Miracle-dz/UniFAR-FTUs huggingface dataset:
“Discourses of discontent: fashion, feminism and the commodification of fat women’s anger”

ABSTRACT This article considers the messy intersections of fat liberation and fashion. In doing so, it analyzes two case studies – Big Beautiful Woman magazine’s “I’M MAD AS HELL! ” complaint slips and the more recent “#MakeMySize” social media movement – through the critical lenses of commodity feminism and commodity activism. When viewed through these lenses, these case studies demonstrate that although some critics have deemed fashion a frivolous preoccupation, it has in fact played an important, if complicated, role in the ongoing project of resignifying fat embodiment and in pushing the fat liberation movement forward.
Optimizing serendipitous detections of kilonovae: cadence and filter selection

The rise of multimessenger astronomy has brought with it the need to exploit all available data streams and learn more about the astrophysical objects that fall within its breadth. One possible avenue is the search for serendipitous optical/near-infrared counterparts of gamma-ray bursts (GRBs) and gravitational-wave (GW) signals, known as kilonovae. With surveys such as the Zwicky Transient Facility (ZTF), which observes the sky with a cadence of ∼3 d, the existing counterpart locations are likely to be observed; however, due to the significant amount of sky to explore, it is difficult to search for these fast-evolving candidates. Thus, it is beneficial to optimize the survey cadence for realtime kilonova identification and enable further photometric and spectroscopic observations. We explore how the cadence of wide field-of-view surveys like ZTF can be improved to facilitate such identifications. We show that with improved observational choices, e.g. the adoption of three epochs per night on a ∼ nightly basis, and the prioritization of redder photometric bands, detection efficiencies improve by about a factor of two relative to the nominal cadence. We also provide realistic hypothetical constraints on the kilonova rate as a form of comparison between strategies, assuming that no kilonovae are detected throughout the long-term execution of the respective observing plan. These results demonstrate how an optimal use of ZTF increases the likelihood of kilonova discovery independent of GWs or GRBs, thereby allowing for a sensitive search with less interruption of its nominal cadence through Target of Opportunity programs.
Impact of Climate Change on International Health Security: An Intersection of Complexity, Interdependence, and Urgency

Climate change (CC) can be defined as a long-term shift in global, continental, and/or local climate patterns. Although many equate CC to the rise in global temperatures, the issue is much more complicated and involves a large number of interconnected factors. Among some of the less discussed considerations of CC are its effects on a broad range of public health issues, including the emergence of novel infectious diseases, the encroachment of infectious disease vectors into previously unaffected geographic distributions, and crop failures resulting in threats of malnutrition and mass migration. This chapter will be devoted to key issues related to CC in the context of international health security (IHS).
Recent Developments in Laser Welding of Aluminum Alloys to Steel

The development of high-performance dissimilar aluminum–steel joints is necessary to promote the feasibility of multi-material design and lightweight manufacturing. However, joining aluminum to steel is a challenging task mainly due to the formation of brittle intermetallic compounds (IMC) at the joint interface. Laser welding is considered a very promising joining process for dissimilar materials, although its application in industry is still limited by the insufficient mechanical performance of the joints. The present paper aims to give a comprehensive review of relevant recent research work on laser joining of aluminum to steel, contributing to highlighting the latest achievements that could boost acceptance of laser joining of dissimilar materials by the modern industries. To this end, the most important challenges in laser joining of aluminum to steel are presented, followed by recent approaches to overcome these challenges, the state-of-art of comprehension of IMC formation and growth, and the different strategies to minimize them.
Data Sensitivity Measurement and Classification Model of Power IOT based on Information Entropy and BP Neural Network

For the problems of privacy data protection caused by massive data sharing in the construction of power Internet of things, a data sensitivity measurement and classification model based on information entropy and BP neural network is proposed. Firstly, a recognition matching algorithm is proposed to identify the sensitive level of attributes in the dataset, and the information entropy is used to determine the weight of attributes sensitivity level, so as to calculate the sensitivity measurement value of records in the dataset; finally, the BP neural network is used to output the data classification results. The experimental results show that the model can achieve accurate measurement and classification of data, with low incorrect judgment rate and error rate.
A Dual-Color Tyr-FISH Method for Visualizing Genes/Markers on Plant Chromosomes to Create Integrated Genetic and Cytogenetic Maps

In situ imaging of molecular markers on a physical chromosome is an indispensable tool for refining genetic maps and validation genome assembly at the chromosomal level. Despite the tremendous progress in genome sequencing, the plant genome assembly at the chromosome level remains a challenge. Recently developed optical and Hi-C mapping are aimed at assistance in genome assembly. For high confidence in the genome assembly at chromosome level, more independent approaches are required. The present study is aimed at refining an ultrasensitive Tyr-FISH technique and developing a reliable and simple method of in situ mapping of a short unique DNA sequences on plant chromosomes. We have carefully analyzed the critical steps of the Tyr-FISH to find out the reasons behind the flaws of this technique. The accurate visualization of markers/genes appeared to be significantly dependent on the means of chromosome slide preparation, probe design and labeling, and high stringency washing. Appropriate adjustment of these steps allowed us to detect a short DNA sequence of 1.6 Kb with a frequency of 51.6%. Based on our results, we developed a more reliable and simple protocol for dual-color Tyr-FISH visualization of unique short DNA sequences on plant chromosomes. This new protocol can allow for more accurate determination of the physical distance between markers and can be applied for faster integration of genetic and cytogenetic maps.
Single‐Crystal Elasticity of MgSiO3 Bridgmanite to Mid‐Lower Mantle Pressure

The combination of seismic observations and mineral physics data represents a unique tool to understand the structure and evolution of the deep Earth's interior. However, to date, elasticity data on both compressional (vP) and shear (vS) wave velocities of MgSiO3 bridgmanite are limited to shallow mantle conditions, hampering the resolution of mineral physics models. Here, we report the first single‐crystal measurements of vP and vS of MgSiO3 bridgmanite up to ∼79 GPa using high‐pressure Brillouin scattering and single‐crystal X‐ray diffraction in a diamond anvil cell. At shallow lower mantle pressures, the elastic anisotropy of MgSiO3 bridgmanite was found to be similar, albeit smaller than that of Fe,Al‐bearing bridgmanite of Kurnosov et al. (2017) but differed significantly from that proposed in the recent study of Fu et al. (2019). Using the elastic stiffness coefficients of bridgmanite obtained in this study at different pressures, we calculate the pressure dependence of the adiabatic bulk modulus, KS0 = 257.1(6) GPa, K'S0 = 3.71(4), and of the shear modulus, G0 = 175.6(2) GPa, G'0 = 1.86(1). These elastic parameters are included in a self‐consistent thermodynamic model to calculate seismic wave velocities along a lower mantle adiabat for a primitive upper mantle bulk composition in the FeO‐CaO‐MgO‐SiO2 system, which is currently the most complex system for which sufficient data exist. This preliminary model provides a good match to the vS and vP of 1D seismic models, implying that the composition of the lower mantle may be closer to pyrolite, rather than being more bridgmanite rich.
Exploring teachers’ inclusive education strategies in rural Indonesian primary schools

ABSTRACT Background Indonesia’s implementation of inclusive education continues to help support students’ development of academic abilities and social skills. However, it also poses challenges for several parties involved in the administration of inclusive education in rural areas: in particular, teachers from general education backgrounds. Purpose This study aimed to investigate teachers’ teaching strategies in inclusive education in a sample of public primary schools in rural Indonesia, in order to better understand the strategies used and the teachers’ perspectives on inclusive education. Methods Participants were 40 classroom teachers from 10 public primary schools in rural Indonesia. The participants taught students from grades 2 to 6 (student ages 8 to 12) and had at least one student with special educational needs and disabilities (SEND) in their class. Observations were conducted to determine the extent to which teachers used differentiation, collaborative learning and inclusive classroom management strategies. After the observations, short interviews were conducted with the teachers in order to explore their perceptions of inclusive education. Data were analysed qualitatively. Findings Analysis of observation data showed that only a small number of teachers used teaching strategies that were considered effective inclusive strategies, such as differentiation and collaborative learning. The interview findings indicated that most of the teachers did not feel confident in using inclusive strategies, due to factors including limited knowledge of strategies, and class size. Conclusions This small-scale study suggests that the implementation of inclusive education remains at an early stage and is challenging in some parts of Indonesia. It highlights that sustaining and advancing teacher education is crucial: through professional development, teachers can be enabled to increase their competence and knowledge of inclusive education strategies, ultimately benefiting all students in the classroom.
Factors related to myopia progression in orthokeratology practice: A 2-year follow-up study

The incidence of myopia in adolescents is gradually increasing, and orthokeratology has achieved effective effects in controlling the progress of myopia, but the effects are mixed. The retrospective study included 30 monocular orthokeratology (ortho-k) lens-treated adolescents to explore the true effectiveness of ortho-k lenses and 36 binocular ortho-k lenses-treated adolescents to study the influencing factors of ortho-k lenses. After 12 months, among 30 adolescents treated with monocular ortho-k lenses, the average axial elongation in the ortho-k group was significantly less than that in the control group (P = 0.002). After 24 months, among 36 adolescents treated binocular ortho-k lenses, the axial elongation in the different initial age groups and different initial myopia groups were significantly different (all P < 0.05). Axial elongation correlated negatively with initial myopia during follow-up periods. In adolescents with myopia, axial elongation can be controlled effectively using an ortho-k lens. Younger children with initial higher myopia will benefit more than older children with initial lower myopia.
Europium Hydroxide Nanorods Mitigate Hind Limb Ischemia in Wistar Rats

Lack of adequate blood supply to the limb extremities results in critical limb ischemia (CLI) and subsequent tissue death. Conventional approaches for CLI treatment include bypass surgery and angioplasty, which achieved limited clinical success due to their complex invasiveness. Of late, growth factor and cell‐based therapies emerged as alternative treatment strategies for CLI. However, these strategies could not achieve much success due to the associated drawbacks. Earlier, this study group develops europium hydroxide nanorods (EHN) which demonstrates potential proangiogenic (stimulating new blood vessel growth) properties. Recently it is shown that EHN could ameliorate the myocardial ischemia in Wistar rats by promoting therapeutic angiogenesis and mitigate cadmium‐induced vascular toxicity. Considering the importance of revascularization in managing CLI, it is hypothesized that the proangiogenic EHN might be useful for its effective treatment. Hence, in the present study, the therapeutic efficacy of EHN is comprehensively evaluated in the hind limb ischemia model of Wistar rats. The EHN‐administered ischemic rats exhibit improved motility, enhanced blood flow to the ischemic limb along with increased expression of angiogenic factors. Additionally, toxicity and pharmacokinetic studies of EHN in mice demonstrate their nontoxic nature, suggesting the feasibility of their practical clinical application for CLI treatment.
Cyclone Signatures in the South-West Indian Ocean from Two Decades of Microseismic Noise

Tropical Cyclones (TC) represent the most destructive natural disaster affecting the islands in the South-West Indian Ocean (SWIO) each year. Monitoring ocean activity is therefore of primary importance to secure lands, infrastructures and peoples, but the little number of oceanographic instruments makes it challenging, particularly in real time. Long-term seismological records provide a way to decipher and quantify the past cyclonic activity by analyzing microseisms, seismic waves generated by the ocean activity and propagating through the solid Earth. In the present study, we analyze this microseismic noise generated by cyclones that develop in the SWIO basin between 1999 and 2020, using broadband seismic stations in La Réunion. The power spectral density (PSD), together with the root mean square (RMS) analyses of continuous seismic data recorded by the permanent Geoscope RER seismic station, indicate the intensification of the microseismic noise amplitude in proportion to the cyclone intensity. Thus, we establish a relationship between the cyclone intensity and the PSD of the Secondary Microseisms (SM) in frequency band ∼0.14 to 0.25 Hz (4 to 7 s period). The Pearson coefficient between the observed and estimated TC intensity are >0.8 in the presence of a cyclone with mean wind speeds >75 km/h and with a seismic station distance-to-storm center D < 3000 km. A polarization analysis in the time and frequency domains allows the retrieval of the backazimuth of the SM sources during isolated cyclone events and well-polarized signal, i.e., CpH > 0.6. We also analyzed the RMS of the Primary Microseisms (PM frequency between ∼0.05 and 0.1 Hz, i.e., for 10 to 20 s period) for cyclones passing nearby La Réunion (D < 500 km), using the available temporary and permanent broadband seismic stations. We also found high correlation coefficients (>0.8) between the PM amplitude and the local wave height issued from the global hindcast model demonstrating that the PM amplitude can be used as a robust proxy to perform a real-time wave-height monitoring in the neighboring ocean. Transfer functions are calculated for several cyclones to infer wave height from the seismic noise amplitude recorded on land. From the analysis of two decades of data, our results suggest that it is possible to quantify the past ocean activity for as long as continuous seismic archives are available, emphasizing microseismic noise as a key observable for quantifying and understanding the climate change.
APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION

Purpose of the study: The purpose of this work is to present an offline Optical Character Recognition system to recognise handwritten English numerals to help automation of document reading. It helps to avoid tedious and time-consuming manual typing to key in important information in a computer system to preserve it for a longer time. Methodology: This work applies Curvature Features of English numeral images by encoding them in terms of distance and slope. The finer local details of images have been extracted by using Zonal features. The feature vectors obtained from the combination of these features have been fed to the KNN classifier. The whole work has been executed using the MatLab Image Processing toolbox. Main Findings: The system produces an average recognition rate of 96.67% with K=1 whereas, with K=3, the rate increased to 97% with corresponding errors of 3.33% and 3% respectively. Out of all the ten numerals, some numerals like ‘3’ and ‘8’ have shown respectively lower recognition rates. It is because of the similarity between their structures. Applications of this study: The proposed work is related to the recognition of English numerals. The model can be used widely for recognition of any pattern like signature verification, face recognition, character or word recognition in another language under Natural Language Processing, etc. Novelty/Originality of this study: The novelty of the work lies in the process of feature extraction. Curves present in the structure of a numeral sample have been encoded based on distance and slope thereby presenting Distance features and Slope features. Vertical Delta Distance Coding (VDDC) and Horizontal Delta Distance Coding (HDDC) encode a curve from vertical and horizontal directions to reveal concavity and convexity from different angles.
Chest x-ray classification using transfer learning on multi-GPU

Since the first quarter of this year, the spread of SARS-CoV-19 virus has been a worldwide health priority. Medical testing consists of Lab studies, PCR tests, CT, PET, which are time-consuming, some countries lack these resources. One medical tool for diagnosis is X-Ray imaging, which is one of the fastest and low-cost resources for physicians to detect and to distinguish among these different diseases. We propose an X-Ray CAD system based on DCNN, using well-known architectures such as DenseNet-201, ResNet-50 and EfficientNet. These architectures are pre-trained on data from Imagenet classification challenge, moreover, using Transfer Learning methods to Fine-Tune the classification stage. The system is capable to visualize the learned recognition patterns applying the GRAD-CAM algorithm aiming to help physicians in seeking hidden features from perceptual vision. The proposed CAD can differentiate between COVID-19, Pneumonia, Nodules and Normal lung X-Ray images.
A Kriging-Assisted Two-Archive Evolutionary Algorithm for Expensive Many-Objective Optimization

Only a small number of function evaluations can be afforded in many real-world multiobjective optimization problems (MOPs) where the function evaluations are economically/computationally expensive. Such problems pose great challenges to most existing multiobjective evolutionary algorithms (EAs), which require a large number of function evaluations for optimization. Surrogate-assisted EAs (SAEAs) have been employed to solve expensive MOPs. Specifically, a certain number of expensive function evaluations are used to build computationally cheap surrogate models for assisting the optimization process without conducting expensive function evaluations. The infill sampling criteria in most existing SAEAs take all requirements on convergence, diversity, and model uncertainty into account, which is, however, not the most efficient in exploiting the limited computational budget. Thus, this article proposes a Kriging-assisted two-archive EA for expensive many-objective optimization. The proposed algorithm uses one influential point-insensitive model to approximate each objective function. Moreover, an adaptive infill criterion that identifies the most important requirement on convergence, diversity, or uncertainty is proposed to determine an appropriate sampling strategy for reevaluations using the expensive objective functions. The experimental results on a set of expensive multi/many-objective test problems have demonstrated its superiority over five state-of-the-art SAEAs.
Observer-based adaptive neural sliding mode trajectory tracking control for remotely operated vehicles with thruster constraints

For a class of remotely operated vehicle (ROV) systems with thruster constraints, immeasurable states, and unknown nonlinearities, the trajectory tracking control problem was discussed in this paper. The unknown nonlinear functions were approximated by radial basis function (RBF) neural networks. An adaptive state observer based on neural networks was designed and the immeasurable states were estimated. Considering the problem of thruster saturation constraints, an auxiliary system for saturation compensation was designed and a saturation factor was constructed by the auxiliary system state. By applying the backstepping design method, an adaptive neural sliding mode trajectory tracking controller was developed, in which the saturation factor is contained in adaptive laws. It was proved that the uniformly ultimately bounded (UUB) of trajectory tracking errors can be obtained. Finally, the effectiveness of the proposed trajectory tracking control approach was checked by simulations.
Textile Triboelectric Nanogenerators with Diverse 3D-Spacer Fabrics for Improved Output Voltage

Electrically superior triboelectric nanogenerators (TENG) using 3D fabric and PDMS show great application potential for biokinetic energy harvesting and multifunctional self-power devices. In this study, TENG with fabric-PDMS-fabric structure was produced using various 3D fabrics and PDMS. The peak-to-peak output voltage of various 3D fabrics was compared. The output voltage changes due to structure and vertical fibers. Also, the coefficient of surface friction between the PDMS and the fabric improves the output voltage. TENG using different 3D-spacer polymeric fabrics showed different maximum peak-to-peak output voltage performance. It is attributed to the stiffness, lateral elasticity and 3D morphology of the fabrics. It is considered that those factors including stiffness, lateral elasticity and 3D morphology influence the densities in vertical and lateral fiber to fiber interaction.
Research on K Nearest Neighbor Skyline Query in Time Dependent Road Network

In recent years, with the increase for query preference, skyline query in road networks has gradually become a research hot topic. In order to help users get the desired query result, we propose a new algorithm. Firstly, we select landmark heuristically and establish corresponding shortest path tree (SPT). Then we construct a skyline matrix index called time dependent matrix index (TDMI), which help us associate the updated result set with dynamic k nearest neighbor (KNN) and simplify the non-space dominance relationship better. Finally, with SPT, TDMI, KNN and dynamic pruning conditions, an algorithm, the TDMI-KS algorithm, is proposed to rapidly answer the query result. Extensive experiments using Oldenburg road network datasets demonstrate the effectiveness and the efficiency of our proposed TDMI-KS algorithms.