Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Backscattering Model with Row Structure on Farmland

The row structure in agricultural surface is key for effectively explaining SAR backscattering coefficient. The row structure is described by a mathematical method at first in this research, and a surface scattering model, called AAIEM (Agriculture Advanced Integral Equation Model) is presented in the paper. In the AAIEM, the row structure is assumed to be the combining of the periodic row surface and small-scale random rough surface over the row surface. Then, the polarimetric deflection angle is correct and the backscattering coefficient is calculated by derived AAIEM. The model is validated by AIEM and field measurements, the results show that comparing to AIEM, the AAIEM can better simulate the backscattering in the row structure surface, and it is more suitable for C/L band SAR signal. Our research provide theoretical foundation and technical support for development of precision agriculture in future.
What You See Is What You Transform: Foveated Spatial Transformers as a bio-inspired attention mechanism

Decoding the semantic content of images is nowadays dominated by the use of deep convolutional neural networks (DCNNs), However, their generalization capability is still undermined by the small translation invariance of their max-pooling layers. Taking inspiration from biological vision, we develop here a new methodology for translation-invariant processing with DCNNs. We build upon a recent model that implements two key biological mechanisms: foveated vision and the separation of the visual processing into a “what” and a “where” pathways. Alongside such foveal vision, we demonstrate the capability of a foveated spatial transformer to learn both pathways in an end-to-end fashion, without any spatial labelling whatsoever. Our results pave the way towards a new class of spatial visual transformers, implementing the principles of active (saccadic) vision over large visual displays.
Capital Plus Warrants

A capital plus warrant (CPW) is a third-party-issued derivative contract (on an underlying portfolio of equities) traded on the Australian Stock Exchange. The investor in a capital plus warrant is guaranteed the initial investment amount plus a return based on the performance of the underlying equity portfolio, but does not receive the dividends on the underlying shares. This article first examines the pricing of this security, which can be modeled as a combination of a risk-free bond and a call option on the equity portfolio. A simple model for valuing capital plus warrants is derived. Second, the article examines the determinants of the terms of the CPW and methods by which the issuing institution might hedge the risk created by the sale of the security. The CPW offers a risk-return profile that the retail investor is not able to obtain using other securities. The issuer is able to reduce the cost of funds from around 7.5% to 6.4%.
A Hyperactive End to the Atlantic Hurricane Season: October–November 2020

The active 2020 Atlantic hurricane season produced 30 named storms, 14 hurricanes, and 7 major hurricanes (Category 3+ on the Saffir-Simpson Hurricane Wind Scale). Though the season was active overall, the final two months (October–November) raised 2020 into the upper echelon of Atlantic hurricane activity for integrated metrics such as Accumulated Cyclone Energy (ACE). This study focuses on October–November 2020, when 7 named storms, 6 hurricanes, and 5 major hurricanes formed and produced ACE of 74 * 104 kt2. Since 1950, October–November 2020 ranks tied for 3rd for named storms, 1st for hurricanes and major hurricanes, and 2nd for ACE. Six named storms also underwent rapid intensification (≥30 kt intensification in ≤24 hr) in October–November 2020—the most on record.This manuscript includes a climatological analysis of October–November tropical cyclones (TCs) and their primary formation regions. In 2020, anomalously low wind shear in the western Caribbean and Gulf of Mexico, likely driven by a moderate intensity La Niña event and anomalously high sea surface temperatures (SSTs) in the Caribbean provided dynamic and thermodynamic conditions that were much more conducive than normal for late-season TC formation and rapid intensification. This study also highlights October–November 2020 landfalls, including Hurricanes Delta and Zeta in Louisiana and in Mexico and Hurricanes Eta and Iota in Nicaragua. The active late season in the Caribbean would have been anticipated by a statistical model using the July–September-averaged ENSO Longitude Index and Atlantic warm pool SSTs as predictors.
Assessing the performance and accuracy of invasive plant habitat suitability models in detecting new observations in Wisconsin

Abstract Land managers require tools that improve understanding of suitable habitat for invasive plants and that can be incorporated into survey efforts to improve efficiency. Habitat suitability models (HSMs) contain attributes that can meet these requirements, but it is not known how well they perform, as they are rarely field-tested for accuracy. We developed ensemble HSMs in the state of Wisconsin for 15 species using five algorithms (boosted regression trees, generalized linear models, multivariate regression splines, MaxEnt, and random forests), evaluated performance, determined variables that drive suitability, and tested accuracy. All models had good model performance during the development phase (Area Under the Curve [AUC] > 0.7 and True Skills Statistic [TSS] > 0.4). While variable importance and directionality was species specific, the most important predictor variables across all of the species' models were mean winter minimum temperatures, total summer precipitation, and tree canopy cover. Post model development, we obtained 5,005 new occurrence records from community science observations for all 15 focal species to test the models' abilities to accurately predict results. Using a correct classification rate of 80%, just 8 of the 15 species correctly predicted suitable habitat (α ≤ 0.05). Exploratory analyses found the number of reporters of these new data and the total number of new occurrences reported per species contributed to increasing correct classification. Results suggest that while some models perform well on evaluation metrics, relying on these metrics alone is not sufficient and can lead to errors when utilized for surveying. We recommend any model should be tested for accuracy in the field before use to avoid this potential issue.
Deep-learning Model for Predicting the Survival of Rectal Adenocarcinoma Patients based on the SEER Database

Background: We collected information on patients with rectal adenocarcinoma in the United States from the Surveillance, Epidemiology, and End Results (SEER) database. We used this information to establish a model that combined deep learning with a multilayer neural network (the DeepSurv model) for predicting the survival rate of patients with rectal adenocarcinoma.Methods: We collected patients with rectal adenocarcinoma in the United States and older than 20 years who had been added to the SEER database from 2004 to 2015. We divided these patients into training and test cohorts at a ratio of 7:3. The training cohort was used to develop a seven-layer neural network based on the analysis method established by Katzman and colleagues to construct a DeepSurv prediction model. We then used the C-index and calibration plots to evaluate the prediction performance of the DeepSurv model.Results: The 49,275 patients with rectal adenocarcinoma included in the study were randomly divided into the training cohort (70%, n=34,492) and the test cohort (30%, n=14,783). There were no statistically significant differences in clinical characteristics between the two cohorts (p>0.05). We applied Cox proportional-hazards regression to the data in the training cohort, which showed that age, sex, marital status, tumor grade, surgery status, and chemotherapy status were significant factors influencing survival (p<0.05). Using the training cohort to construct the DeepSurv model resulted in a C-index of the model of 0.824, while using the test cohort to verify the DeepSurv model yielded a C-index of 0.821. These values show that the prediction effect of the DeepSurv model for the test-cohort patients was highly consistent with the prediction results for the training-cohort patients.Conclusion: The DeepSurv prediction model of the seven-layer neural network that we have established can accurately predict the survival rate and time of rectal adenocarcinoma patients.
Cognitive Preoccupation with Breaking News and Compulsive Social Media Use: Relationships with Online Engagement and Motivations for Use

ABSTRACT The January 6, 2021, insurrection in the United States capitol can offer insight into how individuals engage with breaking news. The insurrection was a moment of national uncertainty; it was unclear who was storming the Capitol or why they were. A survey assessing cognitive preoccupation with breaking news online and compulsive social media use was developed and distributed within 48 hours of the insurrection. A sample of 380 college students revealed relationships between number of social media channels used, cognitive preoccupation with breaking news online, and compulsive social media use. Theoretical implications for the problematic internet use framework are discussed.
Pattern-Moving-Based Partial Form Dynamic Linearization Model Free Adaptive Control for a Class of Nonlinear Systems

This work addresses a pattern-moving-based partial form dynamic linearization model free adaptive control (P-PFDL-MFAC) scheme and illustrates the bounded convergence of its tracking error for a class of unknown nonaffine nonlinear discrete-time systems. The concept of pattern moving is to take the pattern class of the system output condition as a dynamic operation variable, and the control purpose is to ensure that the system outputs belong to a certain pattern class or some desired pattern classes. The P-PFDL-MFAC scheme mainly includes a modified tracking control law, a deviation estimation algorithm and a pseudo-gradient (PG) vector estimation algorithm. The classification-metric deviation is considered as an external disturbance, which is caused by the process of establishing the pattern-moving-based system dynamics description, and an improved cost function is proposed from the perspective of a two-player zero-sum game (TP-ZSG). The bounded convergence of the tracking error is rigorously proven by the contraction mapping principle, and the validity of the theoretical results is verified by simulation examples.
Bioaccumulation and Biotransformation of BDE‐47 Using Zebrafish Eleutheroembryos (Danio rerio)

Polybrominated diphenyl ethers (PBDEs) are well‐known endocrine disrupting chemicals identified as organic persistent pollutants. Their metabolites OH‐BDE and MeO‐BDE have been reported to be potentially more toxic than the postulated precursor PBDEs. One of the most predominant congeners of PBDEs in the environment is BDE‐47, due to its high presence in industrially used mixtures. In the present study, the bioaccumulation and biotransformation of BDE‐47 into its major metabolites is evaluated using zebrafish (Danio rerio) eleutheroembryos adapting a previously developed alternative method to bioconcentration official guideline Organisation for Economic Co‐ordination and Development 305, which reduces the animal suffering, time, and cost. For the simultaneous determination of BDE‐47 and its metabolites in larvae and exposure medium, and considering the polarity difference of the analytes and the small sample size, the development of a validated analytical method is a step to ensure quality results. In the present study, an ultrasound‐assisted extraction followed by a solid phase extraction dispersive clean‐up step and gas chromatography‐mass spectrometry‐microelectron capture detector (GC‐MS‐μECD) with a previous derivatization process was optimized and validated. Bioconcentration factors (BCFs) were calculated using a first‐order one‐compartment toxicokinetic model. The profiles found show rapid absorption in the first hours of larval development and great bioaccumulative capacity, finding BCFs of 7294 ± 899 and 36 363 ± 5702 at nominal concentrations of 10 and 1 μg L−1, respectively. Metabolization studies show increasing concentrations of the metabolites BDE‐28, 2ʹ‐OH‐BDE‐28, and 5‐MeO‐BDE‐47 throughout the exposure time. The results obtained show the feasibility of the method for bioaccumulation and open up the possibility of metabolic studies with zebrafish eleutheroembryos, which is a very underdeveloped field without official testing or regulation. Environ Toxicol Chem 2023;42:835–845. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
The impact of the COVID-19 pandemic on the adoption of e-learning among academics in Saudi Arabia

ABSTRACT Saudi universities have suspended the traditional face-to-face classes and moved to online learning to ensure the continuation of the educational process during the COVID-19 pandemic. The speed at which the move to online learning has happened was unprecedented and staggering. Academics, had to improvise and try different e-learning solutions in less-than-ideal circumstances. No matter how powerful and easy-to-use e-learning solutions might be, many academics understandably found the transition to e-learning stressful. This research project looks at the transition experience to online learning from the viewpoint of academics in Saudi universities. It investigates how the COVID-19 pandemic has affected the adoption of e-learning solutions and how this forced experience would affect the long-term adoption of e-learning solutions. The study employed an online survey administered to academics from all the 24 public universities. The results were quantitatively analysed by using structural equation modelling (SEM). It was found that attitudes toward e-learning, self-efficacy and perceived reliability have significant positive effects on behavioural intention to adopt e-learning. It was also found that COVID-19 has positively affected the long-term adoption of e-learning. Therefore, it is recommended that universities take immediate actions and capitalise on the e-learning experience that academics have gained during COVID-19 pandemic.
CAT Bond Pricing in Uncertain Environment

Catastrophe bonds are among the essential instruments in providing a financial hedge for insurance companies and their policyholders. Catastrophic events are rare, and the shortage of data weakens the inference of using probability theory. Uncertainty theory, on the other hand, is a reliable alternative to model these kinds of indeterminacies. We model the problem of pricing catastrophe bonds as an uncertain optimization problem where the maximization of the cedent insurance company’s profit is constrained to the uncertain measure of ruin defined for the investors. Consequently, we balance the concerns of profitability and reasonable protection for the investors and the cedent of the bond, respectively. The solution of the optimization problem is considered as the spread over the LIBOR, which effectively determines the price of the bond. The results depicted the practicality of the modeling, especially the uncertainty theory, in pricing catastrophe bonds. Finally, the basic part of the methodology, on calculating the ruin index, is implemented on a real-world problem, and the results are compared with those obtained using probability theory.
Siting priorities for congestion-reducing projects in Dhaka: a spatiotemporal analysis of traffic congestion, travel times, air pollution, and exposure vulnerability

Abstract Traffic congestion increases travel time and is a major source of pollution and health damage in developing-country cities. Data scarcity frequently confines traffic improvement projects to sites where congestion can be easily measured. This article uses spatiotemporal data from new global sources to revisit the siting problem in Dhaka, Bangladesh, where local congestion measures are augmented by estimates of citywide travel time, pollution exposure, and pollution vulnerability. We combine Google Traffic data with an econometric model linking traffic, pollution readings from a local monitoring station, and weather data to estimate the spatial distribution of vehicular pollution. We explore pollution-vulnerability implications by incorporating spatial distributions of poor households, children, and the elderly. Using the Open Source Routing Machine and OpenStreetMaps, we estimate systemwide travel-time gains from reducing congestion at each point in a grid covering the Dhaka metro area. We find a large divergence of siting priorities in single-dimensional exercises that focus exclusively on local congestion, citywide travel time, vehicular pollution, or vulnerable-resident pollution exposure. By implication, optimal siting requires a social objective function with explicit weights assigned to each of the four dimensions. The new global information sources permit extending this multidimensional approach to many cities throughout the developing world.
A Review on Battery Modelling Techniques

The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.
Pencegahan Diabetes Melitus Dengan Metode Komunikasi, Informasi dan Edukasi pada Masyarakat

Diabetes Melitus (DM) masih menjadi masalah kesehatan secara global termasuk di Indonesia.Gaya hidup yang tidak sehat seperti mengonsumsi makanan tinggi gula sebagai contoh makanan manis, minum-minuman bersoda dan makanan rendah serat memicu terjadinya peningkatan kadar gula darah seseorang, sehingga timbulnya penyakit degeneratif yaitu DM. Perubahan perilaku masyarakat dilakukan melalui pemberdayaan masyarakat untuk mencegah penyakit DM. Kegiatan pengabdian kepada masyarakat ini melibatkan kelompok masyarakat di RW 19, RW 20 dan RW 21 Dusun Karangjambe banguntapan Bantul Yogyakarta dan berkoordinasi dengan pihak Puskesmas Banguntapan. Metode yang digunakan diantaranya (1) Survei skrining kesehatan dengan pemeriksaan kesehatan, (2) Penyuluhan pencegahan DM dilakukan dengan metode komunikasi, informasi, dan (3) focus group discussion (FGD) untuk menghasilakan kesepakan bersama masyarakat untuk upaya pencegahan DM. Terdapat 12,5% teridentifikasi gejala diabetes mellitus dan mayoritas penderita hipertensi. Peningkatan pengetahuan dan pemahaman masyarakat melalui penyuluhan pencegahan DM sangat penting supaya penyakit tersebut tidak semakin parah. Kesepakatan bersama antara masyarakat dan tokoh yang disegani menghasilakan kesepakatan untuk mengubah pola makan diawali dengan penyediaan kudapan saat pertemuan warga dilakukan.
Monoclonal antibodies in COVID-19 management: a scoping review

Coronavirus disease 2019 (COVID-19) was declared as pandemic on March 11th 2020 by the world health organization (WHO). Vaccination is for preventing COVID-19 morbidity but when people are infected, treatment is required and even after one and half years the effective cure is yet to be discovered. In this context monoclonal antibodies (mAbs) are promising innovative therapeutic agents in controlling COVID-19 infection. Researchers have found more than 50 mAbs against COVID-19 and they are at different stages of development. Scientists are pacing the research on mAbs. mAbs are innovative therapeutic agents in this context a scoping narrative review was done. At present we have evidences from numerous randomized controlled trials (RCT) on mAbs in effective control of respiratory and coagulation related complications due to COVID-19 infection. Many have got emergency use approval and few of which were withdrawn due to absence of enough evidences or adverse reactions. Examples are bamlanivimab, etesevimab, casirivimab and imdevimab. Other than these many investigational (mAbs) are under scrutiny. With the current evidences the article will give an insight to new and repurposed mAbs which are still under investigation in the management of COVID-19 infections.
Photocatalytic Degradation of Rhodamine B and Methylene Orange Using TiO2-ZrO2 as Nanocomposite

The present research reports the synthesis of ZrO2-doped TiO2 photocatalysts at different ZrO2 contents (1, 3 and 5% wt.) synthesized by the sol–gel method. The samples were characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction, attenuated total reflectance-Fourier transform infrared, ultraviolet–visible, X-ray photoelectron spectroscopy and N2 adsorption–desorption analysis. The photocatalytic activity of the ZrO2-doped TiO2 was investigated against the dyes methyl orange and rhodamine B through mineralization studies. The ZrO2-doped TiO2 samples presented a semiglobular-ovoid agglomerate shape around 500–800 nm. The samples presented high crystallinity of the TiO2 anatase phase, XPS suggested the formation of Zr–O–Ti bonds and the samples were classified as mesoporous materials with slight changes in the optical features in comparison with pure TiO2. Our study shows that the ZrO2-doped TiO2 composites exhibited a higher photocatalytic activity than just utilizing the synthetized TiO2 and a commercial P25. The different degradation behaviors are attributed to differences in the textural properties, and to the different optical absorptions of the samples due to structural defects created by the level of doping of Zr4+ ions into the TiO2 lattice. Reaction kinetics parameters were calculated by the Langmuir–Hinshelwood model, and a third run cycle of the ZrO2-doped TiO2 at 1% wt. achieved a photocatalytic degradation of 78.1 and 75.5% for RhB and MO, respectively.