Miracle-dz/UniFAR-FTUs huggingface dataset:
The modelling of tomato crop response to the climate change with different irrigation schemes

: The inequality between available water supplies and growing water demand from diverse sectors, as well as the predicted climate changes are putting significant pressures on Egypt’s food security. There is a nation-wide demand for new scientifically proven on-farm practices to boost water productivity of major food crops. The objective of this study was to explore the use of various deficit irrigation schemes to improve water productivity ( WP ) of tomato cultivated in Egypt under distinct climate change scenarios, RCP4.5 and RCP8.5, in three time-steps of the reference period (2006– 2016), 2030s, and 2050s. The AquaCrop model was used to simulate the influence of climate change on the tomato crop, as well as two deficit irrigation application schemes for the full growing season and the regulated application for the initial and maturity crop stages. With the same irrigation method, the predicted WP increased in a general pattern across all climate change scenarios. The combination of irrigation schedule with the 80% deficit irrigation can enhance WP near the optimum level (approximately 2.2 kg∙m –3 ), especially during early and mature stages of the crop, saving up to 16% of water. The results showed that the expected temperature rise by 2050s would reduce the crop growth cycle by 3– 11 days for all irrigation treatments, resulting in a 1–6% decrease in crop evapotranspiration ( ET c ) and affecting the dry tomato yield with different patterns of increase and decrease due to climate change.
Genetic Variants in IL6R and ADAM19 are Associated with COPD Severity in a Mexican Mestizo Population

ABSTRACT Chronic obstructive pulmonary disease (COPD) is a complex and multifactorial disease with a strong genetic component. Our objective is to identify the genetic variants associated with COPD risk and its severity in Mexican Mestizo population. We evaluated 1285 single-nucleotide polymorphisms (SNPs) of candidate genes in 299 smokers with COPD (COPD-S) and 531 smokers without COPD (SWOC) using an Illumina GoldenGate genotyping microarray. In addition, 251 ancestry informative markers were included. Allele A of rs2545771 in CYP2F2P is associated with a lower risk of COPD (p = 4.02E-10, odds ratio [OR] = 0.104, confidence interval [CI] 95% 0.05–0.18). When the COPD group was stratified by severity according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD; levels III + IV vs. I + II), 3 SNPs (rs4329505 and rs4845626 in interleukin 6 receptor [IL6R] and rs1422794 in a disintegrin and metalloproteinase domain 19 [ADAM19]) were associated with a lower risk of suffering the most severe stages of the disease. rs2819096 in the surfactant protein D (SFTPD) gene was associated with a higher risk of COPD GOLD III + IV (p = 7.79E-03, OR = 1.80, CI 95% 1.16–2.79). Finally, the haplotype in IL6R was associated with a lower risk of suffering from more severe COPD, whereas the haplotype in ADAM19 was associated with a higher risk (p = 7.40E-03, OR = 2.83, CI 95% 1.20–6.86) of suffering from the severe stages of the disease. Our data suggest that there are alleles and haplotypes in the IL6R, ADAM19, and SFTPD genes associated with different severity stages of COPD; in CYP2F2P, rs25455771 is associated with a lower risk of COPD.
On chip optical neural networks based on MMI microring resonators for image classification

We propose a new on-chip optical neural network (OONN) based on multimode interference-microring resonators (MMI-RRs). The suggested structure eliminates the need for wavelength division multiplexers (WDM) to create an optical neuron on a single chip. New microring resonator structure based on 4×4 MMI coupler with a size of 24µm × 2900 µm is used for the basic elements of the computation matrix, as a result a higher bandwidth and free spectral range (FSR) can be achieved. The Si3N4 platform along with the graphene sheet is designed to modulate the signals and weights of the neural networks at a very high speed. The Si3N4 can provide wide range of operating wavelengths and can work directly with the wavelengths of color images. The structure's benefits include rapid computing speed, little loss, and the ability to handle both positive and negative values. The OONN has been applied to the MNIST dataset with a speed faster than 2.8 to 14x times compared with the conventional GPU methods.
Exploring the Potential of Fecal Microbiota Transplantation as a Therapy in Tuberculosis and Inflammatory Bowel Disease

This review explores the potential benefits of fecal microbiota transplantation (FMT) as an adjunct treatment in tuberculosis (TB), drawing parallels from its efficacy in inflammatory bowel disease (IBD). FMT has shown promise in restoring the gut microbial balance and modulating immune responses in IBD patients. Considering the similarities in immunomodulation and dysbiosis between IBD and TB, this review hypothesizes that FMT may offer therapeutic benefits as an adjunct therapy in TB. Methods: We conducted a systematic review of the existing literature on FMT in IBD and TB, highlighting the mechanisms and potential implications of FMT in the therapeutic management of both conditions. The findings contribute to understanding FMT’s potential role in TB treatment and underscore the necessity for future research in this direction to fully leverage its clinical applications. Conclusion: The integration of FMT into the comprehensive management of TB could potentially enhance treatment outcomes, reduce drug resistance, and mitigate the side effects of conventional therapies. Future research endeavors should focus on well-designed clinical trials to develop guidelines concerning the safety and short- and long-term benefits of FMT in TB patients, as well as to assess potential risks.
Constraints on Photosynthesis on Earth and Earth-Like Planets

We discuss how the spectral mismatch between solar radiation at the Earth's surface and the two absorption maxima (440 and 680 nm) of the dominant chlorophyll pigments can be explained as an evolutionary accident of the marine habitat in which photosynthesis evolved on Earth. The geometric rigidity and great stability of chlorophyll make it a likely "universal pigment"; given this, together with the spectral absorption properties of water, the evolution of photosynthesis on Earth-like planets (ELPs) orbiting in the habitable zones of A, F and G stars should be relatively straightforward . However for ELPs orbiting cool K and M stars, the effects of variable UV emission from the ELP at earlier epochs and the high absorption of red light by water could lead to a "feast and famine" existence for photosynthetic organisms in the ELP ocean. Full exploitation of the cool star radiation when UV activity has declined may lead to an evolution of the photosynthetic machinery from the terrestrial two photon to a three photon system.
Restorable Visible and Infrared Image Fusion

Image fusion aims to synthesize multiple source images into a single image to integrate and enhance information. Specifically, we tackle the fusion of visible and infrared images. Previous works generally use structural similarity between the fusion and the paired source images to train a deep-learning-based fusion model. However, only using the structure often results in a texture-insufficient image. In this study, we aim to generate an image rich in texture. This study is inspired by the ability of an autoencoder to learn a compressed representation of the input image. Specifically, we learn a fusion image with the structure and texture of the source images. We propose a novel framework–Restorable visible and infrared Image Fusion, which consists of a fusion and decoupling network. The fusion network synthesizes source images, and the decoupling network restores the source images by decomposing a fusion image. Our framework can be trained by minimizing the difference between the source and restored images. The experimental results demonstrate that the fusion image generated by the proposed method maintains the texture of the source images.
Bit-parallel Computation for String Alignment ⁄

One of the most important ideas in data mining is alignment of two strings. This idea is based on a distance on strings and the most popular and simple one is the edit distance. For two strings of lengths m and n, the alignment and the edit distance is computed in O(mn) time by dynamic programming approach. Bit-parallelism can speed-up the computation of the edit distance w times, where w is the word size of a computer, however this parallelism can not be applied straightforwardly to computing the alignment. This paper proposes a bit-parallel algorithm to compute all the possible alignments.
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)

Development of robust general purpose 3D segmentation frameworks using the latest deep learning techniques is one of the active topics in various bio-medical domains. In this work, we introduce Temporal Cubic PatchGAN (TCuP-GAN), a volume-to-volume translational model that marries the concepts of a generative feature learning framework with Convolutional Long Short-Term Memory Networks (LSTMs), for the task of 3D segmentation. We demonstrate the capabilities of our TCuP-GAN on the data from four segmentation challenges (Adult Glioma, Meningioma, Pediatric Tumors, and Sub-Saharan Africa subset) featured within the 2023 Brain Tumor Segmentation (BraTS) Challenge and quantify its performance using LesionWise Dice similarity and $95\%$ Hausdorff Distance metrics. We demonstrate the successful learning of our framework to predict robust multi-class segmentation masks across all the challenges. This benchmarking work serves as a stepping stone for future efforts towards applying TCuP-GAN on other multi-class tasks such as multi-organelle segmentation in electron microscopy imaging.
Advancements in Plasma-Enhanced Chemical Vapor Deposition for Producing Vertical Graphene Nanowalls

In recent years, vertical graphene nanowalls (VGNWs) have gained significant attention due to their exceptional properties, including their high specific surface area, excellent electrical conductivity, scalability, and compatibility with transition metal compounds. These attributes position VGNWs as a compelling choice for various applications, such as energy storage, catalysis, and sensing, driving interest in their integration into next-generation commercial graphene-based devices. Among the diverse graphene synthesis methods, plasma-enhanced chemical vapor deposition (PECVD) stands out for its ability to create large-scale graphene films and VGNWs on diverse substrates. However, despite progress in optimizing the growth conditions to achieve micrometer-sized graphene nanowalls, a comprehensive understanding of the underlying physicochemical mechanisms that govern nanostructure formation remains elusive. Specifically, a deeper exploration of nanometric-level phenomena like nucleation, carbon precursor adsorption, and adatom surface diffusion is crucial for gaining precise control over the growth process. Hydrogen’s dual role as a co-catalyst and etchant in VGNW growth requires further investigation. This review aims to fill the knowledge gaps by investigating VGNW nucleation and growth using PECVD, with a focus on the impact of the temperature on the growth ratio and nucleation density across a broad temperature range. By providing insights into the PECVD process, this review aims to optimize the growth conditions for tailoring VGNW properties, facilitating applications in the fields of energy storage, catalysis, and sensing.
A Behavioral Test of Contamination Fear in Excessive Health Anxiety

Hypochondriasis and health anxiety are characterized by preoccupation wit h the fear of currently having a serious physical illness. The Diagnostic and Statistical Manual of Mental Disorders conceptualization of hypochondriasis does not include consideration of the fear of acquiring an illness; however, many individuals with severe health anxiety repor t concern about contamination, suggesting that the current conceptualization of hypochondriasis may be incomplete. The present study utilized behavior approach tasks (BATs) to examine t he degree to which contamination fear is present in severe health anxiety. Additionally, perc eived vulnerability to disease (PVD) and disgust were tested as potential mechanis ms i health anxiety and contamination fear. Results showed that health anxious individuals were simila rly avoidant of sources of contamination, and reported similar anxiety and disgust in response to t he BATs. Regression analyses showed that PVD, but not disgust, potentiated the effect of both he alth anxiety and contamination fear in the prediction of disgust experienced during the BATs. The theoretical and clinical implications of these findings are discussed. This dissertation is approved for recommendation to the Graduate Council. Dissertation Director: _______________________________________ Dr. Jeffrey Lohr Dissertation Committee: _______________________________________ Dr. Matthew Feldner _______________________________________ Dr. William Levine DISSERTATION DUPLICATION RELEASE I hereby authorize the University of Arkansas Libraries to duplicate t his dissertation when needed for research and/or scholarship. Agreed __________________________________________ Robert E. Brady Refused __________________________________________ Robert E. Brady ACKNOWLEDGEMENTS I owe special thanks to Dr. Jeff Lohr for his mentorship and guidance throughout my graduate training. He is a model of excellence in the field of psychology. His knowledge, professionalism, intellectual curiosity, and commitment to a science of psyc hology are unparalleled, and I am fortunate to be his student. I would like to thank my colleague, Tom Adams, for his friendship and collaborative spirit during the course of our training. I am greatly appreciative of his he lp during the course of this dissertation, and I wish him the best in his continued training as the sole member of the Lohr Lab. I also thank Ashlyn Testut, Andrew Arnett, and Kara Foster for their help in da ta collection on this study. Finally, I thank my friends and family for their love and support during my graduat e training. DEDICATION This dissertation is dedicated to my grandmother, Penny Brady. TABLE OF CONTENTS
The attack target location method based on quaternion detection array

The bullet point acoustic detection and location system determines the location of the attack target by receiving and measuring the muzzle shock wave of the attack target and the shock wave generated by the projectile flight. This paper designs an attack target location method based on the quaternion detection array through the analysis of the ballistic wave signal, that is, the location of the attack target is preliminarily determined by the time difference of the muzzle shock wave reaching each sensor, Then, the ballistic trajectory is determined by the time difference between the ballistic shock wave generated during the flight of the projectile and each sensor, and the positioning accuracy is improved through the combination of the two.
Identification for Tree-shaped Structural Causal Models in Polynomial Time

Linear structural causal models (SCMs) are used to express and analyze the relationships between random variables. Direct causal effects are represented as directed edges and confounding factors as bidirected edges. Identifying the causal parameters from correlations between the nodes is an open problem in artificial intelligence. In this paper, we study SCMs whose directed component forms a tree. Van der Zander et al. give a PSPACE-algorithm for the identification problem in this case, which is a significant improvement over the general Gröbner basis approach, which has doubly-exponential time complexity in the number of structural parameters. However, they do not show that their algorithm is complete. In this work, we present a randomized polynomial-time algorithm, which solves the identification problem for tree-shaped SCMs. For every structural parameter, our algorithms decides whether it is generically identifiable, generically 2-identifiable, or generically unidentifiable. (No other cases can occur.) In the first two cases, it provides one or two fractional affine square root terms of polynomials (FASTPs) for the corresponding parameter, respectively. In particular, our algorithm is not only polynomial time, but also complete for for tree-shaped SCMs.
Automating Predictive Phage Therapy Pharmacology

Viruses that infect as well as often kill bacteria are called bacteriophages, or phages. Because of their ability to act bactericidally, phages increasingly are being employed clinically as antibacterial agents, an infection-fighting strategy that has been in practice now for over one hundred years. As with antibacterial agents generally, the development as well as practice of this phage therapy can be aided via the application of various quantitative frameworks. Therefore, reviewed here are considerations of phage multiplicity of infection, bacterial likelihood of becoming adsorbed as a function of phage titers, bacterial susceptibility to phages also as a function of phage titers, and the use of Poisson distributions to predict phage impacts on bacteria. Considered in addition is the use of simulations that can take into account both phage and bacterial replication. These various approaches can be automated, i.e., by employing a number of online-available apps provided by the author, the use of which this review emphasizes. In short, the practice of phage therapy can be aided by various mathematical approaches whose implementation can be eased via online automation.
DIABETES TIPO 1 E

: To verify the glycemic safety of a maximal cardiorespiratory test (V̇O2max) in T1DM patients and correlate the maximum cardiorespiratory values with metabolic control parameters such as glycemia, glycated hemoglobin, time of diagnosis (TD), and fat (body [GC] and visceral [GV]). The cross-sectional study evaluated ten T1DM patients (age 24.9 ± 7.5 years; time of diagnosis 11.5 ± 7.4; body mass index 22 ± 2 kg/m 2 ). Metabolic glycemic control variables (before and after), glycated hemoglobin (HbA1c), time since diagnosis and total and visceral fat were collected. The CENESP maximum test protocol was used on the treadmill with direct maximal oxygen uptake (V̇O2max). Normality test, paired t-test and correlations (p <0.05) were performed. In the results, blood glucose pre 189 ± 67 mg/dL vs. post 172 ± 66 mg/dL (p = 0.140). The V̇O2max with a regular average score for men and women (37.4 ± 6.6 ml.kg.min-1). There was a negative and almost perfect correlation with TD, GC and GV (p = 0.028, p <0.000, and p <0.000). We conclude that is possible to perform a CENESP maximal with glycemic safe in T1DM. However, TD, GC, and GV negatively interfere with cardiorespiratory fitness and should be verified before the test.
The Combined Modelling of the Regional Quasigeoid of New Zealand Using Gravity and GPS-levelling Data

The NZGeoid2009 is currently the official quasigeoid model of New Zealand. It was computed to unify 13 separate local vertical datums (LVDs) in New Zealand. This study intends primarily to compute an improved gravimetric quasigeoid model in New Zealand. The computation of a gravimetric quasigeoid model depends on four input data sets namely the gravity data, DTM, GGM and GPS/levelling data. The accuracy of the input data sets plays an important role in the quality of the final gravimetric solution. Therefore, the second objective has been devoted to analysing the levelling networks and investigating recent global geopotential models (GGMs). The joint adjustment approach is used to refine and readjust the levelling networks as well as for the unification of 13 separate local vertical datums (LVDs) in the North and South islands. The unification of LVDs has been done in two steps. The levelling networks in the North and South islands are jointly adjusted at first by fixing two tide gauges in Dunedin andWellington. The gravity anomalies along levelling lines are generated from EGM2008. The cumulative normal to normal-orthometric height correction is computed from levelling and gravity anomaly data. The average offsets of these fixed points are then estimated relative to the World Height System (WHS). The average offset at Wellington and Dunedin are found to be 10.6 and 27.5 cm, respectively. These offsets are added to the newly derived orthometric and normal heights at the levelling networks for each island. The investigation of the recently released GGMs is conducted for a number of 11 GGMs (9 satellite-only and 2 combined models) by testing them using the newly adjusted levelling heights combined to GPS points. Among all tested satellite-only models, the GRACE/GOCE model GOCO-02S has the best RMS fit with GPS/levelling data of 56 cm when using the maximum degree/order 250 of spherical harmonic coefficients. Three methods are reviewed for the gravimetric geoid/quasigeoid modelling and therefore three regional corresponding models have been computed, namely NZGM2010, NZQM2010 and OTG12. NZGM2010 geoid model is compiled using the method of least-squares modification
Toward an integrative approach to nonverbal personality detection: Connecting psychological and artificial intelligence research.

Our ﬁ rst impressions of the people we meet are the subject of considerable interest, academic, and nonacademic. Such initial estimates of another ’ s personality (e.g., their sociality or agreeableness) are vital, since they enable us to predict the outcomes of interactions (e.g., can wetrustthem? ). Nonverbalbehaviorsareakeymediumthroughwhichpersonalityisexpressedanddetected.Thecharacterandreliability of these expression and detection processes have been investigated within two major ﬁ elds: psychological research on personality judgments accuracy and Arti ﬁ cial Intelligence research on personality computing. Communication between these ﬁ elds has, however, been infrequent. In the present perspective, we summarize the contributions and open questions of both ﬁ elds and propose an integrative approach to combine their strengths and overcome their limitations. The integrated framework will enable novel research programs, such as(a),identifyingwhichdetectiontasksbettersuithumansorcomputers,(b),harmonizingthenonverbalfeaturesextractedbyhumansand computers, and (c), integrating human and arti ﬁ cial agents in hybrid systems.