Miracle-dz/UniFAR-FTUs huggingface dataset:
On the teleparallel frame problem

The role played by the congruence in the prediction of the gravitational energy in the teleparallel equivalent of general relativity is discussed. It is shown that some congruences yield unphysical predictions. It is also shown that the energy–momentum tensor density predicted by the proper reference frame of an arbitrary accelerated observer vanishes along the observer’s worldline, regardless of its acceleration. The latter result is discussed and arguments both against and in favor of the use of this type of frame to describe the gravitational energy are presented; arguments against the belief that the principle of equivalence is incompatible with the localization of the gravitational energy are also presented. A gauge condition is applied to the teleparallel frame and its consistence is discussed: using three different tetrads for the pp-wave spacetimes, it is shown that the most consistent prediction comes from the tetrad that satisfies this gauge. The possibility of having a well-defined concept of absolute vacuum is also discussed.
Geographic Information Visualization and Sustainable Development of Low-Carbon Rural Slow Tourism under Artificial Intelligence

This study conducts in-depth research on geographic information visualization and the sustainable development of low-carbon rural slow tourism under artificial intelligence (AI) to analyze and discuss the visualization of geographic information and the sustainable development of low-carbon slow tourism in rural areas. First, the development options related to low-carbon tourism in rural areas are discussed. Then, a low-carbon rural slow tourism recommendation method based on AI and a low-carbon rural tourism scene recognition method based on Cross-Media Retrieval (CMR) data are proposed. Finally, the proposed scheme is tested. The test results show that the carbon dioxide emissions of one-day tourism projects account for less than 10% of the total tourism industry. From the proportion, it is found that air transport accounts for the largest proportion, more than 40%. With the development of time, the number of rural slow tourists in Guizhou has increased the most, while the number of rural slow tourists in Yunnan has increased to a lesser extent. In the K-means clustering model, the accuracy of scenario classification based on the semantic features of scene attributes is 5.26% higher than that of attribute likelihood vectors. On the Support Vector Machine classifier, the scene classification accuracy based on the semantic features of scene attributes is 19.2% higher than that of the scene classification based on attribute likelihood vector features. CMR techniques have also played a satisfying role in identifying rural tourism scenarios. They enable passengers to quickly identify tourist attractions to save preparation time and provide more flexible time for the tour process. The research results have made certain contributions to the sustainable development of low-carbon rural slow tourism.
Performance Analysis and Deep Learning Assessment of Full-Duplex Overlay Cognitive Radio NOMA Networks Under Non-Ideal System Imperfections

In this paper, we investigate the effectiveness of an overlay cognitive radio (OCR) coupled with non-orthogonal multiple access (NOMA) system using a full-duplex (FD) cooperative spectrum access with a maximal ratio combining (MRC) scheme under the various non-ideal system imperfections. In view of practical realization, we ponder the impact of loop self-interference, transceiver hardware impairments, imperfect successive interference cancellation, and channel estimation errors on the system performance. We investigate the performance of the proposed system by obtaining closed-form expressions for outage probability and ergodic rate for primary as well as secondary users using Nakagami- ${m}$ fading channels. As a result, we reveal some notable ceiling effects and present efficacious power allocation strategy for cooperative spectrum access. We further evaluate the system throughput and ergodic sum-rate (ESR) to assess the system’s overall performance. Our findings manifest that the FD-based OCR-NOMA can comply with the non-ideal system imperfections and outperform the competing half-duplex (HD) and orthogonal multiple access (OMA) counterparts. Due to the massive complexity of the suggested system model, direct derivation of the closed-form formula for the ESR becomes cumbersome. To address this problem, we develop a deep neural network (DNN) framework for ESR prediction in real-time situations.
Vortex-induced vibrations of a circular cylinder at low Reynolds numbers

Results are presented for a numerical simulation of vortex-induced vibrations of a circular cylinder of low non-dimensional mass (m* = 10) in the laminar flow regime (60 < Re < 200). The natural structural frequency of the oscillator, fN, matches the vortex shedding frequency for a stationary cylinder at Re = 100. This corresponds to fN D2/ν = 16.6, where D is the diameter of the cylinder and ν the coefficient of viscosity of the fluid. A stabilized space–time finite element formulation is utilized to solve the incompressible flow equations in primitive variables form in two dimensions. Unlike at high Re, where the cylinder response is known to be associated with three branches, at low Re only two branches are identified: ‘initial’ and ‘lower’. For a blockage of 2.5% and less the onset of synchronization, in the lower Re range, is accompanied by an intermittent switching between two modes with vortex shedding occurring at different frequencies. With higher blockage the jump from the initial to lower branch is hysteretic. Results from free vibrations are compared to the data from experiments for forced vibrations reported earlier. Excellent agreement is observed for the critical amplitude required for the onset of synchronization. The comparison brings out the possibility of hysteresis in forced vibrations. The phase difference between the lift force and transverse displacement shows a jump of almost 180° at, approximately, the middle of the synchronization region. This jump is not hysteretic and it is not associated with any radical change in the vortex shedding pattern. Instead, it is caused by changes in the location and value of the maximum suction on the lower and upper surface of the cylinder. This is observed clearly by comparing the time-averaged flow for a vibrating cylinder for different Re. While the mean flow for Re beyond the phase jump is similar to that for a stationary cylinder, it is associated with a pair of counter-rotating vortices in the near wake for Re prior to the phase jump. The phase jump appears to be one of the mechanisms of the oscillator to self-limit its vibration amplitude.
A novel l 1/2 sparse regression method for hyperspectral unmixing

Hyperspectral unmixing (HU) is a popular tool in remotely sensed hyperspectral data interpretation, and it is used to estimate the number of reference spectra (end-members), their spectral signatures, and their fractional abundances. However, it can also be assumed that the observed image signatures can be expressed in the form of linear combinations of a large number of pure spectral signatures known in advance (e.g. spectra collected on the ground by a field spectro-radiometer, called a spectral library). Under this assumption, the solution of the fractional abundances of each spectrum can be seen as sparse, and the HU problem can be modelled as a constrained sparse regression (CSR) problem used to compute the fractional abundances in a sparse (i.e. with a small number of terms) linear mixture of spectra, selected from large libraries. In this article, we use the l 1/2 regularizer with the properties of unbiasedness and sparsity to enforce the sparsity of the fractional abundances instead of the l 0 and l 1 regularizers in CSR unmixing models, as the l 1/2 regularizer is much easier to be solved than the l 0 regularizer and has stronger sparsity than the l 1 regularizer (Xu et al. 2010). A reweighted iterative algorithm is introduced to convert the l 1/2 problem into the l 1 problem; we then use the Split Bregman iterative algorithm to solve this reweighted l 1 problem by a linear transformation. The experiments on simulated and real data both show that the l 1/2 regularized sparse regression method is effective and accurate on linear hyperspectral unmixing.
SOCS3 deficiency-dependent autophagy repression promote the survival of early-stage myeloid-derived suppressor cells in breast cancer by activating the Wnt/mTOR pathway.

Early-stage myeloid-derived suppressor cells (eMDSCs) are a newly defined subset of MDSCs in breast cancer tissues, and was related to poor prognosis of breast cancer patients. Compared with classical MDSCs, eMDSCs display exceptional immunosuppressive ability and accumulate in the tumor microenvironment to suppress innate and adaptive immunity. Previously, we demonstrated that eMDSCs was SOCS3 deficiency-dependent and correlated with differentiation arrest in the myeloid lineage. Autophagy is a major regulator of myeloid differentiation, however, the mechanism by which autophagy regulate the development of eMDSCs has not been elucidated. Here, we constructed EO771 mammary tumor-bearing conditional myeloid SOCS3 knockout mice (SOCS3MyeKO) characterized by abundant tumor-infiltrating eMDSCs and exacerbated immunosuppression in vitro and in vivo. We found that eMDSCs isolated from SOCS3MyeKO mice showed differentiation arrest in the myeloid lineage, which was caused by limited autophagy activation in an Wnt/mTOR-dependent manner. RNA sequencing and miRNA microarray assays revealed that miR-155-induced C/EBPβ downregulation activated the Wnt/mTOR pathway, and promoted autophagy repression and differentiation arrest in eMDSCs. Furthermore, inhibition of Wnt/mTOR signaling suppressed both tumor growth and the immunosuppressive functions of eMDSCs. Thus, SOCS3 deficiency-dependent autophagy repression and their regulatory mechanisms could contribute to the immunosuppressive tumor microenvironment. Our study proposes a novel mechanism for promoting eMDSCs survival, which might shed new light on a potential target of oncologic therapy.
The Transition from the Present Radar Dissemination System to the NEXRAD Information Dissemination Service (NIDS)

The National Weather Service (NWS) will begin replacing its current system of weather radars with Next Generation Weather Radars (NEXRAD) in late 1990. Presented is an overview of previous and current systems, and the replacement NEXRAD Information Dissemination Service (NIDS), for the dissemination of radar products to government and private users. The NIDS will be implemented and operated for the NWS by private sector data providers under a memorandum of agreement. The requirements of the NIDS, the suite of products to be provided, and its implementation milestones are described.
The Host Non-Coding RNA Response to Alphavirus Infection

Alphaviruses are important human and animal pathogens that can cause a range of debilitating symptoms and are found worldwide. These include arthralgic diseases caused by Old-World viruses and encephalitis induced by infection with New-World alphaviruses. Non-coding RNAs do not encode for proteins, but can modulate cellular response pathways in a myriad of ways. There are several classes of non-coding RNAs, some more well-studied than others. Much research has focused on the mRNA response to infection against alphaviruses, but analysis of non-coding RNA responses has been more limited until recently. This review covers what is known regarding host cell non-coding RNA responses in alphavirus infections and highlights gaps in the knowledge that future research should address.
Pre- and Post-Zygotic Barriers Contribute to Reproductive Isolation and Correlate with Genetic Distance in Cucumis

Hybridization between Cucumis species, including cultivated melon (C. melo), is hampered by Interspecific Reproductive Barriers (IRBs). However, the nature of IRBs in Cucumis is largely unknown. This study explores locations, timing, and contribution to reproductive isolation (RI) of pre- and post-zygotic IRBs in Cucumis. To do this, we assessed crossability among Cucumis African wild species and C. melo at the pre-zygotic level by visualizing pollen tubes under fluorescence microscopy and, post-zygotically, by evaluating fruit/seed set and F1 hybrid fertility. Genetic distances among Cucumis species were inferred from Genotyping-by-Sequencing, and its correlation with RI stages was analyzed. Observed pre- and post-zygotic IRBs included pollen tube arrest, fruit set failure, and hybrid male sterility. Unilateral cross-incongruity/incompatibility (UCI) was detected in some hybridizations, and dominant gene action is suggested for pistil-side UCI in interspecific F1 hybrids. Notably, the allotetraploid C. ficifolius was very fertile as a seed parent but infertile in all reciprocal crosses. Contribution to RI was found significant for both pre- and post-zygotic IRBs. Additionally, a significant positive correlation was detected between genetic distance and pre- and post-zygotic RI stages. Interestingly, UCI offers an accessible system to dissect the genetics of IRBs in Cucumis, which may facilitate the use of wild relatives in breeding.
Genome-Wide Characterization of the PIFs Family in Sweet Potato and Functional Identification of IbPIF3.1 under Drought and Fusarium Wilt Stresses

Phytochrome-interacting factors (PIFs) are essential for plant growth, development, and defense responses. However, research on the PIFs in sweet potato has been insufficient to date. In this study, we identified PIF genes in the cultivated hexaploid sweet potato (Ipomoea batatas) and its two wild relatives, Ipomoea triloba, and Ipomoea trifida. Phylogenetic analysis revealed that IbPIFs could be divided into four groups, showing the closest relationship with tomato and potato. Subsequently, the PIFs protein properties, chromosome location, gene structure, and protein interaction network were systematically analyzed. RNA-Seq and qRT-PCR analyses showed that IbPIFs were mainly expressed in stem, as well as had different gene expression patterns in response to various stresses. Among them, the expression of IbPIF3.1 was strongly induced by salt, drought, H2O2, cold, heat, Fusarium oxysporum f. sp. batatas (Fob), and stem nematodes, indicating that IbPIF3.1 might play an important role in response to abiotic and biotic stresses in sweet potato. Further research revealed that overexpression of IbPIF3.1 significantly enhanced drought and Fusarium wilt tolerance in transgenic tobacco plants. This study provides new insights for understanding PIF-mediated stress responses and lays a foundation for future investigation of sweet potato PIFs.
Seaweed Pellets as a Renewable Fuel Feedstock

Seaweed can be a desirable source of renewable energy or fuel after it has been processed by combustion, thermochemical conversion by gasification, pyrolysis, or hydrothermal liquefaction (HTL) or biochemical conversion routes like anaerobic digestion (AD). This work explores how well the measured properties of seaweed pellets match the specifications for the various fuel and energy conversion options listed. Blends of hay, wood chips, sawdust, and seaweed were pelletized. Eight pellet blends with dominant seaweed content and minimum acceptable mechanical strength and stability were produced and their physical and chemical properties were reported. The seaweed pellets had an energy content of around 14 MJ/kg, and each pellet could withstand almost 200 N of compression force. Their water content was around 5% or less and their ash content was around 20–34%. According to the results, a higher wood content increased the energy content of the pellets. Among those properties measured in this project, none of them contradicted the typical specifications of combustion, HTL, and AD. However, the low water content and low strength of some pellet types were unable to meet the specifications for certain types of gasification and pyrolysis.
Disease Occurrence in- and the Transferal of Zoonotic Agents by North American Feedlot Cattle

North America is a large producer of beef and contains approximately 12% of the world’s cattle inventory. Feedlots are an integral part of modern cattle production in North America, producing a high-quality, wholesome protein food for humans. Cattle, during their final stage, are fed readily digestible high-energy density rations in feedlots. Cattle in feedlots are susceptible to certain zoonotic diseases that impact cattle health, growth performance, and carcass characteristics, as well as human health. Diseases are often transferred amongst pen-mates, but they can also originate from the environment and be spread by vectors or fomites. Pathogen carriage in the gastrointestinal tract of cattle often leads to direct or indirect contamination of foods and the feedlot environment. This leads to the recirculation of these pathogens that have fecal–oral transmission within a feedlot cattle population for an extended time. Salmonella, Shiga toxin-producing Escherichia coli, and Campylobacter are commonly associated with animal-derived foods and can be transferred to humans through several routes such as contact with infected cattle and the consumption of contaminated meat. Brucellosis, anthrax, and leptospirosis, significant but neglected zoonotic diseases with debilitating impacts on human and animal health, are also discussed.
A Numerical Model for the Equilibrium Shape of Electrified Raindrops

Abstract The model Beard Chuang, using the differential form of Laplace's formula, has been extended to raindrop shapes under the influence of vertical electric fields and drop charges. A finite volume method was used with a boundary-fitted coordinate system to calculate the shape-dependent electric field. The distorted shape was obtained by numerical integration from the upper to lower pole by iteration to achieve the appropriate drop volume and force balance using shape-dependent stresses. The model prediction of the critical electric field for instability is within a few percent of previous models for a stationary drop, but stability was found to be considerably enhanced for raindrops because of the counteracting aerodynamic distortion. The predicted critical fields for larger raindrops, however, are about 2 kV cm−1 higher than found in the wind tunnel measurements of Richards and Dawson. Model raindrop shapes in a strong, electric field show a pronounced extension of the upper pole, and a flattened ba.
A Dynamic Spatio-Temporal Stochastic Modeling Approach of Emergency Calls in An Urban Context

Emergency calls are defined by an ever-expanding utilisation of information and sensing technology, leading to extensive volumes of spatio-temporal high-resolution data. The spatial and temporal character of the emergency calls is leveraged by authorities to allocate resources and infrastructure for an effective response, to identify high-risk event areas, and to develop contingency strategies. In this context, the spatio-temporal analysis of emergency calls is crucial to understanding and mitigating distress situations. However, modelling and predicting crime-related emergency calls remain challenging due to their heterogeneous and dynamic nature with complex underlying processes. In this context, we propose a modelling strategy that accounts for the intrinsic complex space-time dynamics of some crime data on cities by handling complex advection, diffusion, relocation, and volatility processes. This study presents a predictive framework capable of assimilating data and providing confidence estimates on the predictions. By analysing the dynamics of the weekly number of emergency calls in Valencia, Spain, for ten years (2010-2020), we aim to understand and forecast the spatio-temporal behaviour of emergency calls in an urban environment. We include putative geographical variables, as well as distances to relevant city landmarks, into the spatio-temporal point process modelling framework to measure the effect deterministic components exert on the intensity of emergency calls in Valencia. Our results show how landmarks attract or repel offenders and act as proxies to identify areas with high or low emergency calls. We are also able to estimate the weekly average growth and decay in space and time of the emergency calls. Our proposal is intended to guide mitigation strategies and policy.
Promoting the Application of Lean Automation - Take the Automobile Oil Seal Manufacturing Industry as an Example

With increasingly sophisticated global manufacturing technology and fierce competition in the international market, the automobile manufacturing industry has entered the stage of internationalization. Therefore, it is necessary to innovate and change, improve production technology continuously, strengthen physical fitness, and maintain competitiveness to survive in the rapidly changing market. Lean Management (LM) and automation equipment are important methods to achieve the above goals. Among them, the use of Automated Guided Vehicles (AGV) has become a crucial equipment in the industry that many manufacturers have widely used. AGVs are unmanned vehicles that can travel according to a program or a predetermined path. AGVs can replace the original logistics, shortens the handling time greatly, and enhances the competitiveness of enterprises.This research focuses on the introduction of LM in an automobile oil seal manufacturing industry in Taiwan and focuses on improving logistics efficiency. It uses Value Stream Mapping (VSM) to discuss the logistics problems existing on the production line and proposes specific improvement plans. This study aims to improve logistics efficiency using AGVs and logistics improvement based on its cooperation with Jidoka. This study proposes a new route transportation method, which uses multiple AGVs to perform transportation tasks at the same time, combined with Karakuri’s base-running path transportation mode of multi-functional racks.The results include (1) proposing a new route transportation method, AGVs combined with Karakuri’s running route transportation mode; (2) saving two full-time logistics manpower; and (3) increasing the Cycle Time (CT) to within 5 minutes.
Mathematical Modeling of Lithium-Ion Cell and its Parameter Optimization Using Cuckoo Search Algorithm

An effective model of a battery cell is unveiled in this paper to substantiate the Battery Management System (BMS) on the test bench. This high-fidelity model also characterizes the electrochemical reactions occurring inside the battery cell using a single parallel circuit of resistors and capacitors. The parameters of the cell are optimized by Cuckoo Search (CS) algorithm, which is a nature inspired algorithm to denigrate the error and obtain global best values. The CS algorithm is used to obtain high accuracy in the estimation of parameters. The enhanced model is conspicuous for deliberating the performance of the battery cell under fast charging and discharging conditions of an Electric Vehicle (EV), State of health of the battery to fend off the accelerated deterioration in the life of the battery. The simulation is done using MATLAB for one RC equivalent circuit model, as it is adequate for industrial applications of lithium-ion batteries and to characterize the dynamics of the cell. The results obtained using the CS algorithm provide the best optimized results for parameters with the minimum root mean square error and validate the battery model on the test bench.