Miracle-dz/UniFAR-FTUs huggingface dataset:
Face Mask Image Classification Using Fine-Tuning and The Effect of FGSM And PGD Attacks.

This research seeks to use a fine-tuning method for automatic diagnostics and classification of face masks without face mask images. We trained Deep Convolutional Neural Network models on the face mask dataset obtained from the Mendeley data repository to determine whether individuals will adhere to policies that face mask reduces the spread of SARS-COV-2. The proposed architectures used in this study include the VGG19, MobileNetV3, and InceptionV3 models. These models are known for their role in classifying images. They were trained using the fine-tuning approach and their respective outputs were compared. After training the face mask dataset, it can be said that the fine-tuned InceptionV3 model performed extremely well against the other models obtaining an accuracy of 99.21%, and was able to predict 99.63% of the test dataset. However, the robustness of the fine-tuned model was tested against fast gradient sign method (FGSM) and projected gradient descent (PGD) attacks which generate adversarial images using the gradients of the model. Additionally, the classification report shows after the FGSM attack that the model's accuracy was reduced by 43%. Also, after the PDG attack, the accuracy of the model was reduced by 19%. We assessed the models using many performance metrics such as precision, recall, F1 score, and accuracy before subjecting them to two common adversarial attack techniques: FGSM and PDG. Finally, we demonstrated how the proposed robust method improved the model’s defense against adversarial attacks. The findings emphasize the critical need to increase awareness about adversarial assaults on SARS-COV-2 monitoring systems and to advocate for proactive steps to protect healthcare systems from comparable threats prior to practical deployment.
The Integration of Supply Chain and Digitalization: The Influence on SME’s Business Performance in Chengdu, Sichuan Province, China

The scale of the enterprise organization industry has steadily grown in recent years as a result of the rapid development of the global economy; however, the situation additionally contends with obstacles such as natural catastrophes, policy changes, political situations, and other unforeseen factors that may also trigger threats in the supply chain. The objective of this research is to examine the impact of supply chain digitalization and internal integration, also referred to as Supply Chain 4.0, on the performance of small medium businesses. This research illuminates the advantages and prospects associated with the implementation of contemporary supply chain strategies. It accomplishes this by examining the impact of incorporating digital technologies and integrating supply chain processes on different dimensions of small business performance, including efficiency, cost-effectiveness, and customer satisfaction. The development of conceptual framework in this study will be able to contribute the digital transformation of supply chain theoretically through our framework and expansion of knowledge by examining the influence between digitalization, supply chain integration, and small business performance such as operational efficiency, cost reduction, customer satisfaction, and overall competitiveness.
Improved image stitching method for neutron imaging of large objects with small beam size

Imaging stitching is a solution for radiography and computed tomography (CT) applications where the object is larger than the beam size. Imaging stitching algorithms require a robust noise filter that maintains the landmark features used in stitching. In lens-coupled neutron radiography and CT, a camera is placed away from the neutron beam. Even with shielding, the camera experiences a high radiation dose of mixed gammas and neutrons. The CCD silicon sensor, sensitive to both gammas and neutrons, introduces speckled noise, pixel oversaturation, and blooming effects. Conventional median filters prove inadequate with this type of noise and can result in blurred images. Manual filtering of CT sets is time-consuming and error-prone. An improved image filtering method designed for neutron CT data sets is therefore needed to improve imaging stitching algorithms. We have developed a method that utilizes statistical information in radiographs and variable-sized radii filtration to adequately remove noise while preserving resolution. Once noise has been identified, the algorithm tracks cluster size to inform local filter needs. Filtered radiographs are stitched using a semi-automatic algorithm. This approach works best for data containing features for joint corner detection. It does require specific user inputs, such as object size, features of interest, and alignment, to pinpoint the optimal joining location. Overall, our method represents a significant advancement in neutron CT image processing, offering improved results for imaging stitching and traditional CT applications. We describe the application of this combined filter and stitching algorithm on thermal and fast neutron CT data at OSURR.
Two new species of the Longitarsusviolentus group from China (Coleoptera, Chrysomelidae, Galerucinae, Alticini)

﻿Abstract Two new species of Longitarsus Latreille, 1829 from China are described: L.pekingensis Liang, Konstantinov & Ge, sp. nov. (Beijing) and L.xinjiangensis Liang, Konstantinov & Ge, sp. nov. (Xinjiang). Images of dorsal and lateral habitus, pronotum, head, and male and female genitalia are provided. The records of Longitarsusviolentus Weise, 1893 and Longitarsusweisei Guillebeau, 1895 in China are discussed. Holotypes of L.marguzoricus Konstantinov in Konstantinov & Lopatin, 2000 and L.violentoides Konstantinov in Konstantinov & Lopatin, 2000 are illustrated with images of pronotum and median lobe of aedeagus. A key to species of L.violentus species group is provided.
Seasonal Climate Predictability in a Coupled OAGCM Using a Different Approach for Ensemble Forecasts

Abstract Predictabilities of tropical climate signals are investigated using a relatively high resolution Scale Interaction Experiment–Frontier Research Center for Global Change (FRCGC) coupled GCM (SINTEX-F). Five ensemble forecast members are generated by perturbing the model’s coupling physics, which accounts for the uncertainties of both initial conditions and model physics. Because of the model’s good performance in simulating the climatology and ENSO in the tropical Pacific, a simple coupled SST-nudging scheme generates realistic thermocline and surface wind variations in the equatorial Pacific. Several westerly and easterly wind bursts in the western Pacific are also captured. Hindcast results for the period 1982–2001 show a high predictability of ENSO. All past El Nino and La Nina events, including the strongest 1997/98 warm episode, are successfully predicted with the anomaly correlation coefficient (ACC) skill scores above 0.7 at the 12-month lead time. The predicted signals of some particular e.
Compressive Reconstruction Based on Sparse Autoencoder Network Prior for Single-Pixel Imaging

The combination of single-pixel imaging and single photon-counting technology enables ultra-high-sensitivity photon-counting imaging. In order to shorten the reconstruction time of single-photon counting, the algorithm of compressed sensing is used to reconstruct the underdetermined image. Compressed sensing theory based on prior constraints provides a solution that can achieve stable and high-quality reconstruction, while the prior information generated by the network may overfit the feature extraction and increase the burden of the system. In this paper, we propose a novel sparse autoencoder network prior for the reconstruction of the single-pixel imaging, and we also propose the idea of multi-channel prior, using the fully connected layer to construct the sparse autoencoder network. Then, take the network training results as prior information and use the numerical gradient descent method to solve underdetermined linear equations. The experimental results indicate that this sparse autoencoder network prior for the single-photon counting compressed images reconstruction has the ability to outperform the traditional one-norm prior, effectively improving the reconstruction quality.
Trace element anomaly in fault rock induced by coseismic hydrothermal reactions reproduced in laboratory friction experiments

Friction experiments at high velocities (0.1 to 0.4 m/s) at room temperature under high pore fluid pressures (2 or 5 MPa) and trace element analyses of the run products were carried out on simulated fault gouge derived from the Chelungpu fault, Taiwan. The friction coefficient decreased to ~0.1 during sliding, and the slip surface temperature reached 300°C by 6 m of slip displacement. The slip caused a small, but distinct decrease of Li in the deformed gouge when the slip surface temperature exceeded 300°C. The observed Li depletion agreed with calculated results, indicating that it resulted from a hydrothermal reaction at a high temperature produced by short‐duration (up to 40 s) frictional heating. The geochemical data suggest a small fluid/rock ratio and low reaction temperature in the simulated fault, which is explained by higher normal stress or repeated earthquakes which elevated fluid‐rock ratios in the natural fault.
LC-RSS: A Lane-Change Responsibility-Sensitive Safety Framework Based on Data-Driven Lane-Change Prediction

Although the existing autonomous driving systems (ADS) can implement lane-change behaviors without human operation, they still rely on the lane-change decisions made by human drivers in a complex traffic environment. The reason is that human drivers can reasonably estimate the driving intentions of surrounding vehicles in advance, and decide their own driving trajectory according to their driving experiences. Therefore, the future ADS needs to learn the ability of human beings to predict the driving intentions of surrounding vehicles. Alternatively, it needs to make safer lane-change decisions than human beings without sacrificing the possibility as much as possible. This article develops a Lane-Change Responsibility-Sensitive Safety (LC-RSS) model to improve the safety of lane-change decisions and solve the above research gap. A novel lane-change trajectory planning method is proposed, which considers multiple interactions between the ego vehicle and surrounding vehicles with realistic position constraints and fuel consumption optimization. Specifically, it combines the lane-change prediction of surrounding vehicles to provide a more reasonable recommended lane-change trajectory for the ego vehicle, therefore, could enhance the safety constraints from the Responsibility-Sensitive Safety (RSS) model introduced in the latest published 2846-2022-IEEE Standard. The simulation results have shown that by considering lane-changing prediction in the trajectory planning module, LC-RSS not only enhances the safety of a single vehicle's recommended trajectory but also increases the traffic flow speed of the urban transportation system.
GEOLOGIA DO QUATERNÁRIO COSTEIRO DO ESTADO DE PERNAMBUCO

Este trabalho apresenta um mapa geologico dos depositos quaternarios que ocorrem ao longo da costa do Estado de Pernambuco. Esses depositos compreendem: 1. dois conjuntos de terracos marinhos de idades pleistocenica (120.000 anos A.P.) e holocenica (5.000 anos A.P.); 2. depositos paleolagunares (5.140-6.030 anos A.P.); 3. bancos de arenito e recifes de corais e de algas coralinas com idades entre 1.830 e 5.170 anos A.P.; e 4. depositos aluviais, de pântanos e de mangues, todos de idade holocenica. A integracao desse mapeamento com a ja bem conhecida historia das variacoes do nivel relativo do mar na costa leste brasileira sugere que o modelo de evolucao, ja previamente publicado, para as regioes costeiras dos Estados de Alagoas, Sergipe, Bahia, Rio de Janeiro e Sao Paulo, e tambem valido para a costa do Estado de Pernambuco. Essa historia evolutiva mostra que a sedimentacao ao longo da regiao costeira foi controlada fundamentalmente pelas variacoes ocorridas no nivel do mar durante o Quaternario. Dezoito novas datacoes pelo metodo do carbono 14, realizadas em depositos paleolagunares, bancos de arenito, corais, algas coralinas e incrustacoes de vermetfdeos, sao tambem reportadas no presente trabalho.
On Parameterizing Vertical Mixing of Momentum in Non-eddy Resolving Ocean Models

Abstract We investigate the consequence, at small Ekman number, of adding vertical mixing of momentum terms to the incompressible thermocline equations. We find that choosing the vertical eddy viscosity, ν = Af2/N2, where f is the Coriolis parameter and N is the local value of the buoyancy frequency, leads to isopycnal mixing of fQ, where Q is the reciprocal of potential vorticity, provided A is independent of the vertical coordinate. If, additionally, A is also independent of the north–south coordinate, then on a beta-plane, this implies homogenization of potential vorticity, q, within closed q-contours on isopycnal surfaces. This conclusion extends to spherical geometry if ν is also inversely proportional to β, the gradient of f with respect to latitude, i.e. ν = Af2/(N2β). The connection with the recent work of Gent and McWilliams and the consequences for coarse resolution numerical model studies are discussed.
Remote monitoring of buildings using a ground-based SAR: Application to cultural heritage survey

In this Letter, the authors describe an innovative application of radar interferometric techniques aimed to monitor structural deformations of buildings. The proposed application is based on the use of ground-based instrumentation able to operate as Interferometric Synthetic Aperture Radar (InSAR). Preliminary experimental results on a model of an historical building encourage the development of this technique for use in architectural heritage survey.
The Role of Stochastic Forcing in Ensemble Forecasts of the 1997/98 El Niño

The impact of stochastic intraseasonal variability on the onset of the 1997/98 El Nino was examined using a large ensemble of forecasts starting on 1 December 1996, produced using the Australian Bureau of Mete- orology Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast coupled model. This coupled model has a reasonable simulation of El Nino and the Madden-Julian oscillation, so it provides an ideal framework for investigating the interaction between the MJO and El Nino. The experiment was designed so that the ensemble spread was simply a result of internal stochastic variability that is generated during the forecast. For the initial conditions used here, all forecasts led to warm El Nino-type conditions with the amplitude of the warming varying from 0.58 to 2.78C in the Nino-3.4 region. All forecasts developed an MJO event during the first 4 months, indicating that perhaps the background state favored MJO development. However, the details of the MJOs that developed during December 1996- March 1997 had a significant impact on the subsequent strength of the El Nino event. In particular, the forecasts with the initial MJOs that extended farther into the central Pacific, on average, led to a stronger El Nino, with the westerly winds in the western Pacific associated with the MJO leading the development of SST and thermocline anomalies in the central and eastern Pacific. These results imply a limit to the accuracy with which the strength of El Nino can be predicted because the details of individual MJO events matter. To represent realistic uncertainty, coupled models should be able to represent the MJO, including its propa- gation into the central Pacific so that forecasts produce sufficient ensemble spread.
“Does Physical Exercise Promote Health Benefits for Diabetic Patients during the COVID-19 Pandemic?”: A Systematic Review

Patients affected by COVID-19 are prone to facing disorders in multiple systems and organs, which can lead to deleterious diseases; in addition, people with pre-existing diseases may be more prone to the worst outcomes, and the most vulnerable are patients with type 1 and type 2 diabetes mellitus. The aim of this systematic review was to evaluate the effects of physical activity and/or physical exercise prescribed to individuals with diabetes on the maintenance of plasma glucose and glycated hemoglobin during the COVID-19 pandemic. Studies were found by searching PubMed, SCOPUS, Embase, Web of Science, SciELO, LILACS, SportDiscus, Bireme/BVS and Google Scholar databases. The inclusion criteria were articles that addressed only patients with type 1 or type 2 diabetes (T1D and T2D) who had evaluated the level of physical activity or physical exercise and described the effects on plasma glucose and/or glycated hemoglobin in cross-sectional, retrospective, and observational studies, meeting the main criteria established by GRADE. The PICO and GRADE strategies were used to select and assess the methodological quality of studies. Two reviewers searched and selected the articles in databases independently and blindly, during which oppositions and disagreements about the inclusion of articles were discussed and resolved by a third reviewer. Evidence corroborates that levels of physical activity were reduced due to the lockdown, leading to increased body weight and worse glycemic control. On the other hand, individuals with diabetes mellitus (DM) (T1D and T2D) who maintained and/or increased levels of physical activity or physical exercise showed reduced plasma glucose and glycated hemoglobin (HbA1c) levels. Adequate levels of physical exercise and physical activity are beneficial for glucose and HbA1c control in diabetic patients (type 1 or type 2). In addition, maintaining adequate levels of physical activity can contribute to reducing health problems when these patients are infected with COVID-19.
Distributed Thermal Modeling for Power Devices and Modules With Equivalent Heat Flow Path Extraction

Dynamic temperature information at critical locations has been a critical indicator to safely use power devices and modules; however, most of the existing 3-D thermal modeling methods are time-consuming and complicated, which seriously limits their application in practical temperature distribution estimation. Assisted by finite-element method (FEM), the proposed distributed thermal model takes the cross-coupling effects into account among multiple heat sources. Based on the extracted equivalent heat flow paths from FEM steady-state thermal simulations, a novel method of extracting thermal resistance and thermal capacitance ( $RC$ ) is presented for yielding temperatures at critical positions. More importantly, FEM and experimental results successfully prove the effectiveness of our proposed distributed model in accurately simulating the temperature distribution information for different monitoring points on a single chip, as well as different chips in a power module. Compared with prior-art 3-D thermal modeling methods, the time cost to establish our model is considerably lowered as the transient temperature responses and thermal impedance matrix are no longer required. Meanwhile, reduction in the number of $RC$ parameters simplifies the identification process, which further improves the practicability and generalization ability of the proposed distributed thermal model.
Bone Conduction Cervical Vestibular Evoked Myogenic Potentials as an Alternative in Children with Middle Ear Effusion

Objective: To compare the amplitude ratio and P-wave latency of cervical vestibular evoked myogenic potentials (c-VEMPs) for bone conduction (BC) and air conduction (AC) stimulation in children with otitis media with effusion (OME). Material and methods: This is an observational study of a cohort of 27 children and 46 ears with OME. The c-VEMP amplitude ratio and P-wave latency were compared between BC and AC in children with OME and healthy age-matched children. Results: The c-VEMP response rate in children with OME was 100% when using BC stimulation and 11% when using AC stimulation. The amplitude ratio for BC was significantly higher in the OME group than the age-matched healthy control group (p = 0.004). When focusing on ears with an AC c-VEMP response (n = 5), there was a significant difference in the amplitude ratio between the AC and BC stimulation modes, but there was no significant difference in the AC results between the OME group and the age-matched control group. Conclusions: BC stimulation allows for reliable vestibular otolith testing in children with middle ear effusion. Given the high prevalence of OME in children, clinicians should be aware that recording c-VEMPs with AC stimulation may lead to misinterpretation of otolith dysfunction in pediatric settings.
Comparison of Snow Deposition, the Snow Cover Energy Balance, and Snowmelt at Two Sites in a Semiarid Mountain Basin

Abstract Significant differences in snow deposition, development of the seasonal snow cover, and the timing of melt can occur over small spatial distances because of differences in topographically controlled wind exposure and canopy cover. To capture important intrabasin hydrological processes related to heterogeneous snow cover and energy inputs, models must explicitly account for these differences. The “SNOBAL” point snow cover energy and mass balance model is used to evaluate differences in snow cover energy and mass balance at two sites in a small headwater drainage of the Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains of southwestern Idaho. Though these sites are separated by only 350 m, they are located in distinctly different snow cover regimes. The “ridge” site (elevation 2097 m) is located on a broad shelf on the southern ridge of RCEW, and the “grove” site (elevation 2061 m) is sheltered by topography and forest canopy in a grove of aspen and fir trees just in the lee of th.
Efficient Human Gait Activity Recognition Based on Sensor Fusion and Intelligent Stacking Framework

Wearable sensor-based human gait activity recognition (HGAR) has become increasingly popular for various healthcare applications, such as patient monitoring and postural instability analysis. Despite the promise shown by previous studies using deep and machine learning (ML) methods, many challenges remain to be addressed, such as limited stability and generalization capability, as well as an insufficient study of sensor localization. To address these challenges, this article proposes a novel approach that leverages a new smart belt as a posture data collector and employs an intelligent stacking framework to improve the HGAR performance. Extensive experiments were conducted using a fusion of sensors to validate the effectiveness of the proposed intelligent framework and provide a more comprehensive solution for the HGAR problem. In addition, this study explores the best on-body waist position using the smart belt and investigates the impact of increasing window sequence length on the HGAR performance. Finally, we conducted a performance analysis of the proposed framework using our newly developed smart belt dataset and two open benchmark datasets (UCIHAR and WISDM). The proposed framework attained an overall accuracy of 97.91% on the smart belt dataset while achieving 99.96% and 97.52% accuracies on the other two publicly accessible datasets. The proposed framework outperformed the current state-of-the-art (SOTA) in terms of stability and generalization performance, indicating its considerable potential for real-world applications. In addition, the smart belt dataset used in the study is a valuable resource for future research in the field.