Miracle-dz/UniFAR-FTUs huggingface dataset:
STUDY ON SEISMIC BEHAVIOR OF TRAPEZOIDAL CORRUGATED STEEL PLATE SHEAR WALL STRUCTURE WITH PEC COLUMN

This paper has carried out experimental and numerical research on the hysteretic characteristics of a corrugated steel plate shear wall which has a partially encased composite (PEC) column (PEC-CSPSW). Two single layer PEC-CSPSW cycle tests were conducted. For the sake of simulating the experimental results, the writer made a numerical model and verified it. The capacity of energy dissipation and failure mode of the structure were studied. The results displayed the PEC-CSPSWs had excellent bearing capacity, ductility, and energy dissipation feature, and the bearing capacity declined gradully. The PEC composite column could heighten the frame column’s stiffness, enhance the steel plate anchoring effect , and then give full play to its post-buckling strength. The effects of the thickness, wavelength, wave height of the corrugated steel plate, and the strength of concrete on the lateral force resistance were analyzed. The results indicated that, under the rational parameter design, the CSPSW proposed in this paper had a high bearing capacity and strong energy dissipation feature. Besides, it was an ideal lateral force resisting and energy dissipation member.
Lithium abundances and other clues to envelope burning in small Magellanic Cloud asymptotic giant branch stars

We present a chemical analysis of seven luminous asymptotic giant branch stars and one M supergiant of the Small Magellanic Cloud. The abundances are derived from high-resolution spectra by spectrum synthesis using new, spherically symmetric, opacity sampling model atmospheres. The average metallicity is [Fe/H]=-0.5, in accordance with other determinations of the SMC's metallicity. The AGB stars show signs of envelope burning, being Li rich, C poor, and with a low 12 C/ 13 C ratio. The s-process elements (Rb, Zr, Nd) abundance pattern is different from that in the solar system, and is characteristic of a high exposure at low neutron density
“In the light of evolution:” keratins as exceptional tumor biomarkers

Keratins (KRTs) are the intermediate filament-forming proteins of epithelial cells, classified, according to their physicochemical properties, into “soft” and “hard” keratins. They have a key role in several aspects of cancer pathophysiology, including cancer cell invasion and metastasis, and several members of the KRT family serve as diagnostic or prognostic markers. The human genome contains both, functional KRT genes and non-functional KRT pseudogenes, arranged in two uninterrupted clusters on chromosomes 12 and 17. This characteristic renders KRTs ideal for evolutionary studies. Herein, comprehensive phylogenetic analyses of KRT homologous proteins in the genomes of major taxonomic divisions were performed, so as to fill a gap in knowledge regarding the functional implications of keratins in cancer biology among tumor-bearing species. The differential expression profiles of KRTs in diverse types of cancers were investigated by analyzing high-throughput data, as well. Several KRT genes, including the phylogenetically conserved ones, were found to be deregulated across several cancer types and to participate in a common protein-protein interaction network. This indicates that, at least in cancer-bearing species, these genes might have been under similar evolutionary pressure, perhaps to support the same important function(s). In addition, semantic relations between KRTs and cancer were detected through extensive text mining. Therefore, by applying an integrative in silico pipeline, the evolutionary history of KRTs was reconstructed in the context of cancer, and the potential of using non-mammalian species as model organisms in functional studies on human cancer-associated KRT genes was uncovered.
Advanced USP laser process with deep learning and triangular beam shaping for micro Fresnel lenses fabrication

Industrial molds used to manufacture new type of Fresnel lenses require significant control over the size and shape of the ablation grooves. In particular, for demanding patterns, the objective is to obtain asymmetrical triangular grooves of around 10-20 μm width, micromachined with an ultra-short pulse (USP) laser for a better quality. To obtain this ablation profile we use a specific triangular beam shape obtained thanks to a reflection beam-shaping module. The idea is to move and rotate this triangular shape to have one of the edges of the triangle on one side of the groove, and its opposite point on the other side. In order to have total control of the laser process, we have collected a large amount of data of laser parameters, beam profiles and ablated groove profiles. This database allows us, thanks to the use of a deep learning algorithm, to predict the ablation profile from a set of laser parameters and beam profile pictures used for machining. The use of an artificial intelligence algorithm is justified by the fact that, at such a low resolution and with femtosecond laser pulses, light-matter interactions become complex, in particular due to nonlinear effects, which make using simulations difficult. Our deep learning model has the particularity of being a ”hybrid” model using several types of data: laser parameters, curves and images. This allows the algorithm to have an overview of the process but also to give the end-user a very fine control.
Boosting the Generalized Margin in Cost-Sensitive Multiclass Classification

The boosting algorithm is one of the most successful binary classification techniques due to its relative immunity to overfitting and flexible implementation. Several attempts have been made to extend the binary boosting algorithm to multiclass classification. In this article, a novel cost-sensitive multiclass boosting algorithm is proposed that naturally extends the popular binary AdaBoost algorithm and admits unequal misclassification costs. The proposed multiclass boosting algorithm achieves superior classification performance by combining weak candidate models that only need to be better than random guessing. More importantly, the proposed algorithm achieves a large margin separation of the training sample while attaining an L1-norm constraint on the model complexity. Finally, the effectiveness of the proposed algorithm is demonstrated in a number of simulated and real experiments. The supplementary files are available online, including the technical proofs, the implemented R code, and the real datasets.
Single bubble sonoluminescence

Single-bubble sonoluminescence occurs when an acoustically trapped and periodically driven gas bubble collapses so strongly that the energy focusing at collapse leads to light emission. Detailed experiments have demonstrated the unique properties of this system: the spectrum of the emitted light tends to peak in the ultraviolet and depends strongly on the type of gas dissolved in the liquid; small amounts of trace noble gases or other impurities can dramatically change the amount of light emission, which is also affected by small changes in other operating parameters (mainly forcing pressure, dissolved gas concentration, and liquid temperature). This article reviews experimental and theoretical efforts to understand this phenomenon. The currently available information favors a description of sonoluminescence caused by adiabatic heating of the bubble at collapse, leading to partial ionization of the gas inside the bubble and to thermal emission such as bremsstrahlung. After a brief historical review, the authors survey the major areas of research: Section II describes the classical theory of bubble dynamics, as developed by Rayleigh, Plesset, Prosperetti, and others, while Sec. III describes research on the gas dynamics inside the bubble. Shock waves inside the bubble do not seem to play a prominent role in the process. Section IV discusses the hydrodynamic and chemical stability of the bubble. Stable single-bubble sonoluminescence requires that the bubble be shape stable and diffusively stable, and, together with an energy focusing condition, this fixes the parameter space where light emission occurs. Section V describes experiments and models addressing the origin of the light emission. The final section presents an overview of what is known, and outlines some directions for future research.
Molecular Survey of Toxoplasma gondii in Wild Mammals of Southern Italy

Systematic wildlife surveillance is important to aid the prevention of zoonotic infections that jeopardize human health and undermine biodiversity. Toxoplasma gondii is an opportunistic zoonotic protozoan that can infect all endothermic vertebrates, causing severe disease in immunocompromised humans and cases of congenital transmission. Humans can be infected by ingestion of raw meat containing bradyzoites or water contaminated by oocysts. In our study, we assessed the potential circulation of Toxoplasma gondii in wild mammals by performing surveillance in the Campania region (southern Italy) and surveyed its presence from 2020 to 2022 within the framework of the Regional Plans for Wildlife Surveillance. In detail, 211 individuals belonging to five wild mammals (wolf, fox, wild boar, badger, and roe deer) underwent necropsy and the organs were analyzed by real-time PCR for the detection of the parasite. Toxoplasma gondii was found in 21.8% (46/211) of the subjects examined. No statistically significant differences were noticed between the prevalence and the host’s trophic level or age, rejecting the hypotheses that Toxoplasma gondii will have a higher prevalence in top predators and adult individuals, respectively. Our work emphasized the high circulation of Toxoplasma gondii in wildlife and remarked on the critical role of anthropized areas where domestic cats and wildlife may come into contact, urging a systematic surveillance.
Pollutant accumulation in road mitigation tunnels for amphibians: A multisite comparison on an ignored but important issue

Underpasses or road tunnels are increasingly installed to reconnect habitats and ensure safe wildlife passage, thus preventing habitat fragmentation caused by roads and mortality from collisions with vehicles. In the UK, such underpasses are regularly implemented for amphibians and especially the protected great crested newt, Triturus cristatus. However, roads are also a key source of environmental pollutants including trace metals, road salt, petroleum and diesel hydrocarbons and these might impact road mitigation structures where amphibians are funnelled to, yet the extent and implications of such pollutants are almost never quantified in relation to this aspect, despite the recognised sensitivity of amphibians to chemical pollutants. Sediments were analysed from four amphibian road mitigation tunnel sites across the UK and compared to natural soil formations at local reference sites to determine whether contaminants were indeed accumulating within the tunnels. Three potential contaminants (copper, lead, and total petroleum hydrocarbons) were found in greater concentrations in the underpass sediments than respective reference sites at three of four locations, while one (zinc) was found in greater concentrations at all four studied underpasses compared to reference sites. Aggregated sediment pH value was significantly greater in the underpass sediment than the respective reference sites at all four study sites and in several instances the contaminants reached values that exceeded the thresholds of environmental concern. Despite the large geographic area covered and the significant site differences the absolute values of potential pollutants in tunnels were similar across sites, thus suggesting similar pollution sources and pathways. These results suggest road tunnels installed for ecological mitigation could be a significant pathway for pollutants from road surfaces to amphibians and it is recommended that focussed monitoring and maintenance of the underpasses is enacted given that their short or long-term impacts on amphibians are currently unknown. Potential management options could include regular jet-washing of the underpasses, or alternatively, pre- or post-implementation modifications of mitigation designs should aim to minimise the pollution pathway from road surfaces.
Persamaan Aliran Air Dalam Media Berpori Sebagai Aliran Air Tanah (Groundwater)

Groundwater flow under surface, its usually slow moving, so that in laminer flow condition can find analisys using the Darcy’s law. The combination between Darcy’s law and continuity equation can find differential Laplace equation as general equation groundwater flow in sub surface. Based on Differential Laplace Equation is the equation that can be used to describe hydraulic head and velocity flow distribution in porous media as groundwater. Keyword : groundwater, Laplace’s equation, Darcy’s law, mass conservation Abstrak Pergerakan air ke dalam tanah, pada umumnya bergerak dengan aliran yang relatif lambat, sehingga dalam kondisi laminer dapat dianalisa dengan menggunakan hukum Darcy. Bila hukum Darcy dan hukum kekekalan massa atau prinsip kontinuitas digabungkan maka diperoleh dua persamaan differensial. Persamaan differensial pertama adalah persamaan aliran yang berubah terhadap waktu. Persaman differensial yang kedua adalah persamaan differensial yang tidak berubah terhadap waktu. Gabungan dari persamaan hukum Darcy dan hukum kekekalan massan yang tidak berubah lagi terhadap waktu dikenal dengan persamaan differensial Laplace. Persamaan differensial Laplace sebagai pendekatan persamaan umum aliran air di dalam tanah. Kata kunci: airtanah, persamaan Laplace, hukum Darcy, kekekalan massa
The Role of the Transcription Factor Nrf2 in Alzheimer’s Disease: Therapeutic Opportunities

Alzheimer’s disease (AD) is a common neurodegenerative disorder that affects the elderly. One of the key features of AD is the accumulation of reactive oxygen species (ROS), which leads to an overall increase in oxidative damage. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is a master regulator of the antioxidant response in cells. Under low ROS levels, Nrf2 is kept in the cytoplasm. However, an increase in ROS production leads to a translocation of Nrf2 into the nucleus, where it activates the transcription of several genes involved in the cells’ antioxidant response. Additionally, Nrf2 activation increases autophagy function. However, in AD, the accumulation of Aβ and tau reduces Nrf2 levels, decreasing the antioxidant response. The reduced Nrf2 levels contribute to the further accumulation of Aβ and tau by impairing their autophagy-mediated turnover. In this review, we discuss the overwhelming evidence indicating that genetic or pharmacological activation of Nrf2 is as a potential approach to mitigate AD pathology.
APPLICATION OF SEMIPARAMETRIC METHODS FOR REGRESSION MODELS WITH MISSING COVARIATE INFORMATION

This dissertation addresses regression models with missing covariate data. These methods are shown to be significant to public health research since they enable researchers to use a wider spectrum of data. Unbiased estimating equations are the focus of this dissertation, predominantly semiparametric methods utilized to solve for regression parameters in the presence of missing covariate data. The first aim of this dissertation is to evaluate the properties of an efficient score, an inverse probability weighted estimating equation approach, for logistic regression in a two-phase design. Simulation studies showed that the efficient score is more efficient than two other pseudo-likelihood methods when the correlation between the missing covariate and its surrogate is high. The second aim of this dissertation is to develop a methodology for left truncated covariate data with a binary outcome. To address this problem, we proposed two methods, a likelihood-based approach and an estimating equation approach, to estimate the coefficients and their standard errors for a regression model with a left truncated covariate. The estimating equation technique is close to completion, and once solved should be the most efficient method. The likelihood-based method is compared to standard methods of filling in the truncated values with the lower threshold value or using only the nontruncated values. Simulation studies demonstrated that the likelihood-based method has the best variance correction and moderate bias correction. The application of this method is illustrated in a sepsis study conducted at the University of Pittsburgh.
Radiation trapping forces acting on a two-layered spherical particle in a Gaussian beam

Based on the generalized Lorenz-Mie theory (GLMT), the incident fundamental Gaussian beam is expanded in terms of the vector spherical harmonics. We present an expression of radiation trapping forces acting on a multilayered spherical particle in a Gaussian beam by using the theory of electromagnetic momentum, and, as an example, numerical results are given to an axial two-layer absorbing spherical particle, along with a discussion about the influence of the beam waist radius, relative complex refractive index and thickness of the two layers on trapping forces.
Visual Speech Recognition for Kannada Language Using VGG16 Convolutional Neural Network

Visual speech recognition (VSR) is a method of reading speech by noticing the lip actions of the narrators. Visual speech significantly depends on the visual features derived from the image sequences. Visual speech recognition is a stimulating process that poses various challenging tasks to human machine-based procedures. VSR methods clarify the tasks by using machine learning. Visual speech helps people who are hearing impaired, laryngeal patients, and are in a noisy environment. In this research, authors developed our dataset for the Kannada Language. The dataset contained five words, which are Avanu, Bagge, Bari, Guruthu, Helida, and these words are randomly chosen. The average duration of each video is 1 s to 1.2 s. The machine learning method is used for feature extraction and classification. Here, authors applied VGG16 Convolution Neural Network for our custom dataset, and relu activation function is used to get an accuracy of 91.90% and the recommended system confirms the effectiveness of the system. The proposed output is compared with HCNN, ResNet-LSTM, Bi-LSTM, and GLCM-ANN, and evidenced the effectiveness of the recommended system.
Significant National Declines in Neurosurgical Intervention for Mild Traumatic Brain Injury with Intracranial Hemorrhage: A 13-Year Review of the National Trauma Data Bank

There have been large changes over the past several decades to patient demographics in those presenting with mild traumatic brain injury (mTBI) with intracranial hemorrhage (ICH; complicated mTBI) with the potential to affect the use of neurosurgical interventions. The objective of this study was to characterize long-term trends of neurosurgical interventions in patients with complicated mTBI using 13 years of the National Trauma Data Bank (NTDB). This was a retrospective cohort study of adult (≥18 years) trauma patients included in the NTDB from 2007 to 2019 who had an emergency department Glasgow Coma Scale score 13–15, an intracranial hemorrhage (ICH), and no skull fracture. Neurosurgical intervention time trends were quantified for each ICH type using mixed-effects logistic regression with random slopes and intercepts for hospitals, as well as covariates for time and 14 demographic, injury, and hospital characteristics. In total, 666,842 ICH patients across 1060 hospitals were included. The four most common hemorrhages were isolated subdural hemorrhage (36%), isolated subarachnoid hemorrhage (24%), multiple hemorrhage types (24%), and isolated unspecified hemorrhages (9%). Overall, 49,220 (7%) patients received a neurosurgical intervention. After adjustment, the odds of neurosurgical intervention significantly decreased every 10 years by the following odds ratios (odds ratio [95% confidence interval]): 0.85 [0.78, 0.93] for isolated subdural, 0.63 [0.51, 0.77] for isolated subarachnoid, 0.50 [0.41, 0.62] for isolated unspecified, and 0.79 [0.73, 0.86] for multiple hemorrhages. There were no significant temporal trends in neurosurgical intervention odds for isolated epidural hemorrhages (0.87 [0.68, 1.12]) or isolated contusions/lacerations (1.03 [0.75, 1.41]). In the setting of complicated mTBI, the four most common ICH types were associated with significant declines in the odds of neurosurgical intervention over the past decade. It remains unclear whether changing hemorrhage characteristics or practice patterns drove these trends.
Correlates of Obtaining Informed Consent to Data Linkage

In the United Kingdom, in order to link individual-level administrative records to survey responses, respondents need to give their consent. Using an unprecedented set of respondent, interview, and interviewer characteristics derived from the British Household Panel Survey (BHPS) matched with an interviewer survey, this research investigates which characteristics influence consent to adding health and social security records to the survey responses. We find that consent is related to respondents’ attitudes to privacy, community mindedness and data linkage salience as well as to some interview features such as the “household contagion effect” and the survey “fidelity. ” Interviewer characteristics, including their personality, attitudes to persuading respondents, and job experience, are not associated with consent. Interviewers’ survey experience in the current wave and their task-specific experience, however, do influence consent. Implications of the findings are discussed and areas for future research are identified.
The productive structure and consumption of final energy in Spain: an input–output analysis from a production perspective

ABSTRACT Using an alternative input–output analysis based on the responsibility of the production of the total output of each sector, we determine the relevant sectors in energy consumption in Spain. Our approach offers a different and complementary vision to the usual analyses based on the responsibility of the final demand to help guide environmental and energy policies. The analysis also enables us to observe the importance of final energy consumption by type for the different sectors. We use the input–output table for 2015 from INE and data on energy consumption from EUROSTAT. The most relevant sectors in terms of the energy consumption that their total production induces in other sectors are “Food, beverages and tobacco products” and “Construction,” while various service sectors also stand out for inducing energy consumption in other sectors. In these sectors, policy measures aimed at improving the efficiency in the use of energy-intensive inputs and the substitution of these inputs by others less energy intensive would be effective. The most relevant sectors for being induced to consume energy by the production of other sectors are “Electricity and gas” and “Land Transport. ” The incorporation of technological improvements aimed at improving energy efficiency, as well as the adoption of best practices, would be more effective in these sectors. Finally, the most important sectors due to their own final demand are “Electricity and gas,” and “Coke and refined. ” In these sectors, policies aimed at reducing their final demand would be effective.
Monetary Policy and Banking Sector Stability in Nigeria

This study investigates the impact of monetary policy on banking sector stability in Nigeria, utilizing quarterly data for the period 2007Q1 to 2021Q4. The study employs the autoregressive distributed lag (ARDL) bounds testing approach to cointegration. Results show that a long run relationship exist between banking sector stability and monetary policy in Nigeria. Furthermore, monetary policy rate, liquidity ratio, and cash reserve ratio are found to enhance banking sector stability. The study recommends, among others, that cash reserve and liquidity ratios should be kept at levels that will prevent excess liquidity in the system.