Miracle-dz/UniFAR-FTUs huggingface dataset:
New large volume Calabi-Yau threefolds

In previous work, we have commenced the task of unpacking the 473 800 776 reflexive polyhedra by Kreuzer and Skarke into a database of Calabi-Yau threefolds [R. Altman et al. J. High Energy Phys. 02 (2015) 158. ] (see www.rossealtman.com). In this paper, following a pedagogical introduction, we present a new algorithm to isolate Swiss cheese solutions characterized by “holes,” or small 4-cycles, descending from the toric divisors inherent to the original four dimensional reflexive polyhedra. Implementing these methods, we find 2268 explicit Swiss cheese manifolds, over half of which have h1,1=6. Many of our solutions have multiple large cycles. Such Swiss cheese geometries facilitate moduli stabilization in string compactifications and provide flat directions for cosmological inflation.
Associations of Workplace Violence With Cardiovascular Disease Among United States Workers: Findings From a National Survey

Objectives Recent research indicates a potential association between workplace violence and an increased risk of cardiovascular disease (CVD) in the working-age population. However, the relevant evidence in the United States is sparse. Thus, this study was conducted to explore the possible relationship between workplace violence and CVD among United States workers. Methods We utilized cross-sectional data from the 2015 National Health Interview Survey, which included a representative sample of 18 380 workers, to investigate the associations between workplace violence and the prevalence of CVD using logistic regression. Workplace violence was determined based on self-reported threats, bullying, or harassment at work over the past 12 months, supplemented with additional information regarding frequency. CVD included all forms of heart disease and stroke. Results A total of 1334 workers reported experiences of workplace violence, and 1336 workers were diagnosed with CVD. After adjustment for covariates, participants who reported any instance of workplace violence had significantly higher odds of having CVD (odds ratio [OR], 1.76; 95% confidence interval [CI], 1.35 to 2.30) than those who reported no such violence. Furthermore, the highest odds of CVD (OR, 1.80; 95% CI, 1.23 to 2.63) were observed among those frequently exposed to workplace violence. Even occasional exposure to workplace violence was associated with 74% excess odds of CVD. Conclusions Our study indicates an association between workplace violence and CVD in United States workers, exhibiting a dose-response pattern.
CacheIn: A Secure Distributed Multi-layer Mobility-Assisted Edge Intelligence based Caching for Internet of Vehicles

This paper investigates the feasibility of cache content prediction and coherence in the context of secure communication and search. We introduce a distributed multi-tier mobility-assisted edge intelligence based caching framework for the Internet of Vehicles (IoVs), called CacheIn. The proposed framework leverages user preferences, data correlations, and mobility information to prefetch content to the IoV edge. To enable content management based on mobility, we propose a novel Normalized Hidden Markov Model (NM-HMM) that anticipates a vehicle's future position. The framework also utilizes a mobility-aware collaborative filtering-based federated learning (FL) technique to enhance cache hit, reduce latency, and protect user privacy. To ensure secure cross-domain data sharing and mitigate the risk of data breaches, we also propose an extended ciphertext policy attribute-based encryption (ECP-ABE) mechanism. Compared to content popularity-based caching schemes, CacheIn achieves up to 80%, 38%, and 55% improvement in cache hit ratio for different cache sizes, vehicle densities, and cache lookup scenarios. Moreover, our approach reduces key generation, encryption, and decryption times by 35 %.
Gaussian Process Modeling Using the Principle of Superposition

ABSTRACT Partial differential equation (PDE) models of physical systems with initial and boundary conditions are often solved numerically via a computer code called the simulator. To study the dependence of the solution on a functional input, the input is expressed as a linear combination of a finite number of basis functions, and the coefficients of the bases are varied. In such studies, Gaussian process (GP) emulators can be constructed to reduce the amount of simulations required from time-consuming simulators. For linear initial-boundary value problems (IBVPs) with functional inputs as additive terms in the PDE, initial conditions, or boundary conditions, the IBVP solution is theoretically a linear function of the coefficients conditional on all other inputs, which is a result called the principle of superposition. Since numerical errors cause deviation from linearity and nonlinear IBVPs are widely solved in practice, we generalize the result to account for nonlinearity. Based on this generalized result, we propose mean and covariance functions for building GP emulators that capture the approximate conditional linear effect of the coefficients. Numerical simulations demonstrate the substantial improvements in prediction performance achieved with the proposed emulator. Matlab codes for reproducing the results in this article are available in the online supplement.
Electrocardiogram Data Analysis Using Machine Learning

The electric impulses of the heart are measured using a device called electrocardiogram (ECG), which is a graphical representation of heart signals for abnormality detection. The Electrocardiogram (ECG), which is noninvasive, has developed into a standard diagnosing method for heart related issues in early stages and provides details about heart disorders. The irregularities in heartbeat are discovered using a variety of techniques. This paper puts forward a technique for categorizing ECG records in order to forecast cardiovascular disorders, which affect both middle-aged and older persons and can cause serious illness and even death. Arrhythmia illness was the cause of one of the major abnormalities. In order to find early arrhythmia prediction and save lives, numerous deep learning techniques were utilized. The literature survey revealed that numerous ECG signal classifications were made using already-existing databases, like MIT-BIH arrhythmia. This paper presents an architecture which combines CNN and LSTM for the detection of arrhythmia-related irregularities with an accuracy of 99.7%.
Coffee cooperatives and cross-border side-sales in the Eastern Democratic Republic of Congo: two sides of the same coin

In the Eastern DRC, coffee farmers combine the different sales outlets available to them. Cooperative members sell coffee to the cooperatives they belong to as well as to informal markets, which include a channel of illegal cross-border smuggling. In this conflict affected region, the informal cross-border markets persist irrespective of the presence of cooperatives. This paper seeks to understand the motivating factors of the sideselling behavior of coffee cooperative members. We study the coffee production and sales of 339 cooperative members in the region and use a double hurdle model to understand which farm characteristics relate to the side-selling behavior. The omnipresence of side-selling in the cooperatives suggest that the unstable political and economic environment is conducive to this co-existence of informal trade and cooperative membership. Side-selling seems a deliberate strategy by the farmers that is tolerated by the cooperatives. The results suggest that farmers who are in a more precarious situation are more inclined to engage with informal markets. This is further underscored by the effect of food insecurity and lack of credit. Hence, the informal market is a safety net that allows immediate payment of coffee in contrast to cooperatives that are more formally organized.
Accurate Nonlinear Dynamics and Mode Aberration of Rotating Blades

Nonlinear dynamics and mode aberration of rotating plates and shells are discussed in this work. The mathematical formalism is based on the one-dimensional (1D) Carrera unified formulation (CUF), which enables to express the governing equations and related finite element arrays as independent of the theory approximation order. As a consequence, three-dimensional (3D) solutions accounting for couplings due to geometry, material, and inertia can be included with ease and with low computational costs. Geometric nonlinearities are incorporated in a total Lagrangian scenario and the full Green-Lagrange strains are employed to outline accurately the equilibrium path of structures subjected to inertia, centrifugal forces, and Coriolis effect. A number of representative numerical examples are discussed, including multisection blades and shells with different radii of curvature. Particular attention is focused on the capabilities of the present formulation to deal with nonlinear effects, and comparison with s simpler linearized approach shows evident differences, particularly in the case of deep shells.
Invitation to Mathematical Psychology: Models and Benefits of Formal Theorizing

ABSTRACT In most areas, psychological phenomena tend to be explained only through textual constructions. Several authors, however, point to the need for theories that have a more formal nature, based on mathematical reasoning. In order to encourage broader access to its applications, we present the models and advantages of a mathematical psychology approach to the study of behavior. We review the limitations of verbal theorizing, then a common taxonomy in mathematical psychology follows, that classifies formal models as descriptive, process characterization, and explanatory. As well succeeded cases, we examine the mathematical psychology of decision making, of helping behavior, of memory, and of romantic relationships. Finally, we discuss the potential benefits and uses of this approach. Welcome to mathematical psychology.
Synergistic Effect of Treatment with Highly Pathogenic Porcine Reproductive and Respiratory Syndrome Virus and Lipopolysaccharide on the Inflammatory Response of Porcine Pulmonary Microvascular Endothelial Cells

The highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) often causes secondary bacterial infection in piglets, resulting in inflammatory lung injury and leading to high mortality rates and significant economic losses in the pig industry. Microvascular endothelial cells (MVECs) play a crucial role in the inflammatory response. Previous studies have shown that HP-PRRSV can infect porcine pulmonary MVECs and damage the endothelial glycocalyx. To further understand the role of pulmonary MVECs in the pathogenesis of HP-PRRSV and its secondary bacterial infection, in this study, cultured porcine pulmonary MVECs were stimulated with a HP-PRRSV HN strain and lipopolysaccharide (LPS). The changes in gene expression profiles were analyzed through transcriptome sequencing, and the differentially expressed genes were verified using qRT-PCR, Western blot, and ELISA. Furthermore, the effects on endothelial barrier function and regulation of neutrophil trans-endothelial migration were detected using the Transwell model. HP-PRRSV primarily induced differential expression of numerous genes associated with immune response, including IFIT2, IFIT3, VCAM1, ITGB4, and CCL5, whereas LPS triggered an inflammatory response involving IL6, IL16, CXCL8, CXCL14, and ITGA7. Compared to the individual effect of LPS, when given after HN-induced stimulation, it caused a greater number of changes in inflammatory molecules, such as VCAM1, IL1A, IL6, IL16, IL17D, CCL5, ITGAV, IGTB8, and TNFAIP3A, a more significant reduction in transendothelial electrical resistance, and higher increase in neutrophil transendothelial migration. In summary, these results suggest a synergistic effect of HP-PRRSV and LPS on the inflammatory response of porcine pulmonary MVECs. This study provides insights into the mechanism of severe lung injury caused by secondary bacterial infection following HP-PRRSV infection from the perspective of MVECs, emphasizing the vital role of pulmonary MVECs in HP-PRRSV infection.
Improved Finite Control Set Model Predictive Current Control for Permanent Magnet Synchronous Motor With Sliding Mode Observer

To improve the strong dependence of predictive control parameters on finite control set (FCS) models, an improved robust FCS predictive current control (FCS-MPCC) algorithm for permanent magnet synchronous motor (PMSM) drive systems is proposed. First, the problems of complex calculation and inaccurate selection of voltage vectors in traditional FCS-MPCC are analyzed, and an extended voltage vector-based MPCC (EV2-MPCC) based on the deadbeat principle is proposed to improve the steady-state performance of FCS-MPCC and also improve the anti-disturbance performance of the system. Second, the performance of PMSM under parameter mismatch is analyzed, and a sliding mode observer (SMO)-based stator current and disturbance observer is proposed. The observed disturbance term is used to compensate the reference voltage term for quickly selecting the optimal voltage vector, and the estimated current replaces the traditional predicted current. This method further improves the robustness of EV2-MPCC. Finally, the experimental results prove the effectiveness of the scheme.
The Cultivation Of Students' Music Listening With Computer-Aided Piano Tuning Method

With the increasing popularity of piano, the demand for piano tuning is also increasing. Generally, the professional tuner judges the pitch of the piano through listening. It is inevitable that there will be deviation due to the influence of physical, psychological and objective environment. This paper analyzes and demonstrates the basic methods of piano tuning from the physical characteristics and corresponding physical models of piano tone. The method of music signal processing is systematically analyzed in both time domain and frequency domain. Some algorithms in speech recognition technology are applied to music recognition, and then improved according to the characteristics of piano tuning. In the aspect of pitch detection, aiming at the problem that the piano range is wider than the voice, the pitch detection in the whole frequency band using a single algorithm can not meet the requirements of the piano tuning detection accuracy, and the use of computer-assisted piano tuning method to train students to listen to music is a teaching method developed by the author. It is a new teaching method, which is applicable to students in primary and middle schools. Using the computer-assisted piano tuning method to cultivate students' ability to listen to music can cultivate students' ability to listen to music, improve their sense of music, enhance their memory ability, and develop their artistic imagination. This learning process can also help them play keyboard instruments more skillfully at a very young age. It can help students learn the skills of listening to music and improve their piano playing ability. Using computer-assisted piano tuning method to cultivate students' music listening also helps teachers develop their own teaching skills and can be used in teacher training courses.
The Effect of Caffeine on Heart Rate Variability in Newborns: A Pilot Study

Neonatal apnoea can be treated with caffeine, which affects the central nervous and cardiovascular systems. Heart rate variability (HRV) reflects the activity of the autonomic nervous system (ANS) and might be used as a measure of ANS maturation in newborns. We aimed to establish the effect of caffeine on HRV in newborns and investigated the potential correlation between HRV and postmenstrual age (PMA). In 25 haemodynamically stable newborns hospitalized due to apnoea and treated with caffeine (2.5 mg/kg), we assessed breathing frequency, arterial oxygen saturation, body temperature, and the heart rate while they were sleeping. We assessed HRV by spectral analysis using fast Fourier transformation. The same protocol was reapplied 100 h after caffeine withdrawal to assess the control parameters. Caffeine increased breathing frequency (p = 0.023) but did not affect any other parameter assessed including HRV. We established a positive correlation between postmenstrual age and HRV during treatment with caffeine as well as after caffeine had been withdrawn (total power: p = 0.044; low-frequency band: p = 0.039). Apparently, the maintenance dose of caffeine is too low to affect the heart rate and HRV. A positive correlation between PMA and HRV might reflect maturation of the ANS, irrespective of caffeine treatment.
MATLAB Programming to Implement Quantum Walk Algorithm for Presenting Probability Distributions of Quantum Walks

There are numerous ideas that have been provided by quantum walks for new quantum algorithms. In this article, we surveyed the discrete quantum walk algorithm to present probability distributions. Moreover, we transformed the algorithm into MATLAB programming. Finally, the programming can be utilised to compare coin flip transformations that generate probability distributions as proposed in several previous articles and also to identify a unitarity of a coin flip transformation.
An EXCEL add-in implementation for collocation-based stochastic response surface method

A general probabilistic method called collocation-based stochastic response surface method (CSRSM) was developed previously. It involves the propagation of input uncertainties through a computation model to arrive at a random output vector. It is assumed that the unknown random output can be expanded using a polynomial chaos basis with corresponding unknown coefficients. The unknown coefficients are evaluated using a collocation method because it has the important practical advantage of allowing existing deterministic numerical codes to be used as "black boxes". An EXCEL add-in is developed to produce the basis functions (multi-dimensional Hermite polynomials) without resorting to symbolic algebra practitioners. This is a major practical advantage that would bring realistic probabilistic analyses within reach of the practitioners. Full EXCEL implementation details are illustrated using a simple slope problem involving six input random variables. The results show the ease and successful implementation of the proposed EXCEL-based CSRSM.
Lattice model for failure based on embedded strong discontinuities in dynamic framework

Identifying the failure of materials and structures is still a challenging task and there is no unique approach or model to tackle this problem. Complexities that arise in failure modelling are numerous, starting from mesh dependency for softening to various numerical diﬃculties and instabilities, tracking algorithms, multiple cracking with crack interactions etc. Failure in the dynamic framework is even more challenging bringing inertial eﬀects, crack branching etc. In this paper, lattice model for dynamic failure is presented. The ﬁnal goal is to simulate crack initiation and propagation in 2D brittle and quasi-brittle structures exposed to dynamic environment. The strength of the lattice models is in their successful representation of failure mechanisms. The presented model is based on a triangular lattice of Timoshenko beams which act as cohesive links between the Voronoi cells used to compute beam cross sections. The embedded strong discontinuities in axial and transversal beam directions serve for representation of failure mechanisms in modes I and II, while mass and inertial eﬀects are included into lattice network.
EEG-based Drowsiness and Alertness Recognition Using Feature Fusion and Deep Residual Networks

Drowsiness among drivers is a major cause of road accidents. The purpose of this study is to classify drivers' drowsiness and alertness states using features extracted from electroencephalography(EEG). Artefacts in EEG features diminish the classification model's accuracy. To achieve better classification results, this paper proposes a model based on feature fusion with deep residual networks for drowsiness identification of EEG time-frequency features, and it also investigates the effect of single-channel and multi-channel fused EEG features on the model's classification performance across different sub-bands. The experimental results demonstrated that it had higher accuracy than current drowsiness detection algorithms for EEG signals when tested on the publicly available DROZY dataset.
FPGA implementation of Parameterizable Pattern-generator Modules for LBIST applications

In this paper, a parameterizable Linear Feedback Shift Register design for LBIST applications is designed and implemented in Basys-3 FPGA board using Xilinx Vivado Suite 2020.1. The LFSR mainly helps us in pseudorandom test vector generation which can be used in Pseudo Random Pattern Generator (PRPG) and Multiple Input Signature Register (MISR) units for Logic Built In Self-Test (LBIST) applications. Increasing complexity and usage of Integrated Chips in military, medical, automobile and satellite devices has paved in the way for industries to look into LBIST based on-site chip testing instead of old traditional ATE based testing where the device had to be taken to ATE based test centres for probing, testing and validations. The design proposed in this paper can be reconfigured into multiple configurations like internal, external and complete configurations. Proposed design can also be programmed into multiple polynomial configurations based on intended application. Reconfigurable LFSR configuration features also integrated in the proposed design helping users to test the majority of the random pattern resistant faults present in the circuit while utilising the LFSR to create more sequences without increasing storage needs. The proposed design can generate multiple range of bits based on its intended applications.
Sex Specificity in the Mixed Effects of Blood Heavy Metals and Cognitive Function on Elderly: Evidence from NHANES

The way that males and females react to environmental exposures and negative impacts on their neurological systems is often different. Although previous research has examined the cognitively impairing effects of solitary metal exposures, the relationship between metal mixtures and cognitive function, particularly when considering an individual’s sex, remains elusive. This study aimed to investigate the sex differences in the association between multiple metal combinations and cognitive function in older Americans. This research employed the 2011–2014 NHANES survey of elderly Americans. The association between five mixed metals and four cognitive tests (the animal fluency test (AFT), the digit symbol substitution test (DSST), the instant recall test (IRT), and the delayed recall test (DRT)) were investigated with generalized linear regression model (GLM), Bayesian kernel machine regression model (BKMR), weighted quantile sum regression model (WQS), and quantile g-computation regression model (Qgcomp). A total of 1833 people, including 883 males and 950 females, enrolled in this cross-sectional study. We discovered that blood lead and blood cadmium were negatively associated with cognitive performance, while blood selenium demonstrated a positive association with cognitive function in older people. The negative relationship of heavy metal combinations on cognitive function might be somewhat reduced or even reversed via selenium. The IRT, AFT, and DSST are three of the four cognitive tests where men had more dramatic positive or negative results. There was a sex-specific connection between blood metal ratios and cognitive function among older Americans, as evidenced by the more significant relationship between mixed metals and cognitive performance in men (either positively or negatively). These results emphasize the impacts of ambient heavy metal exposure on cognitive function by employing sex-specific methods.