Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of High Deposition Rate Pin Diodes from Pure SiH and 10% Dilution of SiH in H

In this paper, we present the results of the electrical charac- terization of PIN diodes up to 18 μm thick fabricated on high deposition rates a-Si : H layers obtained from pure SiH and from a 10% dilution of SiH in H , using a 13.56 MHz plasma enhanced chemical vapor deposi- tion (PECVD) with geometrical modifications in the plasma reactor . - and - curves were investigated, concluding that the changes introduced in the deposition reactor to increase the deposition rate did not affect the characteristics of the fabricated diodes. Results are compared with charac- teristics, reported for diodes obtained by standard and other high deposi- tion rate methods.
An Innovative Method for Energy Intensive Routing and Transmission Network Positioning in Integrated Wireless Detector Networks

Sensor nodes in Integrated Wireless Detector Networks (IWDN) may malfunction as a result of abrupt damage or running out of battery power. As a result, IWDN is divided into numerous different networks. In order to restore wireless connectivity on the network, new relay nodes must be added to a partitioned IWDN. This process is known as relay node installation. In IWDN, energy conservation and network lifetime extension are made possible through energy-efficient routing. The placement of relay nodes and energy-efficient routing issues for IWDN are addressed in this work. Rarely have these issues been examined together in the literature. The initial step in this paper is to build a mathematical model for each issue. It is believed that the IWDN comprises an unreachable region where sensor nodes cannot be positioned in order to solve the placement issue for relay nodes. It is converted to the wireless communication path length for energy-efficient routing. A whale optimizer technique is used because the problem is asynchronous polynomial (AP) hard. The report examines the outcomes of three adaptive techniques using the whale optimizer approach. The proposed method for IWDN is put to the test using mathematical simulations. The analysis and discussion highlight the benefits of the recommended strategy for resolving the placement of AP-hard base stations and power concerns for IWDN.
Research and Practice of "Trinity" Construction of Emerging Engineering Big Data Course Teaching Quality Assurance System during the Epidemic

The pandemic of the COVID-19, has brought a great impact to the education and teaching, so the teaching can only be carried out online, in order to ensure the monitoring and management of teaching quality during teaching. This project import the latest information intelligent teaching system, multi-directional to ensure the monitoring and control of teaching quality, the deep integration of "Rain Classroom" technology and teaching in this project, So that the interaction information between teachers and students can be recorded in real time during the online teaching, and organized, analyzed, stored also; Import the online teaching platform, collected the students records that finished homework during the epidemic; Import the online virtual experiment platform, online to finished the remote experimental operation of students. Thus, the trinity builds a three-dimensional teaching quality assurance system to escort high-quality big data course teaching during the epidemic.
Effective Wavelet-Based Regularization of Divergence-free Fractional Brownian Motion

This paper presents a method for regularization of inverse problems. The vectorial bi-dimensional unknown is assumed to be the realization of an isotropic divergence-free fractional Brownian Motion (fBm). The method is based on fractional Laplacian and divergence-free wavelet bases. The main advantage of these bases is to enable an easy formalization in a Bayesian framework of fBm priors, by simply sampling wavelet coe cients according to Gaussian white noise. Fractional Laplacians and the divergence-free projector can naturally be implemented in the Fourier domain. An interesting alternative is to remain in the spatial domain. This is achieved by the analytical computation of the connection coefficients of divergence-free fractional Laplacian wavelets, which enables to easily rotate this simple prior in any sufficiently "regular" wavelet basis. Taking advantage of the tensorial structure of a separable fractional wavelet basis approximation, isotropic regularization is then computed in the spatial domain by low-dimensional matrix products. The method is successfully applied to fractal image restoration and turbulent optic-flow estimation.
SMAC-tuned Deep Q-learning for Ramp Metering

The demand for transportation increases as the population of a city grows, and significant expansion is not conceivable because of spatial, financial, and environmental limitations. As a result, improving infrastructure efficiency is becoming increasingly critical. Ramp metering with deep reinforcement learning (RL) is a method to tackle this problem. However, fine-tuning RL hyperparameters for RM is yet to be explored in the literature, potentially leaving performance improvements on the table. In this paper, the Sequential Model-based Algorithm Configuration (SMAC) method is used to fine-tune the values of two essential hyperparameters for the deep reinforcement learning ramp metering model, the discount factor and the decay of the explore/exploit ratio. Around 350 experiments with different configurations were run with PySMAC (a python interface to the hyperparameter optimization tool SMAC) and compared to Random search as a baseline. It is found that the best reward discount factor reflects that the RL agent should focus on immediate rewards and not pay much attention to future rewards. On the other hand, the selected value for the exploration ratio decay rate shows that the RL agent should prefer to decrease the exploration rate early. Both random search and SMAC show the same performance improvement of 19
The Ubiquitous Complementizer

Complement and adjunct clauses in Bangla often exhibit internal complementizers. Some of these, the present paper suggests, are particles base-generated in situ that covertly move their features to C at LF. This suggestion forms the core of a reasonably complete account of the placement of various clause particles relative to finite verbs in clause structure.
Automated Detection of Biases within the Healthcare System Using Clustering and Logistic Regression

Big data play a vital role in decision-making, especially in the healthcare system such as in cardiovascular disease. However, the findings of algorithms used in decision-making show some disparity as compared to the existing findings of physicians. This is due to the biases in the big data set used for the healthcare system. This will lead to misdiagnosing certain protected groups or attributes like gender. Therefore, it is the major problem to detect biases in a large dataset. In this paper, we have proposed a model and implemented it to detect biases in the large data set of cardiovascular disease. This model uses statistical performance metrics to measure the biases in the dataset. The result shows that if we apply the clustering mechanism with logistic regression along with statistical performance metrics it gives a better result to detect biases in the dataset.
Impact of AIServiceOps on Organizational Resilience

Artificial Intelligence (AI) has been significant technology of the 21st century. This technology is changing every aspect of modern enterprise technology tooling, from strategies to selecting and implementing to adopting digital AI transformation. The rapid development of Artificial Intelligence has prompted many changes in the field of Information Technology (IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on how Artificial intelligence (AI) Powered IT Service Operations impact Organization Resilience and can help IT build optimized resilience by creating value in complex and ever-changing environments as product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps shape an organization's resilience in terms of value creation and sustainability, basically how AIServiceOps makes the IT staff liberation from a low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation tool to increase organizational resilience.
An Integrative Review of Programs for Managing Nurses' Substance Use Disorder in the Workplace

Abstract Background Nurses' substance use disorder (SUD) endangers patient safety and decreases nurses' work ability and health. To better understand the methods, treatments, and benefits of the programs used to monitor nurses with SUD and promote their recovery, a systematic review of international research is needed. Aim The aim was to gather, evaluate, and summarize empirical research on programs for managing nurses with SUD. Design An integrative review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis instructions. Method Systematic searches were conducted in the CINAHL, PsycInfo, PubMed, Scopus, and Web of Science databases between 2006 and 2020, complemented with manual searches. Articles were selected based on inclusion, exclusion, and method-specific evaluation criteria. The data were analyzed narratively. Results The review identified 12 studies of which nine focused on recovery and monitoring programs for nurses with SUD or other impairment and three concerned training programs for nurse supervisors or worksite monitors. The programs were described in terms of their target groups, goals, and theoretical basis. The programs' methods and benefits were described together with challenges in their implementation. Conclusion There has been little research on programs for nurses with SUD, the programs that exist are heterogenous, and the evidence available in this field is weak. Further research and developmental work are needed on preventive and early detection programs as well as rehabilitative programs and programs supporting reentry to workplaces. In addition, programs should not be restricted to nurses and their supervisors; colleagues and work communities should also be involved.
Review of Computational Fluid Dynamics Studies on Jets

The present work reviews the CFD studies carried out in the last more than fifty years to understand the uncontrolled and controlled jet characteristics. The chronological evolution of investigations on jets are reviewed, however they are presented according to their relevance in the discussion. The study also highlights the complexities involved in adaptive turbulence models in a realistic simulation of free jets. These complexities are largely due to entrainment of jets, large perturbations present at low Reynolds numbers in subsonic jets and strong wave interactions in jets exiting at supersonic Mach numbers from convergent-divergent nozzles. The accurate capturing of these perturbations, shock-cell structures in jets poses a real challenge in the CFD analysis. The complexities of capturing shock and expansion waves accurately in supersonic jets, is the critical step in the simulation.
Role of 68Ga-FAPI PET/CT in Assessing Hepatobiliary Malignancies

Introduction and Aim Preliminary studies showed good expression of fibroblast activating protein inhibitor (FAPI) in hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). Our aims were to study the diagnostic performance of 68Ga-FAPI PET/CT in diagnosing the primary hepatobiliary malignancies and to compare its performance with 18F-FDG PET/CT. Patients and Methods Patients suspected to have HCC and CC were recruited prospectively. FDG and FAPI PET/CT studies were completed within 1 week. Final diagnosis of malignancy was achieved by tissue diagnosis (either histopathological examination or fine-needle aspiration cytology) and radiological correlation from conventional modalities. Results were compared with final diagnosis and expressed as sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy. Results Forty-one patients were included. Thirty-one were positive for malignancy and 10 were negative. Fifteen were metastatic. Of 31, 18 were CC and 6 were HCC. For overall diagnosis of the primary disease, FAPI PET/CT performed exceptionally compared with FDG PET/CT with sensitivity, specificity, and accuracy of 96.77%, 90%, and 95.12%, respectively, versus 51.61%, 100%, and 63.41% for FDG PET/CT. FAPI PET/CT clearly outperformed FDG PET/CT for the evaluation of CC with sensitivity, specificity, and accuracy of 94.4%, 100%, and 95.24%, respectively, whereas for FDG PET/CT sensitivity, specificity, and accuracy were 50%, 100%, and 57.14%, respectively. Diagnostic accuracy of FAPI PET/CT was 61.54% for metastatic HCC compared with 84.62% for FDG PET/CT. Conclusions Our study highlights the potential role of FAPI-PET/CT in evaluating CC. It also ascertains its usefulness in the cases of mucinous adenocarcinoma. Although it showed a higher lesion detection rate than FDG in primary HCC, its diagnostic performance in the metastatic setting is questionable.
Efficient wide-range calculation of free energies in solids and liquids using reversible-scaling molecular dynamics

We elaborate a novel and efficient method to obtain multiphase Helmholtz free energies from molecular dynamics (MD) simulations over wide ranges of volume and temperature in materials that can be described by temperature-independent ion forces, with both higher accuracy and order-of-magnitude cost savings compared to direct thermodynamicintegration techniques. Our method leverages and significantly extends the technique of reversible scaling molecular dynamics (RSMD) proposed by de Koning et al. [Phys. Rev. Lett. 83, 3973 (1999)], which allows a free-energy difference in a given phase at constant volume to be calculated as a function of temperature from a single MD simulation. In mechanically stable solid phases, our approach carefully combines quasiharmonic lattice dynamics at low temperatures with an accurate and fully isolated RSMD simulation of the anharmonic vibrational free energy at high temperatures to produce a seamless free energy from zero temperature to above melt along constant-volume isochores. In the liquid, we combine a unique calculation of the free energy along a high-temperature reference isotherm with isochoric RSMD simulations from that temperature to below melt. In metastable solid phases that are mechanically unstable at low temperature, we use two-phase MD melt simulations together with the liquid free energy to obtain the solid free energy along the solidus melt line and then perform isochoric RSMD simulations to temperatures above and below that point. While our free-energy method is general, we have specifically adapted it here to the case of metals in which the ion forces are well described by model generalized pseudopotential theory (MGPT) multi-ion interatomic potentials, and additive electron-thermal free-energy contributions can be
LncRNA109897-JrCCR4-JrTLP1b forms a positive feedback loop to regulate walnut resistance against anthracnose caused by Colletotrichum gloeosporioides

Abstract Walnut anthracnose induced by Colletotrichum gloeosporioides is a disastrous disease that severely restricts the development of the walnut industry in China. Long non-coding RNAs (lncRNAs) are involved in adaptive responses to disease, but their roles in the regulation of walnut anthracnose resistance response are not well defined. In this study, transcriptome analysis demonstrated that a C. gloeosporioides-induced lncRNA, lncRNA109897, located upstream from the target gene JrCCR4, upregulated the expression of JrCCR4. JrCCR4 interacted with JrTLP1b and promoted its transcriptional activity. In turn, JrTLP1b induced the transcription of lncRNA109897 to promote its expression. Meanwhile, transient expression in walnut leaves and stable transformation of Arabidopsis thaliana further proved that lncRNA, JrCCR4, and JrTLP1b improve the resistance of C. gloeosporioides. Collectively, these findings provide insights into the mechanism by which the lncRNA109897-JrCCR4-JrTLP1b transcriptional cascade regulates the resistance of walnut to anthracnose.
What Makes NPOs Sustainable in an Emergency? Examining the Effect of Person-Organization Fit and Generation on Volunteer Activities During the COVID-19 Pandemic

Purpose Despite the unprecedented challenges caused by the COVID-19 pandemic, nonprofit organizations (NPOs) continued providing services, thereby contributing to overcoming the pandemic. What enabled NPOs to sustain their service provision during this global emergency? This study attempts to answer this question by focusing on one of the essential pillars supporting the operation of NPOs: volunteers. More specifically, we aim to investigate how person–organization (P–O) fit and generation, particularly the Millennial generation, are related to engagement in voluntary activities during the COVID-19 pandemic. Methods We collected data through an online survey conducted in March 2021. This US national survey was completed by 2307 respondents, yielding the US Census balanced data regarding gender, age, race, education, and income. To analyze the data, we employed the two-stage Heckman selection model. Results Relying on P-O fit theory and generational theory, the study identifies what led existing volunteers to continue engaging in volunteer activities at their NPO during the COVID-19 pandemic despite the risks. We found that P-O fit mattered in volunteers’ decision to continue engaging. In addition, our study uncovered that when existing volunteers were Millennials, the relationship between P-O fit and engagement in voluntary activities strengthened during the pandemic. Conclusion This study contributes to expanding the explanatory power of the P–O fit theory by testing it in an emergency and extends the generational theory by clarifying under what conditions Millennials (aka Generation Me) transform themselves into Generation We. In addition, linking NPO management and emergency management, this study provides NPO managers with practical implications for securing reliable volunteers who will sustain the capacity of the NPO in a crisis.
Parametric bootstrap inferences for the growth curve models with intraclass correlation structure

ABSTRACT This paper presents parametric bootstrap (PB) approaches for hypothesis testing and interval estimation of the fixed effects and the variance component in the growth curve models with intraclass correlation structure. The PB pivot variables are proposed based on the sufficient statistics of the parameters. Some simulation results are presented to compare the performance of the proposed approaches with the generalized inferences. Our studies show that the PB approaches perform satisfactorily for various cell sizes and parameter configurations, and tends to outperform the generalized inferences with respect to the coverage probabilities and powers. The PB approaches not only have almost exact coverage probabilities and Type I error rates, but also have the shorter expected lengths and the higher powers. Furthermore, the PB procedure can be simply carried out by a few simulation steps. Finally, the proposed approaches are illustrated by using a real data example.
Adjustment of Bank Capital Ratios

This study explores the speed of adjustment of the capital ratio, regulatory ratio, and tier-‎I ‎ratio of ‎commercial banks in China by employing the GMM framework ‎from ‎‎2006 to 2020. The empirical ‎analysis reveals that banks adjust their regulatory ratio and tier-I ‎ratio faster than the capital ratio of ‎Chinses commercial banks. The findings report that the pace of ‎regulatory ratio, a tier-I ratio of ‎well-capitalized, highly liquid, and high growth banks are faster than ‎under-capitalized, low liquid ‎and low growth commercial banks in China. In addition, the speed ‎of adjustment of regulatory ‎ratio, the tier-I ratio is faster than capital ratio during the GFC-2008 in ‎China. These ‎findings suggest ‎that the regulators may consider the heterogeneity in the speed of ‎capital adjustment ‎across ‎different bank characteristics to formulate new bank regulations; ‎particularly, when ‎assessing and ‎adjusting the specific capital requirements through Pillar II of the ‎Basel III agreement. ‎
Graphical tests for the absolute stability of multivariable nonlinear systems.

Three graphical methods, similar to Popov's approach, are derived to test a s t a b i l i t y criterion for a general class of multivariable nonlinear feedback systems. Various simplifications of these methods are then derived for four particular classes of system: the series system, the parallel system, the internal feedback system, and the symmetric system. These s t a b i l i t y tests may be applied to active networks that consist of passive linear time-invariant elements and many nonlinear controlled sources. It is further shown that certain special network structures may be represented as one of the four particular classes of system. TABLE OF CONTENTS Page LIST OF ILLUSTRATIONS v ACKNOWLEDGEMENT v i i
E-Government Services Adoption Assessment From the Citizen Perspective in Jordan

Jordanian government has made a commendable effort towards designing and disseminating e-government portals on the web. However, there are certain barriers that have caused a significant deterioration in the e-government development index (EGDI) rank for Jordan in the last few years. The paper aims to understand the influence of the factors relating to e-government adoption to improve upon the trailing factors and move further on the EGDI. This study proposes a research model considering constructs such as relative advantage, perceived trust, computer self-efficacy, perceived awareness, resistance to change on behavioral intention. The data were collected through questionnaire survey. The data were analysed using structural equation modeling (SEM) of AMOS 25.0. The results of the analysis showed that relative advantage, perceived trust, computer self-efficacy and perceived awareness have a positive and significant influence on behavioral intentions whereas resistance to change found to negatively influence the citizens' behavioral intentions to adopt the e-government services.