Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Intelligence: New breakthrough for Diabetes Mellitus and Regulatory Perspective

Artificial intelligence (AI) is a rapidly evolving science and its application to diabetes has the potential to transform the approach for the diagnosis and treatment of the disease. The inclusion of AI with smart devices, such as medical devices, wearables, smartphones, and sensor technology, will allow for the building of digital machines capable of supervising and monitoring people with diabetes continuously. The new digital technologies, big data-based analytics, and the application of AI to diabetes data will revolutionize the way diabetes and diabetes related complications are treated, as well as their prevention and control. The artificial intelligence is putting considerable effort into developing support systems for patient use, which automatically analyze the patient’s data c ollected by sensors and other portable devices that offer tailored recommendations for therapy adjustments to patients. Patients are increasingly being empowered for self-management of diabetes and both patients and healthcare professionals are benefitting from clinical decision support. It is anticipated that predictive overall performance of AI will soon be maximized by a large amount of organized data and plentiful computational resources. The objectives of this article are to enlist the advancements w.r. t. artificial intelligence in the field of diabetes management using smart devices. Artificial Intelligence is a promising area of research that can significantly improve the quality of life for people affected by this disease. Leading medical device makers as well as technology giants are constantly innovating to bring novel products and solutions to the market.
Proposal for non-Bogomol???nyi-Prasad-Sommerfield D-brane action

In this paper we would propose a form for the action of a non-BPS D-brane, which will be manifestly supersymmetric invariant and T-duality covariant. We also explicitly show that tachyon condensation on the world-volume of this brane leads to the Dirac-Born-Infeld action for a BPS $\mathrm{D}(p\ensuremath{-}1)$-brane.
Methods for linear and nonlinear array data dependence analysis with the chains of recurrences algebra

The presence of data dependences between statements in a loop iteration space imposes strict constraints on statement order and loop restructuring when preserving program semantics. A compiler determines the safe partial ordering of statements that enhance performance by explicitly disproving the presence of dependences. As a result, the false positive rate of a dependence analysis technique is a crucial factor in the effectiveness of a restructuring compiler’s ability to optimize the execution of performance-critical code fragments. This dissertation investigates reducing the false positive rate by improving the accuracy of analysis methods for dependence problems and increasing the total number of problems analyzed. Fundamental to these improvements is the rephrasing of the dependence problem in terms of Chains of Recurrences (CR), a formalism that has been shown to be conducive to efficient loop induction variable analysis. An infrastructure utilizing CR-analysis methods and enhanced dependence testing techniques is developed and tested. Experimental results indicate capabilities of dependence analysis methods can be improved without a reduction in efficiency. This results in a reduction in the false positive rate and an increase in the number of optimized and parallelized code fragments.
A Novel Multi-Objective Binary Chimp Optimization Algorithm for Optimal Feature Selection: Application of Deep-Learning-Based Approaches for SAR Image Classification

Removing redundant features and improving classifier performance necessitates the use of meta-heuristic and deep learning (DL) algorithms in feature selection and classification problems. With the maturity of DL tools, many data-driven polarimetric synthetic aperture radar (POLSAR) representation models have been suggested, most of which are based on deep convolutional neural networks (DCNNs). In this paper, we propose a hybrid approach of a new multi-objective binary chimp optimization algorithm (MOBChOA) and DCNN for optimal feature selection. We implemented the proposed method to classify POLSAR images from San Francisco, USA. To do so, we first performed the necessary preprocessing, including speckle reduction, radiometric calibration, and feature extraction. After that, we implemented the proposed MOBChOA for optimal feature selection. Finally, we trained the fully connected DCNN to classify the pixels into specific land-cover labels. We evaluated the performance of the proposed MOBChOA-DCNN in comparison with nine competitive methods. Our experimental results with the POLSAR image datasets show that the proposed architecture had a great performance for different important optimization parameters. The proposed MOBChOA-DCNN provided fewer features (27) and the highest overall accuracy. The overall accuracy values of MOBChOA-DCNN on the training and validation datasets were 96.89% and 96.13%, respectively, which were the best results. The overall accuracy of SVM was 89.30%, which was the worst result. The results of the proposed MOBChOA on two real-world benchmark problems were also better than the results with the other methods. Furthermore, it was shown that the MOBChOA-DCNN performed better than methods from previous studies.
Novel Battery-Supercapacitor Hybrid Energy Storage System for Wide Ambient Temperature Electric Vehicles Operation

Electric vehicles (EVs) are gaining popularity in recent days to reduce the dependency on fossil fuels. Batteries are the main power source in EVs. However, the capacity of the battery degrades when it operates in low temperatures (< 0°C). Hence, it is essential to maintain the battery temperature (> 0°C) to operate at maximum capacity. Additionally, the battery alone is not suitable to supply the high transient power requirements of EVs. Thus, this brief proposes a novel integrated converter topology, which facilitates battery heating along with power transfer from the hybrid energy storage (battery and supercapacitors). The internal ac heating uses a resonant network for uniform heating of the battery cells in heating mode. The same inductor of the resonant network is also used for power transfer from hybrid energy storage during the driving mode. The Discrete Fourier Transform (DFT) based integrated inductor design ensures effective EV power sharing between battery and supercapacitors and reduces battery heating time. Thus, the proposed integrated converter reduces the number of converters stages, control complexity and overall cost. The detailed analysis and design are included along with the simulation and experiment results. The results show the uniform internal heating of the battery and efficient power transfer from/to the battery and supercapacitors. The experimentation is performed in a 1kW system considering the European driving cycle.
Review and Comparison of Nanofluid Thermal Conductivity and Heat Transfer Enhancements

This study provides a detailed literature review and an assessment of results of the research and development work forming the current status of nanofluid technology for heat transfer applications. Nanofluid technology is a relatively new field, and as such, the supporting studies are not extensive. Specifically, experimental results were reviewed in this study regarding the enhancement of the thermal conductivity and convective heat transfer of nanofluids relative to conventional heat transfer fluids, and assessments were made as to the state-of-the-art of verified parametric trends and magnitudes. Pertinent parameters of particle volume concentration, particle material, particle size, particle shape, base fluid material, temperature, additive, and acidity were considered individually, and experimental results from multiple research groups were used together when assessing results. To this end, published research results from many studies were recast using a common parameter to facilitate comparisons of data among research groups and to identify thermal property and heat transfer trends. The current state of knowledge is presented as well as areas where the data are presently inconclusive or conflicting. Heat transfer enhancement for available nanofluids is shown to be in the 15–40% range, with a few situations resulting in orders of magnitude enhancement.
Enhanced Stability of Vegetal Diamine Oxidase with Trehalose and Sucrose as Cryoprotectants: Mechanistic Insights

Enteric dysfunctions are common for various histamine-related intestinal disorders. Vegetal diamine oxidase (vDAO), an enzyme able to decompose histamine and thus alleviate histamine-related dysfunctions, was formulated in gastro-resistant tablet forms for oral administration as a food supplement and possible therapeutic agent. A major challenge for the use of proteins in the pharmaceutical field is their poor stability. In this study, vDAO was freeze-dried in the absence or in the presence of sucrose or trehalose as cryoprotectants and then formulated as tablets by direct compression. The stability of the obtained preparations was followed during storage at 4 °C and −20 °C for 18 months. In vitro dissolution tests with the vDAO powders formulated as tablets were performed in simulated gastric and in simulated intestinal fluids. The tablets obtained with the powder of the vDAO lyophilized with sucrose or trehalose cryoprotectants offered better protection for enzyme activity. Furthermore, the release of the vDAO lyophilized with the cryoprotectants was around 80% of the total loaded activity (enzyme units) compared to 20% for the control (vDAO powder prepared without cryoprotectants). This report revealed the potential of sucrose and trehalose as cryoprotectants to protect vDAO from freeze-drying stress and during storage, and also to markedly improve the vDAO release performance of tablets obtained with vDAO powders.
Impact of Ketamine on Quality of Recovery after Laparoscopic Surgery: A Single-Centre Single-Blinded Trial Using the QoR-15 Questionnaire

Background The quality of recovery is a cluster of patient-related outcomes that emphasise not only pain but different physical and emotional dimensions. Traditionally, ketamine is used to improve postoperative analgesia and avoid opioid consumption and opioid-related side effects. Objective The present study sought to evaluate if intraoperative ketamine administration (as a part of multimodal analgesia) influences the quality of recovery after laparoscopic surgery. Design A prospective two-armed, single-blinded trial. Settings. Tertiary single-centre trial between July 2021 and January 2022. Patients. From the 146 patients initially admitted to the study, 127 patients were enrolled, 60 in the ketamine group (group K) and 67 in the control group (group NK). Intervention. Both groups received a rigid intraoperative anaesthesia protocol; furthermore, in group K, 0.5 mg/kg of the ideal body weight of ketamine was administered. Main Outcome Measures. The primary outcome was to evaluate the effect of ketamine administration on the postoperative quality of recovery using the Portuguese version of the Quality of Recovery-15 (QoR-15) Questionnaire 24 h after surgery. The total score and minimal clinically significant difference (MCID) of the QoR-15 were compared. Other variables were also assessed such as the presence of emergence delirium (ED), the Numeric Rating Scale (NRS) for pain, and the presence of postoperative nausea and vomiting (PONV). Results A total of 127 patients were allocated to the study groups, 60 in group K and 67 in group NK. Regarding the primary outcome, no differences were found in individual categories (15 items) and in the total score of QoR-15 (p=0.214). Concerning improvement (MCID ≥ 8) or worsening (MCID ≤ 8) in quality of recovery, no difference was found between the groups (24 vs. 32 and 6 vs. 6; p=0.776). Finally, no difference was found in secondary postoperative outcomes including ED (p=0.55), NRS (p=0.401), and PONV (p=0.55). Conclusion In this study, the administration of ketamine in laparoscopic surgery had no impact on the quality of recovery 24 h after surgery. This trial is registered with NCT03724019.
Genome-Wide Association Study of Rice Grain Shape and Chalkiness in a Worldwide Collection of Xian Accessions

Rice (Oryza sativa L.) appearance quality, which is mainly defined by grain shape and chalkiness, is an important target in rice breeding. In this study, we first re-sequenced 137 indica accessions and then conducted a genome-wide association study (GWAS) for six agronomic traits with the 2,998,034 derived single nucleotide polymorphisms (SNPs) by using the best linear unbiased prediction (BLUP) values for each trait. The results revealed that 195 SNPs had significant associations with the six agronomic traits. Based on the genome-wide linkage disequilibrium (LD) blocks, candidate genes for the target traits were detected within 100 kb upstream and downstream of the relevant SNP loci. Results indicate that six quantitative trait loci (QTLs) significantly associated with six traits (qTGW4.1, qTGW4.2, qGL4.1, qGL12.1, qGL12.2, qGW2.1, qGW4.1, qGW6.1, qGW8.1, qGW8.2, qGW9.1, qGW11.1, qGLWR2.1, qGLWR2.2, qGLWR4.2, qPGWC5.1 and qDEC6.1) were identified for haplotype analysis. Among these QTLs, two (qTGW4.2 and qGW6.1), were overlapped with FLO19 and OsbZIP47, respectively, and the remaining four were novel QTLs. These candidate genes were further validated by haplotype block construction.
Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network

It is of great practical significance to quickly, accurately, and effectively identify the effects of rice diseases on rice yield. This paper proposes a rice disease identification method based on an improved DenseNet network (DenseNet). This method uses DenseNet as the benchmark model and uses the channel attention mechanism squeeze-and-excitation to strengthen the favorable features, while suppressing the unfavorable features. Then, depth wise separable convolutions are introduced to replace some standard convolutions in the dense network to improve the parameter utilization and training speed. Using the AdaBound algorithm, combined with the adaptive optimization method, the parameter adjustment time reduces. In the experiments on five kinds of rice disease datasets, the average classification accuracy of the method in this paper is 99.4%, which is 13.8 percentage points higher than the original model. At the same time, it is compared with other existing recognition methods, such as ResNet, VGG, and Vision Transformer. The recognition accuracy of this method is higher, realizes the effective classification of rice disease images, and provides a new method for the development of crop disease identification technology and smart agriculture.
Conditions for the Equivalence of the Autoregressive Latent Trajectory Model and a Latent Growth Curve Model With Autoregressive Disturbances

Curran and Bollen combined two models for longitudinal panel data: the latent growth curve model and the autoregressive model. In their model, the autoregressive relationships are modeled between the observed variables. This is a different model than a latent growth curve model with autoregressive relationships between the disturbances. However, when the autoregressive parameter is invariant over time and lies between-1 and 1, it can be shown that these models are algebraically equivalent. This result can be shown to generalize to the multivariate case. When the autoregressive parameters in the autoregressive latent trajectory model vary over time, the equivalence between the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances no longer holds. However, a latent growth curve model with time-varying autoregressive parameters for the disturbances could be considered an interesting alternative to the autoregressive latent trajectory model with time-varying autoregressive parameters.
Theory and experiment on the low-Reynolds-number expansion and contraction of a bubble pinned at a submerged tube tip

The expansion and contraction of a bubble pinned at a submerged tube tip and driven by constant gas flow rate Q are studied both theoretically and experimentally for Reynolds number Re[Lt ]1. Bubble shape, gas pressure, surface velocities, and extrapolated detached bubble volume are determined by a boundary integral method for various Bond (Bo=ρga2/σ) and capillary (Ca=μQ/σa2) numbers, where a is the capillary radius, ρ and μ are the liquid density and viscosity, σ is the surface tension, and g is the gravitational acceleration. Bubble expansion from a flat interface to near detachment is simulated for a full range of Ca (0.01–100) and Bo (0.01–0.5). The maximum gas pressure is found to vary almost linearly with Ca for 0.01[les ]Ca[les ]100. This correlation allows the maximum bubble pressure method for measuring dynamic surface tension to be extended to viscous liquids. Simulated detached bubble volumes approach static values for Ca[Lt ]1, and asymptote as Q3/4 for Ca[Gt ]1, in agreement with analytic predictions. In the limit Ca→0, two singular time domains are identified near the beginning and the end of bubble growth during which viscous and capillary forces become comparable. Expansion and contraction experiments were conducted using a viscous silicone oil. Digitized video images of deforming bubbles compare well with numerical solutions. It is observed that a bubble contracting at high Ca snaps off.
Markovian Projection to a Displaced Volatility Heston Model

Markovian Projection is an optimal approximation of a complex underlying process with a simpler one, keeping essential properties of the initial process. The Heston process, as the Markovian Projection target, is an example. In this article, we generalize the results of Markovian Projection onto a Heston model to a wider class of approximating models, a Heston model with displaced volatility. As an important application, we derive an effective approximation for FX/EQ options for the Heston model, coupled with correlated Gaussian interest rates. The main technical result is an option evaluation for correlated Heston/Lognormal processes. Unlike the case of exactly solvable (affine) zero correlation or its uncorrelated displacement generalization,considered by Andreasen, non-trivial correlations destroy affine structure and exact solvability. Using the powerful technique of Markovian Projection onto a Heston model with displaced volatility, we produce an effective approximation and present its numerical confirmation.
Beam Size, Shape and Efficiencies for the ATNF Mopra Radio Telescope at 86–115 GHz

Abstract We present data characterising the performance of the Mopra Radio Telescope during the period 2000–2004, including measurements of the beam size and shape, as well as the overall beam efficiency of the telescope. In 2004 the full width half maximum of the beam was measured to be 36 ± 3″ at 86 GHz, falling to 33 ± 2″ at 115 GHz. Based on our observations of Jupiter we measured the beam efficiency of the Gaussian main beam to be 0.49 ± 0.03 at 86 GHz and 0.42 ± 0.02 at 115 GHz. Sources with angular sizes of ∼80″ couple well to the main beam, while sources with angular sizes between ∼80″ and ∼160″ couple to the both the main beam and inner error beam. Measurements indicate that the inner error beam contains approximately one-third the power of the main beam. We also compare efficiency corrected spectra to measurements made at similar facilities and present standard spectra taken towards the molecular clouds Orion-KL and M17-SW.
Hubble's galaxy nomenclature

It is widely written and believed that Edwin Hubble introduced the terms `early' and `late types' to suggest an evolutionary sequence for galaxies. This is incorrect. Hubble took these terms from spectral classification of stars to signify a sequence related to complexity of appearance, albeit based on images rather than spectra. The temporal connotations of the terms had been abandoned prior to his 1926 paper on classification of galaxies.
On the observation of critical indices of primary and secondary order parameters using birefringence

The forms of the birefringence change at phase transitions are discussed for four types of phase transition: (i) ferrodistortive, (ii) cubic systems with an n-dimensional soft mode and order parameter, (iii) cubic systems with a one-dimensional soft mode occurring at n q0 points, and (iv) tetragonal magnetic systems (in which there is no crystallographic symmetry change at the phase transition). The birefringence is shown to vary as ( pi ) for case (i); as the secondary parameter ( pi alpha pi alpha )-(1/n) sigma alpha ( pi alpha pi alpha ) for case (ii); as ( pi )2 for case (iii); and as the energy for case (iv). The advantage of birefringence is that it is not sensitive to fluctuations, while the disadvantages are that one must work with a single-domain single crystal of a transparent substance.
Gene-modified neural progenitor cells for the treatment of neuropathic lysosomal storage diseases

Lysosomal storage diseases: Lysosomal storage diseases (LSDs) are a family of about 70 disorders, with an overall incidence of 1:7000 live births. They are caused by dysfunctional lysosomal hydrolases, eventually leading to the accumulation of undegraded substrate into the lysosome. This results in a wide array of symptoms, which may include: the presence of dysmorphic features, cardio-respiratory disease, bone and joint disease, organomegaly, developmental delay and neurocognitive decline. The majority of these diseases have a neurological component and in the absence of treatment, death often occurs in the first decades of life, with the neurological complications drastically undermining the patient’s quality of life, as well as their families (Boustany, 2013).
Imaging across multiple spatial scales with the multi-camera array microscope

This article experimentally examines different configurations of a novel multi-camera array microscope (MCAM) imaging technology. The MCAM is based upon a densely packed array of"micro-cameras"to jointly image across a large field-of-view at high resolution. Each micro-camera within the array images a unique area of a sample of interest, and then all acquired data with 54 micro-cameras are digitally combined into composite frames, whose total pixel counts significantly exceed the pixel counts of standard microscope systems. We present results from three unique MCAM configurations for different use cases. First, we demonstrate a configuration that simultaneously images and estimates the 3D object depth across a 100 x 135 mm^2 field-of-view (FOV) at approximately 20 um resolution, which results in 0.15 gigapixels (GP) per snapshot. Second, we demonstrate an MCAM configuration that records video across a continuous 83 x 123 mm^2 FOV with two-fold increased resolution (0.48 GP per frame). Finally, we report a third high-resolution configuration (2 um resolution) that can rapidly produce 9.8 GP composites of large histopathology specimens.
Off-center-frequency analysis of a complete planar slotted-waveguide array consisting of subarrays

An iterative procedure for calculating the off-center-frequency performance of a complete planar slotted-waveguide array, which consists of an arbitrary number of subarrays, is presented. The analysis includes the effects of the coupling slots from the main feed lines to the branch line guides, as well as the effects of RF manifolds, power splitters, or comparators used to distribute the power from the antenna input to the multiple subarray main lines. The procedure is applied to a large planar array for which dimensions have been obtained from an accurate metrology exercise. Good agreement between the predicted and measured performance of the array is demonstrated.