Miracle-dz/UniFAR-FTUs huggingface dataset:
FakeCLIP: Multimodal Fake Caption Detection with Mixed Languages for Explainable Visualization

Existing fake news research relies on news propagation or news metadata. Waiting for propagation structure to be enough is a waste of time. Hoping for reliable metadata information is also a waste because all data can be forged. The most natural way for human when verifying a news is through the content itself. In social media, most of the circulating news are in minimal content which consist of image and its text caption. We propose FakeCLIP to examine whether a caption truly describes the corresponding image or not. As far as we know, we are the first one to tackle fake news using fake caption approach. We found mixed languages problem where one single text can consist of many different languages mixed together. We provide explainable visualization for intuitive reasoning of which part contains fake information. Moreover, we also consider alignment of what happens in the image that being discussed in the text caption while showing the fake signal over them. Our proposed method performs better than the current state-of-the-art on Twitter datasets by 11.1%.
RETRACTED: A new approach for transient stability improvement of a grid-connected doubly fed induction generator–based wind generator

Although the doubly fed induction generator–based wind generator has captured approximately 50% of the recent wind energy markets, its transient stability is one of the major concerns in the event of grid faults. In this article, a simple controller-based nonsuperconductor fault current limiter is proposed to augment the transient stability of the doubly fed induction generator–based wind generator system. In simulations, temporary symmetrical and unsymmetrical faults were applied in the tested model system to analyze the transient stability improvement performance of the proposed non-superconductor fault current limiter. Besides, a bridge-type fault current limiter is considered and circuit analysis is also done among them to compare their performance with the proposed non-superconductor fault current limiter. Extensive simulations were executed in MATLAB/Simulink software both for symmetrical and unsymmetrical faults. Simulation results and circuit analysis clearly indicate that the non-superconductor fault current limiter augments the transient stability of the doubly fed induction generator–based wind generator and outperforms the bridge-type fault current limiter.
Identifying and Responding to the Sexual Reproductive Health Needs of Women with Heart Disease: A Qualitative Study

Background: Recognizing the needs of women with heart disease in the field of reproductive health and meeting them can reduce the related complications. The aim of this study was to identify the sexual and reproductive health needs of these women. Materials and Methods: The present study was conducted with qualitative approach (Content analysis). Ten married women of reproductive age with heart disease and 20 providers and managers in, Isfahan, Iran, were selected by using purposive and snowball methods (in 2020). The research setting included heart disease clinics, offices of health team, comprehensive health centers, and the Isfahan Maternal Health Department. Data were collected by semi-structured individual face-to-face interviews in the clinic or other appropriate locations. Some interviews were conducted virtually. Analysis was performed by Qualitative contentment analysis / Conventional content analysis. Results: Sexual and reproductive health needs of women with heart disease were emerged in five main categories, “planned childbearing,” “ssociocultural support,” “Early reproductive health care of girls,” “health team attention to sexual health,” and “health system revision. ” Conclusions: Providing comprehensive reproductive health services before and during pregnancy, family planning, and sexual health for women with heart disease is necessary. It seems that using a multidisciplinary team approach could improve their reproductive health.
A study of heavy rainstorms Over North Bengal

A detailed study of all the heavy rainstorms over North Bengal has been made with a view to finding out whether the rainstorm of October 1968, believed to be unprecedented, has actually contributed the suggesting this study highest depths of precipitation over this area. In all, 85 heavy rainstorms have been selected during the period 1901-1968 and analysed by isohyetal method. Depth-duration curves have been drawn for rainstorms of2-day and of1onger durations and the enveloping curve has been obtained. Depth-area-duration curves for very heavy rainstorms, considered important for any design work in river valley projects, have also been drawn, It has been revealed that the rainstorm of 11-13 June 1950 has contributed the highest depths of precipitation for 1-day to 3-day durations over the area under study during the period. Synoptic situations associated with June 1950 and October 1968 rainstorms along with those of other comparable rainstorms have been described. The coefficient of variability of Rainfall for each of the months May to October and for annual rainfall have been computed in respect of 17 stations of North Bengal. Probable maximum depths of precipitation in one day have been obtained by duration-curve method. A regression equation has also been formul8;ted for obtaining average depths of precipitation over North Bengal from rainfall data of three selected stations.
Academic Management and Analysis Method of MOOC

MOOC (Massive Open Online Courses), is a new mode of distance education courses based on the Internet technology. To solve problems in MOOC, such as lacks of management, supervision and comprehensive evaluation etc., this paper aims to study MOOC academic management and analysis method in contexts of colleges and universities. To supervise and trace students’ learning process, an academic management system of MOOC was constructed. To assess and predict students’ learning trends and achievements, an academic analysis model was established based on learning and interaction data analysis. Experimental results prove the above method insures expected learning effects when applying MOOC in colleges and universities.
A Novel Fault Diagnosis Method Under Dynamic Working Conditions Based on a CNN With an Adaptive Learning Rate

The fault diagnosis problem of prognostics and health management (PHM) is becoming more complicated and challenging due to the increasing demand for timeliness and adaptability under dynamic working conditions. This article studies a new adaptive fault diagnosis (AdaFD) scheme and proposes a deep learning-based model and algorithm to enhance both the timeliness of fault diagnosis and its adaptability to dynamic conditions. The primary AdaFD model is based on a convolutional neural network (CNN). The two key modules of AdaFD are: 1) adaptive and independent learning rate design for model training and 2) adaptive structure optimization approach for model application. During the training phase, we design an adaptive learning rate strategy by distinguishing the weight and bias parameters in the CNN model to accelerate the training process. In the application phase, the adaptive batch normalization (AdaBN) algorithm is adopted to improve the adaptability of the primary CNN model to dynamic working conditions and data noise. Finally, a case study is conducted to validate the superiority of AdaFD based on a benchmark rolling bearing dataset. The results show that the proposed fault diagnosis method has higher accuracy and better adaptability than other common methods and possesses advantages in terms of training efficiency.
Polymeric nanowire architectures and nanodevices

We devised a method, named as scanned electrospinning technique, to form a variety of one-dimensional polymeric nanostructures. It is a straightforward technique that enables the rapid fabrication of oriented polymeric nanowires as well as their integration with lithographically defined and micromachined surfaces. As a result, these orientated polymeric nanowires can be used as building blocks for nanodevices. Using this method we have made and tested a prototype of high-speed single polymeric nanowire chemical detector that is capable of sensing ultra low concentrations of ammonia gas, with rapid and reversible resistance change. As an example of the nanomechanical resonator, mechanical oscillations of individual suspended silica fibers were measured, with a resonant frequency of 10.8 MHz and a mechanical quality factor of 1600. The general approach represents a significant step toward realizing the potential of organized self-assembled nanostructures in nanoscale device architectures.
Perceptions of staff and students about the NC(V) model of workplace Engineering artisan training offered by South African TVET colleges

Vocational training is a contentious issue in South Africa, where large per centages are regularly cited for unemployment statistics, and in particular for youth and post-school unemployment. Vocational study programmes at Technical and Vocational Education and Training (TVET) colleges are often seen as one possible remedy to these problems. The Department of Higher Education and Training’s (DHET) long-term objective is to increase the number of learners that will qualify as artisans, which represent a major scarce/critical skills area in South Africa. However, the throughput and certification rates of TVET students are disconcertingly low, with National Certificate (Vocational) (NC(V)) Engineering courses regularly cited as having some of the lowest rates. This article reports on a small research study on the perceptions of TVET staff and students about NC(V) Engineering programmes, conducted at two TVET colleges in South Africa. Qualitative and quantitative methods were used to collect data from NC(V) Engineering students and lecturers. The most important findings of the study include that there is a critical collaboration gap between industry and the TVET sector and that both NC(V) staff and students experience the need for addressing the (im)balance of time spent on practical versus theoretical training as part of NC(V) programmes. A clear understanding of the perceptions and concerns of TVET NC(V) Engineering lecturers and students may assist in addressing issues locally, at individual TVET colleges, and globally, at national policy and Sector Education and Training Authority (SETA) level, that could in future serve to contribute to improved academic performance, including higher throughput and certification rates, of NC(V) Engineering students.
Impact of ground conditions and excavation depth on selection of excavation support system

Abstract Inappropriate selection of an excavation support system (ESS) may lead to delays, cost overruns and quality and safety problems. Soil conditions, excavation depth and groundwater level are important factors that significantly affect the selection of ESS. However, a guideline that determines the effects of these three factors and accounts for their variation in the selection of ESS is not available. To evaluate such effects, a questionnaire was sent to excavation support experts worldwide. The results of the conducted survey are presented and discussed in the current paper to determine the favourable soil conditions and maximum excavation depths for ESS in dry and wet soils. The focus is on the soldier pile, secant pile, diaphragm and jet grout walls because of their common use worldwide. Clear guidelines that can help in the selection of an appropriate ESS for given site conditions could be established. The obtained findings are compared with the conditions and adopted ESS of reported case histories.
A Low-RCS and Wideband Circularly Polarized Array Antenna Co-Designed With a High-Performance AMC-FSS Radome

In this letter, we investigate a low radar cross-section (RCS) wideband circularly polarized microstrip array antenna co-designed with a high-performance artificial magnetic conductor and frequency selective surface (AMC-FSS) radome. Compared with the absorbent frequency selective radome-based antenna, the proposed co-designed antenna can achieve a relatively low gain loss in an extremely wide frequency range with wideband in-band and out-of-band RCS reduction, due to the introduction of a novel AMC-FSS radome which can achieve low insertion loss and wideband transmission by combining a double-layer miniaturized bandpass FSS and a single-layer chessboard AMC surface. We demonstrate the effectiveness of the proposed AMC-FSS radome and co-designed antenna through simulations and measurements. The results show that the gain deterioration of the co-designed antenna is only 0.42 dB at the central frequency of 4.8 GHz and less than 1 dB in the wide frequency band, ranging from 4.23 to 5.38 GHz with a relative bandwidth of 23.9%. At the same time, the monostatic RCS for a normal incidence is greatly reduced from 4 to 18 GHz.
Hyperspectral and LiDAR Data Classification Using Joint CNNs and Morphological Feature Learning

Convolutional Neural Networks (CNNs) have been extensively utilized for Hyperspectral (HSI) as well as Light Detection and Ranging (LiDAR) data Classification. However, CNNs have not been much explored for joint HSI and LiDAR image classification. Therefore, this article proposes a joint feature learning (HSI and LiDAR) and fusion mechanism using CNN and Spatial Morphological blocks which generates highly accurate land-cover maps. The CNN model comprises three Conv3D layers and is directly applied to the HSIs for extracting discriminative spectral-spatial feature representation. On the contrary, the spatial morphological block is able to capture the information relevant to the height or shape of the different land-cover regions from LiDAR data. The LiDAR features are extracted using morphological dilation and erosion layers which increase the robustness of the proposed model by considering elevation information as an additional feature. Finally, both the obtained features from CNNs and spatial morphological blocks are combined using an additive operation prior to the classification. Extensive experiments are shown with widely used HSIs and LiDAR datasets, i.e., University of Houston (UH), Trento, and MUUFL Gulfport scene. The reported results show that the proposed model significantly outperforms traditional methods and other state-of-the-art deep learning models. The source code for the proposed model will be made available publicly at https://github.com/AnkurDeria/HSI+LiDAR.
Fatty Acids Profile and Healthy Lipids Indices of Native Mexican Guajolote Meat Treated to Two Heat Treatments

Simple Summary The native guajolote meat is an important dietary source of proteins of high biological value for the rural population of Mexico. However, many of its quality characteristics are still unknown. Therefore, the fatty acids (FAs) profile and nutritional indices of breast and leg meat of native guajolote subjected to two heat treatments (boiled and baked) were evaluated. The heat treatments increased the concentration of saturated (SFA) and monounsaturated FA (MUFA) in the meat; in contrast, the concentration of polyunsaturated FA (PUFA) decreased. Likewise, the dietary FA index and the atherogenic index increased in guajolote meat from the effect of the heat treatments, while the essential and hypercholesterolemic FA indices decreased. Based on the results obtained, heat treatments increase the content of SFA and MUFA in breast and leg meat of native guajolote. Baking is less favorable for both types of muscle. Abstract Meat is a complex food with a structured nutritional composition that makes it an essential component of the human diet. In particular, the meat of native guajolote that is traditionally raised in natural conditions is an important dietary source of proteins of high biological value for the rural population of Mexico. The study aimed to evaluate fatty acids (FAs) profile and nutritional indices of breast and leg meat of native guajolote subjected to two heat treatments. For the study, a total of sixty muscle samples (30 breast meat and 30 leg meat) from adult male native guajolotes were used. The FA profile and nutritional indices were evaluated in raw meat (control) and meat subjected to two heat treatments (boiled and baked). The heat treatments, independently of the type of muscle, increased (p ≤ 0.05) the concentration of saturated (SFA) and monounsaturated FA (MUFA); in contrast, polyunsaturated FA (PUFA) decreased. Likewise, the dietary FA index, which has a negative hypercholesterolemic effect, and the atherogenic index increased in guajolote meat from the effect of the heat treatments, while the essential and undesirable hypercholesterolemic FA indices decreased. In conclusion, heat treatments increase the content of SFA and MUFAs in breast and leg meat of native guajolote. Boiling or baking the meat deteriorates PUFAs but increases the nutritional indices. The present investigation would provide valuable information for the guajolote meat product processing.
Stress Concentration Factors of Cross-Bores in Thick Walled Cylinders and Square Blocks

The purpose of this paper is to investigate the stress concentration in stress fields around crossbores for closed-end thick-walled square blocks and cylindrical shells using the finite element method. These stress concentration factors are presented and discussed as a function of the ratio of crossbore radius to the cylinder internal radius (HR/Ri = 0.01 to 0.7) for a range of wall ratios (Y = 1.5 to 5). Charts and simple expressions are provided for ease of use. © 2002 ASME
Improving control of quadrotors carrying a manipulator arm

This paper proposes a simple solution for asymptotical stabilization of a quadrotor carrying a manipulator arm. The manipulator arm is attached to the lower part of the fuselage of a quadrotor to increase the type of missions achievable. The motion of the arm induces torques and disturbances to the quadrotor causing the loss of stability. This work deals with the stabilization of the quadrotor under such disturbances, by means of a bounded quaternion-based feedback. A set of nonlinear control laws are obtained. First, the attitude control stabilizes the quadrotor to a desired position and angular velocities. Then, using a suitable virtual control, the translational dynamics becomes into three independent chains integrators allowing the formulation of a nonlinear control. Both controls consist in saturation functions and allow the stabilization of the quadrotor. Simulations show the effectiveness of the proposed algorithm.
Deep malware hunter based unrivaled malware detection schema thru cache retrospective empiricism

Malware development has been tremendous in recent decades, posing a severe threat to modern systems and Internet security. Conventional approaches to malware detection focus solely on data stored on hard disc drives. To prevent detection, the attackers put harmful material in volatile memory and attack sensitive confidential data. Furthermore, static signature‐based malware detection approaches require a recognized malware signature database to detect the malware. These efforts, however, failed because the malware rapidly modified its signature and easily evaded detection by packaging, obfuscating, or encrypting. As a consequence, it is vital to extract appropriate memory characteristics and let the deep learning classifier that learns the features improve malware detection accuracy. Therefore, in this paper, we propose a Deep Malware Hunter Based Unrivaled Malware Detection Scheme that skillfully detects known and unknown malware without harming the device. In this way, our Unrivaled Malware Detection Scheme efficiently extracts the necessary features through sharp inspection and classifies the malware correctly and benignly at an early stage before it attacks the device. Thus the proposed framework efficiently provides an accuracy of 99% detection rate in the malware hunter method.
Fourier transform near infrared spectroscopy as a tool to predict spawning status in Alaskan fishes with variable reproductive strategies

Fourier-transform near infrared (FT-NIR) spectroscopy of ovarian tissue was used to predict maturity status of fish species with variable reproductive strategies collected at limited time periods of their spawning cycle. Reference data were derived from histologically prepared tissue samples from four species: Pacific cod (Gadus macrocephalus), walleye pollock (Gadus chalcogrammus), Greenland turbot (Reinhardtius hippoglossoides), and northern rockfish (Sebastes polyspinis). Each data set was classified into reproductively immature (non-spawning) and reproductively mature (spawning-capable) categories. Principal component analysis of spectral data showed separation between ovarian tissues of spawning-capable and non-spawning females. Multivariate classification using partial least squares discriminant analysis indicated good discrimination based on spawning status with high predictive accuracy. Greenland turbot and northern rockfish showed clear distinction between ovaries of spawning-capable and non-spawning females and a model validation with 100% and 96.6% classification accuracy, respectively. Pacific cod and walleye pollock had more complicated reproductive patterns at time of collection and classification rates were still 96.6% and 92.1%. This study demonstrated the potential application of FT-NIR spectroscopy to predict spawning status from ovarian tissue even for species with complicated spawning patterns and for collections outside of the preferred spawning period. Future work may include the use of this technology to classify distinct oocyte development stages.
Authentication for Integrated Circuit and Devices Using Blockchain and Physical Unclonable Functions

Secure components and devices have always been and always will be a challenge for the electronics industry. In this sense, there is a constant and growing demand for new solutions that can allow reliability in the use and authenticity of components and devices. The end-user is not able to assess the existing risk, much less if the component or device is reliable in several aspects, mainly improper access to its information. This work presents a new integration of two technologies: Blockchains networks, which implement a kind of decentralized and inviolable database, which can increase resilience, security and guarantee against the alteration of the information registered in its structure; Physical Unclonable Functions (PUF), which allow the generation of a unique cryptographic key, since they use unique physical characteristics of each semiconductor component, considerably increasing security, the protection of industrial property and the opportunity for remote authentication of devices. The unprecedented contribution here is in the integration of existing technologies, in order to obtain an innovative solution of authentication and cyber security for the internet of things and other devices.