Miracle-dz/UniFAR-FTUs huggingface dataset:
Extensive fluid–rock interaction and pressure solution in a UHP fluid pathway recorded by garnetite, Lago di Cignana, Western Alps

This study combines the microstructural and petrological analysis of garnet and its inclusions in quartzite‐hosted garnetite from the ultrahigh‐pressure Lago di Cignana metaophiolite (Western Alps). We present a comprehensive record of metamorphism, compaction, and the state of stress during interaction between oceanic metasediments and infiltrating fluids along a major open‐system fluid pathway. The arrangement of garnet into garnetite layers is attributed to concomitant compaction and dissolution that resulted in the formation of a garnet stylolite. The fluid pulses that were accommodated by this pathway subsequently resulted in several stages of garnet deformation, dissolution, local reprecipitation, and recrystallization. Abundant evidence for pressure solution in garnet, alongside near‐hydrostatic to hydrostatic conditions indicated by elastic strains of quartz inclusions in garnet obtained through Raman spectroscopy, support low differential stresses during fluid–rock interaction. The high degree of dissolution recorded by the garnetite showcases the strong potential for compaction by fluid‐assisted volume loss and mass transfer. Dehydration of subducting rocks with subsequent migration of resulting fluids through the slab provides a mechanism of mass transfer and has the potential to drive compaction and exert control on differential stress along fluid pathways.
Review on Government Budget and Some Recommendations - Situation of Vietnam

The rate of government budget revenue has tended to decrease in recent years because of the policies of tax exemption, relaxation and reduction after 2012 and the annual inflation rate was lower in the 2012 period compared to the 2008 period - 2011 caused the revenue growth rate of some taxes dependent on inflation to decrease. Discipline of government budget expenditure still had some limitations when, for many consecutive years, government budget expenditures were actually higher than estimated. Government budget deficit had been high for many years and was higher than the original estimate, leading to a tendency to increase public debt per GDP and peaked in 2016 at 63.7% of GDP. Government budget revenue in the first 9 months of 2020 decreased by 11.5% compared to the same period in 2019, reaching 975.3 trillion VND, as 64.5% of the estimation. In which, the central budget was estimated at 60.4% of the estimation, the local budget was estimated at 69.8% of the estimate. Domestic revenue in 9 months was at the lowest level compared to the same period in some recent years. This paper gave an assessment of the current government budget revenue, expenditure, overspending situation and public debt and from there on, some recommendations for Vietnam were proposed.
Analysis of the Spatial Properties of Correlated Photon in Collinear Phase-Matching

In this paper, the spatial properties of correlated photon in collinear phase-matching in the process of spontaneous parametric down conversion (SPDC) are researched. Based on the study of the phase-matching angle, non-collinear angle, and correlated photon wavelength, a theoretical model of non-collinear angular variation is derived, which can be used to estimate and predict the width of the correlated photon ring. The experimental measurement is carried out with CMOS camera, and the measurement results are consistent with the theoretical simulation results, which verifies the rationality of theoretical reasoning. Meanwhile, the change of the correlated photon divergence angle outside the crystal is studied, the closer the wavelength is to the degenerate, the smaller the measurement value of the divergence angle, which is agreement with the theoretical simulation. The results of the study play a reference role in the evaluation of the spatial properties of correlated photon and lay a foundation for the measurement of the correlated photon number rate and the calibration of a photodetector.
Fast Monte Carlo-Based Inverse Planning for Prostate Brachytherapy by Using Deep Learning

Inverse planning is an essential tool for optimizing the delivered radiation dose on low-dose-rate (LDR) prostate brachytherapy. Clinical inverse planning systems use the TG-43 dose computation formalism in order to perform a fast optimization. However, this method is an approximation that often leads to a dose overestimation, resulting on suboptimal plans. Alternatively, Monte Carlo simulation (MCS) can be used to obtain an accurate dose distribution, but considerably increasing the estimation time. We propose a fast inverse planning method for LDR prostate brachytherapy that uses a deep convolutional neural network (DCNN) trained on a graphics processing unit (GPU)-based MCS generated database to estimate the dose distribution on the prostate and organs at risk. Segmentations of the organs and seeds’ positions are given as the DCNN input. The mean percent error on the test set was −1.19±0.94% within the prostate. The DCNN was used to estimate the dosimetric parameters in each organ for every configuration of the optimization loop. The dosimetric parameters of the final DCNN-based brachytherapy plans were in good agreement compared to the same plans recalculated with a full MCS. The proposed inverse planning based on DCNN was capable to reach an equivalent level of accuracy with Monte Carlo with a runtime in less than 1 min using conventional GPU card.
Do ministers’ occupational and social class backgrounds influence social spending?

ABSTRACT Recent studies have reported a relationship between legislators’ occupational and social class backgrounds and their economic policy preferences, but these analyses have largely been confined to legislative actors in presidential systems (e.g., Carnes 2012, 2013; Carnes and Lupu 2015; but see O’Grady 2018). This paper extends this research to the study of government executives in parliamentary systems. It uses a new dataset of the occupational backgrounds of cabinet ministers in Canadian (Westminster-style) provincial governments to study if the professional backgrounds of these powerful government actors influence social spending. Examining the expected spending preferences of cabinets based on ministers’ occupations prior to entering politics, this paper shows that ministers’ occupational backgrounds are associated with spending on social services, most notably within center parties.
Observer-based control for a class of hybrid linear and nonlinear systems

An approach to output feedback control for hybrid discrete-time systems subject to uncertain mode transitions is proposed. The system dynamics may assume different modes upon the occurrence of a switching that is not directly measurable. Since the current system mode is unknown, a regulation scheme is proposed by combining a Luenberger observer to estimate the continuous state, a mode estimator, and a controller fed with the estimates of both continuous state variables and mode. The closed-loop stability is ensured under suitable conditions given in terms of linear matrix inequalities. Since complexity and conservativeness grow with the increase of the modes, we address the problem of reducing the number of linear matrix inequalities by providing more easily tractable stability conditions. Such conditions are extended to deal with systems having also Lipschitz nonlinearities and affected by disturbances. The effectiveness of the proposed approach is shown by means of simulations.
I-Talk: Reliable and Practical Superimposed Signal Decoding Without Power Control

Internet-of-Things (IoT) is emerging, while the spectrum is at a premium. To enhance spectrum efficiency, a promising solution is Non-Orthogonal Multiple Access (NOMA) that enables users to communicate with the same resource at the same time, while decoding the superimposed signal at the receiver. Existing NOMA technologies, however, rely on strict power control to decode the superimposed signal, infeasible for heterogeneous and low-cost IoT devices. In contrast, we propose I-Talk, a new NOMA scheme that is designed for IoT and can decode the superimposed signals from two transmitters without power control. Importantly, considering the IoT systems in the wild, both the hardware imperfections and mobility are unavoidable, which can cause severe signal variations, resulting in an unreliable decoding performance. To solve this problem, we design a synthesis channel coefficient to track all signal offsets caused by the hardware imperfection. Meanwhile, we propose a diversity transmission and smart combining scheme to achieve high reliable decoding performance. To demonstrate the feasibility of this new NOMA approach in practical systems, we implement I-Talk with USRP devices and the experimental results illustrate that I-Talk achieves a one-order lower bit-error-rate and a $1.47\times $ higher throughput gain than the state-of-the-art superimposed signal decoding scheme.
Research on Public Opinion and Early Warning Analysis Model of Network Emergencies Based on Decision Tree

Taking the existing network public opinion cases as the research object, the paper analyzes key factors affecting the network public opinions by the machine learning method and the big data technology statistical technology, and builds the early warning system of public opinions based on this. The decision tree model is also built to realize analysis and early warning, and finally the “Qingdao Sky-price Prawns” incident is cited to illustrate the feasibility of the early warning by classification proposed in this paper.
A Privacy-Preserving Optimization of Neighborhood-Based Recommendation for Medical-Aided Diagnosis and Treatment

Nowadays, patients’ physiological data and medical records are outsourced to the medical cloud to help the patients and doctors to obtain valuable and reliable services, i.e., diagnosis decision and treatment recommendation, which provides subtle and precise guidance for medical cases. Neighborhood-based recommendation (NBR) provides an effective and vivid approach to finding like-minded nodes to generate recommendations, automating what is usually known as word of mouth. Unfortunately, both diverse medical data and outsource technology raise stringent privacy concerns since the patient data is generally associated with attribute sets which contain somewhat sensitive information, such as age, gender, epidemic, physiological data and clinic result etc. The great privacy-revealing risk will occur if this sensitive medical information is maliciously exploited by network eavesdroppers, untrusted cloud servers or doctors. Considering the above privacy-revealing issues in medical data, this article proposes a privacy-preserving optimization of neighborhood-based recommendation scheme (namely, PPO-NBR), which achieves a secure and privacy-preserving recommendation for medical-aided diagnosis and treatment without revealing the patients’ sensitive information. We propose a privacy-preserving medical graph construction protocol and a medical treatment recommendation protocol to implement the secure recommendation on the encrypted medical data, in which it employs BGN Cryptosystem as the building modular to encrypt data and then creatively applies graph theory to expand neighbors to search the best similar features, and uses BLS signature to ensure the authentication, and meanwhile, it deploys a ( $t,n$ ) oblivious transfer protocol to preserve the confidentiality of recommendation. A couple of experiments are implemented to evaluate the performance in terms of computational costs and communication overheads and indicate that our scheme is efficient and practical for medical-aided diagnosis.
Analysis of seasonal variability of wave height in Makassar Strait

This study was conducted to examine spatially the seasonal variability of sea wave characteristics in the Makassar Strait waters by utilizing the third generation wave model, MIKE 21 Spectral Wave Model. The study was conducted by observing seasonal variability of sea wave height in Makassar Strait waters for 10 years. The data used are wind data predicted by ECMWF every 6 hours during the period 2006 - 2015 as the main input of wave energy generation. The domain model is adjusted to the wind configuration in a uniform grid size of 5’ x 5’ which is considered to be sufficient to represent the bathymetry of the Makassar Strait waters with location limits of 1,00 0LU - 3,98 0LS and 116,15 0BT - 120,30 0BT and 15 stations. Based on the results of the study, showed that high waves occur in the west season which tends to follow the monsoonal pattern according to the local climate type. The maximum significant wave height tends to occur in the western, central and southern waters of the Makassar Strait with an average observed in the middle of the year in the June, July and August periods (east monsoon). For the study of coastal waters, maximum wave height fluctuations tend to be uniform.
Principle of Computation Power Optimization in Millimeter Wave Massive MIMO Systems

The computation power of baseband units (BBUs) is a major source of power consumption in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with a large number of users due to complex signal processing. The effective reduction of computation power is critical for improving system energy efficiency. In this paper, the principle of reducing the computation power of BBUs is first investigated in mmWave massive MIMO systems with a hybrid precoding structure. A recursive constraint in decomposing the baseband precoding matrix is derived for reducing the computation power of hybrid precoding systems. Furthermore, the optimal number of sub-matrices minimizing the maximum error in decomposing the baseband precoding matrix is obtained. Based on the proposed principle, consisting of the recursive constraint and the optimal number of sub-matrices, a fast Monte Carlo baseband precoding (FMCBP) algorithm is developed to reduce the computation power of BBUs and improve system energy efficiency. Simulation results show that the total transmission rate and energy efficiency of mmWave systems are coupled with the computation power of BBUs, based on the FMCBP algorithm. Moreover, the FMCBP algorithm maximally improves the energy efficiency of multi-user mmWave massive MIMO communication systems by 124 percent, compared with the conventional equivalent zero-forcing algorithm.
Contribution of Family, Behavioral, and Neuropsychological Factors to Long-Term Functional Outcomes in Young Adults with ADHD: A 12-Year Follow-Up Study

The persistent nature of attention deficit hyperactivity disorder (ADHD) and the role of behavioral factors in its continuity have been widely documented in the literature. However, less is known about the role of early family and neuropsychological factors in predicting later adaptive functioning and quality of life in individuals with ADHD. This study aimed to analyze the contribution of early family, behavioral, and neuropsychological factors to long-term functional outcomes in young adults with ADHD. Family (parental mood disorders, family risk index, parenting stress, coherence), behavioral (oppositionism, inattention, emotional lability, hyperactivity), and cognitive factors were examined at baseline. Twelve years later, daily life functioning, quality of life, and executive and behavioral functioning were also evaluated in 61 young adults with a childhood diagnosis of ADHD. Results revealed significant correlations between most of the family and behavioral factors at baseline and the long-term functional outcomes. Specifically, multiple regressions showed that mothers’ depression and laxness predicted later quality of life, executive functioning, and the hyperactivity index, and mothers’ depression and parenting stress at baseline also significantly predicted the hyperactivity index. Oppositionism behavior and inhibition were also significant predictors of the hyperactivity index. Therefore, understanding the family, behavioral, and neuropsychological factors that can contribute to later quality of life, daily functioning, and behavioral and executive functions is a particularly critical issue in detecting and planning efficacious ADHD interventions.
Exploiting the Low Doppler Tolerance of Noise Radar to Perform Precise Velocity Measurements on a Short Set of Data

The extraction of velocity information from radar data by means of the Doppler effect is the driving factor for the investigations presented in this paper. A method for the quantification of the Doppler tolerance in continuous emission (CE) noise radar is introduced, addressing a current lack in literature within the frame of CE noise radars. It is shown that noise radar is highly sensitive to the Doppler effect, an issue that often results in a low Doppler tolerance especially for long coherent integration intervals. In general, the Doppler sensitivity is considered as a drawback but, in this paper, along with the absence of range-Doppler coupling in noise radar, it is turned into an advantage allowing for a very precise Doppler estimation. This new signal processing approach for Doppler extraction is detailed and its feasibility is proven on the basis of experimental data. The presented method requires much less data, i.e., target illumination time, than conventional Doppler analyses and, therefore, is beneficial in terms of radar resource management.
Physics demonstrations: who are the students appreciating them?

ABSTRACT Despite the contemporary emphasis on students’ practical work, lecture demonstrations with their unique power to stimulate students’ interest remain an inseparable part of physics education. At our faculty, performing physics demonstrations for upper secondary students (DEMOs) has a tradition lasting three decades. In our study, we sought to identify factors predetermining students’ positive acceptance of these demonstrations. With a research sample of almost 5000 students, we used the modified Intrinsic Motivation Inventory questionnaire to investigate their intrinsic motivation towards and perception of DEMOs. Subsequently, we examined this data against respondents’ gender, marks in physics, intention to study it at university, and declared attitudes towards physics as a school subject. We found out the acceptance of DEMOs is independent of gender and students’ interest in physics as a school subject. On the other hand, better marks in physics and stronger intention to study it were associated with a more positive assessment of the demonstrations. As the strongest predictors for positive acceptance of DEMOs, giftedness, and diligence in physics (as expressed by respondents themselves) were identified. Cluster analysis uncovered four groups of students, one of which especially seems to be interesting regarding the possibility of their future study of physics – it groups students who do not plan to study physics at this moment but achieve good results in it and have a relatively high intention to study some other STEM discipline. Because they showed a largely positive perception of DEMOs, we discuss the possibilities to use lecture demonstrations to stimulate their interest.
COVID-19 PANDEMİSİNDE GÖREV ALAN SAĞLIK ÇALIŞANLARININ ALGILADIKLARI SOSYAL DESTEĞİN DEPRESYON, ANKSİYETE VE STRES DÜZEYLERINE ETKİSİ

BULGULAR: 197 katılımcının; %58,4’ü doktor, %41,6’sı hemşire/ sağlık memuru, %65’i kadın, %50’si evli ve %40,4’ü COVID-19 servisinde, %24,1’i acil serviste, %22,3’ü COVID-19 yoğun bakım servisinde %11,2’si COVID-19 polikliniğinde çalışıyordu. Orta ve ileri düzeyde depresif belirti gösteren sağlık çalışanı oranı %53,3, anksiyete belirtisi gösterenler %53,8 ve stres belirtileri gösterenlerin oranı ise %48,7’ ydi. Hemşirelerin doktorlardan istatistiksel olarak anlamlı düzeyde yüksek depresyon (p=0,041), anksiyete (p=0,013) ve stres puanlarına sahip oldukları (p=0,009), kadınların erkeklerden DASS-42 tüm alt ölçeklerinde istatistiksel olarak daha yüksek puan aldıkları saptandı. Haftalık çalışma süresi arttıkça depresyon puanlarında artış olduğu (p=0,012), haftada 24 saat üzerinde çalışanlarda anksiyete düzeyleri yüksek bulundu (p<0,05). ÇBASDÖ-aile alt boyutunun DASS-42 depresyon alt ölçeği ile orta derecede kuvvetli, negatif yönde korele (p=0,001, r=-0,439) ve DASS-42 total skorunun ÇBASDÖ-total skoru ile negatif yönlü korele olduğu gözlendi (p<0,001, r=-0,350).
Customização da Regulação Emocional de acordo com a Personalidade dos Estudantes em Sistemas Tutores Inteligentes

As emoções influenciam os processos cognitivos e são essenciais durante o aprendizado. Algumas emoções podem afetar negativamente o aprendizado, como a frustração e o tédio; outras, como o engajamento, podem ter um efeito positivo. Por outro lado, algumas emoções, como a confusão, podem ter um papel duplo, influenciando negativamente ou positivamente. A confusão ocorre quando há discrepâncias entre a informação recebida e as estruturas mentais do indivíduo ou quando há inconsistências no fluxo de informação. Quando experimentam confusão, os estudantes podem se sentir incentivados a buscar seu próprio conhecimento, através do foco e atenção na atividade, resultando em um impacto positivo no aprendizado. No entanto, quando sentem a confusão por um longo período e não conseguem resolvê-la, a confusão pode gerar uma sobrecarga cognitiva nos estudantes e levar a emoções mais negativas à aprendizagem, tais como frustração e tédio. Além disso, o tempo que os estudantes conseguem lidar com a confusão é influenciado por seu traço de personalidade e conhecimentos prévios sobre o assunto. Portanto, a confusão deve ser regulada para maximizar o aprendizado, promovendo um maior envolvimento, e evitar o abandono do exercício ou conteúdo. O problema investigado neste trabalho é como e quando um sistema inteligente de tutoria poderia apoiar a regulação da confusão quando sentida por um longo período por um estudante. Para isso, foi desenvolvido um algoritmo com o objetivo de apoiar na escolha de elementos multimídia (por exemplo, vídeo, figura ou texto) para regular a confusão. A escolha dos elementos considera o conhecimento prévio do estudante e são apresentados de acordo com seu nível de detalhamento (por exemplo, de elementos com poucos detalhes de resolução a elementos apresentados com muitos detalhes). Além disso, quanto tempo o algoritmo espera para regular a confusão do estudante considera a sua personalidade. Para avaliar o algoritmo desenvolvido, foi realizado um experimento com estudantes (N = 111) do ensino fundamental e superior de duas escolas e uma faculdade, durante três meses. Foram analisadas a capacidade do algoritmo de influenciar na regulação da confusão durante a resolução de equações de primeiro grau em um Sistema Tutor Inteligente (PAT2Math), em indivíduos com personalidades de extroversão e neuroticismo. Os resultados mostram que os estudantes que usaram o PAT2Math com o algoritmo desenvolvido de regulação de confusão (grupo experimental) cometeram menos erros e resolveram os exercícios mais rapidamente do que os estudantes que não receberam assistência com relação à regulação de confusão. Os resultados indicam que o sistema estaria regulando a confusão ou emoção negativa decorrente da confusão.
The Role of the Management Accounting System and Decision-Making Style on Managerial performance

This study aimed to provide empirical evidence on the relationship between the management accounting system (MAS) and decision-making style on cooperatives' managerial performance in Ponorogo. Data were collected from 119 questionnaires, while 60 questionnaires could be analyzed. Then, SPSS was applied to test non-response bias and descriptive statistics. SmartPLS was used for hypothesis testing. Research findings indicated that MAS did not affect managerial performance, and the decision-making style affected managerial performance. DOI: https://doi.org/10.26905/jkdp.v25i1. 5330