Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty Propagation in Neutron Activation Cross-Section Measurement Using Unscented Transformation Method

Abstract This paper presents a novel approach for uncertainty propagation of neutron-induced activation cross-section measurement using unscented transformation (UT). Generally, the first-order sensitivity analysis (sandwich formula) method is used for uncertainty propagation in cross-section measurement. It is based on a linear approximation of Taylor series expansion of the function of input parameters and gives satisfactory results for smooth nonlinear functions having relatively small uncertainties. On the contrary, the UT technique is completely defined by the moments of random process and hence produces better results for error propagation in the nonlinear case with large uncertainties. The UT method is easier to implement and gives results as accurate as the sandwich formula and Monte Carlo techniques. This work examines the application of the UT method in nuclear science as an alternate to the sandwich formula and Monte Carlo methods.
Bayesian optical flow with uncertainty quantification

Optical flow refers to the visual motion observed between two consecutive images. Since the degree of freedom is typically much larger than the constraints imposed by the image observations, the straightforward formulation of optical flow as an inverse problem is ill-posed. Standard approaches to determine optical flow rely on formulating and solving an optimization problem that contains both a data fidelity term and a regularization term, the latter effectively resolves the otherwise ill-posedness of the inverse problem. In this work, we depart from the deterministic formalism, and instead treat optical flow as a statistical inverse problem. We discuss how a classical optical flow solution can be interpreted as a point estimate in this more general framework. The statistical approach, whose ‘solution’ is a distribution of flow fields, which we refer to as Bayesian optical flow, allows not only ‘point’ estimates (e.g. the computation of average flow field), but also statistical estimates (e.g. quantification of uncertainty) that are beyond any standard method for optical flow. As application, we benchmark Bayesian optical flow together with uncertainty quantification using several types of prescribed ground-truth flow fields and images.
FPGA Realization of Low Register Systolic Multipliers over GF(2^m)

Qiliang,Shao. M.S.E.E. , Department of Electrical Engineering, Wright State University, 2016. FPGA realization of low register systolic multipliers over GF (2). Finite field multiplication over GF (2) is a critical component for elliptic curve cryptography (ECC). National Institute of Standards and Technology (NIST) has recommended five polynomials (two trinomials and three pentanomials) for ECC implementation. Although there are a lot reports available on polynomial basis multipliers, efficient implementation of a design with flexible field-size is quite rare. There is another basis to represent the field called normal basis. Normal basis multiplication over GF (2) is widely used in various applications such as elliptic curve cryptography (ECC). As a special class of normal basis with low complexity, Gaussian normal basis (GNB) has received considerable attention recently. In this paper, we first propose a novel low-complexity hybrid-size systolic polynomial basis multiplier based on a proposed novel hybrid-size (for both pentanomial and trinomial) algorithm for efficient systolization of finite field multiplications. Next, we propose a novel decomposition algorithm to develop a digitlevel (DL) low critical-path delay and low register-complexity systolic structure for GNB multiplication over GF (2). For the hybrid-size systolic polynomial multipliers, both the theoretical and field-programmable gate array (FPGA) implementation show that, our proposed architectures have lower register-complexity than the existing ones. The proposed hybrid-size multiplier can also be extended to other field-size and can be used as a third-party intellectual property (IP) core for various cryptosystems. At the same time, the proposed systolic Gaussian normal basis multipliers can achieve both low critical-path and low register-complexity through the theoretical and application-specific integrated circuit (ASIC) comparisons with the existing GNB multipliers. Key Words-Gaussian normal basis (GNB), finite field multiplication, systolic structure, irreducible polynomials, low complexity, digit-level, low critical-path delay.
Mechanical properties of hydrogenated electron-irradiated graphene

We report a systematic analysis on the effects of hydrogenation on the mechanical behavior of irradiated single-layer graphene sheets, including irradiation-induced amorphous graphene, based on molecular-dynamics simulations of uniaxial tensile straining tests and using an experimentally validated model of electron-irradiated graphene. We find that hydrogenation has a significant effect on the tensile strength of the irradiated sheets only if it changes the hybridization of the hydrogenated carbon atoms to sp3, causing a reduction in the strength of irradiation-induced amorphous graphene by ∼10 GPa. Hydrogenation also causes a substantial decrease in the failure strain of the defective sheets, regardless of the hybridization of the hydrogenated carbon atoms, and in their fracture toughness, which decreases with increasing hydrogenation for a given irradiation dose. We characterize in detail the fracture mechanisms of the hydrogenated irradiated graphene sheets and elucidate the role of hydrogen and the ex.
Analogue of Bohl theorem for a class of linear partial differential equations

We study the existence and uniqueness of a solution bounded in the entire space for a class of higher order linear partial differential equations. We prove the theorem on the necessary and sufficient condition for the existence and uniqueness of a bounded solution for a studied class of equations. This theorem is an analogue of the Bohl theorem known in the theory of ordinary differential equations. In a partial case the unique solvability conditions are expressed in terms of the coefficients of the equation and we provide the integral representation for the bounded solution.
A Comparative Study on Plant Disease Detection and Classification Using Deep Learning Approaches

: Agriculture is a big sector in nations like India, and it provides a living for many people. To improve crop productivity, it’s very necessary to identify and classify plant diseases and prevent them from spreading further so that they do not affect the whole plant. Artificial intelligence (AI) and computer vision can help detect plant diseases that humans cannot always catch and overcome the shortcomings of continuous human monitoring. In this article, we aim to detect and classify diseases in tomato and apple leaves using deep learning approaches and compare the results between different models. Because tomatoes and apples are important components of the human diet, crop waste can result in losses for both farmers and ordinary people. These plant diseases have an immediate and negative impact on both the amount and quality of yield. Crop diseases must be identified and prevented as soon as possible to improve crop yield. Therefore, we need to monitor and analyze the growth stages of the plants so that the farmers can produce disease-free and with minimal losses to the crop. Furthermore, we used the sequential convolutional neural network (CNN) model followed by transfer learning models like VGG19, Resnet152V2, Inception V3, and MobileNet and compared the models based on accuracy. The performance of the models was evaluated using various factors such as dropout, batch size, and the number of epochs. For both, the datasets, the tomato, and apple MobileNet architecture performed better than the other existing models.
Decision-Making in Patients with Vasovagal Syncope: A Preliminary Study

Simple Summary This study looked at how people with syncope, mainly due to blood phobia or mainly occurring during prolonged standing positions, perform a task that measures their decision-making ability. Out of 332 young participants, 98 were in a control group, 10 in the blood phobia group, and 38 in the position group. The participants performed a computer task to see how well they could handle uncertainty when choosing options. The results showed that people with blood phobia took longer to make decisions in uncertain situations compared to the control and position groups. These results suggest that blood phobia and position syncope are separate conditions and that decision-making can be targeted in the management of patients with blood phobia. Abstract The aim of this study was to evaluate the differences in performance during a decisional conflict task between subjects with emotional/blood phobia and those with an orthostatic vasovagal syncope. A total of 332 young subjects were included, from which 99 were excluded because of their condition or treatment. The subjects were classified into four groups depending on their responses to a questionnaire: 98 in a control group, 10 in an emotional/blood phobia syncope group, 38 in an orthostatic syncope group, and 87 in an unclear status group. This former group was excluded. The subjects performed a decisional conflict task to quantify their conflict-management ability. The task was the computer version of the Simon Task. Emotional/blood phobia syncope subjects showed a delayed reaction time when faced with decisional conflict in comparison with the control and orthostatic syncope subjects (55.8 ± 17.7 ms, 20.5 ± 4.9 ms, and 13.4 ± 9.2 ms, respectively, p ≤ 0.05). Our result suggests that emotional/blood phobia and orthostatic syncope are two clinical entities. Decisions could be a target of management in patients with emotional/blood phobia syncope. The altered decision-making of subjects with emotion/blood phobia syncope emphasized the role of higher cerebral functions in blood pressure control.
Commercialization of teff growers and determinants in west Ethiopia: Double hurdle model analysis

Promoting commercialization of agricultural production is a cornerstone of the rural development strategies of Ethiopia and commercialization of smallholder farming is not yet adequate enough to enable farmers be profitable. This study was designed to analyze the smallholder farmers’ teff commercialization in Guduru District, Western Ethiopia. Two-stages sampling procedure was followed to select 154 teff producer farmers from four randomly selected kebeles. An interview schedule was used to collect household survey data during the 2016/2017 farming season. The Household Commercialization Index was used to assess the levels of market participation. Double Hurdle Model was used to identify the key factors that influence farmers’ teff commercialization. The results revealed that about 78% of sampled farmers sold teff during a production year of 2016/2017. The model result indicated that education of household head, family size, land holding size, land allocated to teff, farm output, participation in off/non-farm activities, lagged teff market price, access to market information and cooperative membership were found to significantly influence the probability of participation in teff output market. Intensity of participation in the teff output market was significantly determined by sex of household head, age of household head, family size, family labor and distance to the nearest market. Based on the findings, the study recommends that government should give emphasize on rural education system, family planning program, productivity improving measures, access to communication facilities and institutional services, enhance the female headed households and improving rural roads.
Understanding the Degradation of Core-Shell Nanogels Using Asymmetrical Flow Field Flow Fractionation

Nanogels are candidates for biomedical applications, and core-shell nanogels offer the potential to tune thermoresponsive behaviour with the capacity for extensive degradation. These properties were achieved by the combination of a core of poly(N-isopropylmethacrylamide) and a shell of poly(N-isopropylacrylamide), both crosslinked with the degradable crosslinker N,N′-bis(acryloyl)cystamine. In this work, the degradation behaviour of these nanogels was characterised using asymmetric flow field flow fractionation coupled with multi-angle and dynamic light scattering. By monitoring the degradation products of the nanogels in real-time, it was possible to identify three distinct stages of degradation: nanogel swelling, nanogel fragmentation, and nanogel fragment degradation. The results indicate that the core-shell nanogels degrade slower than their non-core-shell counterparts, possibly due to a higher degree of self-crosslinking reactions occurring in the shell. The majority of the degradation products had molecule weights below 10 kDa, which suggests that they may be cleared through the kidneys. This study provides important insights into the design and characterisation of degradable nanogels for biomedical applications, highlighting the need for accurate characterisation techniques to measure the potential biological impact of nanogel degradation products.
Evaluating the potential of temporal Sentinel-1A data for paddy rice discrimination at local scales

ABSTRACT A mapping algorithm is proposed in this letter on the application of Sentinel-1A data in discriminating paddy rice from other land-cover categories at local scales. The study region is Chongming Island located in the Shanghai metropolitan area, southeast China. We have acquired five temporal images of the new Sentinel-1A satellite in interferometric wide swath (IW) mode, covering a critical period of the 2015 paddy rice growing season in Chongming Island. Temporal backscatter at vertical transmitted and horizontal received (VH) polarization of Sentinel-1A was exploited and we observed that from early June to mid September, temporal backscatter profiles of the classes water/pond, built/urban, trees/forest and others were relatively stable. On the other hand, paddy rice exhibited a marked change in temporal backscatter coefficients, increasing steeply from flooding/planting to tillering/booting, and decreasing slightly at heading. Backscatter profiles also differ between paddy rice fields of different flooding/planting periods. These observed temporal microwave (radar) backscatter dynamics were employed in a decision tree mapping algorithm to discriminate paddy rice from other land-cover classes. An overall classification accuracy and Kappa statistic of 88.3% and 0.85 were recorded, respectively, which demonstrates the operational applicability of temporal Sentinel-1A data in paddy rice discrimination at local or district scales.
Multi‐Wavelength (L and S Band) Study of Ionospheric Irregularities From an Anomaly Crest Location

This paper reports observation of moderate to intense scintillations, at S band of Indian Regional Navigation Satellite System (IRNSS). During current solar cycle 25, these occurrences are probably being reported for the first time, from a low‐latitude station Calcutta (22.58°N, 88.38°E geographic; magnetic dip 34.54°), located near the northern crest of Equatorial Ionization Anomaly. Efforts have been made to analyze irregularity dynamics at IRNSS S band, along with a comparative study at GNSS L1 to characterize occurrence and evolution of multiscale irregularity structures. Power spectral analysis technique has been applied on recorded C/NO, during scintillation patches, to measure east‐west zonal drift velocity. Efforts are also made to study intensity of signal perturbation at L5 frequency of IRNSS, compared to S band, when observed simultaneously, during period of ionospheric scintillation. Concurrent irregularity dynamics at L1, L5, and S band, particularly from a low‐latitude station, has not been extensively reported earlier. Data is recorded during vernal equinox (February–April 2022) of current solar cycle. Results of this study show occurrence of simultaneous night‐time scintillation at L1, L5, and S band. East‐west zonal drift velocity of irregularity, obtained from S band observations are found to decrease with the progress of time during 20–23 LT having a maximum value of 125 m/s. At GNSS L1, hourly average value of the velocity, is observed to maximize during 20–21 LT. Simultaneous observation of scintillation effects at L5 and S band of IRNSS led to loss‐of‐lock at L5, while no such occurrence was noted at S band.
Formononetin, a Beer Polyphenol with Catabolic Effects on Chondrocytes

Beer consumption has been identified as a risk factor for osteoarthritis (OA), a rheumatic disease characterised by cartilage degradation, joint inflammation, and eventual joint failure. One of the main isoflavonoids in beer is formononetin (FNT), an estrogenic compound also found in multiple plants and herbs. In this study, we aimed to investigate the effect of FNT on chondrocyte viability, inflammation, and metabolism. Cells were treated with FNT with or without IL-1β for 48 h and during 7 days of differentiation. Cell viability was determined via MTT assay. Nitrite accumulation was determined by Griess reaction. The expression of genes involved in inflammation and metabolism was determined by RT-PCR. The results revealed that a low concentration of FNT had no deleterious effect on cell viability and decreased the expression of inflammation-related genes. However, our results suggest that FNT overexposure negatively impacts on chondrocytes by promoting catabolic responses. Finally, these effects were not mediated by estrogen receptors (ERs) or aryl hydrocarbon receptor (AhR). In conclusion, factors that favour FNT accumulation, such as long exposure times or metabolic disorders, can promote chondrocyte catabolism. These data may partially explain why beer consumption increases the risk of OA.
Factors Contributing to the Link between Physical Well-Being and Chronic Pain in Young People from Galicia, Northwest Spain

Introduction: The relation between physical well-being and chronic pain is complex and involves several subjective and objective covariates. We aimed to assess the role of mediator, confounder, or interactor played by covariates, including sleep quality, physical activity, perceived stress, smoking, and alcohol drinking in the relation between physical well-being and chronic pain. Method: We used Poisson regression to obtain incidence rate ratios (IRR) of the association between physical well-being and chronic pain in a cohort study carried out among university students. We applied General Structural Equation Modeling (GSEM) to assess mediation and stratum-specific analyses to distinguish confounding from interaction. We computed Relative Excess Risks due to Interaction (RERI), Attributable Proportion (AP), and the Synergy index (S) to measure additive interaction. Results: High physical well-being is related to a large decrease in the risk of chronic pain (IRRTotal Effect = 0.58; 95% CI: 0.50–0.81). Perceived stress mediates 12.5% of the total effect of physical well-being on chronic pain. The stratum-specific IRRs of current smokers and non-current smokers were different from each other and were larger than the crude IRR (IRR = 1.49; 95% CI: 1.24–1.80), which indicates that smoking could be both confounder and interactor. Interaction analyses showed that physical activity could act as a potential interactor (RERI = 0.25; 95% CI: 0.13, 0.60). Conclusions: Perceived stress is an important mediator of the relation between physical well-being and chronic pain, while smoking is both a confounder and an interactor. Our findings may prove useful in distinguishing high-risk groups from low-risk groups, in the interventions aimed at reducing chronic pain.
A generalized additive model approach to time-to-event analysis

Abstract: This tutorial article demonstrates how time-to-event data can be modelled in a very flexible way by taking advantage of advanced inference methods that have recently been developed for generalized additive mixed models. In particular, we describe the necessary pre-processing steps for transforming such data into a suitable format and show how a variety of effects, including a smooth nonlinear baseline hazard, and potentially nonlinear and nonlinearly time-varying effects, can be estimated and interpreted. We also present useful graphical tools for model evaluation and interpretation of the estimated effects. Throughout, we demonstrate this approach using various application examples. The article is accompanied by a new R -package called pammtools implementing all of the tools described here.
Genomic Analysis of Mycobacterium tuberculosis Strains Resistant to Second-Line Anti-Tuberculosis Drugs in Lusaka, Zambia

The emergence of pre-extensively drug-resistant tuberculosis (pre-XDR-TB) is a threat to TB control programs in developing countries such as Zambia. Studies in Zambia have applied molecular techniques to understand drug-resistance-associated mutations, circulating lineages and transmission patterns of multi-drug-resistant (MDR) Mycobacterium tuberculosis. However, none has reported genotypes and mutations associated with pre-XDR TB. This study characterized 63 drug-resistant M. tuberculosis strains from the University Teaching Hospital between 2018 and 2019 using targeted gene sequencing and conveniently selected 50 strains for whole genome sequencing. Sixty strains had resistance mutations associated to MDR, one polyresistant, and two rifampicin resistant. Among MDR strains, seven percent (4/60) had mutations associated with pre-XDR-TB. While four, one and nine strains had mutations associated with ethionamide, para-amino-salicylic acid and streptomycin resistances, respectively. All 50 strains belonged to lineage 4 with the predominant sub-lineage 4.3. 4.2. 1 (38%). Three of four pre-XDR strains belonged to sub-lineage 4.3. 4.2. 1. Sub-lineage 4.3. 4.2. 1 strains were less clustered when compared to sub-lineages L4.9. 1 and L4.3. 4.1 based on single nucleotide polymorphism differences. The finding that resistances to second-line drugs have emerged among MDR-TB is a threat to TB control. Hence, the study recommends a strengthened routine drug susceptibility testing for second-line TB drugs to stop the progression of pre-XDR to XDR-TB and improve patient treatment outcomes.
Metabolomics and Transcriptomics Analyses Reveals the Molecular Regulatory Mechanisms of Walnut (Juglans regia L.) Embryos in Response to Shade Treatment

The walnut is an important nut that has numerous uses worldwide. However, due to dwarf and close plantation methods as well as continuous cloudy or rainy days that occur during periods of walnut oil accumulation, the walnut fruit exhibits varying degrees of stress under low-light conditions. However, the effects of shade on metabolites and genes in walnut embryos remain unclear in the literature. The purpose of this study is to investigate the lipid biosynthesis process that occurs in walnut embryos under shade treatment via the use of metabolomics and transcriptomics analyses. The results indicate that the oil content decreases significantly under shaded conditions, while the protein content increases significantly. The expression levels of fatty acid desaturase 2 (FAD2) and stearoyl-ACP-desaturase (SAD) involved in the lipid biosynthesis mechanism were significantly reduced in the shaded group, which resulted in reductions in oleic (C18:1), linoleic (C18:2), and α-linolenic (C18:3) acids. The reduced oil content was consistent with the downregulation of genes associated with the lipid biosynthesis mechanism. In the amino acid biosynthesis process, the upregulated cysteine synthase (cscK) and anthranilate synthase beta subunit 2 (trpG) genes promoted the accumulation of L-aspartic acid and L-citrulline. The increase in protein content was consistent with the upregulation of genes related to amino acid biosynthesis. Thus, our study provides new insights into the regulatory mechanisms of shade underlying overall walnut fruit quality.