Miracle-dz/UniFAR-FTUs huggingface dataset:
Moniliformin Occurrence in Serbian Maize over Four Years: Understanding Weather-Dependent Variability

Moniliformin (MON) represents one of the most widespread emerging mycotoxins, whose presence in food and feed can potentially cause harmful effects on the health of both the public and animals. In order to investigate MON occurrence, a total of four hundred (n = 400) samples of unprocessed maize were sampled from different regions (Bačka, Banat, and Srem) of Serbia during a period of four years (2018–2021) and were analyzed using a validated liquid chromatography with tandem mass spectrometry (LC-MS/MS) method. The influence of regional differences and variations from year to year in terms of weather conditions on the occurrence of MON was also assessed. The obtained validation parameters indicated that the LC-MS/MS method is applicable to the determination of MON in maize samples. It can be observed from the acquired findings that all samples were contaminated with MON, while concentration levels varied between the samples, especially from different sampling years. The maize samples showed the greatest levels of MON concentration during the dry and hot climatic conditions experienced in 2021. In maize samples harvested in the period 2018–2020, the MON concentration levels detected were about two to three times lower compared to the year 2021. Additionally, a comprehensive investigation into the correlation between weather conditions and the occurrence of MON in maize grown in Serbia was undertaken by reviewing the outcomes of research undertaken in the past decade.
Total Triterpenes, Polyphenols, Flavonoids, and Antioxidant Activity of Bioactive Phytochemicals of Centella asiatica by Different Extraction Techniques

Obtaining phytochemical-rich plant extracts from natural products where the active ingredients are present in comparatively low levels in the tissue matrix is the critical initial step of any chemical analysis or bioactivity testing. The plant C. asiatica is rich in various phytochemicals, the major constituents being triterpenes and flavonoids, as well as other polyphenols, leading to a number of bioactivities. In this study, an attempt was made to achieve several green technology principles, while optimizing the extraction method for the efficient extraction of bioactive compounds from C. asiatica. Soxhlet extraction (SE), ultrasound-assisted extraction (UAE) with low-frequency sonication, microwave-assisted extraction (MAE) using a closed-vessel microwave digestion system, and subcritical water extraction (SWE) in a high-pressure reactor were employed to extract the bioactive compounds. The solvent system, extraction time, and solid-to-solvent ratio were varied to optimize the extraction. UAE gave the best extraction yield, while MAE gave similar results, with a solid-to-liquid ratio of 1:25, a binary solvent system of 9:1 methanol to water (v/v), and a 20 min extraction time for the extraction of triterpenes, including madecassoside, asiaticoside, madicassic acid, and asiatic acid. Investigation of different solvent systems based on water and methanol also revealed information on the extraction behavior of total triterpene content (TTC), total polyphenolic content (TPC), total flavonoid content (TFC), and the variations in the antioxidant capacity of the extracts. In this study, it was evident that UAE and MAE offer more efficient and effective extraction of bioactive compounds in terms of extraction yield, time, and minimal solvent and energy use. Furthermore, the results showed that the different solvent ratios in the extraction mixture will affect the extraction of bioactive compounds, and a binary solvent system with a combination of methanol and water was the most efficient for the studied compounds in Centella asiatica.
Determining Some Quality Characteristics of Vegan Tarhana Added with Red Beet (Beta Vulgaris Var. Cruenta) Powder

: The present study aims to increase the number of tarhana versions, which have traditionally had a place in our culture, in order to increase the range of vegan products. In the present study, it was aimed to diversify the versions of tarhana, which has had a place in our traditional culture. For this purpose sauced and sauce-free vegan tarhana (yogurt-free) samples added with red beet ( Beta vulgaris var. Cruenta ) powder at different concentrations (control, 0.5%, 1%, 1.5%, 2%, 2.5%, 3%) samples were produced. Several physicochemical, microbiological, and sensorial properties of tarhanas produced were examined. Vegan tarhana with tomato sauce with 1.0% red beet powder had the highest score for consistency, taste, aroma and general acceptability. The highest score of color and odor properties was determined in vegan tarhana with tomato sauce added with 2.0% red beet powder. Ash, total acidity, protein, water-holding capacity, total dietary fiber, total phenolic content, and antioxidant capacity increased in parallel with red beet powder concentration. It was found that microbiological characteristics met the standards for tarhana, that the total number of mesophilic aerobic bacteria was higher than that of total yeast-mold. Staphylococcus aureus and coliform-group bacteria could not be detected in vegan tarhanas with red beet powder. In conclusion, it can be stated that all the vegan tarhanas with red beet powder samples had equivalent physicochemical and bioactive characteristics in comparison to the tarhana samples examined in the literature.
Title Design Of An Extended Architecture For Degraded Modes Of Operation Of AHS Permalink

We propose a hierarchical control architecture for dealing with faults and adverse environmental conditions on an Automated Highway System (AHS). Our design builds on a previously designed control architecture that works under normal conditions of operation. The faults that are considered in our design are classi ed according to capabilities remaining on the vehicle or roadside after the fault has occurred. Information about these capabilities is used by supervisors in each of the layers to select appropriate control strategies. We outline the extended control strategies that are needed by these supervisors of each layer of the hierarchy and, in certain cases, give examples of their detailed operation.
Spatial Variability of Main Trace Elements in Tea Plantation Soils on Low Mountain on Different Scales

Based on the data available,spatial variability of trace elements of the tea plantation soil on the Mengdingshan Mountain was studied by means of principal component analysis and geostatistics.Results indicated(1)trace elements varied,but showed a similar trend in spatial variability on two different scales;and ordinary Kriging revealed that trace element contents were distributed along a strip from northeast to southwest;2)principal-component-based comprehensive mapping reflected pretty well a general rule of the distribution of trace elements;the Kriging maps embodied the characters of trace elements distribution along the slope oft he mountain,which was in accordance with the result of geostatistic analysis;and 3)soil environment conditions were the main factor affecting spatial distribution of trace elements in the tea plantation soil on the Mengdingshan Mountain and slope the main topographic one.
A Deep Reinforcement Learning-Based Motion Planner for Human-Robot Collaboration in Pathological Experiments

This paper presents a robotic motion planner to address the collision issue between the robotic arm and human arm in human-robot collaboration tasks. We transform the motion planning problem of the robotic arm into a Markov decision process based on real pathological experimental scenarios, by constructing models such as state space, action space, and reward function, to achieve motion planning for a pre-designed task. The planner extracts the position information of the human arm from existing trajectory data sets to generate a real-time hand model, which serves as an obstacle in the collaborative workspace. Based on the reinforcement learning algorithm, the robotic arm adjusts its behavior towards the hand obstacle through a self-designed reward function. Meanwhile, we modify the output action policy of the motion planner by adding OU-Noise to enhance the robotic arm’s exploration capability of the overall space. We deploy the pybullet simulation environment on an Ubuntu20 system and conduct multiple experiments using the CR5 collaborative robotic arm model. Experimental results show that this method can efficiently and accurately plan the motion trajectory of the robotic arm in complex collaboration scenarios, achieving avoidance behavior towards the human arm.
Improved motor imagery classification using adaptive spatial filters based on particle swarm optimization algorithm

Background As a typical self-paced brain–computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed spatial filters for specific input signals. In addition, the CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited. Methods To make up for these deficiencies, this study introduces a novel spatial filter-solving paradigm named adaptive spatial pattern (ASP), which aims to minimize the energy intra-class matrix and maximize the inter-class matrix of MI-EEG after spatial filtering. The filter bank adaptive and common spatial pattern (FBACSP), our proposed method for MI-EEG decoding, amalgamates ASP spatial filters with CSP features across multiple frequency bands. Through a dual-stage feature selection strategy, it employs the Particle Swarm Optimization algorithm for spatial filter optimization, surpassing traditional CSP approaches in MI classification. To streamline feature sets and enhance recognition efficiency, it first prunes CSP features in each frequency band using mutual information, followed by merging these with ASP features. Results Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBACSP. The classification accuracy of the proposed method has reached 74.61 and 81.19% on datasets 2a and 2b, respectively. Compared with the baseline algorithm, filter bank common spatial pattern (FBCSP), the proposed algorithm improves by 11.44 and 7.11% on two datasets, respectively (p < 0.05). Conclusion It is demonstrated that FBACSP has a strong ability to decode MI-EEG. In addition, the analysis based on mutual information, t-SNE, and Shapley values further proves that ASP features have excellent decoding ability for MI-EEG signals and explains the improvement of classification performance by the introduction of ASP features. These findings may provide useful information to optimize EEG-based BCI systems and further improve the performance of non-invasive BCI.
Near-Surface Investigation Using High-Resolution Seismic Reflection Techniques

The shallow seismic technique has been used to see subsurface, 595 meters of high resolution seismic reflection profile were carried out. The data were acquired using a Strata Visor with 48-channel, 40 Hz geophones and a vibroseis “IVI Minivib” system as a seismic source. Seismic reflection data were recorded using a CMP (common mid-point) acquisition method. The results for Al muzahimiyah Line are good and show a considerable improvement in signal to noise ratio. There were some problem during processing such as multiples, noise and it was so difficult to see first break because of high frequency. Signal to noise ratio was good in general. Frequent testing was carried out to improve the signal.
Amplitude of ENSO cycles in the Santa Barbara Basin, off California, during the past 15 000 years

Varve thickness time series from ODP Site 893 in the Santa Barbara Basin (off California) were analysed to determine variation in the strength of El Niño/Southern Oscillation (ENSO) cycles during the past 15 000 yr. Mean varve thickness and variance changed over time, with thicker varves before ∼8000 yr BP indicative of wetter than modern climates. A 100‐yr running standardisation was applied to correct for non‐stationarity. The contribution of ENSO‐scale variability was then estimated as the amplitude of 3–8 year bandpassed data. Results show multidecadal‐ to centennial‐scale modulation of the amplitude. On average, however, the amplitude of ENSO scale variability remained constant throughout the past 15 000 yr. We therefore conclude that, although the expression of ENSO cycles may have changed during the Holocene, there is no indication for a significant change in amplitude of interannual variability. Copyright © 2005 John Wiley & Sons, Ltd.
Comparative analysis of lifestyle behaviors and dietary intake among obese and non-obese individuals following bariatric surgery: a secondary data analysis from 2020 to 2022

Objective The aim of this research is to perform a comparative examination of lifestyle habits and dietary consumption between obese and non-obese subjects who have undergone bariatric surgery. This is done with the intent of investigating the disparities in obesity outcomes attributable to these elements. Method This study involves a secondary analysis of cross-sectional data obtained from the Sharik Diet and Health National Survey (SDHNS). To ensure a representative distribution of participants, the SDHNS employs a proportional quota sampling strategy, with stratification based on age, sex, and geographic location within Saudi Arabia’s 13 administrative regions, utilizing the ZDataCloud® system for this purpose. The data, collected between 2020 and 2022 from over 15,000 participants, were screened to identify the eligible records of individuals who underwent bariatric surgery. Results Within the entire sample, a mere 5.0% (806 individuals) had undertook bariatric surgery, with females comprising 54% of this specific subgroup. The average age within this group was 38.85 years (SD 13.02) and range (18–87). Post-operative results showed that 33% of these individuals remained classified as obese. Utilizing the backward likelihood ratio regression model, it was determined that factors including age, decreased consumption of fresh juices and chicken, as well as current tobacco use, were significantly associated with persistent obesity. Conclusion The findings of this study suggest an association between the non-obese group and healthier lifestyle choices, including the consumption of high-protein diets and fresh juices, alongside a decreased prevalence of smoking. These observations underscore the significance of maintaining a healthy lifestyle for positive weight loss outcomes following bariatric surgery.
A Fault Detection and Diagnosis Method Based on the Currents Entropy Indexes for the SRM Drive With a Fault Tolerant Multilevel Converter

Neutral Point Clamped Asymmetric-Half-Bridge (NPC-AHB) has been proposed as one of the power converter topologies for the Switched Reluctance Machine (SRM) drive. This topology is characterized by multilevel operation and the capability to operate in fault tolerant mode, which is most indicated for use in applications that require high reliability. However, one fundamental aspect associated with fault tolerance operation is the necessity to diagnose and identify a fault in the power semiconductors of the converter. Thus, this paper proposes a new approach for the detection and diagnosis of multiple faults in the power semiconductors of this converter. The proposed approach is based on an image analysis, namely through the discrimination of the different eccentricities that will appear. This identification is done through the use of proposed normalized indexes that are developed from the entropy analysis. The proposed approach will allow discriminating multiple power semiconductors in fault, as well as between open and short circuit conditions. The performance and capability of the proposed approach will be tested using a laboratory system.
Model Approach to Thermal Conductivity in Hybrid Graphene–Polymer Nanocomposites

The thermal conductivity of epoxy nanocomposites filled with self-assembled hybrid nanoparticles composed of multilayered graphene nanoplatelets and anatase nanoparticles was described using an analytical model based on the effective medium approximation with a reasonable amount of input data. The proposed effective thickness approach allowed for the simplification of the thermal conductivity simulations in hybrid graphene@anatase TiO2 nanosheets by including the phenomenological thermal boundary resistance. The sensitivity of the modeled thermal conductivity to the geometrical and material parameters of filling particles and the host polymer matrix, filler’s mass concentration, self-assembling degree, and Kapitza thermal boundary resistances at emerging interfaces was numerically evaluated. A fair agreement of the calculated and measured room-temperature thermal conductivity was obtained.
Preference-based Reinforcement Learning

This paper investigates the problem of policy search based on the only expert’s preferences. Whereas reinforcement learning classically relies on a reward function, or exploits the expert’s demonstrations, preference-based policy learning (PPL) iteratively builds and optimizes a policy return estimate as follows: The learning agent demonstrates a few policies, is informed of the expert’s preferences about the demonstrated policies, constructs a utility function compatible with all expert preferences, uses it in a self-training phase, and demonstrates in the next iteration the policy maximizing the current utility function. PPL actually tackles an expensive optimization problem, as each policy assessment relies on the expert’s feedback. The expert’s preference model is used as a surrogate model of the true objective function. Compared to earlier work devoted to active preference learning (see e.g. Brochu et al., 2008), a main lesson is that the surrogate model must preferably be learned on the state × action space, as opposed to, on the policy parametric space. Empirical evidence from the cancer treatment policy domain is provided to support and discuss this claim.
ON LANDSCAPE PATTERN AND BIODIVERSITY IN RURAL AREAS OF JIANGHAN PLAIN——TAKING TWO VILLAGES AS A CASE STUDY

The study was approached based on field investigations,different series of airphoto interpretation and household surveys.Landscape pattern depends on land use changes which were resulted from interactions between large scale and small scale factors.The diversity among households and farming system makes the landscape more heterogeneous.Field margins and hedgerows and woodlots of home gardens are less disturbed elements of the landscape.They are also very diverse structures and may be key elements to conserving biodiversity and agricultural sustainability.
Spectral Analysis of Strontium-Doped Calcium Phosphate/Chitosan Composite Films

Strontium-doped calcium phosphate/chitosan films were synthetized on silicon substrates using the radio-frequency magnetron sputtering technique and the matrix-assisted pulsed laser evaporation technique. The deposition conditions associated with the radio-frequency magnetron sputtering discharge, in particular, include the high temperature at the substrate, which promotes the formation of strontium-doped tetra calcium phosphate layers. The physical and chemical processes associated with the deposition of chitosan on strontium-doped calcium phosphate layers were investigated using Fourier Transform Infrared Spectroscopy, Energy Dispersive X-ray Spectroscopy, and Scanning Electron Microscopy. Mass spectrometry coupled with laser induced ablation of the composite films proved to be a useful tool in the detection of the molecular ions characteristic to chitosan chemical structure.
Co-evolution of phytoplankton and ocean N:P ratios

Co-evolution of phytoplankton C:N:P stoichiometry and the deep ocean N:P ratio T. M. Lenton and C. A. Klausmeier School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK W. K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA Received: 16 June 2006 – Accepted: 4 July 2006 – Published: 17 July 2006 Correspondence to: T. M. Lenton (t.lenton@uea.ac.uk)
Partial Tensorized Transformers for Natural Language Processing

The transformer architecture has revolutionized Natural Language Processing (NLP) and other machine-learning tasks, due to its unprecedented accuracy. However, their extensive memory and parameter requirements often hinder their practical applications. In this work, we study the effect of tensor-train decomposition to improve the accuracy and compress transformer vision-language neural networks, namely BERT and ViT. We focus both on embedding-layer compression and partial tensorization of neural networks (PTNN) through an algorithmic approach. Our novel PTNN approach significantly improves the accuracy of existing models by up to 5%, all without the need for post-training adjustments, breaking new ground in the field of tensor decomposition.
Estimation of Deep Percolation in Sandy-Loam Soil using Water-balanceApproach

The most reliable method used to estimate deep percolation is using Lysimeter which proves to be expensive and time consuming as well. In this study, deep percolation has been estimated by using water balance method. The experimental setup consisted of two lysimeters in the Hydraulic Engineering field lab, I.I. T. , Roorkee in which maize crop was grown. Regular measurements of soil moisture were made at the depths 0-20 cm, 20-40 cm, 40- 60 cm, 60-80 cm and 80-100 cm using gravimetric method. The evapotranspiration estimates were determined using Penman-Moneith equation. The deep percolation is calculated using the water-balance approach. The deep percolation calculated using the water-balance approach is comparable to the observed values of deep percolation which are obtained from the lysimeters.
Patient participation: A qualitative study of immigrant women and their experiences

Patient participation in healthcare is a neglected area of interest in the rather extensive amount of research on immigrant so-called Selma patients in Swedish health care as well as worldwide. The aim is to explore the phenomenon “patient participation” in the context of the Swedish health care from the perspective of immigrants non-fluent in Swedish. A phenomenological lifeworld approach was chosen. Data were collected from patients within a municipal home care setting in Sweden. Eight women agreed to participate. In seven interviews, an interpreter was necessary for the translation of the interview. Five authorized interpreters were used. Data were analysed in accordance to a descriptive phenomenological method for caring research. The analysis led to an essence of the phenomenon with three constituents, “to experience participation,” “to refrain from participation,” and “to be deprived of participation. ” Patient participation from the perspective of immigrant women means that patients are involved and active in their own health and caring processes. For these women, it is particularly important to have the opportunity to express themselves. Patient participation presupposes professional caregivers who act in a way that increases the patients’ opportunities to take part. A skilled interpreter is often necessary in order to enable the patient participation.