Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual Reality Features for E-Commerce 3D PDFAdvertising

This research intention is to investigate which virtual reality design interface features should be included in e-commerce 3D PDF advertising and to enhance user’s level of understanding or awareness of the product by presenting the proposed design features in the VR advertising material. Nowadays, there are already companies adopting virtual reality technology in their ecommerce advertising business. With virtual reality being introduced, there are also many virtual reality features that have emerged which may not be suitable to be applied to e-commerce advertising. Therefore the knowledge of knowing which features to use are essential to ensure that customer will be satisfied in experiencing the advertisement.Though there are many tools which could be used for 3D advertising 3D PDF has proven to be the best option for e-commerce advertising.There are several processes used in completing this paper to guide the research in achieving the paper’s mission. In general the steps are about: 1) Recognizing the problems 2) Analyzing and extracting the possible solutions of which virtual reality features to be used in 3D PDF 3) Devising and conducting experiment and survey for the 3D PDF advertisements which included the tailored virtual reality features 4) Evaluating users’ attitude towards the material Through experiments and user’s evaluation, this study concluded that there are two main categories of virtual reality features that can be use in 3D PDF advertisement which are visual simulation and behavioral simulation. Furthermore the quest to enhance user awareness of the product is successful when the features are presented in the advertisement.
Dynamic Analysis of Alternative Elements in an Automated Packaging System Based on 5G Internet of Things

The arrival of 5G will usher in an era of “Internet of Everything. ” Massive Internet of Things data contains great value in the dynamic analysis of alternative elements of automated packaging systems. From the perspective of the realization of personalized customization functions, this article solves the problem of dynamic analysis of alternative elements in the automated packaging system. We analyze the connection mechanism and interaction method between the cloud service system layer and the mobile terminal service layer, and carry out the corresponding software design. From the perspective of the realization of the intelligent production of the system in this paper, this topic mainly studies the information interaction mechanism and production control mechanism of the cloud service system and the manufacturing system. Based on the hardware of the manufacturing system layer, a flexible production implementation mechanism is formulated to make it the basis for the implementation of intelligent production of the system. Based on the massive data processing capabilities of the cloud service system, the information processing mechanism and the production planning decision-making mechanism are formulated for it, so as to realize the intelligent adjustment of the manufacturing system layer in the production process. For the connection scenario of IoT group paging, based on the application of NB-IoT technology in the next-generation mobile communication network, the focus of network optimization is to ensure the random access performance of IoT devices as much as possible. To this end, this paper proposes a random access optimization strategy for IoT group paging based on time slot scattering. We establish a mathematical model based on queuing theory for the connection scenario of the Io T group paging, then use the mathematical formula to derive the number of IoT devices scattered to each time slot in the initial state, thereby deriving the specific time slot scattering algorithm. This paper establishes a list of credit nodes, changes the participation mode of consensus nodes from static to dynamic, and supports voting to select trusted nodes. We designed a credit evaluation mechanism as a basis for consensus node elections to improve system’s fault tolerance rate. The algorithm process was simplified, and the PBFT algorithm process was simplified from a 3-phase protocol to a 2-phase protocol to further reduce communication bandwidth overhead and algorithm time. Simulation analysis shows that, compared with the PBFT algorithm, the proposed algorithm improves node flexibility and fault tolerance while reducing communication bandwidth overhead by about 45%, packaging throughput by about 4%, and latency by about 3%.
The Black Box - Obscurity and Transparency in the Dutch Coal Supply Chain

Every year more than 50 million tonnes of coal flow into the Netherlands, destined either for combustion in coal-fired power plants in this country or for re-export to other European countries for electricity generation there. The capacity to generate electricity from coal in the Netherlands will more than double if the four new coal-fired power plants currently being planned in the Netherlands go forward as envisioned. Much of that coal comes from countries like Colombia, Russia, and South Africa, where sub-standard social and environmental conditions often prevail. A lack of transparency about the origin of coal used in the Netherlands has allowed the electricity companies responsible for importing irresponsibly mined coal to avoid public and political pressure for improving conditions in their supply chain. Increasing transparency in the coal supply chain is an important first step toward improving the sub-standard conditions at coal mines around the world.This report details the functioning of the coal supply chain and provides a thorough overview of all publicly available information on the origin of coal used in Dutch power plants. The report identifies critical gaps in public knowledge resulting from the lack of transparency provided by electricity companies operating in the Netherlands and analyses whether the degree of transparency provided by electricity companies is in line with international standards.
Ship detection based on medium-low resolution remote sensing data and super-resolved feature representation

ABSTRACT Existing methods enhance medium-low resolution remote sensing ship detection by feeding super-resolved images to the detectors. Although these methods marginally improve the detection accuracy, the correlation between image super-resolution (SR) and ship detection is under-exploited. In this paper, we propose a simple but effective ship detection method called ShipSR-Det, in which both the output image and the intermediate features of the SR module are fed to the detection module. Using the super-resolved feature representation, the potential benefit introduced by image SR can be fully used for ship detection. We apply our method to the SSD and Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN, respectively. Extensive ablation studies validate the effectiveness and generality of our method. Moreover, we compare ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our proposed method.
High performance power-configurable preamplifier in a high-density parallel optical receiver

A power-configurable high performance preamplifier was implemented in standard 180-nm CMOS technology for 12 10 Gb/s high-density ultra-high speed parallel optical communication system. With critical limitations on power consumption, area and fabrication cost, the preamplifier achieves high performance, e.g. high bandwidth, high trans-impedance gain, low noise and high stability. A novel feed-forward common gate (FCG) stage is adopted to alleviate contradictions on trans-impedance gain and bandwidth by using a low headroom con- suming approach to isolate a large input capacitance and using complex pole peaking techniques to substitute induc- tors to achieve bandwidth extension. A multi-supply power-configurable scheme was employed to avoid wasteful power caused by a pessimistic estimation of process-voltage-temperature (PVT) variation. Two representative sam- ples provide a trans-impedance gain of 53.9 dB, a 3-dB bandwidth of 6.8 GHz, a power dissipation of 6.26 mW without power-configuration and a trans-impedance gain of 52.1 dB, a 3-dB bandwidth of 8.1 GHz, a power dis- sipation of 6.35 mW with power-configuration, respectively. The measured average input-referred noise-current spectral density is no more than 28 pA/ p Hz. The chip area is only 0.08 0.08 mm 2 .
Development and Validation of a Head-Neck Finite Element Model for the Study of Neck Dynamic Responses in Car Impacts

Based on the human head-neck anatomic structure,this paper presented a head-neck 3D finite element model,which consisted of skull, brain, cervical vertebra, disks, facet joints, ligaments, and muscles. The model has 17 758 nodes and 21 803 elements. The whole model was validated through the volunteer tests in frontal and rear end impact tests. The boundary and loading conditions were applied according to the frontal impact and rear impact. The output parameters used for validation included the acceleration of the head CG. The head linear acceleration history curve was presented, and the HIC value was calculated. Simulation results showed that the head-neck model had a good biofidelity, which will contribute to the research on neck injury mechanisms and the development of injury protective devices.
International for Processing Purposes: A Critical Examination of DACA Recipients’ Post-Secondary Admissions in Ohio

ABSTRACT In 2011, Ohio passed state-wide legislation prohibiting undocumented students from receiving in-state resident tuition (ISRT). In 2013, a student-led advocacy campaign resulted in the Ohio Board of Regents extending ISRT consideration to recipients of Deferred Action for Childhood Arrivals (DACA), a subsection of the larger population of undocumented students. This qualitative study examines the impact of DACA on ISRT policies and their implementation, particularly in states that have incongruent ISRT policy environments impacting their growing immigrant communities. This qualitative work utilizes a critical race methodology to analyze the perspectives of nine racially diverse DACA recipients as they applied to predominately white institutions (PWIs) of higher education across the state. Regardless of acknowledged or unacknowledged ISRT eligibility, evidence suggests public institutions of higher education in Ohio categorize DACA college applicants as “international for processing purposes (IFPP)” which creates additional, often unclear, administrative steps for applicants. These steps, when coupled with microaggressive interactions with staff who are largely unaware or misinformed about DACA, jeopardize applicants’ college enrollment. The result is a complex and inequitable admissions process full of incongruent policies and reliance on a racist-nativist infrastructure in the implementation of Ohio’s ISRT policy.
The antimicrobial peptide cathelicidin is critical for the development of Th17 responses in severe inflammatory disease

Multiple Sclerosis (MS) is a highly prevalent demyelinating autoimmune condition; the mechanisms regulating its severity and progression are unclear. The IL-17-producing Th17 subset of T cells has been widely implicated in MS and in the mouse model, experimental autoimmune encephalomyelitis (EAE). However, the differentiation and regulation of Th17 cells during EAE remain incompletely understood. Although evidence is mounting that the antimicrobial peptide cathelicidin profoundly affects early T cell differentiation, no studies have looked at its role in longer term T cell responses. Now, we report that cathelicidin drives severe EAE disease. It is released from neutrophils, microglia and endothelial cells throughout disease; its interaction with T cells potentiates Th17 differentiation in lymph nodes and Th17 to exTh17 plasticity and IFN-γ production in the spinal cord. As a consequence, mice lacking cathelicidin are protected from severe EAE. In addition, we show that cathelicidin is produced by the same cell types in the active brain lesions in human MS disease. We propose that cathelicidin exposure results in highly activated, cytokine-producing T cells which drive autoimmunity; this is a mechanism through which neutrophils amplify inflammation in the central nervous system.
Immigration attitudes and the influence of positive, negative, and neutral intergroup exposure

ABSTRACT Largely due to immigration, European societies have grown increasingly diverse. The current study seeks to determine how citizens characterize their experiences with this diversity and whether various types of intergroup exposure can influence immigration attitudes. This analysis takes a unique approach by comparing positive, negative, and neutral intergroup exposure characterizations, along with those reporting no exposure at all. Using internationally representative data from the 2014 European Social Survey, this paper demonstrates how these different characterizations predict citizens’ immigration attitudes. Results indicate that positive and negative experiences have opposing effects that are roughly symmetrical in magnitude and both are more intense when experienced in high frequency. Neutral exposure is actually much more common than negative experiences and represents a net benefit compared to those with no exposure at all. The results present important implications for future research and highlight several encouraging patterns for diversity across Europe.
Survey of Application of Virtual and Augmented Reality in Education

—Virtual Reality and Augmented Reality is immerging with lots of prospective applications in a different area. With the advancement of hardware and software infrastructure, these technologies are more reachable and affordable now. The application of VR and AR in education is now a reality. Students face issues and complexity during their learning process. Sometimes, proper material can not be provided due to cost, and sometimes study material is an imaginary thing without actual existence. Therefore, the learning process can be cumbersome, and VR & AR can be a blessing to improve the learning process in this kind of scenario. This paper will review applications, technology, specific field of application of VR and AR in education.
Propulsive efficiency of small multirotor propellers in fast forward flight

This paper presents a summary of experimental results regarding the propulsive efficiencies of small multirotor propellers, in simulated forward flight conditions. An automated test rig was used in a wind tunnel to measure propeller performance data across a range of flight speeds and angles. Propellers of various pitch were also tested and compared in these conditions. Flight angle was demonstrated to have minimal impact on efficiency within the tested range. Maximal efficiencies were demonstrated at the highest advance ratios and lowest geometric pitches tested.
Is There A Correlation Between COVID-19 and Hepatitis A and Hepatitis E Serum Antibody Level?

Background & Objective: The prevalence of COVID-19 and its severity have been observed to be on a lower level in underdeveloped countries with poorer standards of hygiene. This disparity may be attributed to the higher seroprevalence of other viral diseases, which can result in the presence of antibodies protective against COVID-19. Two of the widespread diseases in such countries are infection to hepatitis A and E viruses (HAV and HEV). In the present study, we explored the relationship between the level of antibodies against these viruses and the susceptibility to COVID-19. Methods: Ninety patients were studied in two groups of controls and cases, each consisting 45 individuals. The cases were patients with the clinical symptoms of COVID-19 and positive RT-PCR test results. The controls were individuals referred to the respiratory triage of Imam Khomeini Hospital Complex and were not demonstrating relevant clinical symptoms of COVID-19 and their RT-PCR test results were negative. Levels of HAV and HEV antibodies were measured and compared in these two groups. Results: The median of HAV antibody level was 13.6 (IQR=11.5-16.9) and 13.2 (IQR =10.7-14.7) in cases and controls, respectively, showing no statistically significant difference (P=0.1). Likewise, the median of HEV antibody level was 6.7 (IQR=5.3-7.1) and 7.1 (IQR=6.3-7.5) in cases and controls, respectively, which again showed no statistically significant difference (P=0.41). Conclusion: The present study was carried out in a region with a relatively high prevalence of HAV and HEV infections. Contrary to our expectations, no statistically significant relationship was observed between the levels of antibodies against these viruses and the susceptibility to COVID-19. Further studies with larger sample sizes and in other countries are needed to come to a definite conclusion.
Fission yeast Ase1PRC1 is required for the G2-microtubule damage response

Schizosaccharomyces pombe delays entry into mitosis following G2 microtubule damage. This pathway is dependent on Rad26ATRIP, the regulatory subunit of the Rad26ATRIP/Rad3ATR DNA damage response (DDR) complex. However, this G2 microtubule damage response pathway acts independently of the G2 DNA damage checkpoint pathway. To identify other proteins in this G2 microtubule damage pathway, we previously screened a cDNA overexpression library for genes that rescued the sensitivity of rad26Δ cells to the microtubule poison thiabendazole. A partial cDNA fragment encoding only the C-terminal regulatory region of the microtubule bundling protein Ase1PRC1 was isolated. This fragment lacks the Ase1PRC1 dimerization and microtubule binding domains and retains the conserved C-terminal unstructured regulatory region. Here, we report that ase1Δ cells fail to delay entry into mitosis following G2 microtubule damage. Microscopy revealed that Rad26ATRIP foci localized alongside Ase1PRC1 filaments, although we suggest that this is related to microtubule-dependent double strand break mobility that facilitates homologous recombination events. Indeed, we report that the DNA repair protein Rad52 co-localizes with Rad26ATRIP at these foci, and that localization of Rad26ATRIP to these foci depends on a Rad26ATRIP N-terminal region containing a checkpoint recruitment domain. To our knowledge, this is the first report implicating Ase1PRC1 in regulation of the G2/M transition.
Data-Driven Modeling of Aggregate Flexibility under Uncertain and Non-Convex Load Models

Bundling a large number of distributed energy resources through a load aggregator has been advocated as an effective means to integrate such resources into whole-sale energy markets. To ease market clearing, system operators allow aggregators to submit bidding models of simple prespecified polytopic shapes. Aggregators need to carefully design and commit to a polytope that best captures their energy flexibility along a day-ahead scheduling horizon. This work puts forth a model-informed data-based optimal flexibility design for aggregators, which deals with the time-coupled, uncertain, and non-convex models of individual loads. The proposed solution first generates efficiently a labeled dataset of (non)-disaggregatable schedules. The feasible set of the aggregator is then approximated by an ellipsoid upon training a convex quadratic classifier using the labeled dataset. The ellipsoid is subsequently inner approximated by a polytope. Using Farkas lemma, the obtained polytope is finally inner approximated by the polytopic shape dictated by the market. Numerical tests show the effectiveness of the proposed flexibility design framework for designing the feasible sets of small- and large-sized aggregators coordinating solar photovoltaics, thermostatically-controlled loads, batteries, and electric vehicles. The tests further demonstrate that it is crucial for the aggregator to consider time-coupling and uncertainties in optimal flexibility design.
Geometric instability of out of distribution data across autoencoder architecture

We study the map learned by a family of autoencoders trained on MNIST, and evaluated on ten different data sets created by the random selection of pixel values according to ten different distributions. Specifically, we study the eigenvalues of the Jacobians defined by the weight matrices of the autoencoder at each training and evaluation point. For high enough latent dimension, we find that each autoencoder reconstructs all the evaluation data sets as similar \emph{generalized characters}, but that this reconstructed \emph{generalized character} changes across autoencoder. Eigenvalue analysis shows that even when the reconstructed image appears to be an MNIST character for all out of distribution data sets, not all have latent representations that are close to the latent representation of MNIST characters. All told, the eigenvalue analysis demonstrated a great deal of geometric instability of the autoencoder both as a function on out of distribution inputs, and across architectures on the same set of inputs.
On maximum Wiener index of directed grids

This paper is devoted to Wiener index of directed graphs, more precisely of directed grids. The grid $G_{m,n}$ is the Cartesian product $P_m\Box P_n$ of paths on $m$ and $n$ vertices, and in a particular case when $m=2$, it is a called the ladder graph $L_n$. Kraner \v{S}umenjak et al. proved that the maximum Wiener index of a digraph, which is obtained by orienting the edges of $L_n$, is obtained when all layers isomorphic to one factor are directed paths directed in the same way except one (corresponding to an endvertex of the other factor) which is a directed path directed in the opposite way. Then they conjectured that the natural generalization of this orientation to $G_{m,n}$ will attain the maximum Wiener index among all orientations of $G_{m,n}$. In this paper we disprove the conjecture by showing that a comb-like orientation of $G_{m,n}$ has significiantly bigger Wiener index.
Multi-round QAOA and advanced mixers on a trapped-ion quantum computer

Combinatorial optimization problems on graphs have broad applications in science and engineering. The quantum approximate optimization algorithm (QAOA) is a method to solve these problems on a quantum computer by applying multiple rounds of variational circuits. However, there exist several challenges limiting the application of QAOA to real-world problems. In this paper, we demonstrate on a trapped-ion quantum computer that QAOA results improve with the number of rounds for multiple problems on several arbitrary graphs. We also demonstrate an advanced mixing Hamiltonian that allows sampling of all optimal solutions with predetermined weights. Our results are a step toward applying quantum algorithms to real-world problems.