Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Thermal Interface Materials on Electrical Machines Thermal Performance With Heat Pipes

Thermal issue has always been a major concern in motor design. Heat pipes (HPs) have been applied in electrical machines with remarkable cooling effects reported. However, critical factors that affect HP thermal benefits have rarely been studied, such as limited contact area between HPs and heat sources, which may lead to high thermal resistance and degrade motor thermal performance. Due to limited thermal conductivities, conventional motor thermal interface materials (TIMs), such as epoxy resin, fail to maximize HP thermal benefits and minimize motor temperature rise. In this case, new TIMs with outstanding thermal properties that have not been used in electrical machines, such as thermal grease and adhesive, are potential candidates to address this issue, while their thermal impact is still unclear. This article conducts both experiments and thermal analyses to quantify the effects of TIM on motor cooling performance. First, the thermal properties of ten typical TIMs in four categories are experimentally characterized. Then, the selected TIMs are applied to a stator-winding assembly with HP to evaluate their thermal impact on motor cooling performance under different cooling conditions. Finally, practical guidelines are given for TIM selection in electrical machines with HPs or other cooling media.
The Effects of Information Layout, Display Mode, and Gender Difference on the User Interface Design of Mobile Shopping Applications

Online shopping has revolutionized our daily lives in the modern era. We can purchase needed goods on mobile shopping applications (apps) anytime and anywhere without leaving home. Especially during the COVID-19 pandemic, we have become increasingly dependent on various mobile shopping activities. However, the visual design of the shopping app interface often affects the user’s interactive experience and the efficiency of browsing product information. In addition, gender differences are also worth being considered in the shopping interface design process. To achieve the goal, the research conducted a user study (N=40) of a $2\times $ 2 x 2 mixed factorial design (i.e., information layout x display mode x gender difference). Each participant performed four tasks during the experiment. The authors measured the task completion time, collected the subjective responses from the SUS and the 7-point Likert scale questionnaire, and interviewed participants. The results revealed that: (1) females perform faster in lighter mode when searching for information location, while males perform faster in darker mode. (2) The information layout affects the user’s visual search performance and subjective evaluation; females prefer the list style, but men prefer the matrix style. (3) Participants (both males and females) perceived matrix style as more popular than list style in dark mode; however, the result was reversed in light mode. The findings generated from the research can serve as a good reference for the development of user experience in the user interface design of mobile shopping apps.
Bit Serial Systolic Architectures for Multiplicative Inversion and Division over GF (2 m )

Systolic architectures are capable of achieving high throughput by maximizing pipelining and by eliminating global data interconnects. Recursive algorithms with regular data flows are suitable for systolization. The computation of multiplicative inversion using algorithms based on EEA (Extended Euclidean Algorithm) are particularly suitable for systolization. Implementations based on EEA present a high degree of parallelism and pipelinability at bit level which can be easily optimized to achieve local data flow and to eliminate the global interconnects which represent most important bottleneck in todays sub-micron design process. The net result is to have high clock rate and performance based on efficient systolic architectures. This thesis examines high performance but also scalable implementations of multiplicative inversion or field division over Galois fields GF (2) in the specific case of cryptographic applications where field dimension m may be very large (greater than 400) and either m or defining irreducible polynomial may vary. For this purpose, many inversion schemes with different basis representation are studied and most importantly variants of EEA and binary (Stein’s) GCD computation implementations are reviewed. A set of common as well as contrasting characteristics of these variants are discussed. As a result a generalized and optimized variant of EEA is proposed which can compute division, and multiplicative inversion as its subset, with divisor in either polynomial or triangular basis representation. Further results regarding Hankel matrix formation for double-basis inversion is provided. The validity of using the same architecture to compute field division with polynomial or triangular basis representation is proved. Next, a scalable unidirectional bit serial systolic array implementation of this proposed variant of EEA is implemented. Its complexity measures are defined and these are compared against the best known architectures. It is shown that assuming the requirements specified above, this proposed architecture may achieve a higher clock rate performance w.r. t. other designs while being more flexible, reliable and with minimum number of intercell interconnects. The main contribution at system level architecture is the substitution of all counter or adder/subtractor elements with a simpler distributed and free of carry propagation delay structures. Further a novel restoring mechanism for result sequences of EEA is proposed
Characterization of Pepper Ripeness in the Field Using Hyperspectral Imaging (HSI) with Back Propagation (BP) Neural Network and Kernel Based Extreme Learning Machine (KELM) Models

Abstract Due to the economic differences of chili peppers at different maturity levels and the absence of differentiation of maturity in existing harvesting processes, it is crucial to accurately identify the maturity during harvesting for improving economic benefits. This paper investigated pepper maturity recognition models using hyperspectral technology to realize intelligent pepper harvesting with high accuracy and monitor maturity. Hyperspectral data (400–1000 nm) for field line peppers were collected, preprocessed using normalization, Savitzky-Golay convolutional smoothing, and standard normal variable transformation and subsequently used to train back propagation neural network (BP) and kernel based extreme learning machine (KELM) models. Principal component analysis (PCA) was used to reduce data dimensionality and identify characteristic spectral wavelengths for pepper maturity at 862.2, 676.9, 578.1, and 980.7 nm; and BP, PCA-BP, KELM, and PCA-KELM models were subsequently established. The precision for the KELM and PCA-KELM models (99.5% and 97.3%, respectively) was superior to the other two models; however, the PCA-KELM model used only four of the feature wavelengths as inputs, hence it required only 1/44 the data compared with the KELM model. Thus, the PCA-KELM model achieved high recognition accuracy and training speed, offering an effective method to discriminate pepper ripeness based on hyperspectral features.
Wildfire smoke exposure and emergency department visits in Washington State

Wildfires are increasing in prevalence in western North America due to changing climate conditions. A growing number of studies examine the impact of wildfire smoke on morbidity; however, few evaluate these impacts using syndromic surveillance data that cover many emergency departments (EDs). We used syndromic surveillance data to explore the effect of wildfire smoke exposure on all-cause respiratory and cardiovascular ED visits in Washington state. Using a time-stratified case crossover design, we observed an increased odds of asthma visits immediately after and in all five days following initial exposure (lag 0 OR: 1.13; 95% CI: 1.10, 1.17; lag 1–5 ORs all 1.05 or greater with a lower CI of 1.02 or higher), and an increased odds of respiratory visits in all five days following initial exposure (lag 1 OR: 1.02; 95% CI: 1.00, 1.03; lag 2–5 ORs and lower CIs were all at least as large) comparing wildfire smoke to non-wildfire smoke days. We observed mixed results for cardiovascular visits, with evidence of increased odds emerging only several days following initial exposure. We also found increased odds across all visit categories for a 10 μg m−3 increase in smoke-impacted PM2.5. In stratified analyses, we observed elevated odds for respiratory visits among ages 19–64, for asthma visits among ages 5–64, and mixed risk estimates for cardiovascular visits by age group. This study provides evidence of an increased risk of respiratory ED visits immediately following initial wildfire smoke exposure, and increased risk of cardiovascular ED visits several days following initial exposure. These increased risks are seen particularly among children and younger to middle-aged adults.
Improvement of ensemble reliability with a new dressing kernel

A new method of combining dynamical and statistical ensembles for the purpose of improving ensemble reliability for underdispersive ensembles is introduced. The method involves adding independent sets of N random four‐dimensional ‘dressing’ perturbations to each of the K members of a dynamical ensemble forecast to obtain an N×K dressed ensemble. The new method mathematically constrains the stochastic process used to generate the statistical dressing perturbations so that it removes seasonally averaged errors in the second moment measures for originally underdispersive ensembles. A random‐number generator experiment and an experiment with the ensemble transform Kalman filter (ETKF) ensemble generation scheme show that the previously proposed ‘best‐member’ dressing method fails to reliably predict the second moment of the distribution of forecast errors, whereas the new dressing method reliably predicts this second moment. After being dressed with the second moment constraint method, the ETKF ensemble is more skilful than the undressed ensemble. The ETKF ensemble postprocessed with the new dressing method is applied for probabilistic forecasts of cooling degree‐days (CDD) for Boston. It is shown that the new kernel's ability to account for temporally correlated forecast errors results in ensemble forecasts of CDDs with reliable spread, whereas the best‐member method leads to an underdispersive ensemble of CDD forecasts. Copyright © 2005 Royal Meteorological Society
A Bayesian Model for Aggregating Rank Data with Covariates

Rank aggregation is the combining of ranking results from different sources to generate a "better" ranking list. Existing rank aggregation methods utilize only rank data. In some real applications, however, rank data come with covariates information as well. Moreover, some rankers may be more informative and reliable than others. Therefore, it is desirable for a rank aggregation method to incorporate covariates information and to distinguish high quality rankers from spam rankers. Here, we propose the Bayesian aggregation of rank-data with covariates (BARC) and its weighted version, BARCW, to achieve the aforementioned goals. Both BARC and BARCW employ latent variable models\textemdash which can incorporate covariates information\textemdash for ranked entities. Furthermore, BARCW quantitatively measures the difference in qualities of rankers. We generate an aggregated ranking and its probability interval based on posterior distributions of latent variables derived from a parameter-expanded Gibbs sampler. Simulation studies show that our methods outperform competing methods in a variety of scenarios. Real-data applications also validate our estimation of reliability parameters and effects of covaraiates.
ASSESSMENT OF ANTICANCER ACTIVITY AND GENE EXPRESSION IN P53 GENE OF CISSUS QUADRANGULARIS L. STEM MEDIATED SILVER NANOPARTICLES

Present study was conducted to evaluate the anticancer activity of Cissus quadrangularis stem mediated silver nanoparticles and its effect on the expression of p53 gene. In the current study the anticancer activity was assessed using the MTT assay on the MCF-7 breast cancer cell lines. In addition, the Quantitative Real Time PCR analysis was carried out to study the expression of p53 genes. The analysis revealed that, Cisplatin exhibits an IC50 value of 5.51 μg/ml when used as a positive control in MCF-7 cell line, whereas the silver nanoparticle extract at the same concentration exhibit an IC 50 value of 56.74 μg/ml. The MCF-7 breast cancer cells that were treated with the Cissus quadrangularis stem mediated silver nanoparticles showed down regulation of the p53 gene, going from 1.0 fold in control cells to 0.43 fold in the treated cells. The relative gene expression of the control is usually set to 1 in comparison to the test sample. In accordance with the MTT test results, the synthesized AgNPs have moderate anticancer efficacy. According to the findings of gene expression, MCF-7 cells treated with synthesized AgNPs from the stem of Cissus quadrangularis exhibit a 0.4564 fold decrease in the expression of p53 genes when compared to MCF-7 control cells.
Oat grains classification using deep learning

Background: Based on their nutritional benefits, oat is classified as a cereal of great importance for both human and animal feeding. Throughout the production process, species and variety identification are vital for agricultural systems. The present work establishes SeedFlow, a method for image acquisition, processing, and classification of oat grains using deep learning techniques. We apply these techniques to the identification of the grains from the different oat species Avena sativa and Avena strigosa and to classify grains as varieties of Avena sativa, such as UPFA Ouro, UPFA Fuerza, and UPFA Gaudéria. Results: To achieve this proposition, we executed our solution considering six different deep learning architectures to evaluate which model presents the best performance. This approach attained an accuracy of 99.7% for oat species identification and 89.7% for oat varieties classification using DenseNet architecture. Conclusions: As a result, this tool can provide high value for practical quality control applications, and it is feasible to use in pre-screening tests, laboratory analysis pipelines, or computer support tools geared toward breeding programs or intellectual property assessment.
Formulation Development and Evaluation of Tadalafil Transdermal Patches Using Various Penetration Enhancers.

Ms. Shikha Baghel Chauhan The aim of this research study was to develop transdermal Patch of tadalafil using various skin penetration enhancers in different concentrations like Eugenol, 1Octanol,DMSO and Peppermint oilfor erectile dysfunction and to understand the role of penetration enhancer on the drug delivery in transdermal patch. The physicochemical compatibility and preformulation studies of the drug and the polymers was studied by Infrared spectroscopy and HPLC. Different formulation of transdermal patches using Chitosan 4% w/w as polymer and polyethylene glycol 400 and glycerol (1:1) as plasticizerwere fabricated.The results suggested no physicochemical incompatibility between the drug and the polymers. Formulated patches were evaluated for folding endurance, thickness, weight uniformity, percentage moisture content, percentage moisture uptake, content uniformity in-vitro permeation studies and in-vitro drug release were performed in 50% v/vethanolic phosphate buffer of pH 7.4. The result showed that formulation DMSO-5 found to be best formulation, as it permeated 59.42% drug through rat Dorsal skin as compare of control patch. Overall results of all permeation enhancers stated that DMSO was found to be the best penetration enhancer, as it has the best penetration ability for tadalafil transdermal drug delivery.
Consistent boundary conditions at nonconducting surfaces of planetary bodies: Applications in a new Ganymede MHD model

The interaction of planetary bodies with their surrounding magnetized plasma can often be described with the magnetohydrodynamic (MHD) equations, which are commonly solved by numerical models. For these models it is necessary to define physically correct boundary conditions for the plasma mass and energy density, the plasma velocity, and the magnetic field. Many planetary bodies have surfaces whose electrical conductivity is negligibly small and thus no electric current penetrates their surfaces. Magnetic boundary conditions, which consider that the associated radial electric current at the planetary surface is zero, are difficult to implement because they include the curl of the magnetic field. Here we derive new boundary conditions by a decomposition of the magnetic field in poloidal and toroidal parts. We find that the toroidal part of the magnetic field needs to vanish at the surface of the insulator. For the spherical harmonics coefficients of the poloidal part, we derive a Cauchy boundary condition, which also matches a possible intrinsic field by including its Gauss coefficients. Thus, we can additionally include planetary dynamo fields as well as time‐variable induction fields within electrically conductive subsurface layers. We implement the nonconducting boundary condition in the MHD simulation code ZEUS‐MP using spherical geometry and provide a numerical implementation in Fortran 90 as supporting information on the JGR website. We apply it to a model for Ganymede's plasma environment. Our model also includes a consistent set of boundary conditions for the other MHD variables density, velocity, and energy. With this model we can describe Galileo spacecraft observations in and around Ganymede's minimagnetosphere very well.
Supplemental materials for \Types, not tokens, are relevant for determining how far to generalize"

The first step for accurately modelling this data is to specify a model of grammar learning (or, more accurately, grammar comparison). We use the model specified in Perfors, Tenenbaum, and Regier (2011), in which grammars are scored according to Bayesian probability theory. Please see Perfors et al. (2011) for the full details, but here it is in a nutshell. The rough idea is that the posterior probability of a grammar given a corpus of sentences is proportional to the prior probability of that grammar times the likelihood of observing those sentences given that grammar. The underlying framework assumes that language is generated according to a multi-stage probabilistic process, and the Bayesian learner inverts this process to learn aspects of the generating grammar from the language data observed. A corpus is generated by first picking a type of grammar T from the prior distribution p(T ). A specific grammar is chosen as an instance of that type with probability p(G|T ), and the data D is then generated from the specific grammar G. The inferences backward are then captured by the joint posterior probability, which according to Bayes rule is:
Rab3A/Rab27A System Silencing Ameliorates High Glucose-Induced Injury in Podocytes

Simple Summary In diabetic nephropathy, the injury of renal cells such as podocytes worsens the glomerular filtration process due to the cells’ loss and detachment from the basal membrane. Intra- and intercellular communications in cells are partially mediated by the Rab3A/Rab27A system. We have observed that silencing the Rab3A/Rab27A system in podocytes experiencing glucose overload has a differential impact on differentiation, cytoskeleton organisation, apoptosis, CD63 distribution, and miRNA levels. Generally, when cells are subjected to glucose stress and the Rab system is silenced, we observed a reduction in the detrimental cell processes. These results suggest that the Rab3A/Rab27A system is a key participant in podocyte injury and vesicular traffic regulation in diabetic nephropathy. Abstract Diabetic nephropathy is a major complication in diabetic patients. Podocytes undergo loss and detachment from the basal membrane. Intra- and intercellular communication through exosomes are key processes for maintaining function, and the Rab3A/Rab27A system is an important counterpart. Previously, we observed significant changes in the Rab3A/Rab27A system in podocytes under glucose overload, demonstrating its important role in podocyte injury. We investigated the implication of silencing the Rab3A/Rab27A system in high glucose-treated podocytes and analysed the effect on differentiation, apoptosis, cytoskeletal organisation, vesicle distribution, and microRNA expression in cells and exosomes. For this, we subjected podocytes to high glucose and transfection through siRNAs, and we isolated extracellular vesicles and performed western blotting, transmission electron microscopy, RT-qPCR, immunofluorescence and flow cytometry assays. We found that silencing RAB3A and RAB27A generally leads to a decrease in podocyte differentiation and cytoskeleton organization and an increase in apoptosis. Moreover, CD63-positive vesicles experienced a pattern distribution change. Under high glucose, Rab3A/Rab27A silencing ameliorates some of these detrimental processes, suggesting a differential influence depending on the presence or absence of cellular stress. We also observed substantial expression changes in miRNAs that were relevant in diabetic nephropathy upon silencing and glucose treatment. Our findings highlight the Rab3A/Rab27A system as a key participant in podocyte injury and vesicular traffic regulation in diabetic nephropathy.
Observations of Large Wind Shear above the Marine Boundary Layer near Point Buchon, California

AbstractParticularly strong winds along the coast of Southern California on 24 May 2012 were measured by the Wyoming King Air research aircraft during the Precision Atmospheric Marine Boundary Layer Experiment (PreAMBLE). The fast flow is bounded laterally by the coastal topography and vertically by a pronounced temperature inversion separating the cool, moist air in the marine boundary layer (MBL) from the warm, dry air aloft. Many studies have investigated the response of this two-layer flow to changes in the coastline by invoking hydraulic theory, which explains the essential characteristics including changes in MBL depth and the attendant wind. Processes occurring just above the MBL are important to the low-level thermodynamic and kinematic structure. Observations on this day demonstrate how the large shear above the MBL can impact the lower atmosphere. A typical two-layer system was observed north of Point Buchon, which was supercritical. Around Point Buchon, the depth of the MBL decreased and wind i.
Application of PID Control Technique Based on Parameter Fuzzy Self-Modify in Dozer Control System

This paper represents the application of the fuzzy mathematics in dealling with the mathematical model of the dozer implement,performs the investigation of the theoretic method,computer simulation and tests on the on-line fuzzy self-modify of the PID parameters. Some results of the computer simulation and tests are given. Then it provides the theoretic and applied basis for the real-time automatical control of doler.