strongly dependent on temperature and concentration of MXene nano‐particles. The values of thermal conductivity are predicted by developing multilayer perceptron (MLP) Artificial Neural Network (ANN) model. The temperature and nano‐particles concentration are taken as inputs and thermal conductivity is taken as output to the model. Mean square error (MSE) and correlation coefficient (R) are used to assess the efficacy of the developed model using trial and error method. The values of MSE and correlation coefficient for the optimum model are found to be 2.6 × 10−07 and 0.99687, respectively. A correlation for thermal conductivity as a function of temperature and concentration is also propounded using the experimental results. The developed optimum ANN model show satisfactory performance, and the predicted thermal conductivity values matches well with experimental thermal conductivity values.
Tornado codes for archival storage

This thesis examines the application of Tornado Codes, a class of low density parity check (LDPC) erasure codes, to archival storage systems based on massive arrays of idle disks (MAID). The fault tolerance of Tornado Code graphs is analyzed, and it is shown that it is possible to identify and mitigate worst-case failure scenarios in small (96 node) graphs through use of simulations that find and eliminate critical node sets that can cause Tornado Codes to fail even when almost all blocks are present. The resulting graph construction procedure is then used to construct a 96-device Tornado Code stripe storage system with capacity overhead equivalent to RAID 10 that tolerates any four device failures. This system is demonstrated to be superior to parity-based RAID. After establishing the fault tolerance of Tornado Codes, a log-structured extent-based file system is constructed using Tornado Coded stripe storage. The file system is combined with a MAID simulator to emulate the behavior of a large-scale storage system, with the goal of employing Tornado Codes to increase fault tolerance in a power-constrained environment. The effect of power conservation constraints and design choices on system throughput is examined, and a policy of placing multiple data nodes on a single device is shown to increase read throughput at the cost of a measurable decrease in fault tolerance. Finally, the system is implemented on a 100-TB Lustre storage cluster, providing GridFTP accessible storage with higher reliability and availability than the underlying storage architecture.
PITX1 Is a Regulator of TERT Expression in Prostate Cancer with Prognostic Power

Simple Summary Most prostate cancer is of an indolent form and is curable. However, some prostate cancer belongs to rather aggressive subtypes leading to metastasis and death, and immediate therapy is mandatory. However, for these, the therapeutic options are highly invasive, such as radical prostatectomy, radiation or brachytherapy. Hence, a precise diagnosis of these tumor subtypes is needed, and the thus far applied diagnostic means are insufficient for this. Besides this, for their endless cell divisions, prostate cancer cells need the enzyme telomerase to elongate their telomeres (chromatin endings). In this study, we developed a gene regulatory model based on large data from transcription profiles from prostate cancer and chromatin-immuno-precipitation studies. We identified the developmental regulator PITX1 regulating telomerase. Besides observing experimental evidence of PITX1′s functional role in telomerase regulation, we also found PITX1 serving as a prognostic marker, as concluded from an analysis of more than 15,000 prostate cancer samples. Abstract The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT, transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT, we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length.