Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovative Characterization and Comparative Analysis of Water Level Sensors for Enhanced Early Detection and Warning of Floods

In considering projections that flooding will increase in the future years due to factors such as climate change and urbanization, the need for dependable and accurate water sensors systems is greater than ever. In this study, the performance of four different water level sensors, including ultrasonic, infrared (IR), and pressure sensors, is analyzed based on innovative characterization and comparative analysis, to determine whether or not these sensors have the ability to detect rising water levels and flash floods at an earlier stage under different conditions. During our exhaustive tests, we subjected the device to a variety of conditions, including clean and contaminated water, light and darkness, and an analogue connection to a display. When it came to monitoring water levels, the ultrasonic sensors stood out because of their remarkable precision and consistency. To address this issue, this study provides a novel and comparative examination of four water level sensors to determine which is the most effective and cost-effective in detecting floods and water level fluctuations. The IR sensor delivered accurate findings; however, it demonstrated some degree of variability throughout the course of the experiment. In addition, the results of our research show that the pressure sensor is a legitimate alternative to ultrasonic sensors. This presents a possibility that is more advantageous financially when it comes to the development of effective water level monitoring systems. The findings of this study are extremely helpful in improving the dependability and accuracy of flood detection systems and, eventually, in lessening the devastation caused by natural catastrophes.
A Review of Social Media Data Utilization for the Prediction of Disease Outbreaks and Understanding Public Perception

Infectious diseases take a large toll on the global population, not only through risks of illness but also through economic burdens and lifestyle changes. With both emerging and re-emerging infectious diseases increasing in number, mitigating the consequences of these diseases is a growing concern. The following review discusses how social media data, with a focus on textual Twitter data, can be collected and processed to perform disease surveillance and understand the public’s attitude toward policies around the control of emerging infectious diseases. In this paper, we review machine learning tools and approaches that were used to determine the correlation between social media activity in disease trends within regions, understand the public’s opinion, or public health leaders’ approaches to disease presentation. While recent models migrated toward popular deep learning methods, neural networks and algorithms that optimized existing models were also explored as new standards for social media data analysis in disease prediction and monitoring. As adherence to public health policies can be improved by understanding and responding to major concerns identified by sentiment analyses, the advancements and challenges in understanding text sentiment are also discussed. Recent sentiment classifiers include more complex classifications and can even recognize epidemiological considerations that affect the spread of outbreaks. The comprehensive integration of locational and epidemiological considerations with advanced modeling capabilities and sentiment analysis will produce robust models and more precision for both disease monitoring and prediction. Accurate real-time disease outbreak prediction models will provide health organizations with the capability to address public concerns and to initiate outbreak responses proactively rather than reactively.
Designing successful internships: exploring the role of duration, formalization and motivational practices

PurposeThis study aims to shed light on how internships' features drive employability outcomes and answers the question of how internships that maximize the employability of interns can be designed. The study assumes that the duration of an internship, degree of formalization and the use of internship-specific motivational practices by companies are likely to influence interns' perceptions of employability.Design/methodology/approachA study among interns investigated the relationship between different internship features and interns' employability. Data on the duration of the internship, motivational practices specific to internships and the formalization of the internship process were collected from a large sample of 13,565 interns in 27 European countries from 15 to 35 years old.FindingsThe findings suggest that internship duration has a curvilinear effect on employability with increasing effects on employability at the beginning of the internship, followed by a decrease over time. In addition, the use of practices specifically designed to motivate interns also favors the interns' employability. Finally, formalization of the internship placement moderates the effect of motivational practices on employability.Originality/valueBy identifying key features of the internship experience – duration, formalization and motivational practices – the authors contribute to the theory related to the development of career resources and employability in young adults and show that these features significantly shape young adults' employability perceptions.
Pupil Scrambling Integral Field Unit (PSI) for the Robert Stobie Spectrograph on SALT

We propose to build an integral field unit (IFU) for the Robert Stobie Spectrograph (RSS) on the Southern African Large Telescope (SALT). This IFU (PSI) will employ the scrambling properties of fibers to address fundamental problems in achieving photon-limited sky subtraction due to variations in pupil illumination during observations. PSI will be fully encapsulated with a compact folding scheme in a standard long slit mask—far thinner than any previous fiber-based implementation. The IFU will cover 14 x 24 arcsec on sky, achieving spectral resolution R ≡ λ/δλ up to 6200 and photon-limited sky subtraction for studies of faint extended gas around galaxies beyond the reach of current 4m-class instruments. It will incorporate new-technology octagonal shaped fiber cores coupled by telecentric, pupil-imaging lenslets to fully optimize pupil scrambling and provide 100% integral sky coverage.
A multi-point fed circularly polarized millimeter wave antenna element with π-shaped coupling apertures

This paper presents an aperture coupled circularly polarized antenna element working in the 26GHz millimeter wave band. The antenna element is fed by four groups of coupling aperture, and composed of parasitic patch, primary patch, coupling apertures, feeding network and square equivalent shield cavity. To satisfy the demand of wide-angle scanning phased array antenna, the overall dimension of the antenna element is 0. 5λ, λ refers to the wavelength at the center frequency. The four groups of coupling apertures are designed with the π-shape to implement miniaturization, moreover, eliminate the energy that coupled to the feed network below. The feeding network is a one-four power divider composed of cascaded one-two Wilkinson power dividers, and in form of stripline. The four output ports of the feed network successively provide signals with equal amplitude and phase difference of 90º to the coupling apertures to realize circularly polarization. The one-four divider feed network is arranged in the outer space of the π-shaped apertures, and completed in one layer of the multilayers PCB, which reduce the number of the layers and simplify the design of the PCB stack. This antenna is designed based on the technology of stacked multilayer dielectric substrates and AIP architecture. The RF chip mounted on the back surface of the multilayer board, and connect with the antenna that designed in the upper part of the multilayer board by the metallic via-holes. By adopting the multi-point feeding technology and π-shaped coupling aperture, the axial ratio of the circularly polarized antenna improved significantly. The simulated results show that the proposed antenna has good performance within the whole working bandwidth 24.25~27.5GHz with VSWR<1.15, AR<1dB, the average radiation efficiency exceeds 80%, and can be a promising candidate for millimeter-wave phased array antenna.
An Analog Side-Channel Attack on a High-Speed Asynchronous SAR ADC Using Dual Neural Network Technique

SUMMARY This brief presents a side-channel attack (SCA) technique on a high-speed asynchronous successive approximation register (SAR) analog-to-digitalconverter(ADC). Theproposeddualneuralnetworkbasedonmultiplenoisewaveformsseparatelydisclosessignandabsolutevalue informationofinputsignalswhicharehiddenbythedifferentialstructureandhigh-speedasynchronousoperation.ThetargetSARADCandon-chip noisemonitorsaredesignedonasingleprototypechipforSCAdemon-stration.Fabricatedin40nm,theexperimentalresultsshowtheproposed attack on the asynchronous SAR ADC successfully restores the input data with a competitive accuracy within 300mV rms error.
Fresnel Diffraction in Vacuum

The goal of this chapter is to develop methods for modeling near-field optical-wave propagation with high delity and some flexibility, which is considerably more challenging than for far-field propagation. This chapter uses the same coordinate convention as in Fig. 1.2. It begins with a discussion of different forms of the Fresnel diffraction integral. These different forms can be numerically evaluated in different ways, each with benefits and drawbacks. Then, to emphasize the different mathematical operations in the notation, operators are introduced that are used throughout Chs. 6-8. The rest of this chapter develops basic algorithms for wave propagation in vacuum and other simulation details. The quadratic phase factor inside the Fresnel diffraction integral is not bandlimited, so it poses some challenges related to sampling. There are two different ways to evaluate the integral: as a single FT or as a convolution. This chapter develops both basic methods as well as more sophisticated versions that provide some flexibility. There are different types of flexibility that one might need. For example, Delen and Hooker present a method that is particularly useful for simulating propagation in integrated optical components. Because the interfaces in these components are often slanted or offset and the angles are not always paraxial, they developed a Rayleigh-Summerfeld propagation method that can handle propagation between arbitrarily oriented planes with good accuracy.28,29 In contrast, the applications discussed in this book involve parallel source and observation planes, and the paraxial approximation is a very good one. When long propagation distances are involved, beams can spread to be much larger than their original size. Accordingly, some algorithms discussed in this chapter provide the user with the flexibility to choose the scaling between the observation- and source-plane grid spacings. Many authors have presented algorithms with this ability including Tyler and Fried,30 Roberts,31 Coles,32 Rubio,33 Deng et al.,34 Coy,35 Rydberg and Bengtsson,36 and Voelz and Roggemann.37 Most of these methods are mathematically equivalent to each other. However, one unique algorithm was presented by Coles32 and later augmented by Rubio33 in which a diverging spherical coordinate system was used by an angular grid with constant angular grid spacing. This was done specifically because the source was a point source, which naturally diverges spherically.
Factors Associated With Enrollment into Inpatient Coronavirus Disease 2019 Randomized Controlled Trials: A Cross-sectional Analysis

Abstract Background Clinical trials for coronavirus disease 2019 (COVID-19) have struggled to achieve diverse patient enrollment, despite underrepresented groups bearing the largest burden of the disease and, presumably, being most in need of the treatments under investigation. Methods To assess the willingness of patients to enroll into inpatient COVID-19 clinical trials when invited, we conducted a cross-sectional analysis of adults hospitalized with COVID-19 who were approached regarding enrollment. Associations between patient and temporal factors and enrollment were assessed by multivariable logistic regression analysis. Results A total of 926 patients were included in this analysis. Overall, Hispanic/Latinx ethnicity was associated with a nearly half-fold decrease in the likelihood to enroll (adjusted odds ratio [aOR], 0.60 [95% confidence interval {CI}, .41–.88]). Greater baseline disease severity (aOR, 1.09 [95% CI, 1.02–1.17]), age 40–64 years (aOR, 1.83 [95% CI, 1.03–3.25]), and age ≥65 years (aOR, 1.92 [95% CI, 1.08–3.42]) were each independently associated with higher likelihood to enroll. Over the course of the pandemic, patients were less likely to enroll during the summer 2021 wave in COVID-19–related hospitalizations (aOR, 0.14 [95% CI, .10–.19]) compared with patients from the first wave in winter 2020. Conclusions The decision to enroll into clinical trials is multifactorial. Amid a pandemic disproportionately affecting vulnerable groups, Hispanic/Latinx patients were less likely to participate when invited, whereas older adults were more likely. Future recruitment strategies must consider the nuanced perceptions and needs of diverse patient populations to ensure equitable trial participation that advances the quality of healthcare for all.
On the Integral of the Product of Three Bessel Functions over an Infinite Domain

Fourier-space representation of the partial differential equations describing nonlinear dynamics of continuous media in cylindrical geometry can be achieved using Chandrasekhar–Kendall (C–K) functions defined over infinite domain as an orthogonal basis for solenoidal vector fields and their generating function and its gradient as orthogonal bases for scalar and irrotational vector fields, respectively. All differential and integral operations involved in translating the partial differential equations into transform space are then carried out on the basis functions, leaving a set of time evolution equations, which describe the rate of change of the spectral coefficient of an evolving mode in terms of an aggregate effect of pairs of interacting modes computed as an integral over a product of spectral coefficients of two physical quantities along with a kernel, which involves the following integral:
Engagement in Aerobic Exercise Is Associated with a Reduced Prevalence of Sarcopenia and Severe Sarcopenia in Italian Older Adults

The present study was conducted to test the association between adherence to specific exercise modalities and sarcopenia severity in Italian older adults. Data were collected as part of the ongoing Longevity Check-Up 7+ (Lookup 7+) project. Lookup 7+ began in June 2015 and has since been conducted in unconventional settings (e.g., exhibitions, malls, social events) throughout Italy. In the present study, we used data on adults 65 years and older. Sarcopenia was identified according to the simultaneous presence of dynapenia and low appendicular muscle mass. Muscle strength was measured by isometric handgrip and sit-to-stand (STS) testing. Sarcopenia was categorized as severe if participants reported difficulty or inability to walk 400 m. Engagement in running and/or swimming (RS) or strength training with or without stretching (SS) was used to define exercise modalities. Analyses were conducted in 3289 participants (mean age: 72.7 ± 5.7 years; 1814 women). The results of the binary regression showed negative associations between RS and the presence of STS-based sarcopenia in women, and between RS and STS-based severe sarcopenia in men. Collectively, these findings indicate that RS is negatively associated with the presence of sarcopenia in large sample of relatively unselected Italian older adults.
Characteristics of Adolescent Life Goals in Contemporary China: A Mixed-Methods Study

Adolescence is a developmental period when individuals actively evaluate and construct their life goals. During the past several decades, China has transformed dramatically toward a highly competitive, market-oriented society. Despite a growing interest in exploring the implications of cultural values for youth adjustment in contemporary China, little is known about what life goals are prevalent among Chinese adolescents. This mixed-methods study aimed to identify the key themes of life goals and to examine gender, grade, and urban-rural differences in the identified themes among Chinese adolescents, using quantitative and qualitative methods. Semi-structured interviews were conducted with a sample of 163 middle- and high-school students in urban and rural China. Thirteen key life goal themes were identified; among them, the most mentioned themes were Family Well-being, Academic Excellence, and Personal Happiness. Quantitative results showed grade and urban-rural differences in the adolescents’ endorsement of the themes of life goals. Specifically, more middle schoolers and rural students endorsed life goals that emphasize social belonging and group well-being, whereas more high schoolers and urban students endorsed life goals that underscore individual independence and uniqueness. These results indicated the implications of social change for adolescents’ life goals in contemporary China.
Unitary learning in conditional modes for deep optics neural networks

Complex-valued backprogation (CVBP) is an essential algorithm parsing a large amount of complex data for statistical inferences to deep optics neural networks. However, the operating optical transformations are almost unitary, which require the unitary backpropagation algorithms to meet the hetero-equivalence from their mathematical to optical mechanisms. This paper mainly compares two compact formulations for a common CVBP and its unitary-variants under holomorphic and compatible conditions, for which composite mechanisms are invented. For unitary weights-update, essentially, the Riemannian gradient of a CVBP is expanded from Euclidean gradient by executing an exponential mapping onto its unitary manifold. The convergent speed and accuracy, as well as the evolutions of their energies and averaging-phases at each layer are progressively investigated and compared, which reflect the dynamic behaviors on the network convergences. We find that not all the nonlinear activations under the respective conditions are always ensured to convergences, and the averaging-phases still remain several jumps although the energies at current layer situate at the convergent districts.
Improved Faster-RCNN algorithm combined with infrared satellite image for tropical cyclone detection

Automatic detection of tropical cyclone (TC) regions from satellite images can provide regions of interest for intelligent TC positioning and intensity determination, and improve the efficiency and accuracy of intelligent disaster weather forecasting. There are currently few studies on automatic detection of TCs from satellite images. In recent years, deep learning technology has developed rapidly in various fields. This paper improves the Faster-RCNN target detection model in deep learning and applies it to the TC detection. The TC detection model designed in this paper is based on the original Faster-RCNN network framework, and the feature extraction network is changed from the original VGG16 network to the ResNet50 network . On this basis, this paper designs a feature fusion network Single Output Feature Fusion Networks (SOFFN). The feature layer used for detection can combine the semantic information of the high-level feature map and the high-resolution feature information of the low-level feature map, fuse different feature layers. At the same time, a new attention mechanism, Channel Linear Weighted Networks (CLWNet), based on the Squeeze-and-Excitation Networks (SENet) channel attention mechanism improvement is added to the model designed in this paper to improve the detection performance. In this paper, China's FY-2D satellite images are used to verify the performance of the proposed model. Experimental results show that the proposed model has achieved good results in TC detection.
Effects of different levels of organic chromium and selenomethionine cocktails in broilers

Selenium (Se) is an essential trace mineral that plays an important role in physiological processes by regulating the antioxidant defense system and enhancing immunity. Chromium is an essential mineral involved in carbohydrate and lipid metabolism and also plays a role in maintaining normal insulin function. Based on these advantages, we hypothesized that the addition of selenomethionine (SeMet) and organic chromium (OC) to broiler diets would increase Se deposition, antioxidant capacity and immune response in meat. Therefore, this study analyzed the effects of OC and SeMet on growh performance, nutrients digestibility, blood profiles, intestinal morphology, meat quality characteristics, and taxonomic analysis of broilers. A total of 168 one-day-old broiler chicken (Arbor Acres) were randomly allotted to 3 groups based on the initial body weight of 37.33 ± 0.24 g with 7 replicate per 8 birds (mixed sex). The experiments period was 28 days. Dietary treatments were folloewd: Basal diets based on corn-soybean meal (CON), basal diet supplemented with 0.2 ppm OC and 0.2 ppm SeMet (CS4), and basal diet supplemented with 0.4 ppm OC and 0.4 ppm SeMet (CS8). Supplementation of OC and SeMet did not affect on growth performance, nutrient digestibility. However, CS8 supplementation increased in duodenum villus height and villus height : crypt depth, and increased in breast meat Se deposition. In addition, CS8 group showed higher uric acid and total antioxidant status than CON group. Taxonomic analysis at phylum level revealed that Proteobacteria and Firmicutes of CS4 and CS8 were lower than CON group. In genus level, the relative abundance of fecal Lactobacillus and Enterococcus of CS4 and CS8 groups were higher than CON group. In short, 0.4 ppm OC and 0.4 ppm SeMet supplementation to broiler diet supporitng positive gut microbiome change, also enhancing antioxidant capacity, and Se deposition in breast meat.
Phytochemicals of Withania somnifera as a Future Promising Drug against SARS-CoV-2: Pharmacological Role, Molecular Mechanism, Molecular Docking Evaluation, and Efficient Delivery

Coronavirus disease (COVID-19) has killed millions of people since first reported in Wuhan, China, in December 2019. Intriguingly, Withania somnifera (WS) has shown promising antiviral effects against numerous viral infections, including SARS-CoV and SARS-CoV-2, which are contributed by its phytochemicals. This review focused on the updated testing of therapeutic efficacy and associated molecular mechanisms of WS extracts and their phytochemicals against SARS-CoV-2 infection in preclinical and clinical studies with the aim to develop a long-term solution against COVID-19. It also deciphered the current use of the in silico molecular docking approach in developing potential inhibitors from WS targeting SARS-CoV-2 and host cell receptors that may aid the development of targeted therapy against SARS-CoV-2 ranging from prior to viral entry until acute respiratory distress syndrome (ARDS). This review also discussed nanoformulations or nanocarriers in achieving effective WS delivery to enhance its bioavailability and therapeutic efficacy, consequently preventing the emergence of drug resistance, and eventually therapeutic failure.