Miracle-dz/UniFAR-FTUs huggingface dataset:
Aromatized Wine-Type Beverages with Added Byproducts of Essential Rose Oil Industry

The aroma bouquet of wine depends mainly on grape (variety, crop, climate, location, and so on), yeasts/microorganisms, and wine aging. Additionally, the wine flavor can be modulated by adding herbs, extracts, byproducts, and so on. The aim of the present study was to characterize aromatized wine-type beverages (AWTBs) prepared by supplementation with rose oil industry byproducts (ROIBs). Three approaches were employed: 1. dried ROIBs were added to the grape must, followed by fermentation with Saccharomyces cerevisiae (ARW_CoF); 2. dried ROIBs were added to preliminarily prepared rosé (ARW_M); 3. 70% (v/v) ethanolic extracts of ROIBs were added to preliminarily prepared rosé (ARW_E). The polyphenol content in the modified beverages increased significantly from 230.00 ± 5.21 (control) to 296.13 ± 3.57 (ARW_M_100), 295.50 ± 3.78 (ARW_CoF_100), and 293.73 ± 4.29 (ARW_E_400) µmol GAE/L. The addition of ROIBs did not alter the formation of higher alcohols, since their contents in the AWTBs and the control were below 65 mg/L. The amount of phenethyl alcohol increased significantly from 1.07 ± 0.15 mg/L to 4.08 ± 0.30, 4.96 ± 0.24, and 5.77 ± 0.28 mg/L in the ARW_E_400, ARW_M_100, and ARW_CoF_100, respectively. The sensory evaluation revealed a preference for AWTBs from the ARW_CoF series. The results suggested that rose byproducts could be successfully utilized for the preparation of new AWTBs enriched with phenolic antioxidants, while exhibiting a distinctive and pleasant rose aroma.
Exploration of probiotic attributes in lactic acid bacteria isolated from fermented Theobroma cacao L. fruit using in vitro techniques

Probiotics are known for their health-promoting properties and are recognized as beneficial microorganisms. The current investigation delves into the isolation and comprehensive in vitro characterization of lactic acid bacteria (LAB) obtained from the Indian-origin Theobroma cacao L. Forastero variety to assess their potential as probiotic candidates. Eleven LAB isolates were obtained, and among them, five exhibited classical LAB traits. These five isolates underwent rigorous in vitro characterization to evaluate their suitability as probiotics. The assessments included resilience against acid and bile salts, which are crucial for probiotic viability. Additionally, the isolates were subjected to simulated gastric and pancreatic fluids and lysozyme exposure to assess their survival rates. Auto- aggregation, co-aggregation, hydrophobicity, and exopolysaccharide production were also examined. The inhibitory potential of α-glucosidase, an enzyme related to glucose metabolism, was measured, and antioxidant activity was evaluated using DPPH and ABTS assays. A safety assessment was conducted to confirm the non-pathogenic nature of the isolates. Among the five isolates, CR2 emerged as a standout candidate with maximal bile salt hydrolase activity, phenol resistance, and lysozyme resistance. CR2 and CYF3 exhibited notable survival rates under simulated conditions. The isolates displayed variable degrees of auto-aggregation, co-aggregation, and hydrophobicity. CR2 exhibited the highest exopolysaccharide production (0.66 mg/mL), suggesting diverse applications in the food industry. CR2 also demonstrated the highest inhibition rate against α-glucosidase (56.55%) and substantial antioxidant activity (79.62% DPPH, 83.45% ABTS). Safety assessment confirmed the non- pathogenic nature of the isolates. Molecular characterization identified CR2 as Lactococcus lactis subsp. lactis and CYF3 as Limnosilactobacillus fermentum. Both strains exhibited commendable probiotic and technological attributes, positioning them as promising candidates for functional foods and beyond. This study provides valuable insights into the in vitro characterization of LAB isolated from Indian Theobroma cacao L. , highlighting their potential as probiotic candidates with advantageous traits, including survival in hostile conditions, beneficial enzymatic activities, bioactivity, and other essential attributes.
Ferrosilicon smelting CURRENT DISTRIBUTION IN SUBMERGED ARC FURNACES FOR SILICON METAL / FERROSILICON PRODUCTION

Current distribution in a Submerged Arc Furnace, is extremely important for the metallurgical processes, and overall furnace operation. This paper describes measurements of current and voltage on an industrial submerged arc furnace producing FeSi75. With a final goal of quantifying the fraction of furnace power generated in the electric arc, and furnace burden, a method was developed to quantify the fractional current in the electric arc and the current bypassing the arc through the charge respectively. The measurements treated in this paper were done on a 48 MVA industrial furnace at Elkem Icelandic Alloys plant in Iceland. Current and voltage waveforms were measured under normal operation, while the furnace was melted down and during start-up of the furnace on a coke bed. The measurements were compared to simulations employing an advanced MFD arc model. The time variability of the furnace resistance is used to estimate the current division between arc and charge. In particular electric arc resistance at current zero is evaluated, and used to estimate the charge resistance during metal production. The main results are that it is possible to use the time variability of phase resistance to distinguish between arc current and charge current. Using that the warmer chemically transformed part of an operating Si metal or FeSi furnace exhibits linear behaviour it is found that it passes close to 50% of the phase current, and similarly half of the furnace power is generated there.
Investigation of the Mechanism of Action of Periploca forrestii Schltr. Extract on Adjuvant Collagen Rats Based on UPLC-Q-Orbitrap-HRMS Non-Targeted Lipidomics

Periploca forrestii Schltr. (P. forrestii) is a classical medicinal plant and is commonly used in traditional medicine for the treatment of rheumatoid arthritis, soft tissue injuries, and traumatic injuries. The aim of this study was to evaluate the anti-arthritic effects of three fractions of P. forrestii alcoholic extracts (PAE), P. forrestii water extracts (PWE), and total flavonoids from P. forrestii (PTF) on Freund’s complete adjuvant (FCA)-induced arthritis in rats, and to use a non-targeted lipidomic method to investigate the mechanism of action of the three fractions of P. forrestii in the treatment of rheumatoid arthritis. To assess the effectiveness of anti-rheumatoid arthritis, various indicators were measured, including joint swelling, histopathological changes in the joints, serum cytokines (tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6)), and the joint inflammatory substance prostaglandin E2 (PGE2). Finally, ultra-performance liquid chromatography–quadrupole-orbitrap–high-resolution mass spectrometry (UPLC-Q-Orbitrap-HRMS) was used to determine the non-targeted lipid histology of the collected rat serum and urine samples to investigate the possible mechanism of action. PWE, PAE, and PTF were all effective in treating FCA-induced rheumatoid arthritis. The administered groups all reduced joint swelling and lowered serum inflammatory factor levels in rats. In the screening of lipid metabolite differences between serum and urine of the rat model group and the normal group, a total of 52 different metabolites were screened, and the levels of lipid metabolites in PWE, PAE, and PTF were significantly higher than those in the normal group after administration. In addition, PWE, PAE, and PTF may have significant therapeutic effects on FCA-induced arthritis by modulating nicotinic acid, nicotinamide, and histidine metabolic pathways.
Road-Pulse From IMU to Enhance HD Map Matching for Intelligent Vehicle Localization

Matching high definition (HD) map and real-time sensor data, which is crucial for map-based intelligent vehicle localization, usually requires robust and distinct road features extracted from both ends. However, road features extacted from sensor data (e.g., image, LiDAR) are susceptible to various conditions, such as lighting, weather conditions, dynamic objects, etc. Moreover, although lane lines can provide continuous lateral constraints on a vehicle's position, it is still challenging to get sufficient longitudinal ones due to the limited road markings. In this article, we employed the longitudinal distance provided by road-pulse, which is the vibration pattern while the vehicle passes lateral road elements (LREs) such as speed bumps, expansion joints, etc., to enhance the localization accuracy. Such road-pulse vibration patterns can be detected accurately with the proposed Sliding Window Gaussian Model (SWGM) from the acceleration data collected using a low-cost inertial measurement unit (IMU). Detection of road-pulse allows accurate and robust matching of LREs within the HD map to provide accurate longitudinal distances. Then the longitudinal and the lateral distance(s) derived from IMU and off-the-shelf around view monitoring (AVM) are mapped into global constraints with the support of the HD map. Finally, these constraints, together with the measurement from the consumer-level inertial navigation system (INS), are fused within a framework of Kalman Filter based on the Markov model (KF-MM) to achieve accurate vehicle localization. The proposed method has been verified on two routes with a total length of 3.7 km. Experimental results demonstrate that road-pulse can significantly enhance the localization results with good robustness to different scenarios. After fusing with road-pulses, the overall localization error can achieve an average decimeter accuracy on both test routes.
Community-Based Diabetes Awareness Strategy With Detection and Intervention: The Mobile Diabetes Education Center

To prevent diabetes and increase equitable access to health care screenings, Touro University California has created and implemented a community outreach program called the Mobile Diabetes Education Center (MOBEC). This program is a joint effort that also involves Sutter Health, the California Department of Public Health, Kaiser Permanente, the Solano County Department of Public Health, and community-based organizations, focusing on advancing health equity in Solano County’s at-risk populations. This article reports on the services and initial successes of MOBEC. With its strong community collaboration, MOBEC has helped to raise awareness of diabetes and ensure access to much-needed health screenings and education. This model can potentially be used as a blueprint for similar efforts nationwide to address the health care needs of medically underserved communities.
Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This data is then processed by a perception subsystem to create semantic knowledge of the world around the vehicle. State-of-the-art ADSs’ perception systems often use deep neural networks for object detection and classification, thanks to their superior performance compared to classical computer vision techniques. However, deep neural network-based perception systems are susceptible to errors, e.g., failing to correctly detect other road users such as pedestrians. For a safety-critical system such as ADS, these errors can result in accidents leading to injury or even death to occupants and road users. Introspection of perception systems in ADS refers to detecting such perception errors to avoid system failures and accidents. Such safety mechanisms are crucial for ensuring the trustworthiness of ADSs. Motivated by the growing importance of the subject in the field of autonomous and automated vehicles, this paper provides a comprehensive review of the techniques that have been proposed in the literature as potential solutions for the introspection of perception errors in ADSs. We classify such techniques based on their main focus, e.g., on object detection, classification and localisation problems. Furthermore, this paper discusses the pros and cons of existing methods while identifying the research gaps and potential future research directions.
Nuclear gluons at RHIC in a multiobservable approach

We explore the possibility of measuring nuclear gluon distributions at the Relativistic Heavy-Ion Collider (RHIC) with $\sqrt{s}=200 \, {\rm GeV}$ proton-nucleus collisions. In addition to measurements at central rapidity, we consider also observables at forward rapidity, consistent with proposed upgrades to the experimental capabilities of STAR and sPHENIX. The processes we consider consist of Drell-Yan dilepton, dijet, and direct photon-jet production. The Drell-Yan process is found to be an efficient probe of gluons at small momentum fractions. In order to fully utilize the potential of Drell-Yan measurements we demonstrate how the overall normalization uncertainty present in the experimental data can be fixed using other experimental observables. An asset of the RHIC collider is its flexibility to run with different ion beams, and we outline how this ability could be taken advantage of to measure the $A$ dependence of gluon distributions for which the current constraints are scarce.
UAV instantaneous power consumption prediction using LR‐TCN with simple moving average

The usage of multi‐rotor unmanned aerial vehicles (UAVs) has rapidly increased in a variety of industries recently, which has caused a quick rise in the quantity of research works on the topic. Remaining battery life prediction, anomaly detection and instantaneous power consumption prediction are among the topics that attract the most attention of researchers. This article presents the development and utilization of a modified temporal convolutional network (TCN) model, a commonly employed approach for anomaly detection and instantaneous power consumption prediction in unmanned aerial vehicles (UAVs). The first modification to the TCN model was to use the Leaky ReLu activation function in place of the rectified linear unit (ReLu) activation function from the original TCN model. In the next step, instantaneous power consumption prediction for UAVs was performed by using the data obtained from both sensors and simple moving average (SMA) algorithm. As a result of the tests performed with the created simulation setup, it has been clearly shown that the proposed method gives better results compared to other deep learning models used for comparison with the lowest RMSE of 0.0496.
The improving strategies and applications of nanotechnology-based drugs in hepatocellular carcinoma treatment

Hepatocellular carcinoma (HCC) is one of the leading causes of tumor-related death worldwide. Conventional treatments for HCC include drugs, radiation, and surgery. Despite the unremitting efforts of researchers, the curative effect of HCC has been greatly improved, but because HCC is often found in the middle and late stages, the curative effect is still not satisfactory, and the 5-year survival rate is still low. Nanomedicine is a potential subject, which has been applied to the treatment of HCC and has achieved promising results. Here, we summarized the factors affecting the efficacy of drugs in HCC treatment and the strategies for improving the efficacy of nanotechnology-based drugs in HCC, reviewed the recent applications’ progress on nanotechnology-based drugs in HCC treatment, and discussed the future perspectives and challenges of nanotechnology-based drugs in HCC treatment.
Highly Selective MIF Ketonase Inhibitor KRP-6 Diminishes M1 Macrophage Polarization and Metabolic Reprogramming

Macrophage polarization is highly involved in autoimmunity. M1 polarized macrophages drive inflammation and undergo metabolic reprogramming, involving downregulation of mitochondrial energy production and acceleration of glycolysis. Macrophage migration inhibitory factor (MIF), an enigmatic tautomerase (ketonase and enolase), was discovered to regulate M1 polarization. Here, we reveal that KRP-6, a potent and highly selective MIF ketonase inhibitor, reduces MIF-induced human blood eosinophil and neutrophil migration similarly to ISO-1, the most investigated tautomerase inhibitor. We equally discovered that KRP-6 prevents M1 macrophage polarization and reduces ROS production in IFN-γ-treated cells. During metabolic reprogramming, KRP-6 improved mitochondrial bioenergetics by ameliorating basal respiration, ATP production, coupling efficiency and maximal respiration in LPS+IFN-γ-treated cells. KRP-6 also reduced glycolytic flux in M1 macrophages. Moreover, the selective MIF ketonase inhibitor attenuated LPS+IFN-γ-induced downregulation of PARP-1 and PARP-2 mRNA expression. We conclude that KRP-6 represents a promising novel therapeutic compound for autoimmune diseases, which strongly involves M1 macrophage polarization.
Disparity estimation in stereoscopic vision by simulated annealing

This paper presents a correspondence algorithm for stereo vision based on an integrated model that includes several units corresponding to different stages: features extraction, minimization of an energy function using simulated annealing, multiresolution and interpolation. Firstly the original images are scaled down to considerably reduce their size. From the reduced images a disparity map is obtained, which is used as the basis to develop the complete process. For this reason an energy function is built and minimized using a multiresolution scheme. The energy function integrates features such as grey level, non parametric transforms, edges, smoothness and uniqueness. The obtained disparity for every resolution is interpolated to work with the following resolution. Our model produces a dense disparity map. The algorithm has been tested with different kinds of real images to show its flexibility.
Chaotic Convection of Viscoelastic Fluid in Porous Medium Under G-Jitter

Abstract The present article aims at investigating the effect of gravity modulation on chaotic convection of a viscoelastic fluid in porous media. For this, the problem is reduced into Lorenz system (non-autonomous) by employing the truncated Galerkin expansion method. The system shows transitions from periodic to chaotic behavior on increasing the scaled Rayleigh number R. The amplitude of modulation advances the chaotic nature in the system while the frequency of modulation has a tendency to delay the chaotic behavior which is in good agreement with the results due to [1]. The behavior of the scaled relaxation and retardation parameter on the system is also studied. The phase portrait and time domain diagrams of the Lorenz system for suitable parameter values have been used to analyze the system.
Barriers to recording calf health data on dairy farms in Ontario

Graphical Abstract Summary: An online survey was completed by a convenience sample of 88 dairy producers in Ontario, Canada, to investigate barriers for dairy farmers for recording calf illnesses and treatments. Fewer than a quarter of farms surveyed recorded all calf illnesses and fewer than half of farms recorded all administered antimicrobial treatments. We identified several factors associated with a producer's likelihood of recording calf illnesses and treatments, such as recording with a computer software system, keeping records in close proximity to the calves, and being a nonfamily employee. Several barriers were also identified, including lack of feedback from analysis of recorded calf data, records not being located near the calves, and records in a paper booklet.
An efficient conditional Monte Carlo method for European option pricing with stochastic volatility and stochastic interest rate

ABSTRACT This paper studies the variance reduction methods for pricing European options under stochastic volatility and stochastic interest rate model. A general conditional Monte Carlo pricing framework is constructed to reduce the variance and save the time cost of Monte Carlo simulation. Based on Martingale Representation Theorem, two efficient martingale control variates are designed to combine with the conditional Monte Carlo simulation. Numerical results show that this hybrid method has great variance reduction effect and robust performance. The idea is also applicable for pricing other financial derivatives with stochastic volatility and/or stochastic interest rate.
Characterizing Near-Nadir and Low Incidence Ka-Band SAR Backscatter From Wet Surfaces and Diverse Land Covers

The Surface Water and Ocean Topography (SWOT) satellite and AirSWOT airborne instrument are the first imaging radar-altimeters designed with near-nadir and low incidence, 35.75 GHz Ka-band InSAR for mapping terrestrial water storage variability. Remotely sensed surface water extents are crucial for assessing such variability but are confounded by emergent and inundated vegetation along shorelines. However, because SWOT-like measurements are novel, there remains some uncertainty in the ability to detect certain land and water classes. This study examines the likelihood of misclassification between 15 land cover types and develops the Ka-band Phenomenology Scattering (KaPS) model to simulate changes to Ka-band backscatter as a result of changing surface water fraction and surface structure, including vegetation morphology and water surface waves. Using a separability metric, the study finds that water is five times more distinct compared with dry land classes, but has the potential to be confused with littoral zone and wet soil cover types. The KaPS scattering model simulates AirSWOT backscatter for incidence angles 1–27°, identifying the conditions under which open water is likely to be confused with littoral zone and wet soil cover types. KaPS characterization of the sensitivity of near-nadir and low incidence Ka-band SAR to small changes in both wet area fraction and surface structure enables a more nuanced classification of inundation area. These results provide additional confidence in the ability of SWOT to classify water inundation extent and open the door for novel hydrological and ecological applications of future Ka-band SAR missions.
Molecular dynamics simulation on engine oil nanolubricant boundary lubrication conditions

The employment of nanoparticle additives in engine oil provides an effective technique for improvement in engine performance and emission. In this study, graphene, graphene–MWCNT, and graphene–SiO2 hybrid nanoparticles have been dispersed in engine oil (n‐decane) to measure the effects of microscopic characteristics of interfacial layer between the carbon–carbon, carbon–hydrogen, carbon–oxygen, and hydrogen–oxygen molecules under fixed shear rate 600 s−1. The boundary lubrication conditions are depicted with nanoindentation operation, in which interaction between the atoms generates the molecular forces under indentation and scratching operation that provide the nucleation and propagation of dislocation in the planes. The result shows that in an early stage of nanoindentation, plastic deformation occurs in the lubricant substrate around the indenter. Furthermore, forces generated in the z‐direction have a significant effect as compared with the forces x‐ and y‐directions. Nanoindentation results concluded that for graphene–MWCNT/engine oil (n‐decane) nanoparticles, at a volume fraction of 1.8%, the nanoparticles outperformed all other nanolubricants. At a volume fraction of 0.3%, the adhesion between the solid and liquid interface was increased by 2.5% graphene nanolubricant, 4% graphene–MWCNT nanolubricant, and 8% graphene–SiO2 nanolubricant. At 1.8% volume fraction, 240% increase in graphene–MWCNT nanolubricant, 72% increase in graphene nanolubricant, and 20% increase in graphene–SiO2 nanolubricant. This trend indicates that the performance of graphene SiO2 nanolubricants is superior at low‐volume fractions, whereas the performance of graphene nanolubricants and graphene–MWCNT nanolubricants is superior at high‐volume fractions. Also we observed the scratching effect of indenter on nanolubricants.