Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Edible and Nutritional Quality of Roasted Duck Breasts through Variable Pressure Salting: Implications for Protein Anabolism and Digestion in Rats

Variable pressure salting (VPS) is considered a novel salting approach to improve meat quality. This study aimed to investigate the effects of roasted duck’s edible and nutritional quality after VPS through serum biochemical indicators and in vivo digestion properties in rats. The results show that roasted duck after VPS led to an increase in the total protein content (57.24 g/L) and blood glucose levels (6.87 mmol/L), as well as a decrease in the blood urea nitrogen content (11.81 mmol/L), in rats. Compared to rats fed base diets and roasted duck after static wet salting (SWS), those ingesting roasted duck after VPS exhibited higher values of apparent protein digestibility (51.24%), pepsin activity (2.40 U/mg), and trypsin activity (389.80 U/mg). Furthermore, VPS treatment improved the textural properties and microstructure of duck breasts shown by a higher immobilized water relaxation area and more ordered protein structures (α-helixes and β-sheets). These improvements enhanced the protein anabolism capacity and in vivo digestion properties in rats. Therefore, VPS represents a beneficial salting method for promoting effective digestion and absorption in rats.
Simulation and Assessment of Daily Evapotranspiration in the Heihe River Basin over a Long Time Series Based on TSEB-SM

The high spatial and temporal resolution of recently developed evapotranspiration (ET) products facilitates agricultural water-savings in irrigated areas as well as improved estimates of crop yield, especially in arid and semi-arid regions. However, cloud cover interferes with ET estimates, in particular when using thermal-infrared-based models in temperate and tropical regions. Previous studies have shown that the two-source energy balance (TSEB) model coupled with soil moisture (TSEB-SM) has great potential for estimating surface ET by overcoming this issue. In this study, the TSEB-SM model was first used to generate a spatiotemporally continuous 1 km daily ET dataset across the Heihe River Basin in China from 2000 to 2020, which was then evaluated against four spatially distributed sites (Arou, Huazhaizi, Daman, and Sidaoqiao) and further compared with the two most widely used daily ET datasets (PML-V2 (Penman–Monteith–Leuning) and SEBAL (surface energy balance algorithm for land)). The results showed that the newly developed ET dataset agrees well with ground-based observations and outperforms the PML-V2 and SEBAL products in precisely characterizing the seasonal fluctuations and spatial distribution as well as the spatiotemporal trends of ET. In particular, ET in the Heihe River Basin exhibits clear regional differences. The upstream and midstream grassland and irrigated oasis areas provide much higher annual ET than the downstream desert areas, with a difference of up to 600 mm/year. A three-cornered hat (TCH)-based pixel-by-pixel analysis further demonstrated that the TSEB-SM and PML-V2 products have substantially smaller relative uncertainties as compared to SEBAL ET. In general, the proposed ET datasets are expected to be more beneficial for irrigation scheduling and to provide more efficient water management across the Heihe River Basin.
Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin

Deep learning has grown among the prediction tools used within renewable energy options. Solar energy belongs to the options with the lowest atmosphere impact after considering their limitations. In the last five years, Brazil has seen the expansion of wind and solar options almost all over the country, and to preserve the Amazon rainforest, the use of solar energy has helped large and small cities towards a greener future. The novelty of this research covers the use of Deep Learning with data from twelve cities in the state of Amazonas to forecast solar irradiation (W.h/ $\text{m}^{2}$ ) within 30 days. The data input came from ground stations, as much as possible, and NASA satellite models, with a daily time aggregation. The types of neural networks considered are Long Short-Term Memory (LSTM), a Multi-Layer Perceptron (MLP), and an LSTM Gated Recurrent Unit (GRU). Among the metrics used to check the algorithm’s performance, the Mean Absolute Percentage Error (MAPE) indicates that the values of this research are coherent with other scenarios to forecast solar energy; the boundary conditions were not the same, however. The lowest MAPE was observed in the city of Labrea with the LSTM GRU.
Contigency based UPFC Placement for Steady State Analysis

Due to increase in the power demand, there is huge pressure on the generation, transmission and distribution. This leads to failure of the power system components, including poor efficiency and unstable operation of the power systems. The introduction of fast operating power electronics devices can address 20-30% of these problems for healthy operation of the power systems. In this paper, contingency based UPFC placement is carried out using MiPower software. The obtained results compared with the analytical method. The chosen method gives the best solution for UPFC placement and it can be used for placement of any FACTS devices.
Character Motion Synthesis Based on Deep Learning: A Survey

Character motion synthesis can be more cost-effective, flexible, and time-efficient compared to motion capture or traditional animation. As character motion synthesis regards multiple major industries, along with the development in deep learning techniques, character motion synthesis based on deep learning conspicuously receives substantial attention, resulting in numerous related studies that need to be analyzed and summarized. This paper presents an overview of character motion synthesis based on deep learning. Firstly, it epitomizes methods incorporating different types of neural networks, then encapsulates methods that did not utilize neural networks but simply deep learning, such as deep reinforcement learning, and lastly summarizes and evaluates the advantages and limitations of different deep learning methods on character motion synthesis.
A Comparative Analysis of Deep Reinforcement Learning-Enabled Freeway Decision-Making for Automated Vehicles

In application, advanced autonomous driving technologies still face numerous challenges. Deep Reinforcement Learning (DRL) has emerged as a widespread and effective approach to address artificial intelligence challenges, due to its substantial potential for autonomous learning and self-improvement. In this study, four DRL algorithms—Deep Q-Learning (DQN), along with its enhanced algorithm, Double DQL, Dueling DQL, and Priority Replay DQL(PR-DQN), are employed to address decision-making challenges for autonomous vehicles on highways, with a comprehensive comparative analysis conducted. The decision-making model is constructed as a Markov Decision Process, guided by specially designed reward functions, enabling the target vehicle to learn safe and efficient decision-making strategies through multiple environmental explorations. Through the analysis and discussion of a series of experimental results, the feasibility of DRL-based decision strategies is demonstrated. Finally, through comparing the experimental outcomes of different algorithms, the connection between autonomous driving results and the inherent learning features of these DRL technologies is analyzed.
CTM Tongue Image Consulting System based on Deep Learning Technology

Internet is an important development step in information times. With the tide of Internet development, internet plus health will become a trend of new times. The Chinese Traditional Medicine (CTM) tongue image consulting system based on deep learning technology has created a more perfect and intelligent personal health management system by connecting smart devices to mobile platforms. The system will serve customers perfectly through health food therapy, medical consultation, etc.
Research on Stock Price Prediction based on LSTM Modeling-A Stock Market as a Case Study

This research intends to predict the closing values of the Shanghai Stock Exchange 50 Index using an enhanced Long Short-Term Memory (LSTM) neural network approach. The investigation leverages a comprehensive data set from August 2013 to August 2023, encompassing various market conditions. Alongside the primary LSTM analysis, the study introduces supplementary methodologies, specifically the Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF), to validate and enrich the time-series analysis. This study verified the non-stationary of the original data and processed them with method. 80%of the data was allocated for training, while the subsequent 20% was reserved for testing, the LSTM model, with its intricate architecture, exhibits superior predictive capabilities, particularly in stable market conditions. However, its performance wanes during volatile market periods, suggesting further refinements are necessary. This research contributes to the existing literature by applying machine learning methodologies to the turbulent and complex A-share market, offering invaluable insights for both academic researchers and market investors.
The Phenotype of Mesenchymal Stromal Cell and Articular Chondrocyte Cocultures on Highly Porous Bilayer Poly-L-Lactic Acid Scaffolds Produced by Thermally Induced Phase Separation and Supplemented with Hydroxyapatite

Bilayer scaffolds could provide a suitable topology for osteochondral defect repair mimicking cartilage and subchondral bone architecture. Hence, they could facilitate the chondro- and osteogenic lineage commitment of multipotent mesenchymal stromal cells (MSCs) with hydroxyapatite, the major inorganic component of bone, stimulating osteogenesis. Highly porous poly-L-lactic acid (PLLA) scaffolds with two layers of different pore sizes (100 and 250 µm) and hydroxyapatite (HA) supplementation were established by thermally induced phase separation (TIPS) to study growth and osteogenesis of human (h) MSCs. The topology of the scaffold prepared via TIPS was characterized using scanning electron microscopy (SEM), a microCT scan, pycnometry and gravimetric analysis. HMSCs and porcine articular chondrocytes (pACs) were seeded on the PLLA scaffolds without/with 5% HA for 1 and 7 days, and the cell attachment, survival, morphology, proliferation and gene expression of cartilage- and bone-related markers as well as sulfated glycosaminoglycan (sGAG) synthesis were monitored. All scaffold variants were cytocompatible, and hMSCs survived for the whole culture period. Cross-sections revealed living cells that also colonized inner scaffold areas, producing an extracellular matrix (ECM) containing sGAGs. The gene expression of cartilage and bone markers could be detected. HA represents a cytocompatible supplement in PLLA composite scaffolds intended for osteochondral defects.
Extensional tectonics in the Campania Volcanic Zone (eastern Tyrrhenian Sea, Italy): new insights into the relationship between faulting and ignimbrite eruptions

The Campania Volcanic Zone (CVZ), located on the eastern margin of the Tyrrhenian Sea, is a region characterized by active tectonics and large ignimbrite eruptions starting from 290 ka. A reconstruction of the three-dimensional architecture of the ignimbrite deposits and faults of the CVZ (Campania Plain and Bay of Naples) was made by means of the interpretation of an integrated geological geophysical database. This reconstruction, based on several geological sections combined into two fence diagrams and isopach maps showing the distribution of ignimbrites, reveals: (i) a regional fault pattern characterized by NE-SW, NNE-SSW, NW-SE and E-W structures active over the last 290 ka during ignimbrite eruptions; (ii) a location of the ignimbrite vents over the whole CVZ in correspondence to NE-SW and NW-SE faults; (iii) volcano-tectonic subsidence along normal separation faults that produced asymmetric basins. Furthermore, we have established the tectonic evolution of the CVZ from the emplacement of the ignimbrite deposits, and infer the presence of a WNW-ESE trending extension associated with NNE-SSW normal faults, E-W transfer faults and the reactivation, as transtensional faults, of the NE-SW and the NW-SE structures. The results of this study conflict with the occurrence of a caldera at Campi Flegrei and Naples Bay.
Mechanism of Explosive Eruptions from Moment Tensor Analyses of Explosion Earthquakes at Sakurajima Volcano, Japan

The source processes of explosion earthquakes that accompany explosive eruptions at an andesitic volcano Sakurajima, are investigated to clarify the mechanics of explosive eruption. Waveforms of explosion earthquakes are composed of a compressional P-wave first motion (P phase), following a dilatational motion with larger amplitude (D phase) and the largest amplitude motion, with longer period of 2 s (LP phase), which appears 2 to 3 s after the arrival of the P-wave first motion. Analyses of the particle motion, wave attenuation, and propagation velocity show that the P and D phases are composed of P-waves and the LP phase is composed of Rayleigh waves. Moment tensors for these phases are estimated by a waveform inversion method. The waveforms of explosion earthquakes are simulated by 2 sources at depths of about 2 km and 2 sources just beneath the crater. The three diagonal components of the moment tensors of the 1st source generating the P phase have similar positive values for all the events and ranging from 1.1 × 10 11 to 8.1 × 10 11 Nm. The non-diagonal components are negligibly small compared with the diagonal components. The 2nd source corresponding to the D phase is a contraction source with vertical dipoles of negative values ranging from - 0.4 × 10 12 to - 6.1 × 10 12 Nm. The horizontal dipoles are about 10 12 Nm and about twice as large as the vertical dipoles, ranging from -0.9 × 10 12 to -11.8 × 10 12 Nm. These results show that the P phase is generated by an isotropic expansion and the source of the D phase is approximated by a contraction of a cylinder at a depth of 2 km beneath the crater. The seismic moment of the cylindrical contraction is 5 to 20 times larger than that of the isotropic expansion, and the origin time of cylindrical contraction source is delayed by 0.2 to 0.5 s from the onset of the isotropic expansion. The LP phase is excited by an isotropic expansion and subsequent horizontal contraction at depths of 0.25-0.5 km beneath the crater bottom. The seismic moments of the shallow isotropic expansion and horizontal contraction sources range from 2.5 × 10 11 to 12.8 × 10 11 Nm and -2.9 × 10 11 to - 13.8 × 10 11 Nm, respectively. Origin times of the shallow isotropic expansions are delayed by 0.9-1.1 s from the occurrence of explosion earthquakes and coincide with the generation time of the air-shock at the crater bottom. Seismic moments of the shallow isotropic expansion sources are also correlated with the amplitudes of the air-shocks. The source depths of the shallow isotropic expansion and horizontal contraction coincide with that of the rapid deflations that are caused by an outbreak of gas pocket at the uppermost part of the volcanic conduit. It is inferred that the isotropic expansion is caused by instantaneous volume increase of the gas pocket formed at the uppermost region of the volcanic conduit which generates the air-shock. The horizontal contraction reflects decrease in volume caused by pressure decrease due to collapse of the gas pocket.
Land Use and Cover Changes in Brazilian Biomes between 1985 and 2018

In the last decades, there have been changes in land use and cover in the Brazilian territory, many resulting from anthropic activities. However, these changes impact the environmental system, compromising biodiversity and ecological services. In this context, integrative geographic information about environmental changes in the Biomes allows the identification of the main Spatio-temporal changes in land use and cover and can contribute to efficient environment planning. Thus, this paper proposes to describe and synthesize the Spatio-temporal changes in Brazilian Biomes between 1985-2018 using spatial statistics techniques and Principal Component Analysis with data from the MapBiomas project. It has employed the Google Earth Engine and QGIS for the analysis, with emphasis on spatial statistics and Principal Components Analysis which enabled the identification of the main changes in land use and cover. From the Principal Components Analysis, it was possible to reduce the data set with 34 images to two Principal Components that represent significant percentages of the variance of the original database. The results showed that the Brazilian Biomes had undergone significant changes over 34 years. The main vectors that drove these transformations were anthropic activities, such as urban growth, the advancement of agriculture, and cattle ranching in the countryside. Amazon and Cerrado present the largest area altered in square kilometers. And the fastest changes occurred on Amazon, Cerrado, and Pantanal. This work presents a synthesis of the main patterns and spatial-temporal changes in the use and cover of Brazilian Biomes, which can assist in the national environmental planning.
Does Samaritan, a Digital Support Platform, Help Improve Social Determinants of Health for Mental Health Offenders in Jacksonville, Florida?

Background The objective of this study is to evaluate if access to Samaritan, a digital support platform, improves the social determinants of health (SDOH) needs for patients enrolled in a jail diversion program in Jacksonville, FL. Methodology A total of 59 patients who were enrolled in a jail diversion program for homeless mentally ill misdemeanor offenders in Jacksonville, FL, participated in the study. Of the 59 patients, 47 individuals consented to participate in Samaritan while 12 declined participation. Demographics and the Health Leads Social Needs Screening Tool scores from the electronic health record were compared between groups along with average financial support from Samaritan. These non-normally distributed variables were compared using Wilcoxon rank-sum tests. Results The majority of study participants were male (92%, n = 43). The average age of study participants was 42 years. The average income from donors on the platform over three months for those who opted in was $48.80 (SD = 53.75). Among the individual Health Leads Social Needs Screening Tool questions, intact Housing was statistically significant (Z = -2.002, p = 0.045), suggesting access to a digital technology such as Samaritan might help improve SDOH needs. Conclusions Access to digital technologies, such as Samaritan, might help offenders with mental illness adjust to the many challenges they face upon reentry into the community. As such, these devices may represent one means for improving SDOH needs for disadvantaged mental health patients.
Explicit and implicit large eddy simulation of turbulent combustion with multi-scale forcing

The context of this study is the optimization of premixed turbulent combustion of syngas for clean energy production. A Bunsen-type CH4/air turbulent premixed burner with a multi-scale grid generator is simulated with different Large Eddy Simulation (LES) strategies and compared to experimental results. A low-Mach formulation of a compressible Navier-Stokes solver based on different numerical methods, ranging from 4th order central finite difference to 5th order advanced WENO schemes, is developed and applied. Classical test cases (1D laminar premixed flame, decaying HIT), and 2D simulations of the turbulent premixed flame are performed to assess the numerical methodology. Implicit LES (ILES), i.e. LES without any explicit subgrid modeling, and explicit LES with the Thickened Flame model and subgrid scale flame wrinkling modelling (TFLES) are applied to simulate numerically the 3D experimental burner. Results show that TFLES with a high-order low dissipation scheme predicts quite well the experimental flame length and flame surface density. ILES with advanced WENO schemes produces a slightly shorter although realistic flame provided the grid spacing is of order of the laminar flame thickness. The representation of flame/turbulence interactions in TFLES and ILES are however quite different.
Comparative Analysis of Three Semiconductor Technology Companies

This paper presents a meticulous financial analysis of Nvidia, Intel, and Texas Instruments, three leading companies in the semiconductor industry. And these three companies also operate in different section in the semiconductor industry. The study aims to provide insights into effective investment strategies of these companies. The investigation encompasses an analysis of recent financial indicators for these companies like exploring the meaning of various data, along with an exploration of asset selection challenges faced by nine distinct investor profiles including Value, Income, PEG, Index, Ratio analysis, DCF, Momentum, Insider buying and Stock buyback investors. Leveraging established financial models, the research reveals that Index, Momentum, Insider Buying, and Stock Buyback investors are inclined to invest in all three stocks. Additionally, Ratio Analysis and Discounted Cash Flow (DCF) investors are inclined towards investing Texas Instruments. The insights gained from this analysis contribute to the development of tailored investment strategies, ultimately aiming to optimize financial gains within the semiconductor sector.
High-Impact Teaching Practices in Higher Education: Understanding Barriers, Concerns, and Obstacles to Their Adoption

This research explores the barriers, concerns, and obstacles undergraduate STEM educators face when implementing high-impact teaching practices (HIPs), the application of which may improve student learning outcomes. Because our study took place during the COVID-19 pandemic, our results also shed light on the unique challenges of utilizing HIPs in asynchronous online-learning environments. Thirteen undergraduate instructors were interviewed about their current teaching practices in order to identify barriers to or support for adopting HIPs. Data collected through semi-structured interviews revealed administrative and financial restraints as barriers to effective teaching which have been found in previous research. A number of new and unique obstacles emerged out of teaching remotely or online during the pandemic, including a heightened concern over the instructor’s ability to connect with students and engage in the best teaching practices. This research extends our current understanding of barriers and concerns about adopting HIPs in undergraduate STEM courses because of the unique perceived threats that emerged during the pandemic. We identify strategies to equip faculty with the support they need to provide equitable learning experiences, including access to consultants who support curriculum development and implementation in the classroom, ongoing educational coaching, and increased access to professional-development opportunities and a community of inquiry to discuss teaching strategies.