Miracle-dz/UniFAR-FTUs huggingface dataset:
Does flaxseed and chia use affect postprandial glucose, insulin and subjective saturation response in healthy individuals?

Background/Aim: In recent years, there has been an increase in the prevalence of obesity and its complications, along with a growing awareness of healthy nutrition. As a result, consumers are seeking to incorporate more functional foods into their diets. Chia and flax seeds have gained popularity due to their soluble fiber and antioxidant capacity. This study aims to compare the effects of consuming cakes made with the addition of chia and flax seeds on blood glucose and insulin levels, as well as evaluate their impact on post-consumption satiety response in individuals. Methods: This randomized, double-blind, self-controlled experimental study involved 12 volunteers (19–64 years old) who were free from acute or chronic diseases. The participants had a body mass index (BMI) value between 18.5 and 24.9 kg/m2 and a Beck Depression Inventory score of 8 or below. The study investigated the effects of standard and test cakes containing 50 g of digestible carbohydrates, including chia-added cake, flaxseed-added cake, and chia+flaxseed-added cake. Postprandial blood sugar, insulin, and subjective satiety responses were assessed. A standard nutrition program (diet: 60% carbohydrates, 20% protein, 30% fat) was implemented at least one week before the study, and participants were asked to maintain 24-h food consumption records the day before the test days. Throughout the study period, individuals were instructed to avoid caffeine, medication, nutritional supplements, and heavy physical activity. Cake consumption sessions were conducted at the research center, with participants visiting four times in total, with at least 1-week intervals. Fasting for 10–12 h prior to each visit, saturation responses were measured using a visual analog scale at 0, 15, 30, 60, 90, 120, and 180 min. Blood samples were also collected to assess blood glucose and insulin levels. Results: The study revealed that cakes containing chia and flaxseeds, compared to the standard cake, as well as flaxseed-added cake compared to chia-added cake, resulted in higher plasma glucose under-curve values and saturation responses and lower hunger responses (P=0.038, P=0.016, P=0.004, respectively). Conclusion: The findings indicate that both chia and flax seeds impact glycemic control and the sensation of satiety, with flaxseed exhibiting greater effectiveness than chia.
Dynamic Output Feedback and Neural Network Control of a Non-Holonomic Mobile Robot

This paper presents the design and synthesis of a dynamic output feedback neural network controller for a non-holonomic mobile robot. First, the dynamic model of a non-holonomic mobile robot is presented, in which these constraints are considered for the mathematical derivation of a feasible representation of this kind of robot. Then, two control strategies are provided based on kinematic control for this kind of robot. The first control strategy is based on driftless control; this means that considering that the velocity vector of the mobile robot is orthogonal to its restriction, a dynamic output feedback and neural network controller is designed so that the control action would be zero only when the velocity of the mobile robot is zero. The Lyapunov stability theorem is implemented in order to find a suitable control law. Then, another control strategy is designed for trajectory-tracking purposes, in which similar to the driftless controller, a kinematic control scheme is provided that is suitable to implement in more sophisticated hardware. In both control strategies, a dynamic control law is provided along with a feedforward neural network controller, so in this way, by the Lyapunov theory, the stability and convergence to the origin of the mobile robot position coordinates are ensured. Finally, two numerical experiments are presented in order to validate the theoretical results synthesized in this research study. Discussions and conclusions are provided in order to analyze the results found in this research study.
Input-Convex Deep Networks

• We introduce a new neural network architecture: – Input-Convex Neural Networks (ICNNs) • Definition: Scalar-valued neural network f(x; θ) – f is convex in the input x – (f is not convex in the parameters θ = {Wi, bi}) • Model allows global optimization over some of the inputs to the network, given fixed values for other inputs • Many existing neural-network architectures can be “easily” made input-convex
Shock deceleration in interplanetary coronal mass ejections (ICMEs) beyond Mercury’s orbit until one AU

The CDPP propagation tool is used to propagate interplanetary coronal mass ejections (ICMEs) observed at Mercury by MESSENGER to various targets in the inner solar system (VEX, ACE, STEREO-A and B). The deceleration of ICME shock fronts between the orbit of Mercury and 1 AU is studied on the basis of a large dataset. We focus on the interplanetary medium far from the solor corona, to avoid the region where ICME propagation modifications in velocity and direction are the most drastic. Starting with a catalog of 61 ICMEs recorded by MESSENGER, the propagation tool predicts 36 ICME impacts with targets. ICME in situ signatures are investigated close to predicted encounter times based on velocities estimated at MESSENGER and on the default propagation tool velocity (500 km s−1 ). ICMEs are observed at the targets in 26 cases and interplanetary shocks (not followed by magnetic ejecta) in two cases. Comparing transit velocities between the Sun and MESSENGER ( ) and between MESSENGER and the targets ( ), we find an average deceleration of 170 km s−1 (28 cases). Comparing to the velocities at the targets (v Tar ), average ICME deceleration is about 160 km s−1 (13 cases). Our results show that the ICME shock deceleration is significant beyond Mercury’s orbit. ICME shock arrival times are predicted with an average accuracy of about six hours with a standard deviation of eleven hours. Focusing on two ICMEs detected first at MESSENGER and later on by two targets illustrates our results and the variability in ICME propagations. The shock velocity of an ICME observed at MESSENGER, then at VEX and finally at STEREO-B decreases all the way. Predicting arrivals of potentially effective ICMEs is an important space weather issue. The CDPP propagation tool, in association with in situ measurements between the Sun and the Earth, can permit to update alert status of such arrivals.
Exotic order in magnetic systems from Majorana fermions

This thesis explores the theoretical representation of localised electrons in magnetic systems, using Majorana fermions. A motivation is provided for the Majorana fermion representation, which is then developed and applied as a mean-field theory and in the path-integral formalism to the Ising model in transversal-field (TFIM) in one, two and three dimensions, on an orthonormal lattice. In one dimension the development of domain walls precludes long-range order in discrete systems; this is as free energy savings due to entropy outweigh the energetic cost of a domain wall. An argument due to Peierls exists in 2D which allows the formation of domains of ordered spins amidst a disordered background, however, which may be extended to 3D. The forms of the couplings to the bosons used in the Random Phase Analysis (RPA) are considered and an explanation for the non-existence of the phases calculated in this thesis is discussed, in terms of spare degrees of freedom in the Majorana representation. This thesis contains the first known application of Majorana fermions at the RPA level. Declarations Candidate’s declarations I, Edmund Bennett, hereby certify that this thesis, which is approximately 60,000 words in length, has been written by me, that it is the record of work carried out by me and that it has not been submitted in any previous application for a higher degree. I was admitted as a research student in September 2009 and as a candidate for the degree of Doctor of Philosophy in September 2009; the higher study for which this is a record was carried out in the University of St Andrews between 2009 and 2013. Date: Signed: (candidate) Supervisor’s declarations I hereby certify that the candidate has fulfilled the conditions of the Resolution and Regulations appropriate for the degree of Doctor of Philosophy in the University of St Andrews and that the candidate is qualified to submit this thesis in application for that degree. Date: Signed: (supervisor)
Editorial: Case reports in neuroimaging and stimulation

COPYRIGHT © 2023 Battaglia, Schmidt, Hassel and Tanaka. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Case reports in neuroimaging and stimulation
Improving visibility in limited-view scenarios with dynamic particle-enhanced optoacoustic tomography

Limited-view artefacts affect most optoacoustic (photoacoustic) imaging systems due to geometrical constraints that impede achieving full tomographic coverage as well as limited light penetration into scattering and absorbing objects. Indeed, it has been theoretically established and experimentally verified that accurate optoacoustic images can only be obtained if the imaged sample is fully enclosed (< π angular coverage) by the measuring locations. Since in many cases full angular coverage cannot be achieved, the visibility of structures along certain orientations is hampered. These effects are of particular relevance in the case of hand-held scanners with the imaged volume only accessible from one side. Herein, a new approach termed dynamic particle-enhanced optoacoustic tomography (DPOT) is described for accurate structural imaging in limited-view scenarios. The method is based on the non-linear combination of a sequence of tomographic reconstructions representing sparsely distributed moving particles. Good performance of the method is demonstrated in experiments consisting of dynamic visualization of flow of suspended microspheres in three-dimensions. The method is expected to be applicable for improving accuracy of angiographic optoacoustic imaging in living organisms.
Smith ScholarWorks Smith ScholarWorks

Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people’s understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deﬁciency of having one-size-ﬁts-all visualization interfaces, as well as the signiﬁcance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence-based reviews, and spur future inquiry.
The 7th AI City Challenge

The AI City Challenge’s seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential. The 2023 challenge had five tracks, which drew a record-breaking number of participation requests from 508 teams across 46 countries. Track 1 was a brand new track that focused on multi-target multi-camera (MTMC) people tracking, where teams trained and evaluated using both real and highly realistic synthetic data. Track 2 centered around natural-language-based vehicle track retrieval. Track 3 required teams to classify driver actions in naturalistic driving analysis. Track 4 aimed to develop an automated checkout system for retail stores using a single view camera. Track 5, another new addition, tasked teams with detecting violations of the helmet rule for motorcyclists. Two leader boards were released for submissions based on different methods: a public leader board for the contest where external private data wasn’t allowed and a general leader board for all results submitted. The participating teams’ top performances established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.
Machine learning-based 3D resist model

Accurate prediction of resist profile has become more important as technology node shrinks. Non-ideal resist profiles due to low image contrast and small depth of focus affect etch resistance and post-etch result. Therefore, accurate prediction of resist profile is important in lithographic hotspot verification. Standard approaches based on a single- or multiple-2D image simulation are not accurate, and rigorous resist simulation is too time consuming to apply to full-chip. We propose a new approach of resist profile modeling through machine learning (ML) technique. A position of interest are characterized by some geometric and optical parameters extracted from surroundings near the position. The parameters are then submitted to an artificial neural network (ANN) that outputs predicted value of resist height. The new resist 3D model is implemented in commercial OPC tool and demonstrated using 10nm technology metal layer.
Engineering of extracellular matrix from human iPSC-mesenchymal progenitors to enhance osteogenic capacity of human bone marrow stromal cells independent of their age

Regeneration of bone defects is often limited due to compromised bone tissue physiology. Previous studies suggest that engineered extracellular matrices enhance the regenerative capacity of mesenchymal stromal cells. In this study, we used human-induced pluripotent stem cells, a scalable source of young mesenchymal progenitors (hiPSC-MPs), to generate extracellular matrix (iECM) and test its effects on the osteogenic capacity of human bone-marrow mesenchymal stromal cells (BMSCs). iECM was deposited as a layer on cell culture dishes and into three-dimensional (3D) silk-based spongy scaffolds. After decellularization, iECM maintained inherent structural proteins including collagens, fibronectin and laminin, and contained minimal residual DNA. Young adult and aged BMSCs cultured on the iECM layer in osteogenic medium exhibited a significant increase in proliferation, osteogenic marker expression, and mineralization as compared to tissue culture plastic. With BMSCs from aged donors, matrix mineralization was only detected when cultured on iECM, but not on tissue culture plastic. When cultured in 3D iECM/silk scaffolds, BMSCs exhibited significantly increased osteogenic gene expression levels and bone matrix deposition. iECM layer showed a similar enhancement of aged BMSC proliferation, osteogenic gene expression, and mineralization compared with extracellular matrix layers derived from young adult or aged BMSCs. However, iECM increased osteogenic differentiation and decreased adipocyte formation compared with single protein substrates including collagen and fibronectin. Together, our data suggest that the microenvironment comprised of iECM can enhance the osteogenic activity of BMSCs, providing a bioactive and scalable biomaterial strategy for enhancing bone regeneration in patients with delayed or failed bone healing.
Effect of Parity, Body Condition Score at Calving, and Milk Yield on the Metabolic Profile of Gyr Cows in the Transition Period

Simple Summary Zebu cattle can produce food in high quantity and quality under adverse farming conditions due to their greater tolerance to heat stress and resilience to parasites compared to taurine breeds. With advances in genetic improvement programs, Gyr dairy cows currently have production rates comparable to those of some taurine herds. The present study aimed to characterize the metabolic profile (energy, protein, and mineral metabolism) of Gyr cows during the transition period, which comprises the 21 days before and after parturition. This period constitutes a fundamental phase in the life of a dairy cow, as significant hormonal and metabolic changes occur, which can predispose the cow to diseases, in addition to affecting its productive indices. This study provides relevant information by showing that high-yielding Gyr cows are metabolically balanced and less predisposed to disease during the transition period compared to taurine dairy breeds. The possibility of rearing this Zebu breed in the tropical regions of the world with excellent production rates highlights the importance of this subject. Abstract This study aimed to evaluate the effects of parity, body condition score (BCS) at calving, and milk yield on the metabolic profile of Gyr (Zebu) cows. Healthy cows in late pregnancy were grouped according to parity (primiparous, biparous, and multiparous); to BCS scale at calving (high—HBCS and normal—NBCS); and to milk yield (high—HP and moderate—MP production). BCS was assessed, and blood samples were collected on −21, −7, 0, 7, 21, and 42 days relative to parturition. The concentrations of non-esterified fatty acids (NEFA), beta-hydroxybutyrate (BHB), cholesterol, glucose, total protein (TP), albumin, total calcium (Ca), phosphorus (P), and magnesium (Mg); and activities of aspartate aminotransferase and gamma-glutamyltransferase were measured. Data were analyzed by two-way repeated measures ANOVA. The frequencies of high lipomobilization, subclinical ketosis, subclinical hypocalcemia (SCH), and the occurrence of diseases during early lactation were established. Regardless of grouping, NEFA, BHB, and cholesterol increased during early lactation; glucose showed higher values at calving; TP and albumin were higher at 21 and 42 DIM; and Ca, P, and Mg were lower at calving. Parity had little effect on the metabolic profile, HBCS did not differ from NBCS cows, and HP did not differ from MP cows in most metabolites. High lipomobilization in early lactation and SCH at calving were the most common imbalances but were not related to postpartum diseases. High-yielding Gyr cows have a balanced metabolic profile during the transition period, with few biologically relevant effects of parity, BCS at parturition, or milk yielded.
On the Exploration of Quasars and the Astrometric Microlensing Effect with the Gaia Satellite

For two years, the Gaia satellite has been scanning the sky to obtain an unmatched positional accuracy of stars and a variety of data. In this thesis, we investigate two different kinds of questions with respect to Gaia. In our first research focus we explore Gaia data that is suitable to learn more about the structure of quasars. For this, we simulate low- and high-resolution quasar spectra as they would be generated by Gaia’s blue and red photometers and the radial velocity spectrograph (RVS). Furthermore, we investigate bright SDSS quasars with broad emission lines (BELs) redshifted into the wavelength range of Gaia’s RVS. We find out that quasar low-resolution spectra enable the study of equivalent widths, continuum variability, and the so called Baldwin effect. For about seventy bright quasars, Gaia will obtain high-resolution spectra from which BEL variability can be studied using the line profile. From our analysis we conclude that Gaia provides a large amount of data which we can use for a better understanding of variability in quasar spectra. In our second research focus, we improve the prediction of astrometric microlensing events with Gaia data. When we predict such events precisely, we could plan to observe the corresponding photometric amplification simultaneously. To improve our published search for microlensing events (Proft et al. 2011), we increase the search radius around a potential lensing star and check once more the proper motion of the lenses. Due to the revision of our program, we find fifty new potential microlensing events. Six have estimated positional changes that could lead to a detectable Gaia signal. Currently, the errors of our calculated quantities are too large to follow up the events in real time. We will monitor whether our microlensing predictions are correct and with future Gaia data we can make more precise predictions in order to develop observational campaigns.
Three-dimensional mode selection of the flow past a rotating and inline oscillating cylinder

This paper studies the transition to three-dimensional flow in the wake of a cylinder immersed in a free stream, where the cylinder is externally forced to continuously rotate about its axis and to linearly oscillate in the streamwise direction. Floquet stability analysis is used to assess the stability of the nominal two-dimensional flows at a Reynolds number $Re=100$ and rotation rate $\unicode[STIX]{x1D6FC}=\unicode[STIX]{x1D714}D/U=3$ to three-dimensional perturbations, as a function of the amplitude and frequency of the linear oscillations. Two modes of instability are found, distinguished by their spatial structure, temporal behaviour and apparent mechanism. The first mode has a shorter wavelength in the spanwise direction and appears to be linked to a centrifugal instability in the layer of fluid near the rotating body. The second mode has a longer wavelength and is linked to an instability of the vortex cores in the wake that is subharmonic, leading to a period doubling. Either mode can be stable while the other is unstable, depending primarily on the frequency of the oscillation of the cylinder. This indicates that either mode can control the transition to a three-dimensional flow. The results are compared to the fully three-dimensional simulation results of a rotating cylinder elastically mounted and free to oscillate in the streamwise direction from Bourguet & Lo Jacono (J. Fluid Mech., vol. 781, 2015, pp. 127–165), and appear to be able to explain the surprising switching of the observed spanwise wavelength in that flow as a change in the dominant mode, and therefore mechanism, of instability.
AN EFFICIENT PLANT LEAF DISEASE DETECTION MODEL USING SHALLOW-CONVNET

In recent time, deep convolution neural networks have seen an exponential growth in their use in phytopathology. However, deep convolutional neural network needs a lot of processing power because of its intricate structure consisting of a large stack of layers. In this article, authors have introduced a novel lightweight sequential CNN architecture-Shallow-ConvNet for the diagnosis of leaf diseases. The suggested approach contains fewer layers and around 75% fewer attributes than pre-trained CNN-based approaches. For the experiments and performance evaluation, authors utilized a hybrid dataset consisting of 7166 images of tomato & potato with real field and laboratory-conditions affected with early and late blight diseases. The performance of the proposed architecture is compared against three recently priorly trained CNN architectures such as ResNet-50, VGG-16, and VGG-19. The average accuracy percentage reported by the proposed architecture is 97.22, and the time consumed in training is also much better. The experimental findings demonstrate that the suggested approach outperforms the recent existing trained CNN approaches and has a very less number of layers and parameters which significantly reduces the number of computing resources and time needed to train the model which could be a better choice for real-time plant disease diagnosis applications on resource constrained computing devices.