Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cool-Season Tornadoes of California and Southern Australia

Abstract Examples of cool-season tornadic thunderstorms in California and southern Australia are examined. Almost one-half of the reported Australian tornadoes and the majority of those in California occur in the cool season. It is shown that in both areas the typical synoptic pattern shows an active midlatitude trough just upstream, with a strong jet streak aloft. In both areas the tornadic thunderstorms occur with weak to moderate levels of thermodynamic instability in the lower troposphere but with extremely high values of low-level positive and bulk shear. Statistical tests on null cases (nontornadic thunderstorms) in the Central Valley of California indicate that large values of 0–1-km shear provide a discriminator for more damaging (F1–F3) tornadoes, whereas bulk measures of buoyancy, such as CAPE, do not. Australian case studies and tornado proximity soundings show similar characteristics. A “cool-season tornadic thunderstorm potential” diagnostic for Australian conditions, based on regional NWP an.
Neural Network-Based Recent Research Developments in SLAM for Autonomous Ground Vehicles: A Review

The development of autonomous vehicles has prompted an interest in exploring various techniques in navigation. One such technique is simultaneous localization and mapping (SLAM), which enables a vehicle to comprehend its surroundings, build a map of the environment in real time, and locate itself within that map. Although traditional techniques have been used to perform SLAM for a long time, recent advancements have seen the incorporation of neural network techniques into various stages of the SLAM pipeline. This review article provides a focused analysis of the recent developments in neural network techniques for SLAM-based localization of autonomous ground vehicles. In contrast to the previous review studies that covered general navigation and SLAM techniques, this paper specifically addresses the unique challenges and opportunities presented by the integration of neural networks in this context. Existing review studies have highlighted the limitations of conventional visual SLAM, and this article aims to explore the potential of deep learning methods. This article discusses the functions required for localization, and several neural network-based techniques proposed by researchers to carry out such functions. First, it presents a general background of the issue, the relevant review studies that have already been done, and the adopted methodology in this review. Then, it provides a thorough review of the findings regarding localization and odometry. Finally, it presents our analysis of the findings, open research questions in the field, and a conclusion. A semisystematic approach is used to carry out the review.
Analytical formulae for longitudinal slip lengths over unidirectional superhydrophobic surfaces with curved menisci

This paper reports new analytical formulae for the longitudinal slip lengths for simple shear over a superhydrophobic surface, or bubble mattress, comprising a periodic array of unidirectional circular menisci, or bubbles, protruding into, or out of, the fluid. The accuracy of the formulae is tested against results from full numerical simulations; they are found to give small relative errors even at large no-shear fractions. In the dilute limit the formulae reduce to an earlier result by Crowdy (Phys. Fluids, vol. 22, 2010, 121703). They also extend analytical results of Sbragaglia & Prosperetti (Phys. Fluids, vol. 19, 2007, 043603) beyond the limit of a small protrusion angle.
Marginal Curvatures and Their Usefulness in the Analysis of Nonlinear Regression Models

Abstract Interval estimation for individual parameters in nonlinear regression is examined in relation to curvature of the solution locus. Criteria are developed to define severe curvature and distinguish cases in which linear methods may be used from those in which parameter transformations are necessary. A formula for improving the linear approximation in cases of relatively mild curvature is proposed. For example, suppose that interest is focused on one specific parameter, θ j from among θ = {θ1 θ2, …, θ p } in a nonlinear regression model. A crude approach to setting 95% confidence limits to θ j might proceed as follows: First calculate the standard error of the estimate as the square root of its asymptotic variance. Then call the confidence limits. In this article, higher-order correction terms Γ and β are proposed that enable us to develop a power series expansion for the confidence limits, as and respectively. Here denotes some estimate of σ2, the variance of a single observation.
A Novel Microwave Hot Pressing Machine for Production of Fixed Oils from Different Biopolymeric Structured Tissues

A microwave hot pressing machine (MHPM) was used to heat the colander to produce fixed oils from each of castor, sunflower, rapeseed, and moringa seed and compared them to those obtained using an ordinary electric hot pressing machine (EHPM). The physical properties, namely the moisture content of seed (MCs), the seed content of fixed oil (Scfo), the yield of the main fixed oil (Ymfo), the yield of recovered fixed oil (Yrfo), extraction loss (EL), six Efficiency of fixed oil extraction (Efoe), specific gravity (SGfo), refractive index (RI) as well as chemical properties, namely iodine number (IN), saponification value (SV), acid value (AV), and the yield of fatty acid (Yfa) of the four oils extracted by the MHPM and EHPM were determined. Chemical constituents of the resultant oil were identified using GC/MS after saponification and methylation processes. The Ymfo and SV obtained using the MHPM were higher than those for the EHPM for all four fixed oils studied. On the other hand, each of the SGfo, RI, IN, AV, and pH of the fixed oils did not alter statistically due to changing the heating tool from electric band heaters into a microwave beam. The qualities of the four fixed oils extracted by the MHPM were very encouraging as a pivot of the industrial fixed oil projects compared to the EHPM. The prominent fatty acid of the castor fixed oil was found to be ricinoleic acid, making up 76.41% and 71.99% contents of oils extracted using the MHPM and EHPM, respectively. In addition, the oleic acid was the prominent fatty acid in each of the fixed oils of sunflower, rapeseed, and moringa species, and its yield by using the MHPM was higher than that for the EHPM. The role of microwave irradiation in facilitating fixed oil extrusion from the biopolymeric structured organelles (lipid bodies) was protruded. Since it was confirmed by the present study that using microwave irradiation is simple, facile, more eco-friendly, cost-effective, retains parent quality of oils, and allows for the warming of bigger machines and spaces, we think it will make an industrial revolution in oil extraction field.
Three-term asymptotic expansion: A semi-Markovian random walk with a generalized beta distributed interference of chance

ABSTRACT A semi-Markovian random walk process (X(t)) with a generalized beta distribution of chance is considered. The asymptotic expansions for the first four moments of the ergodic distribution of the process are obtained as E(ζn) → ∞ when the random variable ζn has a generalized beta distribution with parameters (s, S, α, β); α, β > 1, 0 ⩽ s < S < ∞. Finally, the accuracy of the asymptotic expansions is examined by using the Monte Carlo simulation method.
Adaptive estimation in a random coefficient autoregressive model

This paper proves the local asymptotic normality of a stationary and ergodic first order random coefficient autoregressive model in a semiparametric setting. This result is used to show that Stein's necessary condition for adaptive estimation of the mean of the random coefficient is satisfied if the distributions of the innovations and the errors in the random coefficients are symmetric around zero. Under these symmetry assumptions, a locally asymptotically minimax adaptive estimator of the mean of the random coefficient is constructed. The paper also proves the asymptotic normality of generalized M-estimators of the parameter of interest. These estimators are used as preliminary estimators in the above construction.
Pandemic Populism: Explaining Support for the People's Party of Canada in the 2021 Federal Election

Abstract The COVID-19 pandemic has seen opponents of public health mandates deploy a range of populist and anti-elite arguments. The 2021 Canadian federal election was an exceptional “pandemic election” in which the COVID-19 health crisis took centre stage. But the election campaign also saw the populist People's Party of Canada (PPC) rise to prominence by opposing pandemic-related public health restrictions. While the party failed to win a seat, it did manage to triple its vote share (1.6 per cent to 4.9 per cent). It is unclear, however, what factors led to the rise in support for the PPC. To explore this issue, we draw on an original post-election survey (n = 18,950) and focus on populist attitudes and opposition to COVID-19-related public health restrictions. Results from regression models and structural equation models (SEMs) indicate that opposition to public health restrictions was a much stronger factor than populism in shaping support for the PPC.
Adaptive Synchronization of Second-Order Leader-Following Multiagent Systems

This article considers a distributed adaptive control scheme for a class of second-order leader-following multiagent systems with only position information as output. An auxiliary network for the agent network is modeled to estimate the unmeasurable velocity information and make the output-based distributed adaptive control protocol effective. Under the distributed adaptive control scheme based on the auxiliary network model, several distributed synchronization criteria are established, and the corresponding convergence analysis is provided based on the stability theory. Finally, several simulation examples are provided to conform to the presented criteria.
Pilot-Scale Production of Washed Cottonseed Meal and Co-Products

Enhanced utilization of defatted cottonseed meal (CSM)-based products as biobased raw materials would increase the profitability of cotton growers and processors. In this work, a multiple-step procedure that can be used to produce water washed cottonseed meal (WCSM) and a co-product water soluble cottonseed protein (CSPw) from CSM was tested at pilot scale. Alternatively, the procedure can also be used to produce CSPw, alkali soluble protein (CSPa) and the protein extracted insoluble residue (CSIR). The chemical composition of these pilot-produced products was generally comparable to that of the corresponding laboratory-prepared products. The fiber content was higher in the pilot-produced WCSM than in laboratory-produced product mainly due to the higher fiber content in the mill-based starting meal material used in the pilot trial. The protein content in the pilot-produced CSPw was only 64.4% of the solid matter, indicating that additional rinse is needed before drying the HCl-precipitated CSPw fraction. Per the yield and composition data, it was concluded that it was feasible to produce the four products WCSM, CSPw, CSPa, and CSIR from CSM in a large scale. Thus, adoption of this procedure would provide the necessary quantities for exploring the practical utilization of these products in biomaterial industry.
Topological EEG-Based Functional Connectivity Analysis for Mental Workload State Recognition

Mental workload (MWL) assessment is crucial in fatigue evaluation applications to avoid potential health problems or serious accidents. This article proposes an MWL recognition approach developed with a topological investigation of the electroencephalography (EEG)-based brain functional connectivity (FC) network. In this work, the graph-filtration-based features are extracted to reveal the brain state variations using the persistent homology technique from the topological data analysis (TDA) area. Three public open benchmark datasets are used to test and verify the recognition ability of the proposed method, which are developed with the MWL assessment experiments of Simultaneous Capacity (SIMKAP) test tasks, arithmetic calculations, and Multi-Attribute Task Battery II (MATB-II) MWL tasks. The experimental results show that the proposed topological FC network analysis scheme shows excellent distinguishing ability in brain state recognition, comparable to or better than the state-of-art results with similar settings. This work is the first investigation of EEG-based MWL evaluation with the persistent homology analysis of multivariate time series. The proposed topological features are effective and robust brain states’ indicators, providing an alternative feature in designing novel brain–computer interface systems.
A Linguistic Approach to Children's Absolute Interpretation of Differential Comparatives

Comparison problems are notoriously difficult for kids, which require them to determine a differential between two sets, e.g., “A boy has three apples and a girl has two apples. How many more apples does the boy have than the girl? ” Even young school children wrongly answer the number of the boy’s apples, three, instead of answering the differential between the two sets of apples, namely one. Children seem to interpret the differential phrase, how many not as comparative but as absolute. This paper introduces a pilot experiment to settle a controversy over the source of children’s absolute interpretation: linguistic or cognitive, and shows their absolute interpretation is not caused by their limited cognitive resources for quantifying a differential between two sets. Another pilot experiment scrutinizes their absolute interpretation and reveals what underlying representation it has. Finally, reviewing previous semantic analyses of differential comparatives, we argue that child grammar lacks a functional head which introduces a degree argument and that this lack causes their absolute interpretation of differential comparatives.
Structural characteristics of the supersonic turbulent boundary layer subjected to concave curvature

By employing the Nanoparticle-based Planar Laser Scattering method and the particle image velocimetry, structural responses of a Mach 2.95 turbulent boundary layer to two concave curvatures (radiuses are 113 mm and 350 mm, respectively) are experimentally investigated. Large scale vortices formed in the flat plate region are observed to break up into smaller ones immediately after they flow into the concave region. Larger curvature tends to introduce more immediate breakup of the vortices. Fractal analysis reveals that after the turning angle and impulse of bulk dilatation reach 11° and 0.6, respectively, the breakup of the vortices slows down evidently.
Neutral Weed Communities: The Intersection between Crop Productivity, Biodiversity, and Weed Ecosystem services

Abstract Weeds are a fundamental component of agroecosystems and, if not appropriately managed, can cause severe crop yield losses. New perspectives on weed management are required, because current approaches, such as herbicide application or soil tillage, have significant environmental and agronomic drawbacks. We propose the concept of “neutral weed communities,” which are weed communities that coexist with crops and do not negatively affect crop yield and quality compared with weed-free conditions. Management practices that promote neutral weed communities can enable reduced use of herbicides and soil tillage while enhancing ecosystem services and biodiversity. We report scientific evidence of neutral weed communities and survey ecological explanations for why different weed communities have different effects on crop production. We also propose two weed management approaches for attaining neutral weed communities. The first approach aims to maximize weed biodiversity using traditional approaches such as cropping system diversification and integrated weed management. Higher weed biodiversity is associated with lower dominance of competitive weed species that reduce crop yield. The second approach relies on modern tools such as robots and biotechnology to manipulate the density of specific weed species. This approach can remove highly problematic species and minimize niche overlap between the weeds and crops. Given the complexity of interactions among crops, weeds, and other components of the agroecosystem, we highlight the need for multidisciplinary research to illuminate mechanisms that determine the neutrality of weed communities.
New Frontiers in Autoimmune Diagnostics: A Systematic Review on Saliva Testing

(1) Background: Immunological laboratory testing is known to be complex, and it is usually performed in tertiary referral centers. Many criticalities affect diagnostic immunological testing, such as limited availability, the need for specifically trained laboratory staff, and potential difficulties in collecting blood samples, especially in the most vulnerable patients, i.e., the elderly and children. For this reason, the identification of a new feasible and reliable methodology for autoantibody detection is urgently needed. (2) Methods: We designed a systematic review to investigate the available literature on the utilization of saliva samples for immunological testing. (3) Results: A total of 170 articles were identified. Eighteen studies met the inclusion criteria, accounting for 1059 patients and 671 controls. The saliva collection method was mostly represented by passive drooling (11/18, 61%), and the most frequently described methodology for antibody detection was ELISA (12/18, 67%). The analysis included 392 patients with rheumatoid arthritis, 161 with systemic lupus erythematosus, 131 with type 1 diabetes mellitus, 116 with primary biliary cholangitis, 100 with pemphigus vulgaris, 50 with bullous pemphigoids, 49 with Sjogren syndrome, 39 with celiac disease, 10 with primary antiphospholipid syndromes, 8 with undifferentiated connective tissue disease, 2 with systemic sclerosis, and 1 with autoimmune thyroiditis. The majority of the reviewed studies involved adequate controls, and saliva testing allowed for a clear distinction of patients (10/12 studies, 83%). More than half of the papers showed a correlation between saliva and serum results (10/18, 55%) for autoantibody detection, with varying rates of correlation, sensitivity, and specificity. Interestingly, many papers showed a correlation between saliva antibody results and clinical manifestations. (4) Conclusions: Saliva testing might represent an appealing alternative to serum-based testing for autoantibody detection, considering the correspondence with serum testing results and the correlation with clinical manifestations. Nonetheless, standardization of sample collection processing, maintenance, and detection methodology has yet to be fully addressed.
Analysis of Factors Affecting Brand Trust Toddler Care Products in Jabodetabek Brand Study: Zwitsal Baby

Parents, especially mother whose role as decision maker in purchasing baby toiletries, certainly want to provide the best products for their baby. Baby toiletries that known as high functional risk products, make mothers really have to be aware and very careful when making decisions. In that highrisk situation, moth-ers can minimize the risk by choosing a brand that they trust. Through this quantitative research by distrib-uting questionnaires to mother who use Zwitsal Baby products in Jabodetabek, obtained the results of measurements of the factors forming the brand trust. This study aims to measure how strong rhe influence of the factors forming the brand trust, the influence of the mediator variables, and moderator variabels de-rived from mothers’ personality traits. The results of this study indicate that the forming aspects of brand trust, which are brand predictability, brand innovativeness, and brand intimacy have a positive and signifi-cant impact on cognitive and affective brand trust. Cognitive brand trust which is a mediator variable in this study, also have a positive and significant influence in the formation of affective brand trust. As for the moderator vaiable, the study shows that openness does not influence the formation of brand trust, but con-scientiousness and agreeableness have a significant relationship, but has a fairly weak influence. The result of this research can be used to develop or form brand trust in baby toiletries products, especially for Zwitsal Baby brand.
In Vitro Evaluation of Biphasic Calcium Phosphate Scaffolds Derived from Cuttlefish Bone Coated with Poly(ester urea) for Bone Tissue Regeneration

This study investigates the osteogenic differentiation of umbilical-cord-derived human mesenchymal stromal cells (hUC-MSCs) on biphasic calcium phosphate (BCP) scaffolds derived from cuttlefish bone doped with metal ions and coated with polymers. First, the in vitro cytocompatibility of the undoped and ion-doped (Sr2+, Mg2+ and/or Zn2+) BCP scaffolds was evaluated for 72 h using Live/Dead staining and viability assays. From these tests, the most promising composition was found to be the BCP scaffold doped with strontium (Sr2+), magnesium (Mg2+) and zinc (Zn2+) (BCP-6Sr2Mg2Zn). Then, samples from the BCP-6Sr2Mg2Zn were coated with poly(ԑ-caprolactone) (PCL) or poly(ester urea) (PEU). The results showed that hUC-MSCs can differentiate into osteoblasts, and hUC-MSCs seeded on the PEU-coated scaffolds proliferated well, adhered to the scaffold surfaces, and enhanced their differentiation capabilities without negative effects on cell proliferation under in vitro conditions. Overall, these results suggest that PEU-coated scaffolds are an alternative to PCL for use in bone regeneration, providing a suitable environment to maximally induce osteogenesis.
Deep Generative Models for Synthetic Data: A Survey

A growing interest in synthetic data has stimulated the development and advancement of a large variety of deep generative models for a wide range of applications. However, as this research has progressed, its streams have become more specialized and disconnected from one another. This is why models for synthesizing text data for natural language processing cannot readily be compared to models for synthesizing health records anymore. To mitigate this isolation, we propose a data-driven evaluation framework for generative models for synthetic sequential data, an important and challenging sub-category of synthetic data, based on five high-level criteria: representativeness, novelty, realism, diversity and coherence of a synthetic data-set relative to the original data-set regardless of the models’ internal structures. The criteria reflect requirements different domains impose on synthetic data and allow model users to assess the quality of synthetic data across models. In a critical review of generative models for sequential data, we examine and compare the importance of each performance criterion in numerous domains. We find that realism and coherence are more important for synthetic data natural language, speech and audio processing tasks. At the same time, novelty and representativeness are more important for healthcare and mobility data. We also find that measurement of representativeness is often accomplished using statistical metrics, realism by using human judgement, and novelty using privacy tests.