Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Accuracy of Probabilistic Electricity Price Forecasting: A Comparative Study of Novel Quantile Regression Averaging Generalization

This paper presents a comparative study of two novel approaches for probabilistic electricity price forecasting, namely Smoothing Quantile Regression Averaging (SQRA) and LASSO Quantile Regression Averaging (LQRA). Both methods are generalizations of the well-established Quantile Regression Averaging (QRA) model. In this study, a novel LASSO Quantile Regression Machine (LQRM) is introduced, and the Bayesian Information Criterion (BIC) is proposed as a solution to determine the optimal tuning parameter value for the LQRM method. The models are evaluated on Polish power market datasets using 25 point forecasts obtained from calibration windows of varying lengths. The results show that both methods significantly improve forecast accuracy, with a slight advantage for the SQRA approach. Both generalizations offer promising potential for enhancing accuracy of electricity price forecasts, and as a result improves the risk management and supporting decision-making in electricity markets.
A New Robust Control Framework for Robot Manipulators without Velocity Measurements: A Modified Dual-loop Control Scheme

This paper proposes a new framework for the computed torque method (CTM) of robot manipulators without velocity measurements. We first introduce the Luenberger-observer-based CTM with only position measurements. We then clarify that the external disturbance affects not only the tracking performances with respect to the plant but also the estimation accuracies relevant to the state observer. To address this problem, we establish a new architecture for the so-called dual-loop control scheme, by which both the tracking performances and estimation accuracies can be simultaneously improved, in contrast to its existing structure. A guideline for taking control parameters corresponding to the proposed control structure is also provided with respect to the stabilization of the overall closed-loop systems. Finally, simulation and experimental results are provided to demonstrate the validity and practical feasibility of the developed structure.
Reference points for getting a target in a soccer competition. A probabilistic model with applications to the Spanish and English Premier League

Abstract We model the probabilities that a soccer team gets a target, for example, to play the Champions League, the UEFA Europa League or preserve the category. Taking into account the points won until de mth matchday of the competition, when the winter transfer window is closed. We give closed-form expressions for the probabilities of reaching the goals. We also introduce a risk measure which is going to give us the smallest initial points needed to ensure that the probability of getting the target is larger than a given level.
Demand forecasting in smart homes using deep learning techniques

As energy usage grows quickly, smart energy management solutions have gained in popularity. Smart meters are used to track daily energy consumption in realtime. Demand forecasting is very important in smart homes where the smart meter data is used to forecast the load of a particular household and it can be even used for a neighbourhood. Prediction of electrical loads can be done using different deep learning techniques and can be used for demand management. Deep learning techniques proposed in this work include LSTM, Bi-LSTM and GRU. The proposed model for the prediction of energy consumption consists of data preprocessing, model generation and validation. The main benefit of this demand forecasting is to decrease energy wastage and improve energy consumption effectiveness.
Trends in General Medicine

COVID-19 has had profound mental health effects for young adults and college students. While individuals endure crisis situations through various management protocols, art viewing is a common coping mechanism. Some universities have public art displays, in highly trafficked areas. We examined perceptions and acknowledgments of on-campus art, as well as how these displays affect the coping, comfort, and wellbeing of students, particularly in the height of the pandemic. On a scale of one (art is never a form of comfort) to seven (art is always a form of comfort), 95.92% students felt that art was a four or above. Amidst the COVID-19 pandemic, 95.83% of students indicated a four or above (on a scale of one (strong negative effect) to seven (strong positive affect)), with most students selecting five or seven. More positively than negatively, students feel art is a form of comfort and affected their wellbeing during COVID-19.
A Hybrid Generator Architecture for Controllable Face Synthesis

Modern data-driven image generation models often surpass traditional graphics techniques in quality. However, while traditional modeling and animation tools allow precise control over the image generation process in terms of interpretable quantities — e.g., shapes and reflectances — endowing learned models with such controls is generally difficult. In the context of human faces, we seek a data-driven generator architecture that simultaneously retains the photorealistic quality of modern generative adversarial networks (GAN) and allows explicit, disentangled controls over head shapes, expressions, identity, background, and illumination. While our high-level goal is shared by a large body of previous work, we approach the problem with a different philosophy: We treat the problem as an unconditional synthesis task, and engineer interpretable inductive biases into the model that make it easy for the desired behavior to emerge. Concretely, our generator is a combination of learned neural networks and fixed-function blocks, such as a 3D morphable head model and texture-mapping rasterizer, and we leave it up to the training process to figure out how they should be used together. This greatly simplifies the training problem by removing the need for labeled training data; we learn the distributions of the independent variables that drive the model instead of requiring that their values are known for each training image. Furthermore, we need no contrastive or imitation learning for correct behavior. We show that our design successfully encourages the generative model to make use of the internal, interpretable representations in a semantically meaningful manner. This allows sampling of different aspects of the image independently, as well as precise control of the results by manipulating the internal state of the interpretable blocks within the generator. This enables, for instance, facial animation using traditional animation tools.
Cardiovascular prevention in childhood: a consensus document of the Italian Society of Cardiology Working Group on Congenital Heart Disease and Cardiovascular Prevention in Paediatric Age

Cardiovascular diseases (CVD) may be manifested from a very early age. Genetic and environmental (epigenetic) factors interact to affect development and give rise to an abnormal phenotypical expression of genetic information, although not eliciting changes in the nucleotide sequence of DNA. It has been scientifically proven that increased oxidative stress (OS) caused by disease (overweight, obesity, diabetes), nutritional imbalances, unhealthy lifestyles (smoking, alcohol, substance abuse) in the mother during pregnancy may induce placental dysfunction, intrauterine growth restriction, prematurity, low birth weight, postnatal adiposity rebound, metabolic alterations and consequent onset of traditional cardiovascular risk factors. OS represents the cornerstone in the onset of atherosclerosis and manifestation of CVD following an extended asymptomatic period. OS activates platelets and monocytes eliciting the release of pro-inflammatory, pro-atherogenic and pro-oxidising substances resulting in endothelial dysfunction, decrease in flow-mediated arterial dilatation and increase in carotid intima-media thickness. The prevention of CVD is defined as primordial (aimed at preventing risk factors development), primary (aimed at early identification and treatment of risk factors), secondary (aimed at reducing risk of future events in patients who have already manifested a cardiovascular event), and tertiary (aimed at limiting the complex outcome of disease). Atherosclerosis prevention should be implemented as early as possible. Appropriate screening should be carried out to identify children at high risk who are apparently healthy and implement measures including dietary and lifestyle changes, addition of nutritional supplements and, lastly, pharmacological treatment if risk profiles fail to normalise. Reinstating endothelial function during the reversible stage of atherosclerosis is crucial.
Breast Pseudoaneurysm in a Woman after Core Biopsy: Intravascular Glue Embolization

Core needle biopsy of breast masses is a common procedure for tissue diagnosis of breast lesions. The incidence of complications is low, with pseudoaneurysm (PA) after core biopsy has been described in the literature, and the subsequent need for surgical management. Ultrasonography is the most common modality used for not only diagnosis but also treatment of a PA. Color Doppler images show a heterogeneous echoic lesion with whirling flow inside of the lesion. We describe a patient whose breast PA that developed after core needle biopsy was successfully treated with sonographically-guided intravascular glue embolization.
Impact of the Secondary Insider Trading on Financial Markets

The insider trading phenomenon is based on the situation when traders use material information not publicly available to make their investment decisions. In most countries of the world, insider trading is illegal and is punishable by ne or imprisonment. Insider trading has many economic e ects, which in the light of available scienti c research can be both positive and negative. Insider traders can be divided into primary and secondary traders depending on whether their contact with insider information is direct or indirect. Primary insider trading has been discussed in many research studies and scienti c descriptions, while secondary insider trading has not been investigated yet. This paper's aim is to ll this gap. Since the empirical data for secondary insider trading is in practice impossible to obtain, the research analysis is based on simulations of two probabilistic models. The rst one concerns the use of insider information for an in nite long period of time after its acquisition, while the second one on the day of its receipt. The results of the simulation are related to three basic models of nancial market functioning: the e cient market hypothesis, the fractal market hypothesis, and the coherent market hypothesis.
Adhesions Detection and Staging Classification for Preoperative Assessment of Difficult Laparoscopic Cholecystectomies: A Prospective Case–Control Study

Background: Laparoscopic cholecystectomy (LC) is the treatment of choice for cholelithiasis; however, there are procedural difficulties in determining preoperative detection of a difficult LC. The current methods using clinical and sonographic variables to identify difficult LCs have limitations to identify gallbladder adhesions which form the most common cause. We present a new method of evaluation using acoustic radiation force impulse (ARFI)-based virtual touch imaging (VTI) for the detection and classification of these patients. Methods: Fifty consecutive patients of cholelithiasis were evaluated preoperatively using conventional scoring system (CSS) and by new adhesion detection and staging (ADS) system, and patients were classified into three classes (I–III) with class I being easy, II and III being moderate-to-high difficulty LCs. Peroperative classification was done based on the difficulty level during surgery after visualization of gallbladder adhesions. The sensitivity, specificity, and area under the curves (AUCs) of both systems were compared. Results: Out of 50 patients, 72% and 54% of patients were in class I by CSS and ADS classification, while 28% and 46% were in class II and III, respectively, and were labeled as difficult LC cases; differences being two classifications were statistically significant (P = 0.02). Sensitivity, specificity, negative predictive value, and accuracy for ADS were 91%, 100%, 93.1%, and 96.0%, and for CSS, 60.9%, 100%, 75%, and 82% with AUCs of 1.0 and 0.63, respectively. Conclusion: ARFI-based VTI accurately detects gallbladder adhesions and can determine the difficult cases of LCs preoperatively using ADS classification and shows higher accuracy than CSS classification, which results in lower operative time and risk of complications.
Does the Presence of Extended Jugular Lymphatic Sacs Add More Risk to Nuchal Thickness for Genetic and Structural Abnormality?

Background: The risks added by extended jugular lymphatic sacs (EJLS) to increased nuchal translucency (NT) including genetic and structural abnormalities and pregnancy outcomes have not been previously investigated, which this study aims to investigate. Methods: The data of 155 singleton pregnancies with increased fetal NT (≥95th percentile) of these 20 with fetal EJLS were evaluated retrospectively. Patients were stratified according to NT thickness such that ≥95th percentile - 3.5 mm, 3.6–4.4 mm, 4.5–5.4 mm, 5.5–6.4 mm, ≥6.5 mm, and grouped according to the presence of EJLS. Pregnancy outcomes, genetic and structural abnormalities were assessed by comparing EJLS with non-EJSL cases (n-EJLS). Results: Associated with NT, the incidence of the presence of EJLS increased with NT, from 4.5% at the ≥95th percentile - 3.5 mm to 30.8% when NT ≥5.5 mm. In the n-EJLS group, the proportion of fetuses with structural and genetic abnormalities increased as the measurement of NT increased. This correlation was not observed in the EJLS group. Compared to n-EJLS, cases with EJLS had a higher rate of fetal structural (38.5% vs. 75%, P = 0.003) and genetic (18.5% vs. 45%, P = 0.005) anomalies and a lower term live birth rate (59.3% vs. 15%, P < 0.001). Conclusion: The increasing rate of EJLS was seen as NT increased. Compared to n-EJLS, the EJLS cases had a higher rate poor pregnancy outcomes and fetal genetic and structural abnormalities.
Assessment Knowledge, Attitude, and Willingness to Care for Patients with HIV/AIDS among Midwifery Students of Selected Universities in Iran in 2020

Background: Stigmatization and discrimination by health workers, particularly midwives are obstacles to the achievement of universal access to Human Immunodeficiency Virus (HIV) prevention, treatment, and care programs. Therefore, it is necessary to evaluate midwifery students' knowledge and attitude regarding Acquired Immune Deficiency Syndrome (AIDS) and HIV, and their willingness to care for patients with AIDS and HIV before they enter the field of health and medical activities. Thus, the aim of this study was to investigate the level of knowledge and attitude in this regard, and willingness to care for patients with AIDS and HIV among midwifery students of selected universities in Iran in 2020. Materials and Methods: A descriptive-analytical, cross-sectional study was performed on 618 midwifery students in Iran in 2019–2020. Data were collected using a five-part questionnaire: a demographic characteristics form, an academic profile form, the HIV Knowledge Questionnaire (HIV-KQ), the AIDS Attitude Scale (AAS), and the Jordan Standard Questionnaire. Data analysis was performed through descriptive and inferential statistical methods. p value of ≤ 0.05 was considered significant. Results: The mean (SD) age of the subjects was 23.10 (5.63) years. The mean (SD) of knowledge, attitude, and willingness scores were 26.93 (6.64), 80.45 (9.27), and 29.55 (9.10), respectively. Single individuals had higher attitude scores. Conclusions: Iranian midwifery students had the desired level of knowledge, although the mean score was not much higher than the threshold. The level of the midwifery students' attitude was appropriate and more than that, but none of them had a 100% positive attitude. They had a moderate or neutral willingness to provide services to and care for patients with AIDS.
Pulsed radiofrequency to stellate ganglion for brachial plexus injury-induced complex regional pain syndrome: A case series

Brachial plexus injury (BPI) occurs commonly in young adults following trauma. This may result in the development of complex regional pain syndrome (CRPS) following injury, which is difficult to treat. We present a group of patients with CRPS secondary to BPI. These patients were managed with neuromodulation of the stellate ganglion (SG) with pulsed radiofrequency (PRF) and followed up for a period of 3 months to assess for pain relief and a decrease in the intake of medications after the intervention. PRF to SG was found to have significant pain relief lasting around three months.
Design and Analysis of Radiation Hardened by Design Non-Volatile RAM for Space Applications

- The non-volatile semiconductor memory chips such as flash memory, EEPROM, FRAM etc must be used for data storage. When operating in space, memory microcircuits are exposed to radiation, which can lead to data corruption and functional failures. When compared to other non-volatile memories MRAM is been considered as the good one used for space application to withstand against radiation effect. Therefore Magneto-resistive Random-Access Memories (MRAMs) are preferred for space and radiation-hardened electronics applications. The reason MRAM is selected is based on solid-state form factor, non-volatility, radiation hardness, modularity, reliability, scalability, fault tolerance, support for mission assurance, small size, low mass, and low power consumption. MRAMs provide higher bit storage densities. Radiation hardened MRAM design is a crucial aspect of space and defence applications where memory devices must withstand harsh radiation environments. Rad-Hard MRAM is designed and analysed for various parameters which shows the tolerance level for radiation effects. The design of MRAM memory will consider the impact of radiation on the device's electrical characteristics and performance. MRAM could operate at the temperature between −40 and +150°C which is prone to 2 types of radiation effects which are TID and SEE. The designed MRAM memory will mitigate the Single Event Effects (SEE) like Single Event Upset (SEU) and SEL (Single-Event Latch-up) and TID (Total Ionizing dose). In this work, MRAM circuit will be implemented using LTSpice and verify the basic read /write operation and delay levels as radiation induced current potentially leads to read/write failures and will verify the amount of radiation the circuit is immune to in terms of rad.
A Portrait of MWrite as a Research Program: A Review of Research on Writing-to-Learn in STEM through the MWrite Program

The writing-to-learn (WTL) literature is varied in how assignments are structured and implemented in the classroom, making it difficult for instructors to identify how to incorporate writing effectively. Drawing on the WTL literature, the MWrite program was established to work with STEM faculty to design, implement, and assess evidencebased WTL assignments. Herein we present a review of the WTL research generated through the MWrite program, situating our findings in a four-dimensional framework of engagement to identify how the MWrite WTL assignment design and implementation has supported students’ learning. Our analysis indicates that the multi-faceted design of MWrite WTL assignments supports students’ development of conceptual knowledge and disciplinary thinking. The assignments’ rhetorical features (i.e., context, audience, and genre) guide how students write about content, and peer review and revision stages encourage a collaborative, knowledge building process between students and their peers.
A New Multivariate Digital-Signature Scheme by Mixing Oil-Vinegar with Triangles

Multivariate cryptography is based on multivariate quadratic or MQ (i.e. multivariate quadratic root-finding) problem which is known to be NP-hard; thus it is conjectured to be post-quantum secure. UOV (unbalanced Oil-Vinegar) is a popular technique (Kipnis et.al.1999) used to design the central polynomials in an MQ-based signature scheme; for instance, the Rainbow scheme (ACNS’05) is one of the more well-known candidates. A powerful attack on Rainbow was recently proposed by Beullens (CRYPTO’22), which significantly decreases its security. Our work proposes two new MQ-based digital-signature schemes, called TriRainbow (short for ‘Triangular Rainbow’). TriRainbow effectively combines, in a simple way, two well-known methods— Rainbow and Trian-gular schemes. Our signature scheme, proposed in this work, is secure against Beullens’s attack. TriRainbow needs one Gaussian elimination during the signing phase, so it is as efficient as Rainbow, in addition, it offers better security compared to Rainbow.
CA++: Enhancing Carrier Aggregation Beyond 5G

Carrier aggregation (CA) is an important component technology in 5G and beyond. It aggregates multiple spectrum fragments to serve a mobile device. However, the current CA suffers under both high mobility and increased spectrum space. The limitations are rooted in its sequential, cell-by-cell operations. In this work, we propose CA++, which departs from the current paradigm and explores a group-based design scheme. We thus propose new algorithms that enable concurrent channel inference by measuring one or few cells but inferring all, while minimizing measurement cost via set cover approximations. Our evaluations have confirmed the effectiveness of CA++. Our solution can also be adapted to fit in the current 5G OFDM PHY and the 3GPP framework.
Fractional Brownian motion satisfies two-way crossing

We prove the following result: For (Z_t)_{t∈R} a fractional Brownian motion with arbitrary Hurst parameter, for any stopping time τ, there exist arbitrarily small e > 0 such that Z_{τ+e} < Z_τ, with asymptotic behaviour when e ↘ 0 satisfying a bound of iterated logarithm type. As a consequence, fractional Brownian motion satisfies the “two-way crossing” property, which has important applications in financial mathematics.
O EFEITO DA FRAGMENTAÇÃO FLORESTAL NA COMPOSIÇÃO E RIQUEZA DE ÁRVORES NA REGIÃO DA RESERVA MORRO GRANDE (PLANALTO DE IBIÚNA, SP)

Diversas modificações estruturais e funcionais têm sido indicadas como decorrência do processo de fragmentação das comunidades vegetais. Para avaliar alguns desses efeitos, foram comparadas a composição e a riqueza arbórea em diferentes áreas em duas paisagens do Planalto de Ibiúna (Cotia e Ibiúna, SP). As duas paisagens localizam-se sob condições abióticas similares, mas uma é predominantemente florestal (Reserva Morro Grande - 9.400 ha, onde foram estudadas 6 áreas) e outra agrícola (onde foram estudados 21 fragmentos com ampla gama de tamanhos – 0,9 a 275 ha). Utilizou-se o método de quadrantes para indivíduos com Diâmetro à Altura do Peito ≥ 5 cm, sendo que o esforço amostral incluiu 2.400 indivíduos na Reserva e 5.000 indivíduos na paisagem fragmentada. Foram amostradas 362 espécies, pertencentes a 171 gêneros e 71 famílias, sendo as mais ricas Myrtaceae, Lauraceae, Fabaceae, Rubiaceae e Euphorbiaceae. Considerando-se o conjunto dos fragmentos foram observadas diferenças na composição de espécies e menor riqueza. Nos fragmentos ocorreram mais indivíduos e espécies anemocóricas e barocóricas e pioneiras e secundárias iniciais. Inversamente, ocorreram mais indivíduos e espécies secundárias tardias e umbrófilas e zoocóricas na Reserva Morro Grande, destacando a importância desta área para a manutenção da biodiversidade regional. Apesar desses claros efeitos da fragmentação, algumas espécies ameaçadas de extinção, pouco conhecidas ou mesmo inéditas para São Paulo, foram amostradas exclusivamente nos fragmentos florestais, evidenciando a necessidade de incluir estes fragmentos remanescentes, da região sul do Planalto Paulistano, em mecanismos de preservação e promover a restauração florestal, para garantir a manutenção da biodiversidade regional.