Miracle-dz/UniFAR-FTUs huggingface dataset:
Mothers As New Teachers During The COVID-19 Pandemic: Challenges and Opportunities

COVID-19, as a global pandemic, has generated extreme disruptions and challenges worldwide in social, economic, healthcare, and educational systems. To reduce the virus’s transmission, education systems moved to remote learning in the spring of 2020, with little to no time for preparation. This paper examines the educational experiences of parents of students with disabilities whose children attended PreK-6th public schools in the US during the COVID-19 pandemic using a Disability Studies in Education theoretical framework. In this qualitative phenomenological study, we interviewed 15 mothers of students with disabilities on Zoom. Using constant-comparative data analysis, we identified a theme revealing mothers’ new identities as teachers of their children with disabilities at home, which brought unique challenges and opportunities. The mothers’ challenges were related to the school’s overreliance on them in remote learning, balancing multiple responsibilities, the need to re-learn academic content and new technology, and the collision of teacher and mom identities and school and home boundaries. These challenges strained mother-child relationships and negatively influenced the mothers’ mental health and well-being. However, their new identity as new teachers also generated new opportunities. Particularly, remote learning made school practices transparent, and mothers reconceptualized their children’s abilities and realized their competence for learning.
The Role of International Organizations and Partnerships for Emergency Education in Ethiopian Higher Education Institutions: Online Learning During COVID-19 Pandemic

The purpose of the study was to explore the role of international organizations and partnerships that provided support to the higher education institutions of Ethiopia during the active COVID-19 emergency period. In addition, the study aimed to explore the experiences and challenges of students in working with the online learning platforms that were made available during the pandemic. Qualitative case study was employed. Official email communications and documents were reviewed from two USA based partner organizations that responded to the higher education teaching need during the pandemic. In addition, 30 graduate students from counseling psychology program of the School of Psychology, Addis Ababa University (AAU) were involved in responding to a qualitative questionnaire in Google forms. The qualitative data were analyzed using thematic analysis. All partnership and collaboration efforts were important in supporting the teaching need of the local institutions during the pandemic. The major themes that emerged as positive experiences were (a) ensuring continuity, (b) new opportunity, (c) safety and economic management, and (d) working with different platforms. The major themes that emerged as challenges were (a) difficulty to adjust oneself, (b) lack of personal touch, and (c) internet and power disruption. The study provided critical message of social infrastructures are highly important as that of physical infrastructure need during emergency education. Additional implications are highlighted in making education systems more resilient.
Research on Information Recommendation in E - commerce Systems

This article discusses methods of information recommendation in e - commerce systems from the perspective of information service. These methods can be divided into two kinds: active information recommendation and passive information recommendation. At last, as one method of the active infonnation recommendation, arithmetic of user- based collaborative filtering is mentioned in detail. At the same time we compare the two information recommendation methods.
A Comparison of Deep Learning Algorithms Dealing With Limited Samples in Hyperspectral Image Classification

Hyperspectral Imaging, also known as Spectroscopy, is used in various areas such as medicine, defense, submarine, remote sensing, and environmental monitoring. Several supervised or unsupervised deep learning algorithms have been developed to classify such hyperspectral images. A significant problem in HSI is insufficient data availability, as annotating the samples is time-consuming and labor-intensive. This study provides a comparison of deep learning algorithms that have been developed to deal with the limited data problem in the HSI domain. It compares the performance, classification accuracy and other relevant parameters that exist during the development of such algorithms.
Unsteady analysis of natural convection in a carbon nanotube-water filled cavity with an inclined heater

ABSTRACT A finite element solution has been performed in this work to solve unsteady governing equations of natural convection in a carbon nanotube–water-filled cavity with inclined heater. The temperature of ceiling and left vertical walls is lower than that of the heater while the other walls are adiabatic. The main governing parameters are nanofluid volume fraction and Rayleigh number (Ra). It is found that the heat transfer rate shows different trends based on Rayleigh number and it increases with increase in nanoparticle volume fraction. It has been estimated that average Nusselt number (Nu) is dependent onRa through power regression models with strong positive linear correlation between ln (Nu) and ln (Ra). In particular, for the maximum time, when the solid volume fraction is varied from 0 to 0.1 the dependence between average Nu and linear Ra, on a logarithmic scale, is very high.
Comparison of e-government acceleration in five regions: Case studies following the issuance of Presidential Regulation 95/2018

One year following the issuance of Presidential Regulation Number 95 of 2018 (Perpres 95/2018), all local governments are competing to implement an electronic-based government system (SPBE/e-Government/eGov). This study, which was conducted in the 2021-2022 period, aimed to examine e-Gov planning and then compare its practice in the five regions (Surabaya, Banyuwangi, Sleman, Gresik, and Kulon Progo) focusing on the results of the 2019 SPBE evaluation and the availability of a legal umbrella in the form of Regent/Mayor Regulations governing the implementation of e-Gov. Starting with the e-Gov theoretical framework, this study used a qualitative-descriptive method with internet secondary data, library research, SPBE index review, and supporting documents. The results of this study are: (a) there are four phases of e-Gov planning: automation, optimization, reengineering, and transformation. The acceleration of the implementation of the SPBE includes the integration of planning, budgeting, procurement, personnel data, archives, public complaints, and data centers; (b) responsively, the five regions compared in this study already have a legal umbrella in the form of a Regent/Mayor Regulations one year following the issuance of Presidential Decree 95/2018; (c) The SPBE service domain is a reliable indicator among the five regions, while the SPBE governance and policy domain displays different dynamics among them. This study concludes that Indonesia’s performance in e-Gov practice is still not convincing among other countries globally and recommends more innovative implementation of e-Gov from authorities (government) without neglecting periodic evaluations.
Risk of penalized least squares, greedy selection andl 1-penalization for flexible function libraries

For function estimation using penalized squared error criteria, we derive generally applicable risk bounds, showing the balance of accuracy of approximation and penalty relative to the sample size. Attention is given to linear combinations of terms from a given class (such as used in neural network models, projection pursuit regression, function aggregation and multiple linear regression). The risk bounds apply to forward stepwise selection and other relaxed greedy algorithms with penalty on the number of terms, and to ‘1penalized least squares, for which we develop a fast algorithm. 1. Introduction. Flexible regression models are built by combining simple functional forms. Fitting such models to data in a training sample, there is a role for empirical performance criteria such as penalized squared error in selecting components of the function from a given library of candidate terms. With suitable penalty, optimizing the criterion adapts the total weights of combination or the number of components as well as the subset of terms to include. The aim is to produce function estimates which accurately predict responses for new input values with the same distribution as the sample. This generalization capability is characterized by the mean squared error as the statistical risk. In this context, our paper has several interwoven objectives: 1. To analyze performance of penalized least squares estimators with theory of acceptable penalties, such that the estimator optimizing the empirical criterion has risk characterized by a corresponding population property of tradeoff of approximation and penalty relative to the sample size. 2. To allow for flexible function fitting using linear combinations of terms selected from various large or even infinite libraries of functions. 3. To establish that a greedy term selection solves the‘1 penalized squared error problem with bounds on accuracy that compare favorably with competing convex optimization algorithms for large libraries. 4. To demonstrate that different estimators, one based on forward stepwise selection with penalty on the number of terms and another with penalty on the ‘1 norm of coefficients, both achieve approximately the same risk, for target functions that have control on the ‘1 norm of coefficients and for functions in the interpolation classes between these and all of L2.
Ethical Considerations When Conducting Pan-European Research with and for Adolescent Young Carers

ABSTRACT Adolescent young carers (AYCs) are a sub-group of young carers who carry out significant or substantial caring tasks and assume a level of responsibility which would usually be associated with an adult. They are a potentially vulnerable group of minors because of the risk factors associated with their caring role. AYCs face a critical transition phase from adolescence to adulthood often with a lack of tailored support from service providers. The recently completed European funded ‘ME-WE’ project, which forms the focus of this paper, aimed to change the ‘status quo’ by advancing the situation of AYCs in Europe, via responsive research and knowledge translation actions. This paper outlines the participatory, co-creation approach employed in the project to optimise AYC’s involvement. It describes the ethical framework adopted by the project consortium to ensure the wellbeing of AYCs within all project activities. Ethical issues that arose in the field study work in all six countries are presented, followed by a discussion of the level of success or otherwise of the consortium to address these issues. The paper concludes with lessons learned regarding ethically responsible research with and for AYCs that are likely transferable to other vulnerable research groups and pan-European projects.
Diurnal Variability of Precipitation in the Northeastern United States

Abstract The diurnal and semidiurnal variations of precipitation over the northeastern United States (New England, New York, New Jersey and Pennsylvania) are investigated using 25 years of hourly precipitation data for 271 stations. The hourly data were harmonically analyzed for all measurable precipitation events ranging upwards to amounts < 10.0 mm. Additionally, a regional probability of precipitation analysis was performed for homogeneously grouped stations. The winter season (November–March) displays a general early-to-midmorning diurnal maximum for all measurable precipitation events across the Northeast with a shift to just before noon southward into Pennsylvania. For heavier precipitation events, much of the region retains the morning maximum, a notable exception being the northern mountains of New York and New England, where there is a late-evening maximum. The spring (April–May) season is quite similar to winter, although the diurnal amplitudes become weaker everywhere except from southeastern P.
Cardiac sensitivity to rewards in cognitively inflexible nonclinical participants

Background In psychopathologies characterized by compulsive decision-making, core impairments include cognitive inflexibility and excessive sensitivity to rewards. It has been posited that traits shared by nonclinical individuals and psychiatric patients could help explain the pathogenesis of compulsive decision-making. Methods To investigate whether cognitive inflexibility predisposes nonclinical individuals to poor choices and hyper-reactivity to reward, we recruited people with high and low scores for cognitive persistence and used the Iowa Gambling Task to assess decision-making and cardiac reactivity to monetary gains/losses. Results As is frequently observed in psychophysiological research, the data indicated discrepancies among self-reports, behavior, and physiology. Cognitive inflexibility was not related to worse performance; however, monetary gains, in line with the literature, prompted marked cardiac accelerations. Consistent with our research goal, only inflexible participants showed large cardiac accelerations during the largest monetary wins. Discussion Taken together, the data confirm an association between cognitive persistence and physiological reward sensitivity in a nonclinical population. The findings are in line with recent theories on the development of compulsive behaviors that consider cognitive inflexibility as a transdiagnostic impairment and predisposing factor for excessive reactivity to rewards, and might act both as a preexisting individual trait and drug-induced deficit.
Machine Learning Security Algorithms and Framework for IOT System

This paper cover different security issues needs along with attack and vectors attacks and also highlighted current issues and solutions for IoT security for networks are all thoroughly reviewed in this study. Here we cover ML and DL methods for holes in these security solutions that demand strategies are then highlighted. We also go into great detail about the ML and DL technologies that are now being used to solve a range of security harms to different IoT networks. In this study we highlighted the future research prospects for ML (Machine Leraning) and DL (Deep Learning) based IoT security based on the thorough analysis of the already published solutions. This work focused on a brand-new (ML) machine learning based security framework that adapts automatically to the evolving security needs of the IoT domain. For the purpose of reducing various vulnerabilities, this framework formulate utilize of both (NFV) Network Function Virtualization enabler’s and (SDN) Software Defined Networking. To accomplish this result we are using supervised learning and their framework that system more secures and distributed data mining system, and neural networks.
A Systematic Mapping Study of the Advancement in Software Vulnerability Forecasting

Developing software securely remains a challenge even with great advancements made in AI, vulnerability databases, dynamic and static code analysis. Research into aspects of detecting, identifying, and refactoring vulnerabilities has shown promise, but further work is needed throughout all stages of the software development lifecycle. We will show that current research suffers from a lack of focus on forecasting where vulnerabilities will occur. Investigations into predicting vulnerabilities in a reliable, scalable way provide developers with insights that could prevent security failures in the future. Increasing software security is an initiative which can aid efforts from national security to saving lives affected by hospital ransomware attacks. Through our first questions, we seek to provide an overview of the vulnerabilities that current researchers are targeting and the methods they use to uncover them. Subsequently, we investigated the fields of impact that the research is contributing to and look for unique contributions. As this paper shows, wide distribution coupled with poor security practices has increased the attack angles available to malicious actors and is statistically linked with increased vulnerabilities. We argue that combinations of automated and manual techniques are necessary to achieve more impactful and adaptable methods to increase security in software development.
Developmental Pattern Formation: Spanish Contributions from a Biophysical Perspective

During the last few decades, developmental pattern formation has evolved from being a descriptive discipline to a quantitative one. That process has been possible due to the implementation of multidisciplinary approaches where biophysicists and mathematicians have played a key role. In this review, we highlight relevant Spanish contributions and stress their biophysical approaches, as well as provide some historical context. Finally, this work also aimed at bridging the concepts from biology to physics/math (and back) and at shedding light on some directions for future research.
DEMOIVRE'S QUINTIC AND A THEOREM OF GALOIS

Explicit formulae for the five roots of DeMoivre's quintic polynomial are given in terms of any two of the roots. If f(x) is an irreducible polynomial of prime degree over the rational field Q, a classical theorem of Galois asserts that f(x) is solvable by radicals if and only if all the roots of f(x) can be expressed as rational functions of any two of them, see for example [2, p. 2541. It is known that
INCREASED UNDERSTANDING OF BEAM LOSSES FROM THE SNS LINAC PROTON EXPERIMENT

Beam loss is a major concern for high power hadron accelerators such as the Spallation Neutron Source (SNS). An unexpected beam loss in the SNS superconducting linac (SCL) was observed during the power ramp up and early operation. Intra-beam-stripping (IBS) loss, in which interactions between H- particles within the accelerated bunch strip the outermost electron, was recently identified as a possible cause of the beam loss. A set of experiments using proton beam acceleration in the SNS linac was conducted, which supports IBS as the primary beam loss mechanism in the SNS SCL.
Twisted N = 4 Super Yang-Mills Theory in -background

We study the twisted N = 4 super Yang-Mills theories in the -background with the constant R-symmetry Wilson line gauge field. Based on the classification of topological twists of N = 4 supersymmetry (the half, the Vafa-Witten and the Marcus twists), we construct the deformed off-shell supersymmetry associated with the scalar supercharges for these twists. We find that the -deformed action is written in the exact form with respect to the scalar supercharges as in the undeformed case.
Re-thinking notions of evidence and proof for sentencing: Towards a more communitarian model

Judges and magistrates are often criticised for failing to take sufficient account of social factors such as poverty and social deprivation when sentencing offenders. The implication is that the sentencing practices of the courts lack an important social dimension—that of ‘social justice’—namely, the perception that the punishment of criminalised behaviour by the state is fair and non-discriminatory. This article asserts that the notion of ‘social justice’ sits uneasily with the values that sustain the existing paradigm of adversarial trial. It is argued that shifting the focus of the adversarial trial away from its narrow preoccupation with individual accountability towards a more communitarian model of penal accountability would significantly enhance the moral credibility of sentencing and its social impact. A more flexible approach to the admissibility and evaluation of evidence is advocated, one conceived within a communitarian ideology whose purpose is to promote penal interventions which enhance social justice.
Posture Estimation and Trajectory Tracking for SQuRo: a Small-sized Quadruped Robotic Rat

Large quadruped robots have shown high environmental adaptability and promising applications. However, due to the difficulty of integrating multiple sensors or highperformance CPU in such a limited dimension, which strongly hindered the perception and calculation ability, quadruped robots in small scale haven’t shown equally performance. To overcome this limitation, we integrated the inertial measurement unit (IMU) into the small-sized robotic rat SQuRo, and proposed simplified control strategies to enhance its robustness. The improvements includes three parts: 1) A posture control strategy is proposed and verified by posture adjustment in a slope test; 2) Straight line tracking was achieved by detecting and controlling the yaw angle of SQuRo; 3) The autonomous fall recovery was achieved by detecting the fall and executed a predefined fall recovery strategy.
Vision and Vibration Data Fusion-Based Structural Dynamic Displacement Measurement with Test Validation

The dynamic measurement and identification of structural deformation are essential for structural health monitoring. Traditional contact-type displacement monitoring inevitably requires the arrangement of measurement points on physical structures and the setting of stable reference systems, which limits the application of dynamic displacement measurement of structures in practice. Computer vision-based structural displacement monitoring has the characteristics of non-contact measurement, simple installation, and relatively low cost. However, the existing displacement identification methods are still influenced by lighting conditions, image resolution, and shooting-rate, which limits engineering applications. This paper presents a data fusion method for contact acceleration monitoring and non-contact displacement recognition, utilizing the high dynamic sampling rate of traditional contact acceleration sensors. It establishes and validates an accurate estimation method for dynamic deformation states. The structural displacement is obtained by combining an improved KLT algorithm and asynchronous multi-rate Kalman filtering. The results show that the presented method can help improve the displacement sampling rate and collect high-frequency vibration information compared with only the vision measurement technique. The normalized root mean square error is less than 2% for the proposed method.
Optimization of hydraulic turbine diffuser

Hydraulic turbine diffuser recovers pressure energy from residual kinetic energy on turbine runner outlet. Efficiency of this process is especially important for high specific speed turbines, where almost 50% of available head is utilized within diffuser. Magnitude of the coefficient of pressure recovery can be significantly influenced by designing its proper shape. Present paper focuses on mathematical shape optimization method coupled with CFD. First method is based on direct search Nelder-Mead algorithm, while the second method employs adjoint solver and morphing. Results obtained with both methods are discussed and their advantages/disadvantages summarized.