Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound-Assisted Extraction of Phenolic Compounds from Adenaria floribunda Stem: Economic Assessment

Adenaria floribunda is a native species found in tropical regions of South America used as a traditional medicine. Ultrasound-assisted extraction (UAE) is an extraction process known to increase the extraction yield, reduce extraction times, and use low temperatures. This study aims to obtain water-based extracts from A. floribunda stems using UAE, hot water extraction (HWE), and Soxhlet extraction and perform an economic analysis. The global extraction yield (GEY) and total phenolic compounds (TPC) of extracts ranged from 5.24% to 10.48% and from 1.9 ± 0.44 mg GAE g−1 DW to 6.38 ± 0.28 mg GAE g−1, respectively. Gallic acid, catechin, and ferulic acid were identified in the extract using HPLC-UV. Results indicate that Soxhlet extraction has the best performance regarding GEY and TPC. However, after performing an economic assessment, the cost of manufacturing (COM) of Soxhlet extraction (US$ 5.8 flask−1) was higher than the UAE (US$ 3.86 flask−1) and HWE (US$ 3.92 flask−1). The sensitivity results showed that obtaining extracts from A. floribunda by UAE and HWE is economically feasible when the selling price is above US$ 4 flask−1. Soxhlet extraction is a feasible technique when the selling price is above US$ 7 flask−1.
A Comprehensive Study on Fruit Classification and Grading Techniques

Traditional methods for fruit grading are based on sensory or psychometric evaluation, which is costly and time-consuming. Different approaches were developed for different fruit like Dates, Apple, Mango, Tomato, and Mulberry based on their features like shape, color, Freshness, acidity, and some other methods also include cellular level analysis and Classification. The most prevalent traits used to detect diseases, ripeness, and fruit class are color, texture, and morphological characteristics. Agriculture automation is used to boost production and quality and promote economic growth. The techniques have evolved to grade fruits in cost-effective, less time-consuming using computer vision and machine learning algorithms that include Artificial Neural Networks and Support Vector Machine. ML techniques, spectrometric sensors, and non-invasive sensing provided better results. In this article, different Machine Learning and Deep Learning methods used along with traditional designs are considered and analyzed. Their mathematical model and algorithms are interpreted, and deep analysis is done.
Design and Analysis of Printed Planar Compact Broadband Dual Frequency Antenna in FR4 Substrate for Signal Processing Applications

This research study proposes a printed compact dual frequency broadband antenna, which can radiate at two resonant frequencies such as 2.1GHz and 5.80 GHz. The proposed antenna is designed in Ansoft Magus CAD EM simulation tool. The material substrate used here is FR4 substrate with εr=2.2, tanδ =0.001, and h =0.254 mm. The operating frequency is designed for ISM band 5G application for performing multi-band switching in mobile phones. The antenna dimension considered here is 23mmX 68mmX 254μm, a compact structure which can be easily embedded in any random area. The simulated output involves simulated parameter values, which are acceptable for fabrication & the testing total gain is 2.17 dBi with VSWR = 2 and Zin= 45Ω.
Functionalized 10-Membered Aza- and Oxaenediynes through the Nicholas Reaction

The scope and limitations of the Nicholas-type cyclization for the synthesis of 10-membered benzothiophene-fused heterocyclic enediynes with different functionalities were investigated. Although the Nicholas cyclization through oxygen could be carried out in the presence of an ester group, the final oxaenediyne was unstable under storage. Among the N-type Nicholas reactions, cyclization via an arenesulfonamide functional group followed by mild Co-deprotection was found to be the most promising, yielding 10-membered azaendiynes in high overall yields. By contrast, the Nicholas cyclization through the acylated nitrogen atom did not give the desired 10-membered cycle. It resulted in the formation of a pyrroline ring, whereas cyclization via an alkylated amino group resulted in a poor yield of the target 10-membered enediyne. The acylated 4-aminobenzenesulfonamide nucleophilic group was found to be the most convenient for the synthesis of functionalized 10-membered enediynes bearing a clickable function, such as a terminal triple bond. All the synthesized cyclic enediynes exhibited moderate activity against lung carcinoma NCI-H460 cells and had a minimal effect on lung epithelial-like WI-26 VA4 cells and are therefore promising compounds in the search for novel antitumor agents that can be converted into conjugates with tumor-targeting ligands.
Research into influencing factors in user experiences of university mobile libraries based on mobile learning mode

PurposeThe purpose of this study is to analyse and discuss the influencing factors of user experience in university mobile libraries and the improvement path of user experience in the context of mobile learning.Design/methodology/approachThe study adopted the grounded theory research method, and the sample included 28 students from five universities, with mobile libraries as the research objects and semi-structured interview as data acquisition method. A step-by-step coding analysis of the original interview materials was conducted, which comprehensively identified the main concerns and problems encountered by users of the university mobile library apps especially in the mobile learning behaviour mode, and then a theoretical model of the influencing factors of the app user experience of the university mobile library was constructed.FindingsA theoretical model of influencing factors was constructed, which determined that system quality, interaction quality, content quality, interface quality and function quality were the key elements of mobile library user experiences. Furthermore, based on the research results and user feedback obtained in the research process, the content and key points relating to the user experience can be elaborated in detail. In addition, this study was able to determine users' perspectives and their behavioural characteristics when engaging in mobile learning.Originality/valueThis study establishes a theoretical model of the factors influencing of the user experience of university mobile libraries based on mobile learning, which could provide a valuable reference for the design of other programs and strategies to promote user learning experiences of mobile library app in colleges and universities.
Superconducting transition temperature in thin films

By considering the Ginzburg-Landau model, compactified in one of the spatial dimensions, and using a modified Matsubara formalism, we determine the dependence of the superconducting transition temperature (Tc) of a film as a function of its thickness (L). We show that Tc is a decreasing linear function of L, as has been found experimentally. The critical thickness for the suppression of superconductivity is expressed in terms of the Ginzburg-Landau parameters.
Sexuality and mental health during the pandemic: Associations among couples’ COVID-19 anxiety, sexual communication, and sexual satisfaction

ABSTRACT The present study explored the associations among COVID-19 anxiety, sexual communication, and sexual satisfaction in couples cohabitating together during the pandemic lockdown. Forty-one couples living in three major cities in the United States completed a survey during the early months of the pandemic. Multilevel modeling revealed that individuals’ sexual communication was associated with their own, but not their partner’s, reported sexual satisfaction. However, COVID-related anxiety was not indirectly associated with sexual satisfaction through sexual communication nor did it moderate the association between sexual communication and sexual satisfaction. These findings reveal that the well-established link between sexual communication and sexual satisfaction remained during the pandemic and that anxieties related to the pandemic did not impact this association.
Synthesis of boron nitride nanosheets incorporated with Pd nanoparticles for reduction of nitroaromatics

As an analogous to graphene, hexagonal boron nitride nanosheets (BNNSs) posses interesting and unique properties. Here, we successfully exfoliated commercial h-BN powder to BNNSs with large lateral sizes of 200 nm and thicknesses of 2–5 nm by N -methyl-2-pyrrolidone aqueous solution. The various loading amount of palladium nanoparticles with sizes of about 7 nm were uniformly distributed on BNNSs. The XPS, FTIR and TEM results indicated a strong interaction between Pd nanoparticles and BNNSs. The prepared Pd incorporated BNNSs showed superior catalytic activity and cycle stability for the hydrogenation reduction of p -nitrophenol. Graphical abstract
Drawing planar partitions III: Two constrained embedding problems

In two previous papers, we studied the following problem: Given a planar graph G = (V; E) and a partition V = A B of the vertices. Can we draw G without crossing such that the partition is clearly visible? For three models used to display the partition, we developed necessary and suucient conditions for the existence of such a drawing. The time to test these conditions was dominated by verifying whether there exists a planar embedding of G that satisses certain additional properties. In this paper, we study how these constrained embedding tests can be done in linear time.
Tropical–temperate comparisons in insect seed predation vary between study levels and years

Abstract The biotic interaction hypothesis, which states the species interaction becomes stronger in the tropics, is deeply rooted in classic ecological literature and widely accepted to contribute to the latitudinal gradients of biodiversity. Tests in latitudinal insect–plant interaction have emphasized leaf‐eating insects on a single or a few plant species rather than within an entire community and mixed accumulating evidence, leaving the biotic interaction hypothesis disputed. We aimed to test the hypothesis by quantifying insect seed predation in a pair of tropical and temperate forest communities with similar elevations. We applied a consistent study design to sample predispersal seeds with systematically set seed traps in 2019–2020 and examined internally feeding insects. The intensity of seed predation was measured and further applied to tropical versus temperate comparison at two levels (cross‐species and community‐wide). Our results showed every latitudinal pattern associated with different study levels and years, that is, negative (greater granivory in the tropics in community‐wide comparison in 2020), positive (less granivory in the tropics in community‐wide and cross‐species comparison in 2019), and missing (similar level of granivory in the tropics in cross‐species comparisons in 2020). The cross‐species level analyses ignore differences among species in seed production and weaken or even lose the latitudinal trend detected by community‐wide comparisons. The between‐year discrepancy in tropical–temperate comparisons relates to the highly variable annual seed composition in the temperate forest due to mast seeding of dominant species. Our study highlights that long‐term community‐level researches across biomes are essential to assess the latitudinal biotic interaction hypothesis.
A Platinized Carbon Fiber Microelectrode-Based Oxidase Biosensor for Amperometric Monitoring of Lactate in Brain Slices

Background: Direct and real-time monitoring of lactate in the extracellular space can help elucidate the metabolic and modulatory role of lactate in the brain. Compared to in vivo studies, brain slices allow the investigation of the neural contribution separately from the effects of cerebrovascular response and permit easy control of recording conditions. Methods: We have used a platinized carbon fiber microelectrode platform to design an oxidase-based microbiosensor for monitoring lactate in brain slices with high spatial and temporal resolution operating at 32 °C. Lactate oxidase (Aerococcus viridans) was immobilized by crosslinking with glutaraldehyde and a layer of polyurethane was added to extend the linear range. Selectivity was improved by electropolymerization of m-phenylenediamine and concurrent use of a null sensor. Results: The lactate microbiosensor exhibited high sensitivity, selectivity, and optimal analytical performance at a pH and temperature compatible with recording in hippocampal slices. Evaluation of operational stability under conditions of repeated use supports the suitability of this design for up to three repeated assays. Conclusions: The microbiosensor displayed good analytical performance to monitor rapid changes in lactate concentration in the hippocampal tissue in response to potassium-evoked depolarization.
Effects of Compression of the Ulnar Artery on the Radial Artery Catheterization

Background: The study was designed to evaluate the effects of compression of the ulnar artery on blood flow (BF) and internal cross-sectional area (CSAi) of the radial artery. We also evaluated the success rate and time of successful ultrasound-guided radial artery catheterization at the first attempt with or without compression of the ulnar artery. Methods: Patients were randomly allocated to the Compression group or Standard group to be treated with or without the application of ulnar artery compression, respectively. Hemodynamic stability was confirmed, and ultrasound-guided radial artery catheterization was performed. In the Compression group, an assistant compressed the ulnar artery at 5 cm above the wrist crease and the catheterization was performed after the loss of the distal ulnar artery BF. In the Standard group, the catheterization was performed without compression of the ulnar artery. Before and after the catheterization, BF and CSAi of the radial artery were evaluated. Success rate and time to successful catheterization at the first attempt were recorded. Results: BF and CSAi of the radial artery were similar in the two groups (37.5 [19.3–66] vs. 37.0 [20.6–53.7] mL/min, respectively, p = 0.63; 4.0 [4.0–6.0] vs. 4.0 [3.0–5.0] mm2, respectively, p = 0.095). In the Compression group, BF and CSAi were changed to 80.9 [35.9–128.5] mL/min (p < 0.001) and 5.0 [4.0–7.0] mm2 (p < 0.001), respectively, after compression of the ulnar artery. There was a trend that the success rate of ultrasound-guided radial artery catheterization at the first attempt was higher in the Compression group than in the Standard group (58/59 vs. 53/59, respectively, p = 0.05), although the difference was not statistically significant. However, the time to successful ultrasound-guided radial artery catheterization at the first attempt was significantly shorter in the Compression group than in the Standard group (34 [27–41] s vs. 46 [36–60] s, p < 0.001). Conclusion: Compression of the ulnar artery augmented BF and CSAi of the radial artery. It resulted in a significantly shorter success time for ultrasound-guided radial artery catheterization at the first attempt.
Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net

Computed Tomography (CT) is commonly used for cancer screening as it utilizes low radiation for the scan. One problem with low-dose scans is the noise artifacts associated with low photon count that can lead to a reduced success rate of cancer detection during radiologist assessment. The noise had to be removed to restore detail clarity. We propose a noise removal method using a new model Convolutional Neural Network (CNN). Even though the network training time is long, the result is better than other CNN models in quality score and visual observation. The proposed CNN model uses a stacked modified U-Net with a specific number of feature maps per layer to improve the image quality, observable on an average PSNR quality score improvement out of 174 images. The next best model has 0.54 points lower in the average score. The score difference is less than 1 point, but the image result is closer to the full-dose scan image. We used separate testing data to clarify that the model can handle different noise densities. Besides comparing the CNN configuration, we discuss the denoising quality of CNN compared to classical denoising in which the noise characteristics affect quality.
Ionizing Radiation from Radiopharmaceuticals and the Human Gut Microbiota: An Ex Vivo Approach

This study aimed to determine the effect of three widely used radiopharmaceuticals with intestinal excretion on selected relevant bacteria that are part of the human gut microbiota, using an ex vivo approach. Fecal samples obtained from healthy volunteers were analyzed. Each sample was divided into four smaller aliquots. One served as the non-irradiated control. The other three were homogenized with three radiopharmaceutical solutions ([131I]NaI, [99mTc]NaTcO4, and [223Ra]RaCl2). Relative quantification of each taxa was determined by the 2−ΔΔC method, using the ribosomal gene 16S as an internal control (primers 534/385). Twelve fecal samples were analysed: three controls and nine irradiated. Our experiment showed fold changes in all analyzed taxa with all radiopharmaceuticals, but results were more significant with I-131, ranging from 1.87–83.58; whereas no relevant differences were found with Tc-99m and Ra-223, ranging from 0.98–1.58 and 0.83–1.97, respectively. This study corroborates limited existing research on how ionizing radiation changes the gut microbiota composition, providing novel data regarding the ex vivo effect of radiopharmaceuticals. Our findings justify the need for future larger scale projects.
Comparing image segmentation methods using data envelopment analysis

In this paper, a model based on data envelopment analysis is used for comparing diﬀerent image segmentation methods and also for the purpose of ﬁnding the best parameter among certain values for a method. The criteria for choosing inputs and outputs are explained and in the end, some examples are presented to demonstrate how this model works. Copyright c (cid:13) 2022 Shahid Beheshti University.
Calibration of stellar and atmospheric models using the Hyades

Calibration and benchmarking of evolutionary and atmospheric models is essential for the study of low-mass stars and brown dwarfs, as for isolated objects these models are the only way to determine basic parameters like mass and age. The Hyades star cluster with an age of around 625 Myr and located at the distance of ~ 45 pc, is the most accessible cluster in the solar neighborhood. R\"oser et al. (2011) establish a list of 724 likely Hyades members. Using available literature data (HST, HIPPARCOS, WDS, Patience et al. 1998, Mermilliod et al. 2009, Morzinski 2011) and our own lucky imaging observations with AstraLux at the 2.2m telescope in Calar Alto, we establish a single-star sequence containing 255 Hyades members spanning the mass range ~ 0.2--1.5 solar masses . This sequence is used for testing and calibration of various existing stellar and atmospheric models (PADOVA, DARTMOUTH, BCAH, BT-Settl)
Creating a Japanese Dialogue Corpus with Multi-level Topic Analysis

The study of generative dialogue systems has become a hotspot with the recently well-studied natural language understanding and generation techniques. However, most works have been focusing on those widely used languages, such as English and Chinese, with huge dialogue corpora being collected and thoroughly analyzed for training the dialogue generation models. In addition, retaining the human-like diversity and spontaneity of speech in these corpora is challenging for building dialogue systems. In this paper, we propose a method to build a lage Japanese dialogue corpus by using the conversations posted on Twitter and to annotate the dialogue- and utterance-level topic labels and the corresponding probabilistic scores automatically by analyzing the similarity between the word clusters and the dialogue clusters of the corpus in the same semantic space. The advantage of this method is that it does not require the expensive time and effort of human workers for mimicking dialogues and annotating labels, which is specifically useful for those less widely used languages, such as Japanese. We compare four filtering settings with respect to the lower-bound of utterance length for corpus creation and topic annotation and report the effect of the utterance length to the quality of our dialogue corpus, based on a manual evaluation. Based on this corpus, we further propose two topic-based dialogue generation tasks, that is, the next-response-topic prediction task and the next-topic-based response generation task. The Japanese dialogue corpus is available on GitHub1.
Accurate Formulation of the Skin and Proximity Effects in High-Speed Cable System

An efficient transmission line model of high-speed cables is established, to predict its response at a high-frequency region (up to 20 GHz). Especially, an appropriate conformal mapping technique is applied to formulate the per-unit-length parameters of high-speed cables. Moreover, the skin and proximity effects of the conducting wires and the outer shield are derived in the closed form. As a result, the AC resistance/ inductance of all the conducting materials due to the skin and proximity effects are accurately incorporated for both layered and unlayered conductors. Furthermore, the mixed-mode S-parameters are precisely predicted for both balanced and unbalanced transmission line cables in the high-frequency region. In addition, the effects of the nonuniformities and cable geometry deformation on the mode conversions are also investigated. The proposed model is firstly validated with commercial software tools, COMSOL, FEKO, and HFSS; then, we further performed physical measurements to verify our new algorithm.
Exploring personalized structural connectomics for moderate to severe traumatic brain injury

Abstract Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.