Miracle-dz/UniFAR-FTUs huggingface dataset:
Pandemic Policing and Community Engagement: Preparedness, Legitimacy and Public Support During the COVID-19 Crisis in Nigeria

This study brings to the fore, insights into three key factors that had been widely noted to play significant roles in driving effective pandemic policing. These are the preparedness of the police as first responders to a public health crisis, the level of public trust in the police as a legitimate power holder, and community engagement as a tool to drive public support and participation in fighting COVID-19. Using the Nigeria police as a case study, with the damning reports of abuse of power and other misconduct, this study examined how the police responded to COVID-19 mandates and community participation. Interviews with 40 police officers who enforced the COVID-19 lockdown, 16 senior police officers, and 18 community leaders within Lagos and Ogun states were conducted, and a thematic analysis of the narratives was carried out. Findings indicated that community engagement was not effectively deployed by the Nigerian police in the course of pandemic policing. This was due to a lack of police preparedness, over-reliance on the use of force for public control, public distrust in the police, and a lack of prior practice of community engagement by the police. Public distrust in the police was found to be central to peoples’ disobedience to COVID-19 rules which worsened police-community relations, culminating in protests against the police and its formations. These findings have important policy and practical implications if police legitimacy and post-COVID police-community relations are to improve.
GFF-CARVING: Graph Feature Fusion for the Recognition of Highly Varying and Complex Balinese Carving Motifs

The recognition of Balinese carving motifs is challenging due to the highly varying and interrelated motifs of Balinese carvings and in addition to the scantiness of Balinese carving data. This study proposed a method named GFF-CARVING for the recognition of Balinese carving motifs. GFF-CARVING is a deep learning architecture based on the Graph Convolutional Network (GCN) and Convolutional Neural Network (CNN) to extract image and graph features. GFF-CARVING applies feature fusion to improve the discriminative ability of the model to overcome these challenges and therefore improve its recognition performance. The proposed method consists of three main modules, the image representation learning module, the graph representation learning module, and the prediction module. The image representation learning module is based on ResNet and extracts the image features using global max pooling. The graph representation learning module is based on GCN and extracts the graph features. The graph features are handcrafted features that are built based on the occurrence relationship between the constituent sub-motifs of Balinese carvings. The feature fusion generates new features that take into account the occurrence relationship between the sub-motifs. These new features are used in the prediction module to accurately recognize the Balinese carving motifs. Based on the experimental results, GFF-CARVING achieved the highest recognition accuracy of 98.93% compared to other state-of-the-art models. These results indicated that feature fusion based on the handcrafted graph features and image features improved the discriminative ability of GFF-CARVING in recognizing Balinese carving motifs.
A 55nm CMOS Linearized Oscillator for Audio VCO-ADCs achieving 78dBA of SNDR with $153\mu \mathrm{W}$

In this work, we present a current-controlled ring oscillator (CCRO) with an analog feedback loop that linearizes the CCRO's signal-to-frequency tunning curve. The feedback loop is based on a frequency-dependent resistor (FDR) and a current divider. This linearization technique reduces the oscillator voltage swing, simplifying the implementation of a transconductor signal driver. We investigate the circuit parameters to optimize the trade-off between linearization and power consumption. Furthermore, an experimental 55nm CMOS chip implementing the proposed architecture has been designed and measured. The fabricated chip is intended for audio VCO-ADCs and achieves 78.15 dBA of peak SNDR with a power consumption of $153\mu \mathrm{W}$.
A resampling approach to estimate variance components of multilevel models

In a multilevel model for complex survey data, the weight‐inflated estimators of variance components can be biased. We propose a resampling method to correct this bias. The performance of the bias corrected estimators is studied through simulations using populations generated from a simple random effects model. The simulations show that, without lowering the precision, the proposed procedure can reduce the bias of the estimators, especially for designs that are both informative and have small cluster sizes. Application of these resampling procedures to data from an artificial workplace survey provides further evidence for the empirical value of this method. The Canadian Journal of Statistics 40: 150–171; 2012 © 2012 Statistical Society of Canada
Nonstationarity in Multifactor Models of Discrete Jump Processes, Memory, and Application to Cloud Modeling

AbstractBecause of the mathematical and numerical limitations, standard statistical methods known from the literature are not applicable to inferring jump processes under exogenous influence. Such processes can be considered, for example, in the atmosphere (transitions between different cloud types) and in the ocean (phase transitions between water and ice). Reasons for these intrinsic limitations of standard methods are investigated and a method for the inference of discrete microscopic jump models based on macroscopic ensemble observations is presented. It significantly extends the recently developed methods of nonstationary Markov model parameterization (which are constrained to a single exogenous factor and to direct individual observations of the jump process realizations). The main advantage of the new method is the possibility of inference from indirect ensemble observations with multiple exogenous factors. Moreover, this method allows for a new possibility to test whether the available time series.
Global Error Bounds for Convex Inequality Systems in Banach Spaces

We study conditions under which a global error bound in terms of a natural residual exists for a convex inequality system. Specifically, we obtain an error bound result, which unifies many existing results assuming a Slater condition. We also derive two characterizations for a convex inequality system to possess a global error bound; one is in terms of metric regularity, and the other is in terms of an associated convex inequality system. As a consequence, we show that in \font\bbbabstract=msbm10 at 8pt% $\mbox{\bbbabstract R}^n$ a global error bound holds for such a system under the assumption of the zero vector in the relative interior of the domain of an associated conjugate function along with metric regularity at every point of the feasible set defined by the system. Finally, we discuss some applications of these results to convex programs.
UV-B Sensor Based on a SnO2 Thin Film

A photoconductive-type SnO2-based photosensor was fabricated for monitoring UV-B rays that are harmful to human health. A SnO2 film was grown by plasma-assisted molecular beam epitaxy and postannealed in air to increase the resistivity of the film for decreasing dark current. The film had an optical bandgap of 4.1 eV, which is suitable for sensing UV-B rays. The sensor exhibited a large photoresponsivity in the UV-B region. The external quantum efficiency at 290 nm was 10%. These results support the development of simple and low-cost UV-B sensors based on SnO2.
Alzheimer's Disease Segmentation and Classification on MRI Brain Images Using Enhanced Expectation Maximization Adaptive Histogram (EEM-AH) and Machine Learning

Alzheimer’s disease (AD) is an irreversible ailment. This ailment causes rapid loss of memory and behavioral changes. Recently, this disorder is very common among the elderly. Although there is no specific treatment for this disorder, its diagnosis aids in delaying the spread of the disease. Therefore, in the past few years, automatic recognition of AD using image processing techniques has achieved much attraction. In this research, we propose a novel framework for the classification of AD using magnetic resonance imaging (MRI) data. Initially, the image is filtered using 2D Adaptive Bilateral Filter (2D-ABF). The denoised image is then enhanced using Entropy-based Contrast Limited Adaptive Histogram Equalization (ECLAHE) algorithm. From enhanced data, the region of interest (ROI) is segmented using clustering and thresholding techniques. Clustering is performed using Enhanced Expectation Maximization (EEM) and thresholding is performed using Adaptive Histogram (AH) thresholding algorithm. From the ROI, Gray Level Co-Occurrence Matrix (GLCM) features are generated. GLCM is a feature that computes the occurrence of pixel pairs in specific spatial coordinates of an image. The dimension of these features is reduced using Principle Component Analysis (PCA). Finally, the obtained features are classified using classifiers. In this work, we have employed Logistic Regression (LR) for classification. The classification results were achieved with the accuracy of 96.92% from the confusion matrix to identify the Alzheimer’s Disease. The proposed framework was then evaluated using performance evaluation metrics like accuracy, sensitivity, F-score, precision and specificity that were arrived from the confusion matrix. Our study demonstrates that the proposed Alzheimer’s disease detection model outperforms other models proposed in the literature.
“That Was the Happiest Time of My Life”: Understanding Childhood Eco-Connections in Appalachian Communities

The goal of this study was to explore facets of childhood eco-connections, using retrospective qualitative data from adults. One hundred and forty-five adults from predominantly rural and small-town communities in southern Appalachia (71.7% female), average age 36.23 years (SD = 12.08) participated in semi-structured qualitative interviews on key life experiences and the places they grew up. Mentions about place-related childhood memories or interactions were grouped into four themes based on exploratory thematic content analysis: nature nostalgia (positive reminiscences about nature experiences); nature immersion (extensive contact with the natural world during childhood); formative experiences with nature (nature interactions that taught a skill or life lesson); and rhythms of nature (appreciation of seasonal or cyclical patterns). Childhood eco-connections are multifaceted and often emerge from early impactful or intense experiences. Pro-environmentalism messages to youth may benefit from more references to childhood eco-connections.
Design of a Gagenbuer Polynomial based Low Voltage Ride ThroughScheme for three-phase system

This paper describes the design and implementation of a Gegenbuer polynomial (GP)-based controller for the operation of a three-phase grid coupled to a renewable PV array. The PV source is linked at the DC link of the voltage source converter (VSC). Least Mean Square (LMS) is used to train the suggested GP method for calculating the fundamental load current value. The controller has many modes of operation and is designed to handle power quality (PQ) concerns. In addition to its normal mode of operation, it can withstand voltage dips. The VSC injects reactive power into the grid in case of low voltage ride through (LVRT) mode of operation. The findings illustrate both normal grid conditions, in which the system will function in unity power factor (UPF), and abnormal grid scenarios, in which the system must run in LVRT mode. Simulation and experimental findings for the different operating modes have been validated using the proposed GP-based controller.
Urine Galectin-3 binding protein reflects nephritis activity in systemic lupus erythematosus

Background Lupus nephritis (LN) is a major and severe organ involvement in systemic lupus erythematosus (SLE), whose diagnosis and treatment necessitate to perform kidney biopsy, which is an invasive procedure. Non-invasive urine biomarkers are an active area of investigation to support LN diagnosis and management. Objective To investigate the role of urinary galectin-3 binding protein (u-Gal-3BP) as a candidate biomarker of renal disease in biopsy proven LN. Patients and methods Levels of u-Gal-3BP were investigated in a cross-sectional fashion by ELISA in 270 subjects: 86 LN patients, 63 active SLE patients with no kidney involvement, 73 SLE patients with inactive disease and 48 age and sex-matched population-based controls (PBC). Moreover, urine samples were analysed separately by ELISA for additional markers of kidney pathology: neutrophil gelatinase-associated lipocalin (NGAL), osteopontin (OPN), kidney injury molecule-1 (KIM-1) and galectin-3 (Gal-3). The concentrations of all studied molecules were normalized to urine creatinine levels. In 10 patients, post-treatment levels of the biomarkers were measured. Results Normalized u-Gal-3BP levels were higher in LN patients compared to the other groups (p < .0001). Comparing different LN classes, u-Gal-3BP levels were higher among patients with proliferative (class III/IV) and membranous (class V) as compared to mesangial (class II) forms (p = .04). In proliferative forms, u-Gal-3BP levels correlated with the activity index in renal biopsies (r = 0.42, p = .004). Moreover, in a subset of 10 patients with repeated kidney biopsy and urine sampling before and after induction treatment, a significant decrease of u-Gal-3BP was observed (p = .03). Among the other markers, KIM-1 was also able to discriminate LN from the other groups, while NGAL, OPN and Gal-3 could not in this cohort. Conclusion Given its ability to discriminate LN patients from active non-renal and inactive SLE patients, the observed correlation with the activity index in renal biopsies, and its levels declining following treatment, u-Gal-3BP shows promise as a non-invasive urinary biomarker to help detecting and to monitor renal involvement in SLE patients and should be validated in larger cohorts.
VVC Interpicture Prediction Using SAD with Imprecise Subtractors: A Quantitative Analysis

Real-time video encoding demands intense computational effort resulting in large power dissipation and energy consumption. Therefore, energy efficiency becomes mandatory especially for energy-constrained systems such as battery-powered mobile devices. In this context, approximate computing arose as a promising approach to improve energy efficiency. This work presents an analysis of the SAD distortion metric using approximate subtractors in interpicture prediction considering the VVC reference encoder. The approximate 10-bit sub tractors were evaluated considering four levels of imprecision (in number of approximate bits). The results achieved show the potential in the adoption of approximate subtractors both in coding efficiency and in power reduction. The SAD architecture can achieve a power reduction of 16.45% when compared to SAD using RCA subtractor, while not degrading the compression efficiency (less than 0.1% BD-Rate increase).
Derivation of the 1964 CIE 10° XYZ colour‐matching functions and their applicability in photometry

The 1931 2° standard colorimetric observer can be used for photometry as well as for colorimetry. But there is confusion as to whether this applies also to the 1964 10° standard colorimetric observer. This system was derived by Judd, who did not fully report his procedure. Its validity in photometry and the way in which Judd derived the transformation equations are examined. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 67–75, 2001
Effects of Gaze Stabilization Exercises on Gait, Plantar Pressure, and Balance Function in Post-Stroke Patients: A Randomized Controlled Trial

This study aims to explore the effects of gaze stabilization exercises (GSEs) on gait, plantar pressure, and balance function in post-stroke patients (≤6 months). Forty post-stroke patients were randomly divided into an experimental group (n = 20) and a control group (n = 20). The experimental group performed GSEs combined with physical therapy, while the control group only performed physical therapy, once a day, 5 days a week, for 4 weeks. The Berg Balance Scale (BBS) was used to test the balance function and the risk of falling, which was the primary outcome. The Timed Up and Go test (TUGT) evaluated the walking ability and the fall risk. The envelope ellipse area and the plantar pressure proportion of the affected side were used to measure the patient’s supporting capacity and stability in static standing. The anterior–posterior center of pressure displacement velocity was used to test the weight-shifting capacity. Compared to the control group, the swing phase of the affected side, swing phase’s absolute symmetric index, envelope ellipse area when eyes closed, and TUGT of the experimental group had significantly decreased after GSEs (p < 0.05); the BBS scores, TUGT, the anterior–posterior COP displacement velocity, and the plantar pressure proportion of the affected side had significantly increased after 4 weeks of training (p < 0.05). In conclusion, GSEs combined with physical therapy can improve the gait and balance function of people following stroke. Furthermore, it can enhance the weight-shifting and one-leg standing capacity of the affected side, thus reducing the risk of falling.
Smooth optimum kernel estimators near endpoints

SUMMARY Kernel estimators for smooth curves like density, spectral density or regression functions require modifications when estimating near endpoints of the support, both for practical and asymptotic reasons. The construction of such boundary kernels as solutions of a variational problem is addressed and representations in orthogonal polynomials are given, including explicit solutions for the most important cases. Based on explicit formulae for certain functionals of the kernels, it is shown that local bandwidth variation might be indicated near boundaries. Various bandwidth variation schemes are discussed and
Stock Price Prediction with Golden Cross and Death Cross on Technical Analysis Indicators Using Long Short Term Memory

The stock market price movement has become an interesting topic to research due to the large profit potential. Market traders use technical analysis to forecast stock prices by analyzing stock transaction data. Because technical analysis predictions are subjective, an automated system is needed to produce more objective and faster results. In the previous, next prices were predicted by combining price and transaction volume features with technical analysis indicators such as Stochastic, Moving Average, Moving Average Convergence Divergence, and Relative Strength Index values. In most cases, the addition of the indicator's value reduces the accuracy of the forecast. This is because the value of technical analysis indicators is not useful enough when they are used directly without transformation. The transformation of technical analysis indicators to a crossing value between the fast and slow signal on these indicators, known as the golden cross and death cross, has a greater impact on stock price movement. Therefore, it is recommended to use the price and the golden cross and death cross on technical analysis indicators as inputs to improve stock price predictions. Long Short Term Memory is used in the proposed method, which is a reliable method for predicting sequence data. The stock price forecasts are based on 1200 days of trading data from the Indonesia Stock Exchange for 10 LQ45 group shares. The test was conducted by calculating the Root Mean Squared Error and the Mean Absolute Percentage Error value of the predicted close price next day.
On the interchange of subdifferentiation and epi-convergence

Abstract Given a sequence (ƒ n ) of nonconvex nondifferentiable functions, or, more generally, a parametrized family of such functions which epi-converges to some function ƒ 0 we give conditions in order to have ∂ − ƒ 0 = lim n ∂ − ƒ n , where ∂ − ƒ 0 is the Frechet subdifferential of ƒ 0 . We also give conditions in order to get inclusons instead of equality. We point out an application to second-order epi-derivatives.
Research on compound scattering modeling and imaging methods of sea surface ship target for GEO-UAV BiSAR

The bistatic imaging system based on Geosynchronous Earth Orbit SAR (GEOSAR) launch-unmanned aerial vehicle(UAV) SAR reception has the advantages of high resolution, flexible configuration, fast maneuvering response, and continuous reconnaissance for large areas, especially in high-resolution ship target imaging and recognition technology. However, under the long synthetic aperture, the ship’s target imaging will be affected by the complex structure of the bistatic radar system and marine dynamics factors, resulting in the failure of the traditional SAR imaging algorithm, so it is urgent to develop the new method of high-resolution imaging of moving ship targets for complex sea background. Based on this, this paper combines the geometric difference between GEO-UAV BiSAR geometric structure and the actual complex sea surface ship target to study the sea surface scattering and ship dynamics motion model, and establish a high confidence sea surface motion ship composite scattering model. On this basis, the precise focusing imaging algorithm of moving ship targets in rough sea is proposed. Finally, the effectiveness of the proposed method is verified by simulation analysis.
DBTap-A Digital Transformation App

To create a one-stop digital shop for college administrative and academic needs. The recent academic shift to an online format resulted in a lack of coordination among college administrative personnel and students. This problem statement into account by DBTap, which addresses it using three key modules: No Dues, Convocation, and Letter Of Recommendation (LOR). This digital system enables students to apply for No Dues and LOR online and assists every faculty member participating in the process in approving or rejecting applications with justifications. It assists teachers in keeping track of all alumni by scanning copies of admit cards for further education. The database approach helps gather data for each student and analyse it for convocation purposes, where student skills are evaluated based on academic, athletic, technical, and extra-curricular data.