Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Peak Pressure by Blast Wave Propagation Between Buildings Using a Conditional 3D Convolutional Neural Network

To predict the damage resulting from an explosion in the middle of a city, where buildings are concentrated, the peak pressure reaching the walls of the buildings or in between buildings should be accurately and rapidly calculated. However, predicting peak pressure between buildings is known to be very difficult because of the diffraction and reflection of blast waves, which have generally been analyzed by numerical analysis methods. However, numerical analysis is not suitable in a military operation environment which requires rapid analysis, because it takes considerable time and resources. This study proposes a deep neural network that quickly and accurately predicts the peak pressure caused by the propagation of blast waves, for the effective analysis of weapon effectiveness and damage in urban environments. The proposed deep learning model is based on a 3-dimensional convolutional neural network (3D CNN) model that processes the spatial information of explosion and measurement in the 3D spaces using 3D kernels. To predict the peak pressure between buildings separated by an arbitrary distance using a single model, we also propose using conditional convolution, which modulates the prediction output according to the building distance. The proposed models were trained with a dataset constructed through finite element analysis with various building distances, explosion locations, and explosive weights. The experiment with a fixed building distance showed that the relative error of the proposed 3D CNN is less than 7%, which is 2.5 times more accurate than a simple multi-layer perceptron (MLP) model. For unseen building layouts, the conditional 3D convolution showed 3.6 times lower error than the MLP model, demonstrating the effectiveness of the conditional convolution for prediction in arbitrary building layouts. Most importantly, the proposed deep learning models took less than one minute per prediction, which is significantly faster than finite element analysis, which takes 6 to 8 hours to analyze a single simulation case.
Comparative transcriptome analysis reveals the molecular mechanism of heat-tolerance in Neopyropia yezoensis induced by Sargassum horneri extract

Introduction Heat stress is one of the main threats for the seaweed aquaculture, leading to the decay of the blades and causing serious economic losses. A newly developed biostimulant, Sargassum horneri extract (SHE), can enhance the heat-tolerance in Neopyropia yezoensis, which can be applied in the seaweed aquaculture. However, the molecular mechanism underlying this acquired heat-tolerance is still unclear. Method In this study, we examined the transcriptional regulation of N. yezoensis under heat stress after the SHE pre-treatment by performing the RNA-Seq on illumina Novaseq platform and differentially expressed gene analysis. Result and discussion Our results show that, under heat stress, the SHE pre-treatment can alleviate the suppression of genes involved in normal cell activities, such as ribosome biogenesis, photosynthesis, respiration and cell cycle. On the other hand, SHE pre-treatment can also induce some key genes involved in the heat-tolerance. We also found that the combination of SHE pre-treatment and heat stress can induce some genes related to epigenetic modification, suggesting that SHE may facilitate the formation of heat stress memory in N. yezoensis. Our study provides information for understanding the molecular mechanism of heat-tolerance in N. yezoensis and for promoting the application of seaweed-derived biostimulants in the seaweed aquaculture.
Face relighting based on virtual irradiance sphere and reflection coefficient

We present a novel method to estimate the light source direction and relight a face texture image of a single 3-D model under arbitrary unknown illumination conditions. We create a virtual irradiance sphere to detect the light source direction from a given illuminated texture image using both normal vector mapping and weighted bilinear interpolation. We then induce a relighting equation with estimated ambient and diffuse intensity. We provide the result of a series of experiments on light source estimation, relighting, and face recognition to show the efficiency and accuracy of the proposed method in restoring the shading and shadow areas of a face texture image. Our approach for face relighting can be used for illuminant-invariant face recognition applications.
Fast, Conservative Algorithm for Solving the Transonic Full-Potential Equation

A fast, fully implicit approximate factorization algorithm designed to solve the conservative, transonic, fullpotential equation in either two or three dimensions is described. The algorithm uses an upwind bias of the density coefficient for stability in supersonic regions. This provides an effective upwind difference of the streamwise terms for any orientation of the velocity vector (i.e., rotated differencing), thereby greatly enhancing the reliability of the present algorithm. A numerical transformation is used to establish an arbitrary body-fitted, finite-difference mesh. Computed results for both airfoils and simplified wings demonstrate substantial improvement in convergence speed for the new algorithm relative to standard successive-line over-relaxation algorithms.
A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho = 0.88, $p < 0.0001$ ), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies.
The Moderating Role of Self-Care Behaviors in Personal Care Aides of Older Adults during the COVID-19 Pandemic

The COVID-19 pandemic has brought new challenges and work changes for formal caregivers such as personal care aides with an impact on their quality of life (QoL). This cross-sectional study aims to analyze the relationships and contribution of sociodemographic and psychological variables towards QoL including the moderating role of self-care. This study included 127 formal caregivers from Portugal who were assessed on depression, anxiety and stress (DASS-21); professional self-care (SCAP); quality of life (SF-12); COVID-19 traumatic stress (COVID-19TSC) and preventive COVID-19 infection behaviors (PCOVID-19 IBS). Professional self-care was positively associated with QoL and also moderated the relationship between distress and QoL (p < 0.001). According to results, nursing homes should provide formal caregivers, such as personal care aides, with the professional support they need in order to promote their QoL and prevent burnout.
Two-Step Reactive Sputtering of Piezoelectric AIN Thin Films on Mo Electrodes for FBAR Applications

A series of two-step pulsed-DC reactive sputtering methods were employed to explore the feasibility of making fully (0002)-textured AlN thin films with a better control of its film quality. By varying the process parameters, e.g., working pressure, discharge power, and reactive atmosphere, of the first-step sputtering, while those of the second-step sputtering kept constant, we observed that the full width at half maximum of AlN (0002) rocking curve became smaller than that of the corresponding one-step sputtering on Si. The residual stress in two-step working pressure method on Si also became smaller in magnitude and varied less with pressure compared to that of its corresponding one-step counterpart. These phenomena were rationalized in terms of film thickness effects and deposition mechanisms. For deposition on Mo, on the other hand, the quality of the AlN piezoelectric film is found to be totally decided by the underlying Mo film, regardless of the sputtering parameters for AlN deposition including working pressure, discharge power, and reactive atmosphere.
In-Person or Online? Exploring Student Pharmacists’ Perceived Change in Interprofessional Skills between Two Delivery Formats

The COVID-19 pandemic drastically changed how education is delivered. Many academic programs quickly transformed their curriculum to online distance learning. This rapid transition may have compromised the rigor and fidelity of these activities. The Interprofessional Standardized Patient Experience (ISPE) is an interprofessional education activity (IPE) involving a team of students from six different healthcare professions that switched to an online delivery format. This manuscript compares pharmacy students’ perceived change in interprofessional skills between the two formats. Following the ISPE, second-year pharmacy students completed the revised Interprofessional Collaborative Competencies Attainment Survey (ICCAS-R). The ICCAS-R assesses the change in interprofessional collaboration-related competencies in healthcare students before and after IPE training using a retrospective pre–post approach. For each ICCAS-R item and each delivery format (44 in-person and 51 online), paired Student’s t-test on pre- and post-ISPE scores, and Cohen’s d were calculated. Every item of the ICCAS-R was significant (p < 0.001), regardless of delivery format. Nearly all ICCAS-R items had a large effect size, and the remaining items had a medium effect size. The amount of change pre- and post-ISPE for each ICCAS-R item was calculated. Student’s t-test was used to compare the magnitude of change in interprofessional skills between the two delivery formats. Only one difference was noted between the two delivery formats—ICCAS-R item 16, which measured actively listening to interprofessional team members (p = 0.0321). When switching to an online format, the high-fidelity dimension of the ISPE was retained. The ISPE is an effective IPE activity at increasing pharmacy students’ self-perceived interprofessional collaboration skills regardless of delivery format. Even though students reported the ISPE increased their ability to actively listen to the perspectives of interprofessional team members in both formats, the magnitude of the benefit was more profound in the in-person group.
Odorant-Binding Proteins and Chemosensory Proteins in Spodoptera frugiperda: From Genome-Wide Identification and Developmental Stage-Related Expression Analysis to the Perception of Host Plant Odors, Sex Pheromones, and Insecticides

Spodoptera frugiperda is a worldwide generalist pest with remarkable adaptations to environments and stresses, including developmental stage-related behavioral and physiological adaptations, such as diverse feeding preferences, mate seeking, and pesticide resistance. Insects’ odorant-binding proteins (OBPs) and chemosensory proteins (CSPs) are essential for the chemical recognition during behavioral responses or other physiological processes. The genome-wide identification and the gene expression patterns of all these identified OBPs and CSPs across developmental stage-related S. frugiperda have not been reported. Here, we screened for genome-wide SfruOBPs and SfruCSPs, and analyzed the gene expression patterns of SfruOBPs and SfruCSPs repertoires across all developmental stages and sexes. We found 33 OBPs and 22 CSPs in the S. frugiperda genome. The majority of the SfruOBP genes were most highly expressed in the adult male or female stages, while more SfruCSP genes were highly expressed in the larval or egg stages, indicating their function complementation. The gene expression patterns of SfruOBPs and SfruCSPs revealed strong correlations with their respective phylogenic trees, indicating a correlation between function and evolution. In addition, we analyzed the chemical-competitive binding of a widely expressed protein, SfruOBP31, to host plant odorants, sex pheromones, and insecticides. Further ligands binding assay revealed a broad functional related binding spectrum of SfruOBP31 to host plant odorants, sex pheromones, and insecticides, suggesting its potential function in food, mate seeking, and pesticide resistance. These results provide guidance for future research on the development of behavioral regulators of S. frugiperda or other environmentally friendly pest-control strategies.
Development and Validation of the German Version of the Community of Inquiry Survey

The Community of Inquiry (CoI) Framework describes success factors for collaborative online-based learning. The CoI Survey is a validated instrument to measure these factors from the perspective of course participants. Until now, no validated translation of this Survey to German was available. The aim of this work was to translate the original English Survey to German and to validate the translated Survey instrument. After a systematic translation process, we validated the German translation in two higher education settings in two countries (entire data set of n=433 Surveys). By conducting item analysis, reliability analysis, exploratory factor analysis, and confirmatory factor analysis, we were able to confirm the reliability and validity of the German CoI Survey. Only one item (CP6) shows cross-loadings on two factors, a finding that was already discussed for the original CoI Survey. To conclude, the validated German version of the CoI Survey is now available.
Aluminum Foil vs. Gold Film: Cost-Effective Substrate in Sandwich SERS Immunoassays of Biomarkers Reveals Potential for Selectivity Improvement

The first application of aluminum foil (Al F) as a low-cost/high-availability substrate for sandwich immunoassay using surface-enhanced Raman spectroscopy (SERS) is reported. Untreated and unmodified Al F and gold film are used as substrates for sandwich SERS immunoassay to detect tuberculosis biomarker MPT64 and human immunoglobulin (hIgG) in less than 24 h. The limits of detection (LODs) for tuberculosis (TB) biomarker MPT64 on Al foil, obtained with commercial antibodies, are about 1.8–1.9 ng/mL, which is comparable to the best LOD (2.1 ng/mL) reported in the literature for sandwich ELISA, made with fresh in-house antibodies. Not only is Al foil competitive with traditional SERS substrate gold for the sandwich SERS immunoassay in terms of LOD, which is in the range 18–30 pM or less than 1 pmol of human IgG, but it also has a large cost/availability advantage over gold film. Moreover, human IgG assays on Al foil and Si showed better selectivity (by about 30–70% on Al foil and at least eightfold on Si) and a nonspecific response to rat or rabbit IgG, in comparison to the selectivity in assays using gold film.
Numerical and Experimental Investigation of the Heating Process of Glass Thermal Slumping

The glass thermal forming process provides a high volume, low cost approach to producing aspherical reflectors for x-ray optics. Thin glass sheets are shaped into mirror segments by replicating the mold shape at high temperature. Heating parameters in the glass thermal slumping process are crucial to improve surface quality of the formed glass. In this research, the heating process of a thermal slumping glass sheet on a concave parabolic mold was simulated with the finite-element method (FEM) to investigate the effects of heating rate and soaking temperature. Based on the optimized heating conditions, glass samples 0.5 mm thick were formed in a furnace with a steel concave parabolic mold. The figure errors of the formed glass were measured and discussed in detail. It was found that the formed glass was not fully slumped at the edges, and should be trimmed to achieve better surface deviation. The root-mean-square (RMS) deviation and peak-valley (PV) deviation between formed glass and mold along the axial direction were 2.3 μm and 4.7 μm respectively.
From UbiComp to Universe—Moving Pervasive Computing Research Into Space Applications

Humanity's burgeoning crewed and uncrewed presence in space is creating increasing opportunity for ideas and approaches gestated for terrestrial use to be adapted and deployed in space applications. To illustrate this from the perspective of the Pervasive Community, this article overviews a selection of recent and ongoing space-oriented projects in the MIT Media Lab's Responsive Environments Group, and chronicles the roots that most of them had in our prior Pervasive Computing research program. These projects involve wearables, smart fabrics, sensor networks, cross-reality systems, pervasive/reactive displays, microrobots, responsive space habitat interiors, and self-assembling systems for in-space infrastructure. Many of them have been tested in zero-gravity and suborbital flights, on the International Space Station, or will be deployed during an upcoming lunar mission. Assessed together, this portfolio of work points forward to the broad role that some of the tenets of Pervasive Computing (e.g., novel sensing technologies, “smart materials,” and best-in-class modern HCI infrastructure) will play in our near-term space future. This work marks an important inflection point in the space industry, where academic research experiments are rapidly maturing—on the scale of months, not years—to influence the products, tools, and human experiences in low earth orbit and beyond.
Co-Design of a Controller and Its Digital Implementation: The MOBY-DIC2 Toolbox for Embedded Model Predictive Control

Several software tools are available in the literature for the design and embedded implementation of linear model predictive control (MPC), both in its implicit and explicit (either exact or approximate) forms. Most of them generate C code for easy implementation on a microcontroller, and the others can convert the C code into hardware description language code for implementation on a field programmable gate array (FPGA). However, a unified tool allowing one to generate efficient embedded MPC for an FPGA, starting from the definition of the plant and its constraints, was still missing. The MOBY-DIC2 toolbox described in this brief bridges this gap. To illustrate its functionalities, the tool is exploited to embed the controller and observer for a real buck power converter in an FPGA. This implementation achieves a latency of about $30~\mu \text{s}$ with the implicit controller and 240 ns with the approximate explicit controller.
Deep Learning-Based Semantic Segmentation Methods for Pavement Cracks

As road mileage continues to expand, the number of disasters caused by expanding pavement cracks is increasing. Two main methods, image processing and deep learning, are used to detect these cracks to improve the efficiency and quality of pavement crack segmentation. The classical segmentation network, UNet, has a poor ability to extract target edge information and small target segmentation, and is susceptible to the influence of distracting objects in the environment, thus failing to better segment the tiny cracks on the pavement. To resolve this problem, we propose a U-shaped network, ALP-UNet, which adds an attention module to each encoding layer. In the decoding phase, we incorporated the Laplacian pyramid to make the feature map contain more boundary information. We also propose adding a PAN auxiliary head to provide an additional loss for the backbone to improve the overall network segmentation effect. The experimental results show that the proposed method can effectively reduce the interference of other factors on the pavement and effectively improve the mIou and mPA values compared to the previous methods.
An evaluation of sexual dimorphism in head size and shape of Red Salamanders (Pseudotriton ruber)

For many secretive species of amphibians and reptiles, observations of reproductive behavior are limited to sparse anecdotes from the field or to animals held in captivity. However, a careful examination of morphological differences between the sexes can help shape hypotheses about behaviors with which they may be correlated. For example, sexual dimorphism in head size and shape are correlated with courtship, territoriality, and mate-defense behaviors in some salamanders. One widespread species with a poorly described reproductive natural history is the Red Salamander (Pseudotriton ruber). Here, we measure head size and employ geometric morphometric methods to evaluate head shape in P. ruber, with the goals of: 1) quantifying and visualizing sexual variation; 2) forming hypotheses about reproductive behavior. We found preliminary evidence for differences in head size and shape that are consistent with mate-guarding behavior, and we remark upon directions for future research.
Quality Assurance Program for Surface-guided Radiation Therapy: A Review of Guidelines

SUMMARY Surface-guided radiation therapy (SGRT) has gained wide popularity across radiation oncology community due to its non-radiographic characteristic and real-time motion monitoring capability. Nevertheless, it has not yet gained its full potential in routine clinical practice. Implementing SGRT system into the clinical practice requires not only the definition of steps in clinical workflow, but also establishment of the comprehensive quality assurance (QA) program including commissioning, acceptance and periodic QA test to facilitate a safe, and efficient use of SGRT system in clinical settings. This review focuses on the latest recommendation of American Association of Physicists in Medicine and European Society for Radiotherapy and Oncology guidelines about the implementation of comprehensive QA program for SGRT system.
A Representation Theorem for L-fuzzy Pseudo-metrics

Through this article, the definitions of an L -fuzzy pseudo-metric and a family of pseudo-metric that satisfies the property of lower-semicontinuity (or named nests of pseudo-metrics) are introduced. After that, we indicated that an L -fuzzy pseudo-metric can be constructed from nests of pseudo-metrics and nests of pseudo-metrics can be constructed from an L -fuzzy pseudo-metric. Therefore, we establish a each to each association between L - fuzzy pseudo-metrics and nests of pseudo-metrics.