Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic evaluation of drag coefficients for flow over scale-similar complex boundaries

A vehicle is disclosed which is normally supported by a plurality of wheels and tires, the vehicle having an article carrying space, with a floor formed of a first material and having an opening. A spare wheel and tire storage well is mounted in the opening through the floor and is formed of a second material different from the first material. The storage well is configured to accommodate a spare wheel and tire substantially equal in width and diameter to at least one of the plurality of wheels and tires supporting the vehicle. Also disclosed is a method for modifying a vehicle to carry a full size spare wheel and tire, including removing a portion of the floor of the vehicle, which portion includes a first storage well, to form an opening in the floor, placing a second storage well in the opening in the floor, the second storage well being configured and to accommodate a full size spare wheel and tire having a maximum width and diameter substantially equal to at least one of the wheels and tires normally supporting the vehicle such that a major portion of a maximum width of the full size spare wheel and tire is located below an upper surface of the floor, and attaching the second storage well to the floor. A lifting mechanism is disclosed to assist in the removal of the spare wheel and tire from the storage well. A force generating mechanism raises the spare wheel and tire when a securing device is removed.
Deep Learning-based Real-time Segmentation for Edge Computing Devices

Recently, due to the rapid improvement of artificial intelligence technology, numerous studies are considered to solve various problems using deep learning. Typical deep neural networks for semantic segmentation require the high computation with a large capacity to extract abundant amounts of contextual information for accurate prediction. Our live demonstration will show real-time semantic segmentation operation on an NVIDIA Jetson-Xavier board with the BiSeNet-based method compressed using a novel knowledge distillation method.
Aggregate Modeling and Non-Disruptive Control of Residential Air Conditioning Systems with Two-Zone Cooling Capacity

Modern power grids are rapidly shifting from fossil fuel-based generation to carbon-free renewable sources. This transition brings the problem of balancing power supply and demand to a higher level – due largely to the high uncertainty of renewable generation such as wind and solar. Aggregations of thermostatically controlled loads such as residential air conditioners (AC) can provide balancing services by leveraging the thermal inertia of houses. Existing work has focused on single-zone houses; however, a significant fraction of houses have more than one zone. In these houses a single AC provides cooling to multiple distinct areas each with their own thermostat. Moreover, air temperature dynamics are coupled across zones. In this paper we propose a mathematical model describing the dynamical behavior of two-zone ACs and develop the corresponding aggregate system model. In addition, we also design non-disruptive aggregate control strategies – called the uniform and priority controllers – to perform frequency regulation services and investigate different Kalman Filter-based state estimation approaches to reduce real-time information needs. It is revealed from numerical simulations that the uniform controller yields the best power tracking performance while the priority controller is able to minimize the total ACs cycles, though it requires more computation time.
LiTIV: A Lightweight Traceable Data Integrity Verification Scheme for Version Control Systems

Conventional data integrity solutions for Version Control Systems (VCSs) include hash verification, commit signature and log-based schemes. However, existing schemes either bring heavy burden to the VCS client, or place high requirements on the VCS server. These schemes are incapable of offering a lightweight service. Moreover, existing schemes cannot offer corruption provenance that facilitates corruption location and version recovery. To address these challenges, we present LiTIV, a lightweight traceable data integrity verification scheme for VCS. LiTIV introduces a third-party verification server, namely Data Verifier (DV), which effectively reduces impacts on the VCS client and server. LiTIV exploits index history table (IHT) along with signed commit information to achieve both integrity and traceability. Besides, a balanced Merkle hash tree (BMT) is designed and adopted to achieve fast data updating and efficient integrity verification. In case of malicious tampering of V CS file system disks, LiTIV can rely on the BMT structure to effectively detect implicit inconsistency, and achieve backtracking for tam-pering provenance with IHT query. We also design a probabilistic sampling verification mechanism on historical commits to achieve high performance under security premise. Extensive evaluations show that LiTIV achieves traceable integrity security against various attacks and is highly efficient.
The Nonequilibrium Statistical Mechanics of Open and Closed Systems

Part 1 Introduction: evolution of complex systems langevin equations some problems. Part 2 Evolution of probability: stochastic rate equations single degree of freedom delta-correlated gaussian fluctuation many degrees of freedom fluctuations with a finite correlation time (colored noise) statistical measures. Part 3 Open systems: thermodynamic properties (nonequilibrium) dye laser with a fluctuating pump parameter chemical reaction with a fluctuating rate coefficient bistable stochastic systems mechanical oscillators with fluctuating frequences kubo oscillator (semiclassical) Lorenz model (connection with chaos) odds and ends. Part 4 Closed systems (Hamiltonian systems) thermodynamic properties (nonequilibrium) projection operator techniques explicit integration method harmonic oscillator in a real fluid. Part 5 Reduced descriptions of non-Hamiltonian systems: statistical fluctuations in fluid flow decimation procedure projection method explicit integration method. Part 6 Evolution of quantum systems: reduced density matrix (dynamical bath) reduced density matrix (c-number fluctuations). Part 7 Open systems (c-number fluctuations): linear oscillator Haken-Strobi-Reineker (HSR) model spin relaxations in an external fluctuating magnetic field. Part 8 Closed quantum systems (operator fluctuations): linear quantum oscillators two-evel system in a linear bath stochastic liouville equations nonlinear coupling in bath variables conclusions.
A Deep-Learning-Based Method for Gaining More Biomechanical Parameters with Fewer Sensors in Fast and Complex Movements

Real-time biomechanical feedback can provide direct and objective quantified information for any practitioners, such as the athletes/coaches, to assist in accelerating their motor skills’ learning and training process. However, it is usually difficult to monitor the human motion in full and acquire the key biomechanical parameters (i.e., the kinematic and kinetic data, the EMG, etc.) just with few sensors in some elite sports involving fast movements and complex motor skills. Using too many sensors in the field tests may limit the athletes’ motor ability and affect the collected data’s validity and reliability. In this paper, we employ a deep learning method to immensely reduce the number of sensors required for providing the real-time biomechanical feedback in field, according to the hammer-throw local motion features found from our pilot study. Based on the Keras API imported from the TensorFlow open-source platform, two Sequential Neural Network models are implemented and compared. One model has two inputs (i.e., vertical displacements and velocities on waist) and six outputs (i.e., vital joint angles on lower limbs). The other one has four inputs (i.e., vertical displacements and velocities on wrist and waist) and thirteen outputs (i.e., vital joint angles on both upper and lower limbs). The experimental results demonstrate that the vital joint angles on the upper and lower limbs have strong correlation with the vertical wrist and waist/hip displacements respectively. This study indicates that fewer wearable sensors can be applied in fast and complex movements to obtain the most significant kinematic data, whereas more biomechanical parameters can be further gained by prediction.
Data-Driven Learning Control for Building Energy Management

Designing model-based control methods for real building energy optimization using physics-based models is complex and time-consuming. A promising alternative is to use data-driven models, but weather and occupancy changes may cause changes in operating conditions of the building, such that data-driven modeling accuracy and control performance degrade. In this work, we propose a data-driven learning framework for building energy control that addresses the non-stationary nature of building operations caused by factors like weather and occupancy. Our approach relies on a set of data-driven models that characterize the building energy behavior and a deep reinforcement learning (DRL) controller trained on such models. Defining all possible operating modes is intractable for most buildings, therefore, we propose a continual adaptation approach based on non-stationary change detection. We compare our proposal against a DRL state-of-the-art algorithm called proximal policy optimization (PPO), and data-driven model predictive control (MPC) using a five-zone building benchmark. We demonstrate satisfactory results for different non-stationary changes in terms of energy efficiency, thermal comfort, and actuation safety metrics.
Migration Mapper: Identifying movement corridors and seasonal ranges for large mammal conservation

Modern tracking technology has facilitated a novel understanding of terrestrial mammal movement while revealing that movements are being truncated and lost. The first step towards conserving mobile animals is identifying movement corridors and key seasonal ranges. Yet, the identification and subsequent mapping of these important areas has remained a challenge due to the analytical skills necessary to conduct such analyses. Migration Mapper (MM) is a user‐friendly software that provides tools to analyse global positioning system (GPS) collar data to create season‐specific, population‐level polygons representing areas where most of a population moves (i.e. movement corridors) and areas where most of a population spends time (e.g. high‐use areas, seasonal ranges). MM consists of six standalone modules including data cleaning and review, seasonal movement delineation, movement model application, calculation of population‐level outputs and visualization of results. Analysis of GPS data using MM can provide the spatial polygons necessary to facilitate conservation and policy planning. New initiatives at the local and global levels are already beginning to use MM to facilitate conservation of large, terrestrial mammals.
ISOMETRIES AND JORDAN-ISOMORPHISMS ONTO C -ALGEBRAS

Let A be a C -algebra, and B a complex normed non-associative algebra. We prove that, if B has an approximate unit bounded by one, then, for every linear isometry F from B onto A, there exists a Jordan-isomorphism G : B ! A and a unitary element u in the multiplier algebra of A such that F(x) = uG(x) for all x in B. We also prove that, if G is an isometric Jordan- isomorphism from B onto A, then there exists a self-adjoint element ' in the centre of the multiplier algebra of the closed ideal of A generated by the commutators satisfying k'k 6 1 and G(xy) = 1 (G(x)G(y) + G(y)G(x) + '(G(x)G(y) G(y)G(x)))
Design and Implementation of Robot Middleware Service Integration Framework Based on DDS

The distributed real-time system composed of industrial robots usually needs to meet the support of low-latency, high-reliability data transmission and service invocation. Based on the Data Distribution Service (DDS) publish-subscribe communication paradigm, this paper designs a service integration framework model called Dynamic-DDS-RPC with a request-response mechanism to achieve low-latency and flexible configuration of the robot service remote procedure call function. First of all, we use JSON to implement a set of service description specifications to support service definition and program interface implementation, and uses dynamic library automatic loading technology to solve the problem that DDS-RPC does not support dynamic loading and discovery of services. Secondly, we implement a topic-centric service request-response mechanism based on the publish-subscribe communication mode of DDS, and realize low-latency data communication. Finally, we develop a robot middleware software based on the framework, and use the middleware to compare the performance of the service-request response speed with the WebService-based middleware and the DDS-RPC-based middleware. The results show that the service integration framework proposed in this paper can realize the remote procedure call function with low latency and high flexible configuration, which verifies the effectiveness of the framework.
Symptom‐based case definitions for COVID‐19: Time and geographical variations for detection at hospital admission among 260,000 patients

Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID‐19‐hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory‐confirmed COVID‐19, in the ISARIC prospective cohort study database, meeting widely used case definitions.
Principal component analysis in linear systems: Controllability, observability, and model reduction

Kalman's minimal realization theory involves geometric objects (controllable, unobservable subspaces) which are subject to structural instability. Specifically, arbitrarily small perturbations in a model may cause a change in the dimensions of the associated subspaces. This situation is manifested in computational difficulties which arise in attempts to apply textbook algorithms for computing a minimal realization. Structural instability associated with geometric theories is not unique to control; it arises in the theory of linear equations as well. In this setting, the computational problems have been studied for decades and excellent tools have been developed for coping with the situation. One of the main goals of this paper is to call attention to principal component analysis (Hotelling, 1933), and an algorithm (Golub and Reinsch, 1970) for computing the singular value decompositon of a matrix. Together they form a powerful tool for coping with structural instability in dynamic systems. As developed in this paper, principal component analysis is a technique for analyzing signals. (Singular value decomposition provides the computational machinery.) For this reason, Kalman's minimal realization theory is recast in terms of responses to injected signals. Application of the signal analysis to controllability and observability leads to a coordinate system in which the "internally balanced" model has special properties. For asymptotically stable systems, this yields working approximations of X_{c}, X_{\bar{o}} , the controllable and unobservable subspaces. It is proposed that a natural first step in model reduction is to apply the mechanics of minimal realization using these working subspaces.
On the Formulation of a Stochastic User Equilibrium Model Consistent with the Random Utility Theory – A Conjugate Dual Approach

The results presented in this paper include: (1) establishing the correspondence relation between the entropy maximization model and the random utility model; (2) depriving the user equilibrium within the context of conjugate correspondence; and (3) proposing a computation procedure based on the contraction mapping method. All of these results are expected to serve for making further research possible. Therefore, it is appropriate to touch on a few of the remaining problems of each result mentioned above for further research.
Border securocracy: Global expansion of the U.S. Department of homeland security bureaucratic apparatus before, during and beyond covid-19

Abstract The U.S. border security apparatus is moving around the globe as climate change, the coronavirus pandemic and corporatization create political and economic chaos. Global north governments seek to keep out migrants and refugees from the global south while corporations in the global north want protection to maintain their wealth. U.S. government bureaucratic agencies such as Custom and Border Protection’s Border Patrol Tactical Unit are sent abroad to expand U.S. influence in an empire of borders to train receptive government security and border forces and to regulate, detain and prevent migrants and refugees well beyond the U.S. border. Governments are waging war against the people, creating a “securocracy” comprised of profit seeking military arms corporations and allied government agents to quell resistance and border crossers. Examined are the effects and impacts of the U.S. Department of Homeland Security on global border securocracy beginning with an analysis of the Mexico-U.S. border, moving to international borders in Latin America and beyond. The theoretical concept of border securocracy is expanded from the securocracy literature in the context of the north versus south globalization conflict.
Prevalence of Caries among School Children in Saudi Arabia: A Meta-Analysis

Introduction Children's dental health has become the primary concern, because of the increase in the prevalence of caries amongst school children in Saudi Arabia. Therefore, a meta-analysis was conducted to assess the prevalence and severity of dental caries among school children in Saudi Arabia. Method A systematic search of Scopus, ISI Web of Science, EMBASE, Saudi digital library, Google Scholar, and MEDLINE via Ovid for cross-sectional studies with healthy participants between 5and –15 years. Two authors independently extracted the prevalence of caries. With 95% confidence intervals (CIs) using a random-effects model, we calculated caries prevalence. Results Dental caries prevalence data were extracted from 18 cross-sectional studies (n = 56,327 children). The pooled estimate for the caries prevalence among 5–7 years' children was 84% (95% CI: 0.81–0.87%; I2 = 91%) while among 12–15 years' children was 72% (95% CI: 0.63–0.79; I2 = 96.2%). Discussion. In this systematic review, the summary estimate of the prevalence of dental caries among children of 5–7 years and 12–15 years were 84% and 72%, respectively. Further research is required to identify approaches for preventing and treating dental caries in schoolchildren.
On singular moduli.

0. The values of the modular function 7 (τ) at imaginary quadratic arguments τ in the upper half plane are known s singular moduli. They are all algebraic integers. In this paper we will study the prime factorizations of the differences of two singular moduli. These differences turn out to be highly divisible numbers. For instance, we will determine the set of primes dividing the absolute norm ofy'(T) — 1 728 =7 (τ) —7(1) (and the multiplicities with which they occur); they turn out to be contained among the prime divisors of the positive integers of the form d—x, where d is the discriminant of τ, and hence smaller than or equal to d, e.g. :
State-Of-Health Estimation Pipeline for Li-ion Battery Packs with Heterogeneous Cells

The internal condition of lithium-ion batteries, in particular State-of-Health (SoH), needs careful monitoring to ensure safe and efficient operation. In this paper, we propose a hybrid online SoH estimation pipeline for series-connected heterogeneous cells. Implementing a single cell parameter estimation scheme for a battery pack with hundreds to thousands of cells is computationally intractable. This challenge is solved in this work using feature-based adaptive polling of cells with "extreme" parameter values. Furthermore, the electrical parameters for the polled cells are estimated using online recursive least squares with forgetting factor. The key novelty lies in accounting for the uncertain state dependence of the parameters. We use sparse Gaussian process regression to obtain the parameter bounds as a function of both SOC and temperature. The pipeline is validated through a simulation study, using experimental data from Li-NMC cells.
Associations Between Children's Emotion Regulation, Mindful Parenting, Parent Stress, and Parent Coping During the COVID-19 Pandemic

Stress among parents has increased during the COVID-19 pandemic. Research prior to the pandemic indicates that parents of children who struggle with emotion regulation (ER) and who themselves are less mindful report more stress and diminished coping abilities. We know little, however, about these associations in the context of COVID-19. To prevent COVID-related deteriorations in parent well-being and child outcomes and to support parents during this potentially challenging time, it is important to understand the factors that are associated with increased stress as well as adaptive coping. This paper discusses the association between children's ER, mindful parenting (MP), parent stress, and parents’ coping with parenting during the pandemic in a sample of 217 caregivers of school-aged children (91.0% mothers). Results indicated that children's ER was associated with parents’ self-reported coping with parenting in the pandemic but was not associated with increased stress. Further, MP moderated the association between children's ER and coping, such that parents who were the most mindful and had children with better ER skills reported significantly greater ability to cope with pandemic parenting. Coping was lower for other combinations of ER and mindful parenting. These findings contradict those from before COVID, suggesting the relationship between children's ER and parent outcomes may differ in the COVID-19 context, and offering insights into which parents may be most likely to struggle with coping with pandemic parenting.
The NEAT Equating Via Chaining Random Forests in the Context of Small Sample Sizes: A Machine-Learning Method

The part of responses that is absent in the nonequivalent groups with anchor test (NEAT) design can be managed to a planned missing scenario. In the context of small sample sizes, we present a machine learning (ML)-based imputation technique called chaining random forests (CRF) to perform equating tasks within the NEAT design. Specifically, seven CRF-based imputation equating methods are proposed based on different data augmentation methods. The equating performance of the proposed methods is examined through a simulation study. Five factors are considered: (a) test length (20, 30, 40, 50), (b) sample size per test form (50 versus 100), (c) ratio of common/anchor items (0.2 versus 0.3), and (d) equivalent versus nonequivalent groups taking the two forms (no mean difference versus a mean difference of 0.5), and (e) three different types of anchors (random, easy, and hard), resulting in 96 conditions. In addition, five traditional equating methods, (1) Tucker method; (2) Levine observed score method; (3) equipercentile equating method; (4) circle-arc method; and (5) concurrent calibration based on Rasch model, were also considered, plus seven CRF-based imputation equating methods for a total of 12 methods in this study. The findings suggest that benefiting from the advantages of ML techniques, CRF-based methods that incorporate the equating result of the Tucker method, such as IMP_total_Tucker, IMP_pair_Tucker, and IMP_Tucker_cirlce methods, can yield more robust and trustable estimates for the “missingness” in an equating task and therefore result in more accurate equated scores than other counterparts in short-length tests with small samples.