Miracle-dz/UniFAR-FTUs huggingface dataset:
Plasma-enhanced atomic layer deposition of nickel and nickel oxide on silicon for photoelectrochemical applications

The production of hydrogen fuel through sunlight-driven water splitting has the potential to harness and store large quantities of solar energy in a clean and scalable chemical state, suitable for later use in a range of energy applications. Silicon (Si) possesses many of the required properties to be used effectively as a photoelectrochemical (PEC) water-splitting photoanode. However, its sensitivity to corrosion during the oxygen evolution reaction limits its performance in photoanode applications, thus requiring additional overlayer materials to protect the underlying Si substrate. Nickel oxide (NiO) is one material that acts as an effective protective layer, being transparent, suitably conductive and stable. In this work, we present NiO deposition via state-of-the-art atomic layer deposition and photoemission studies to grow and characterize NiO and Ni–metal protective films. Early-stage nucleation of deposited thin films is illustrated along with the effects of post-deposition annealing and argon milling on depth profile information. Previous reports on the effects of slow argon milling are explored and counter arguments are proposed. Protective films are subjected to PEC testing, which shows enhancement of stability and photocurrent output as a result of the deposited films and plasma annealing on these thin films.
Time-varying rational expectations models: Solutions, stability, numerical implementation

While rational expectations models with time{varying (random) coefficients have gained some esteem, the understanding of their dy- namic properties is still in its infancy. The paper adapts results from the theory of random dynamical systems to solve and analyze the stability of rational expectations models with time{varying (random) coefficients. This theory develops a \linear algebra" in terms of Lya- punov exponents defined as the asymptotic growth rates of trajecto- ries. They replace the eigenvalue analysis used in constant coefficient models and allow the construction of solutions in the spirit of Blan- chard and Kahn (1980). The usefulness of these methods and their numerical implementation is illustrated using a canonical New Key- nesian model with a time{varying policy rule.
Security issues of quantum cryptographic systems with imperfect detectors

Security Issues of Quantum Cryptographic Systems with Imperfect Detectors Viacheslav Burenkov Doctor of Philosophy Graduate Department of Physics University of Toronto 2015 The laws of quantum physics can be used to secure communications between two distant parties in a scheme called quantum key distribution (QKD), even against a technologically unlimited eavesdropper. While the theoretical security of QKD has been proved rigorously, current implementations of QKD are generally insecure. In particular, mathematical models of devices, such as detectors, do not accurately describe their reallife behaviour. Such seemingly insignificant discrepancies can compromise the security of the entire scheme, especially as novel detector technologies are being developed with little regard for potential vulnerabilities. In this thesis, we study how detector imperfections can impact the security of QKD and how to overcome such technological limitations. We first analyze the security of a high-speed QKD system with finite detector dead time τ . We show that the previously reported sifting approaches are not guaranteed to be secure in this regime. More specifically, Eve can induce a basis-dependent detection efficiency at the receiver’s end. Modified key sifting schemes that are basis-independent, and thus secure in the presence of dead time and an active eavesdropper, are discussed and compared. It is shown that the maximum key generation rate is 1/(2τ) for passive basis selection, and 1/τ for active basis selection. The security analysis is also extended to the decoy state BB84 protocol. We then study a relatively new type of single-photon detector called the superconducting nanowire single-photon detector (SNSPD), and discover some unexpected behaviour. We report an afterpulsing effect present when the SNSPD is operated in the high bias current regime. In our standard set-up, the afterpulsing is most likely to occur at around
The association between statin administration and renal cell carcinoma; a systematic review and meta-analysis

Introduction: Renal cell carcinoma (RCC) is the most prevalent renal cancer in adults, with a rising global incidence. There has long been an ambiguity about the effect of statin administration on the incidence of RCC. The present meta-analysis aims to evaluate the relationship between statin usage and RCC. Materials and Methods: Cochrane, Web of Science, Scopus, and PubMed databases, as well as the Google Scholar search engine, were queried for relevant articles. The data were statistically analyzed by STATA 14 software. The significance level of the tests was considered P<0.05. Results: In 15 reviewed articles, 46 735 subjects used statin, and 673 752 did not. The odds ratio (OR) between statin usage and the risk incidence of RCC was 0.86 (OR: 0.86; 95% CI: 0.63, 1.17) overall and estimated as 0.94 (OR: 0.94; 95% CI: 0.69, 1.28) in males and 0.92 (OR: 0.92; 95% CI: 0.66, 1.28) in females. The odds ratio of statin administration and the incidence risk of RCC was 0.74 (OR: 0.74; 95% CI: 0.37, 1.49) in case-control and 0.96 (OR: 0.96; 95% CI: 0.79, 1.17) in cohort studies. In addition, the impact of statin usage on overall survival (OS) in RCC was 0.65 (HR: 0.65; 95% CI: 0.53, 0.80), and this relationship was statistically significant. However, the effect of statin usage was 0.68 (HR: 0.68; 95% CI: 0.45, 1.02) on progression-free survival (PFS) and 1.24 (OR: 1.24; 95% CI: 0.66, 2.32) on disease progression (DP), and these relationships were statistically non-significant. Conclusion: The OS of RCC patients was 35% higher in statin users than in non-users. However, no relationship between statin usage and the incidence risk of RCC was found. Meta-analysis Registration: This study has been compiled based on the PRISMA checklist, and its protocol was registered on the PROSPERO website (CRD42023393647).
Indonesian Stock Market Reaction: Effects of Uncertainty Policy Shocks in the United States and China

The purpose of this study is to examine and analyze the effect of economic policy uncertainty US and China on the Indonesian stock market. The data uses time series, from January 2000-July 2022. The methods used are the Structural Vector Error Correction Model (SVECM). The results show that the uncertainty of the US and China's economic policies has a negative and significant effect on the Indonesian stock market. The response of Indonesian stock market responded negatively to the economic policy uncertainties of the US and China. The results of the study show that the uncertainty of the US and China's economic policies has a negative and significant effect on the Indonesian stock market. The response of Indonesian stock market responded negatively to the economic policy uncertainties of the United States and China. The results of the study show that the uncertainty of the US and China's economic policies has a negative and significant effect on the Indonesian stock market. Indonesian stock market responded negatively to the economic policy uncertainties of the United States and China.
Arc protein, a remnant of ancient retrovirus, forms virus-like particles, which are abundantly generated by neurons during epileptic seizures, and affects epileptic susceptibility in rodent models

A product of the immediate early gene Arc (Activity-regulated cytoskeleton-associated protein or Arc protein) of retroviral ancestry resides in the genome of all tetrapods for millions of years and is expressed endogenously in neurons. It is a well-known protein, very important for synaptic plasticity and memory consolidation. Activity-dependent Arc expression concentrated in glutamatergic synapses affects the long-time synaptic strength of those excitatory synapses. Because it modulates excitatory-inhibitory balance in a neuronal network, the Arc gene itself was found to be related to the pathogenesis of epilepsy. General Arc knockout rodent models develop a susceptibility to epileptic seizures. Because of activity dependence, synaptic Arc protein synthesis also is affected by seizures. Interestingly, it was found that Arc protein in synapses of active neurons self-assemble in capsids of retrovirus-like particles, which can transfer genetic information between neurons, at least across neuronal synaptic boutons. Released Arc particles can be accumulated in astrocytes after seizures. It is still not known how capsid assembling and transmission timescale is affected by seizures. This scientific field is relatively novel and is experiencing swift transformation as it grapples with difficult concepts in light of evolving experimental findings. We summarize the emergent literature on the subject and also discuss the specific rodent models for studying Arc effects in epilepsy. We summarized both to clarify the possible role of Arc-related pseudo-viral particles in epileptic disorders, which may be helpful to researchers interested in this growing area of investigation.
Antimicrobial activity evaluation of black currant (Ribes nigrum L.) variety Čačanska crna juice and extract

Aim. This study aimed to evaluate and quantify the antimicrobial activity of lyophilized fruit juice (BCLJ) and waste extract (BCLW) obtained from the black currant (Ribes nigrum L.) variety Čačanska crna. Materials and method. The study was conducted using four Gram (+) (Bacillus cereus, Listeria monocytogenes, Staphylococcus aureus, Enterococcus faecalis) and five Gram (-) bacteria (Escherichia coli, Pseudomonas aeruginosa, Salmonella enteritidis, Proteus mirabilis, Enterobacter aerogenes) as well as one yeast (Candida albicans). Cyanidin-3-O-glucoside, delphinidin-3-O-rutinoside and delphinidin-3-Oglucoside present in black currant were used as standards, so the second aim was to determine their influence on the total antimicrobial activity. Results. The tested samples showed moderate antimicrobial activity. The inhibitory effect of BCLJ was shown on all Gram (+) bacteria (B. cereus, E. faecalis, S. aureus), apart from L. monocytogenes, for which the extracts were not effective. It was noted that BCLJ did not suppress the growth of Gram (-) bacteria. Black currant waste extract on the other hand was shown to be efficient on Gram (+) as well as on Gram (-) bacteria. The results of minimum inhibitory concentrations MIC (MFC) of BCLJ and BCLW were 100 mg/mL and MBC was higher than 100 mg. The MIC/MBC (MFC) of standards were 0.13 - 0.5 mg/mL. Conclusion. Results indicate that these black currant lyophilizates might be potentially used as antimicrobial agents.
Multiwavelength study of the flaring activity of the supermassive black hole Sgr A* at the center of the Milky Way

Sgr A*, le trou noir supermassif le plus proche de nous, emet une luminosite quiescente tres faible ainsi que des eruptions en infrarouge proche (NIR), rayons X et radio. Cette these a pour but d'etudier l'effet du passage de DSO/G2 pres de Sgr A* sur les eruptions. J'ai utilise et ameliore trois methodes pour l'etude en rayons X : les blocs Bayesiens en deux passes pour detecter les eruptions avec une certaine probabilite, le lissage des courbes de lumieres pour diminuer le bruit de Poisson et la methode de Monte Carlo par chaines de Markov pour l'ajustement des spectres des eruptions. J'ai contraint les parametres physiques de la source pour une des 3 eruptions detectees en rayons X en 2011 et pour 3 eruptions detectees en rayons X et NIR durant la campagne multi-longueurs d'onde de fevrier-avril 2014. L'activite en rayons X et NIR de fevrier-avril 2014 correspond a celle observee avant le passage de DSO/G2 pres de Sgr A*. J'ai calcule le taux d'eruption intrinseque en rayons X de Sgr A* en 1999-2015 et detecte une plus faible activite a partir du 28 octobre 2013. L'energie stockee pendant cette periode peut expliquer la plus forte activite observee du 30 aout au 9 septembre 2014.
Bayesian Regression Trees for High-Dimensional Prediction and Variable Selection

ABSTRACT Decision tree ensembles are an extremely popular tool for obtaining high-quality predictions in nonparametric regression problems. Unmodified, however, many commonly used decision tree ensemble methods do not adapt to sparsity in the regime in which the number of predictors is larger than the number of observations. A recent stream of research concerns the construction of decision tree ensembles that are motivated by a generative probabilistic model, the most influential method being the Bayesian additive regression trees (BART) framework. In this article, we take a Bayesian point of view on this problem and show how to construct priors on decision tree ensembles that are capable of adapting to sparsity in the predictors by placing a sparsity-inducing Dirichlet hyperprior on the splitting proportions of the regression tree prior. We characterize the asymptotic distribution of the number of predictors included in the model and show how this prior can be easily incorporated into existing Markov chain Monte Carlo schemes. We demonstrate that our approach yields useful posterior inclusion probabilities for each predictor and illustrate the usefulness of our approach relative to other decision tree ensemble approaches on both simulated and real datasets. Supplementary materials for this article are available online.
Quantitative Simultaneous Tc/ I SPECT: Design Study and Validation With Monte Carlo Simulations and Physical Acquisitions

Simultaneous dual isotope imaging ( Tc/ I) has potential clinical applications but has not been implemented in the clinic. The aim of this work was to design an artificial neural network (ANN) for crosstalk and scatter correction using a smaller number of energy windows (8) than we had previously proposed (26) to allow implementation on some clinical cam- eras, and to validate our approach using realistic Monte Carlo simulations and anthropomorphic brain phantom acquisitions. Monte Carlo simulations of dual isotope SPECT studies of a digital brain phantom and physical acquisitions of the striatal brain phantom were used to validate our approach. Corrected projections were reconstructed using an iterative ordered subsets expectation maximization (OSEM) algorithm that modeled nonuniform attenuation and variable collimator response in the projector/backprojector. Results: In Monte Carlo simulations, ANN26 and ANN8 yielded similarly accurate quantitation of I activity (bias 7%) in all brain structures. An asymmetric windowing method (AW) yielded accurate estimation in the striata (bias 7%) but not in other brain structures. The estimation bias of Tc primary activity was 10% in all brain structures with ANN26 and ANN8. This bias was greater than 25% in all brain structures with AW. In physical acquisitions, ANN26 and ANN8 yielded accurate estimation of I activity in striata of both normal and reduced activity concentration (bias 7%). Bias was significantly higher with AW when estimating activity in the "pathologic" striata (5%) than in the normal one (9%). The bias of the Tc activity estimate in the brain was less than 6% with ANN26 and ANN8 but greater than 20% with AW. ANN8, which can be implemented more easily in the clinic than ANN26, yielded estimation bias and precision comparable to those of ANN26 in Monte Carlo simulations and physical acquisitions. Index Terms—Artificial neural networks, attenuation correc- tion, brain single photon emission compted tomography (SPECT), crosstalk correlation, dual isotope imaging, iterative reconstruc- tion, ordered subsets expectation maximization (OSEM), scatter correction.
The Role of School Principal Leadership Style, Work Environment and Family Support on PAUD Teacher Performance

Teacher performance is an important part of achieving the goals of an educational institution. The good or bad of an educational institution or school can also be seen from the level of teacher performance. To avoid the bad side of teacher performance, a comfortable work environment, the right leadership style and support from the family are needed so that performance can be more optimal. This research intends to analyze the performance of teachers in PAUD in Cengkareng District, West Jakarta, which is affected by the work environment, leadership style and family support. Questionnaire data as primary data and school IKP and EKI documents as secondary data. There were 22 respondents in this research who were sampled. Multiple linear regression analysis as data analysis and t-test as a hypothesis test were all processed through SPSS version 26. The results of this research describe that performance is not partially affected by the work environment. Meanwhile performance is significantly affected positively by leadership style. Furthermore, performance is significantly positively affected by family support. Other test results, performance is significantly positively affected by the work environment, leadership style and family support simultaneously.
Neural Network-based Finite-time Control of Nonlinear Systems with Unknown Dead-zones: Application to Quadrotors

Over the years, researchers have addressed several control problems of various classes of nonlinear systems. This article considers a class of uncertain strict feedback nonlinear system with unknown external disturbances and asymmetric input dead-zone. Designing a tracking controller for such system is very complex and challenging. This article aims to design a finite-time adaptive neural network backstepping tracking control for the nonlinear system under consideration. In addition, all unknown disturbances and nonlinear functions are lumped together and approximated by radial basis function neural network (RBFNN). Moreover, no prior information about the boundedness of the dead-zone parameters is required in the controller design. With the aid of a Lyapunov candidate function, it has been shown that the tracking errors converge near the origin in finite-time. Simulation results testify that the proposed control approach can force the output to follow the reference trajectory in a short time despite the presence of asymmetric input dead-zone and external disturbances. At last, in order to highlight the effectiveness of the proposed control method, it is applied to a quadrotor unmanned aerial vehicle (UAV).
Maximizing Social Welfare in Score-Based Social Distance Games

Social distance games have been extensively studied as a coalition formation model where the utilities of agents in each coalition were captured using a utility function $u$ that took into account distances in a given social network. In this paper, we consider a non-normalized score-based definition of social distance games where the utility function $u^s$ depends on a generic scoring vector $s$, which may be customized to match the specifics of each individual application scenario. As our main technical contribution, we establish the tractability of computing a welfare-maximizing partitioning of the agents into coalitions on tree-like networks, for every score-based function $u^s$. We provide more efficient algorithms when dealing with specific choices of $u^s$ or simpler networks, and also extend all of these results to computing coalitions that are Nash stable or individually rational. We view these results as a further strong indication of the usefulness of the proposed score-based utility function: even on very simple networks, the problem of computing a welfare-maximizing partitioning into coalitions remains open for the originally considered canonical function $u$.
Specific challenges posed by artificial intelligence in research ethics

Background The twenty first century is often defined as the era of Artificial Intelligence (AI), which raises many questions regarding its impact on society. It is already significantly changing many practices in different fields. Research ethics (RE) is no exception. Many challenges, including responsibility, privacy, and transparency, are encountered. Research ethics boards (REB) have been established to ensure that ethical practices are adequately followed during research projects. This scoping review aims to bring out the challenges of AI in research ethics and to investigate if REBs are equipped to evaluate them. Methods Three electronic databases were selected to collect peer-reviewed articles that fit the inclusion criteria (English or French, published between 2016 and 2021, containing AI, RE, and REB). Two instigators independently reviewed each piece by screening with Covidence and then coding with NVivo. Results From having a total of 657 articles to review, we were left with a final sample of 28 relevant papers for our scoping review. The selected literature described AI in research ethics (i.e., views on current guidelines, key ethical concept and approaches, key issues of the current state of AI-specific RE guidelines) and REBs regarding AI (i.e., their roles, scope and approaches, key practices and processes, limitations and challenges, stakeholder perceptions). However, the literature often described REBs ethical assessment practices of projects in AI research as lacking knowledge and tools. Conclusion Ethical reflections are taking a step forward while normative guidelines adaptation to AI's reality is still dawdling. This impacts REBs and most stakeholders involved with AI. Indeed, REBs are not equipped enough to adequately evaluate AI research ethics and require standard guidelines to help them do so.
Genomic Characterization and Functional Description of Beauveria bassiana Isolates from Latin America

Beauveria bassiana is an entomopathogenic fungus used in agriculture as a biological controller worldwide. Despite being a well-studied organism, there are no genomic studies of B. bassiana isolates from Central American and Caribbean countries. This work characterized the functional potential of eight Neotropical isolates and provided an overview of their genomic characteristics, targeting genes associated with pathogenicity, the production of secondary metabolites, and the identification of CAZYmes as tools for future biotechnological applications. In addition, a comparison between these isolates and reference genomes was performed. Differences were observed according to geographical location and the lineages of the B. bassiana complex to which each isolate belonged.
A Fused Speech Enhancement Framework for Robust Speaker Verification

Robust speaker verification (RSV) under noisy conditions is still a challenging task. Recently, some task-specific speech enhancement (SE) approaches are proposed and achieve excellent performance on RSV. However, all these works adopt only one kind of SE network and thus can not remove noise from different aspects, limiting the performance of the RSV task. In this letter, we propose a fused SE framework (FSEF) for RSV, which integrates both T-F masking-based and feature mapping-based SE networks to collect complementary information and improve the robustness against noise. Two FESF-RSV systems are constructed based on two kinds of fusion methods: score fusion and feature fusion. In addition, we present a Multi-Scale Attentive Context Aggregation Network (MSACAN) as the backbone structure in the FSEF. The MSACAN can not only extract and fuse multi-scale features adaptively but also enhance speaker characteristics against noise and interfering speakers. Experiments conducted on the noise-simulated VoxCeleb1 dataset demonstrate both the FSEF and the MSACAN can improve the performance of RSV compared to previous approaches.