Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Essential Oils on the Nervous System: A Scoping Review

Essential oils are a mixture of natural aromatic volatile oils extracted from plants. The use of essential oils is ancient, and has prevailed in different cultures around the world, such as those of the Egyptians, Greeks, Persians, and Chinese. Today, essential oils are used in traditional and complimentary medicines, aromatherapy, massage therapies, cosmetics, perfumes and food industries. The screening effect of essential oils has been studied worldwide. They demonstrate a range of biological activities, such as antiparasitic, antifungal, antibacterial, antiviral, antioxidant, anti-inflammatory, anticancer, antiaging, and neuroprotective properties. In this scoping review, we provide a 10-year updated comprehensive assessment of volatile oils and their effects on the nervous system. MEDLINE, Scopus, and Google Scholar were systematically and strategically searched for original studies investigating these effects from 2012 to 2022. Approximately seventy studies were selected as included studies. Among these studies, several outcomes were reported, including antistress, antianxiety, analgesic, cognitive, and autonomic effects. Some essential oils showed developmental benefits, with the potential to induce neurite outgrowth. The neurotransmitter receptor level can also be modified by essential oil application. Physiological and pathophysiological outcome measures were reported. For physiological outcomes, arousal, cognitive performance, circadian eating behavior, emotional modulation, consumer acceptance, preferences, and willingness to buy were investigated. For pathophysiological conditions, pain, depression, anxiety, stress, sleep disorder, mental fatigue, agitated behavior, and quality of life were measured. In conclusion, essential oils showed promising effects on the nervous system, which can be further applied to their use in functional foods, drinks, and alternative therapy.
Proof of Theorem A

Proof of Theorem 1: Beginning with triangular inequality |x+ y| ≤ |x|+ |y| for x, y ∈ R, we have Equation (2) ∣∣∣Exi∼P (xi|ai=0)L(c(zi), pi)− Exj∼P (xj |aj=1)L(c(zj), pj)∣∣∣ ≤ Exi∼P (xi|ai=0),xj∼P (xj |aj=1,|pi−pj |≤ p) ∣∣∣L(c(zi), pi)− L(c(zj), pj)∣∣∣ (8) For x1 ∼ P (x1 | ai = 0), z1 = g(x1) and x2 ∼ P (x2 | ai = 1), z2 = g(x2), L(c(z1), p1) and L(c(z2), p2) are bounded by: L(c(z1), p2)− p L ≤ L(c(z1), p1) ≤ L(c(z1), p2) + p L, L(c(z2), p1)− p L ≤ L(c(z2), p2) ≤ L(c(z2), p1) + p L, (9)
Exploring Approaches for Estimating Parameters in Cognitive Diagnosis Models with Small Sample Sizes

Cognitive diagnostic models (CDMs) are increasingly being used in various assessment contexts to identify cognitive processes and provide tailored feedback. However, the most commonly used estimation method for CDMs, marginal maximum likelihood estimation with Expectation–Maximization (MMLE-EM), can present difficulties when sample sizes are small. This study compares the results of different estimation methods for CDMs under varying sample sizes using simulated and empirical data. The methods compared include MMLE-EM, Bayes modal, Markov chain Monte Carlo, a non-parametric method, and a parsimonious parametric model such as Restricted DINA. We varied the sample size, and assessed the bias in the estimation of item parameters, the precision in attribute classification, the bias in the reliability estimate, and computational cost. The findings suggest that alternative estimation methods are preferred over MMLE-EM under low sample-size conditions, whereas comparable results are obtained under large sample-size conditions. Practitioners should consider using alternative estimation methods when working with small samples to obtain more accurate estimates of CDM parameters. This study aims to maximize the potential of CDMs by providing guidance on the estimation of the parameters.
Effectiveness of the Combination of Imidacloprid/ Moxidectin/ Praziquantel and Moxidectin/ Praziquantel Applied Topically in Dogs with Endoparasites and Ectoparasites

Topically applied antiparasitic drugs used in dogs have evolved in terms of active ingredients, but progress has also been made through the development of drugs that control both ectoparasites and enteroparasites (nematodes and cestodes). The aim of this study is to evaluate the effectiveness of Moxidectin 3.5g/ Praziquantel 10.0g and Imidacloprid 10.0g/ Moxidectin 3.5g/ Praziquantel 10.0g combinations applied topically in dogs with helminths (cestodes and nematodes) and ectoparasites, respectively. A total number of 276 dogs were analyzed. Dogs with nematodes or cestodes were treated with Moxidectin 3.5g/ Praziquantel 10.0g. All the positive cases for ectoparasites were treated with Imidacloprid 10.0g/ Moxidectin 3.5g/ Praziquantel 10.0g. The effectiveness of Moxidectin/ Praziquantel was ≥99.8% from day 14 to day 35 on all the nematodes analyzed. The effectiveness of Moxidectin 3.5g/ Praziquantel 10.0g for Dipylidium caninum was significant on day 7 (75%) and reached 100% by day 14. A significant efficacy for Taenia spp. was achieved by day 7 and 100% from day 28 AT. Eighty-three dogs tested positive for 11 classes of ectoparasites. The Imidacloprid 10.0g /Moxidectin 3.5g/ Praziquantel 10.0g treatment proved to be effective in all the ectoparasites from day 7, until reaching 100% effectiveness by day 14 in the Linognathus setosus, Ctenocephalides canis, Amblyomma americanum, Ixodes spp. , Rhipicephalus sanguineus, and Dermacentor reticulatus sp. We found a positive correlation (r= 0.7430, p= <0.0001) between the pruritus scale and lesion scale. The use of Moxidectin 3.5g /Praziquantel 10.0g applied topically was effective against nematodes and cestodes. Imidacloprid 10.0g/ Moxidectin 3.5g/ Praziquantel 10.0g was effective against ectoparasites and pruritus.
Directed Evolution of Seneca Valley Virus in Tumorsphere and Monolayer Cell Cultures of a Small-Cell Lung Cancer Model

Simple Summary Serial passaging of oncolytic viruses in a new host system can increase their infectivity and anti-cancer efficacy by the directed evolution of naturally occurring variants. RNA viruses have a high nucleotide substitution and proof-reading error rate due to the low fidelity of viral RNA-dependent RNA polymerase. The directed evolution of oncolytic RNA viruses is a practical strategy to select for virus variants with an increased therapeutic efficacy. The Seneca Valley virus (SVV) is a promising oncolytic virotherapy candidate for a range of human cancers. Here, we used deep genome sequencing to analyse viral genome changes during the serial passaging of the SVV in two cell culture models of small-cell lung cancer, i.e., monolayer cells and tumorsphere. We observed the improved infectivity of the SVV in tumorspheres over time associated with the accumulation of several mutations across the genome, possibly involved in the optimization of infectiousness in tumors. Abstract The Seneca Valley virus (SVV) is an oncolytic virus from the picornavirus family, characterized by a 7.3-kilobase RNA genome encoding for all the structural and functional viral proteins. Directed evolution by serial passaging has been employed for oncolytic virus adaptation to increase the killing efficacy towards certain types of tumors. We propagated the SVV in a small-cell lung cancer model under two culture conditions: conventional cell monolayer and tumorspheres, with the latter resembling more closely the cellular structure of the tumor of origin. We observed an increase of the virus-killing efficacy after ten passages in the tumorspheres. Deep sequencing analyses showed genomic changes in two SVV populations comprising 150 single nucleotides variants and 72 amino acid substitutions. Major differences observed in the tumorsphere-passaged virus population, compared to the cell monolayer, were identified in the conserved structural protein VP2 and in the highly variable P2 region, suggesting that the increase in the ability of the SVV to kill cells over time in the tumorspheres is acquired by capsid conservation and positively selecting mutations to counter the host innate immune responses.
Radio studies of relativistic SN 2009bb

Abstract A local sub-population of type Ib/c supernovae (stripped envelope SNe) with mildly relativistic outflows have been detected as sub-energetic Gamma Ray Bursts (GRBs) or X-ray Flashes (XRFs) and as radio afterglows without detected GRB counterpart. SN 2009bb belongs to the last class of objects. The long term radio observations with (J)VLA and GMRT of this SN map the dynamics of the relativistic ejecta characteristic of Central Engines associated with GRBs. We present here GMRT observations of this SN from October 2009 onwards.
A Turnstile Quad-Ridge Orthomode Transducer (OMT) for Octave-Bandwidth Receiver Front-Ends (24–51 GHz)

We report on the design, fabrication, and measured results of a turnstile quad-ridge orthomode transducer (OMT) suitable for millimeter-wavelength receivers used in radio telescopes for astronomy. The quad-ridge OMT is directly compatible with a quad-ridge feed horn to implement dual-polarization low-noise receivers that exceed the single-mode bandwidth of a rectangular waveguide. Using a bandwidth of 24–51 GHz, the OMT demonstrated an insertion loss of 0.3–0.4 dB along with cross-polarization leakage and isolation below −40 dB. An OMT is a key part of a dual polarization receiver front-end, and our long-term goal is to realize low-noise, wideband receivers for radio astronomy utilizing ridge waveguides throughout the entire front-end. Design details are provided for each ridge waveguide feature within the OMT, including a quad-ridge waveguide turnstile junction, <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane bend, and <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane T-junction. To facilitate future layouts and integration, additional ridge waveguide component designs have been included for a 3-dB coupler, <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-plane bend, and <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-plane T-junction.
A Novel Cascadable TCAM Using RRAM and Current Race Scheme for High-Speed Energy-Efficient Applications

In this work, a novel ternary content addressable memory (TCAM) design is proposed using resistive random-access memory (RRAM) array in 2T2R configuration. The suggested memory array adopts the current-race (CR) sensing mechanism incorporated with a match-line (ML) booster in the sensing amplifier (SA) to improve energy efficiency, search speed and tolerance to RRAM switching variation. Several innovations are implemented to enhance the design further. For large TCAM arrays, match-line sensing amplifier (MLSA) direct cascading (DC) and an SR-latch cascading (SRC) schemes are proposed and compared in search speed, energy efficiency and MLSA noise margin. A same clock phase cascading (SCPC) scheme is also introduced to reduce latency in cascading structure by placing evaluation phase of all stages in the same clock phase. Furthermore, an RRAM-based tunable delay element (RRAM-TDE) is used in the TCAM design to provide flexibility and robustness against RRAM switching variation. The resulting system demonstrates excellent speed, energy and area efficiency against other TCAM designs using CMOS and emerging non-volatile memory (eNVM). To the best of our knowledge, the proposed 64-bit 1-stage TCAM system's speed and energy consumption match the best performance reported by other eNVM-based TCAM designs. The proposed design on a 128-bit 2-stage system also has speed and energy consumption comparable to SRAM-based TCAMs with the extra advantages of (a) compact size (90% reduction) and (b) non-volatility.
Effect of Anthocyanin-Enriched Brine on Nutritional, Functional and Sensory Properties of Pickled Baby Corn

Considering the great potential of black soybean seed coat as a source of bioactive compounds, the objective of this study was to investigate the effect of anthocyanin-rich brine from the seed coat on functional properties of pickled baby corn, as well as its sensory properties. Given that the ears of sweet corn, popping corn and semi-flint corn were used for pickling in the pre-pollination phase, the effect of genotype and its growing stage on the chemical composition of Baby corn product was also taken into consideration. The brine of black soybean with a total anthocyanins content of 11,882.9 mg CGE/kg (cyanidin 3-glucoside equivalent) and an antioxidant capacity of 399.5 mmol Trolox Eq/kg determined by QUENCHER method had a positive impact on the functional potential of baby corn products. The content of total anthocyanins in the obtained products ranged from 748.6 to 881.2 mg CGE/kg, the predominant anthocyanin was cyanidin-3-glucoside (184.6 to 247.5 μg/g), while their colour was red. Compared to the commercial sample, baby corn products pickled in the enriched solution had a 26% to 46% and 17% to 26% higher content of total free phenolic compounds and antioxidant capacity, respectively. Contrarily, the control sample had higher sugar and fibre content. As established, pickled popping corn had the best sensory properties.
Deep Learning for Metro Short-Term Origin-Destination Passenger Flow Forecasting Considering Section Capacity Utilization Ratio

Origin-destination (OD) short-term passenger flow forecasting (OD STPFF) in urban rail transit (URT) is essential for developing timely network measures. The capacity utilization ratios of critical sections are key factors in developing these measures. The OD pairs passing through critical sections require a higher prediction accuracy than others; however, most studies have raised equal concerns on the prediction accuracy of each OD pair, namely, prediction at the network level. To address this problem, we raise heterogeneous time-variant concerns on OD pairs and employ an operation-oriented deep-learning architecture called the spatiotemporal convolutional neural network (STCNN) for realizing short-term OD passenger flow prediction. The architecture contains OD pair importance calculation, lagged spatiotemporal relationship construction, lagged spatiotemporal learning, real-time information learning, and sequential-temporal learning blocks. To this end, critical OD pairs are ascertained first, and the topological lagged spatiotemporal relationship among critical OD pairs are constructed and then normalized into grid-shaped data. The third block utilizes a convolutional neural network (CNN) to learn the grid-shaped lagged spatiotemporal feature and real-time information in parallel. A temporal convolutional neural network (TCN) is utilized for learning the sequential-temporal feature at last. Further, we design a time-varying weighted masked loss function to jointly reinforce the concerns on critical OD pairs during model training. Finally, we test the proposed STCNN and its components on a field dataset from Chengdu Metro. Although the proposed STCNN performs only slightly better at the network level than the other models, it outperforms state-of-the-art methods with significant superiority on critical OD pairs.
Optical proximity correction with PID control through reinforcement learning

Model-based optical proximity correction (MB-OPC) consists of fragmentation which is decomposed into segments and iterative simulations and corrections with a feedback system. Mask bias for each segment is iteratively corrected by heuristic rule-based PID control. Although mask pattern is various, the same PID parameters are adopted. We apply reinforcement learning (RL) as a PID parameters predictor. Pattern-aware adaptive PID control through RL has the benefit of EPE convergence. RL model receives layout features and PFT values as its inputs. The reward of RL model is designed for minimizing EPE from the current mask.
Prediction Equations in Quantitative Genetics

One of the fundamental concepts in the application of statistical methods to the analysis of the inheritance of characters showing continuous variation is the additive genetic variance ao , the variance in any character in a population that is due to the average effects of genes. If this is expressed as a fraction of the total variance, oa, we get the related parameter, h2, the heritability (in the narrowest sense) of the character. It can be shown without great labour that the heritability is also equal to the regression coefficient of breeding value on performance or phenotype. This short paper presents an alternative derivation from the point of view of the combination of information from different sources, an approach which may be useful in teaching. Several other important prediction equations in quantitative genetics can be fitted into the same pattern. If we have a measurement P of an individual in a population in which the character measured has a mean P, we may consider ourselves as having two independent pieces of information on the animal's breeding value. They are: (i) that the animal is a member of a population whose mean breeding value is P with variance ao2; (ii) that the animal's own performance is P and that this will have variance ao_ oa2 about the true breeding value. If we knew only (i), we should take P as the best estimate of the individual's breeding and it would have error variance <,, . If we knew only (ii), we should take P as the best estimate with error variance 2 2 O7) U. In combination, the correct weight to give the two estimates is the reciprocal of their respective variances. We then have
Clinical outcomes and thermodynamics aspect of direct brain cooling in severe head injury

Background: Brain cooling therapy is one of the subjects of interest, and currently, data on direct brain cooling are lacking. Hence, the objective is to investigate the clinical outcomes and discuss the thermodynamics aspect of direct brain cooling on severely injured brain patients. Methods: This pilot study recruited the severely injured brain patients who were then randomized to either a direct brain cooling therapy group using a constant cooling temperature system or a control group. All studied patients must be subjected to an emergency neurosurgical procedure of decompressive craniectomy and were monitored with intracranial pressure, brain oxygenation, and temperature. Further, comparison was made with our historical group of patients who had direct brain cooling therapy through the old technique. Results: The results disclosed the direct brain cooling treated patients through a newer technique obtained a better Extended Glasgow Outcome Score than a control group (P < 001). In addition, there is a significant outcome difference between the combined cooling treated patients (new and old technique) with the control group (P < 0.001). Focal brain oxygenation and temperature are likely factors that correlate with better outcomes. Conclusion: Direct brain cooling is feasible, safe, and affects the clinical outcomes of the severely traumatized brain, and physics of thermodynamics may play a role in its pathophysiology.
A regression based approach to a maximum margin classifier for separation of linearly inseparable pattern classes

Abstract This paper proposes a method by which we can arrive at a maximum margin nonlinear decision boundary that exists between two pattern classes that are linearly inseparable. The technique involves sampling the feature space within an area bounded by the curves of best fit to the two pattern classes and arriving at the optimal boundary point between the two classes in each sampled region. The boundary points are then smoothened to obtain the non-linear decision boundary. The proposed technique was tested on the chromosomal data set and a classification accuracy of 95% was achieved.
Numerical Modelling of Turbulent Two Phase Flows Using the Two Fluid Approach

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Numerical Modelling of Turbulent Two Phase Flows Using the Two Fluid Approach Jean-Marc Hérard
Linear Quadratic Gaussian (LQG) online learning

Optimal control theory and machine learning techniques are combined to propose and solve in closed form an optimal control formulation of online learning from supervised examples. The connections with the classical Linear Quadratic Gaussian (LQG) optimal control problem, of which the proposed learning paradigm is a non trivial variation as it involves random matrices, are investigated. The obtained optimal solutions are compared with the Kalman-filter estimate of the parameter vector to be learned. It is shown that the former enjoys larger smoothness and robustness to outliers, thanks to the presence of a regularization term. The basic formulation of the proposed online-learning framework refers to a discrete time setting with a finite learning horizon and a linear model. Various extensions are investigated, including the infinite learning horizon and, via the so-called "kernel trick", the case of nonlinear models. Subjects: Optimization and Control (math.OC) Cite as: arXiv:1606.04272 [math.OC] (or arXiv:1606.04272v2 [math.OC] for this version)
Impacts of Random Atomic Defects on Critical Buckling Stress of Graphene under Different Boundary Conditions

Buckled graphene has potential applications in energy harvest, storage, conversion, and hydrogen storage. The investigation and quantification analysis of the random porosity in buckled graphene not only contributes to the performance reliability evaluation, but it also provides important references for artificial functionalization. This paper proposes a stochastic finite element model to quantify the randomly distributed porosities in pristine graphene. The Monte Carlo stochastic sampling process is combined with finite element computation to simulate the mechanical property of buckled graphene. Different boundary conditions are considered, and the corresponding results are compared. The impacts of random porosities on the buckling patterns are recorded and analyzed. Based on the large sampling space provided by the stochastic finite element model, the discrepancies caused by the number of random porosities are discussed. The possibility of strengthening effects in critical buckling stress is tracked in the large sampling space. The distinguishable interval ranges of probability density distribution for the relative variation of the critical buckling stress prove the promising potential of artificial control by the atomic vacancy amounts. In addition, the approximated Gaussian density distribution of critical buckling stress demonstrates the stochastic sampling efficiency by the Monte Carlo method and the artificial controllability of porous graphene. The results of this work provide new ideas for understanding the random porosities in buckled graphene and provide a basis for artificial functionalization through porosity controlling.