Miracle-dz/UniFAR-FTUs huggingface dataset:
How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.
Socio-Economic Determinants of Rice Farmers' Loan Size in Shama, Ghana

2 Abstract: Farm credit can stimulate the transfer of technology into agriculture and hence lead to increased crop yield. However, most often than not farmers are faced with the problem of loans received being far lower than what they applied for. The objectives of this study are therefore to: (i) analyze the size of loan applied for and received (ii) identify the constraints regarding loan obtainment and (iii) analyze the socioeconomic determinants of farmers' loan size. A random sample of 91 rice farmers from Shama in the western region of Ghana was used for the study. A well structured interview schedule was the main tool for data collection, while descriptive statistics and multiple regression analysis were the main analytical techniques. The farmers interviewed stressed loan availability and accessibility as major problems. The size of loan received by farmers was far lower and significantly different from the amount applied for. Empirical results from the regression analysis find the farm size, income and years of farming experience as positive and significant predictors of farm loan size.
Detection and assessment scheme of voltage and current unbalance for three phase synchronous generators using dual numerical techniques based on correlation and deviation percentage concepts

A differentiation in three-phase voltages or currents is termed as unbalance. Unbalance can be caused by unbalanced loads and power system faults. This disturbance results in serious problems in system elements, particularly to synchronous generators, due to the deviation of magnitude, frequency or phase shift. The ability to detect unbalance as well as to identify the severity level of unbalance is very important in power grids. This paper describes a protection scheme for voltage and current unbalance in synchronous generators. The scheme is based on dual numerical techniques application utilizing correlation and deviations percentages for voltage and current signals to detect and assess the unbalance situations. Different types of disturbances are simulated to verify the effectiveness of the proposed scheme using ATP and MATLAB programs. The simulation results prove that the scheme is successful for unbalance detection and assessment of synchronous generators. The different faults and operating conditions have no effect on the proposed scheme reliability. Also, the results show that the proposed scheme is able to classify the unbalance into different severity levels. Besides the obtained results reveal this scheme is effective and secure to the acceptable overloading and temporary faults in power systems.
Study the Relationship of PCSK9 Gene Polymorphism and PCSK9 Protein on Serum Bad Cholesterol and Risk of CVDs in Iraqi Patient

Cardiovascular disease responsible for number 1 of death worldwide. Many genetic and environmentalfactors influence the development of CVDs. PCSK9 protein responsible for degradations of LDLRwhich lead to increase blood level of bad cholesterol. Gain of function mutation in PCSK9gene result inhypercholesterolemia and loss of function lead to hypocholesterolemia. Aim of this study: to investigatethe polymorphism of rs775278198 of PCSK9 gene and serum level of PCSK9 with their effect on LDL-Clevel in CVDs patients. Materials and methods: a common SNP PCSK9 gene was studied using Real-Time PCR and technique to show association between PCSK9 SNP with risk of developing CVDs. fifty ofcardiovascular diseases patients who were clinically diagnosed by physicians and fifty apparently healthyindividuals were conducted within this study. Blood samples were collected from all subjects after overnightfasting. Genomic DNA was extracted from blood samples and analyzed for rs775278198 SNP in PCSK9gene with specific primers and probes using Real-Time PCR technique. Also, serum levels of PCSK9 wereevaluated using Elisa kit supplied by Bioassay Technology Laboratory. In addition LDL-C levels weremeasured by lipid profile test kit (CHOL2) supplied by Roch. Identification of PCSK9 SNP polymorphismand PCSK9 serum levels with LDL-C levels in this study success percentage of the methods. Results: inReal Time PCR, CVDs patients appeared the TT (20 vs 4, OR=6, p=0.028) genotypes were significantlyhigher in patients when compared to controls group (20 vs 4%, OR=6, P=0.028) . In comparison of theserum levels of PCSK9 in CVDs patient were higher than PCSK9 serum levels in controls group (p value=f0.000005) at the same time there is a positive relationship between PCSK9 serum levels and LDL-C levelsas measured by Pearson Correlation.
Synthesis and in vitro cytotoxicity evaluation of ruthenium polypyridyl-sensitized paramagnetic titania nanoparticles for photodynamic therapy

Magnetite/silica/titania core–shell–shell nanoparticles were synthesized by sol–gel methods and characterized by various methods. The hydrodynamic radius of the final nanoparticle sample was measured by dynamic light scattering and found to be 99 nm. Following hydrothermal treatment of the final nanocomposites, a ruthenium polypyridyl dye was anchored to the surface. The total potential reactive oxygen species (ROS) generation and singlet oxygen (1O2) production in solution by the hybrid NPs (naked NPs + ruthenium dye) were evaluated by fluorescence and UV-Vis spectroscopy. Under 532 nm light illumination, more than a five-fold increase in ROS production was observed with the hybrid NPs in comparison to the bare NPs or the ruthenium dye alone. Similar results were obtained under white light illumination as well. Moreover, singlet oxygen generation produced by the hybrid NPs was found to be negligible. The photodynamic effect of the excited NPs on the viability of lung cancer cells (A549) was evaluated in vitro. ROS production in A549 NP-loaded cells was measured and a live/dead cell assay was conducted. There was a significant, light excitation-dependent increase in ROS production and induction of cell death in light excited A549 NP-loaded cells as compared to control cells and NP loaded cells without excitation. These findings demonstrate that the hybrid NPs can potentially act as type I photodynamic therapy (PDT) agents generating free radicals, unlike the currently employed ones in medicine which follow a type II mechanism predominantly (generating singlet oxygen). This type of photosensitizers can prove advantageous in eradicating PDT-resilient hypoxic tumors, avoiding type II photosensitizers-induced hypoxia in non-hypoxic tumor cells.
The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.
Measurement report: Effects of NOx and seed aerosol on highly oxygenated organic molecules (HOMs) from cyclohexene ozonolysis

Abstract. Cyclohexene ( C6H10 ) is commonly used as a proxy for biogenic monoterpenes, when studying their oxidation mechanisms and secondary organic aerosol (SOA) formation. The ozonolysis of cyclohexene has been shown to be effective at producing highly oxygenated organic molecules (HOMs), a group of molecules known to be important in the formation of SOA. Here, we provide an in-depth look at how the formation and fate of the broad range of observed HOMs changed with perturbations from NOx and seed particles. HOMs were produced in a chamber from cyclohexene ozonolysis and measured with a chemical ionisation mass spectrometer (CIMS) using nitrate ( NO 3 - ) as reagent ion. As high-resolution CIMS instruments provide mass spectra with numerous ion signals and a wealth of information that can be hard to manage, we employed a primarily statistical approach for the data analysis. To utilise as many individual HOM signals as possible, each compound was assigned a parameter describing the quality of the observed signal. These parameters were then used as weights or to determine the inclusion of a given signal in further analyses. Under unperturbed ozonolysis conditions, known HOM peaks were observed in the chamber, including C6H8O9 as the largest HOM signal and C12H20O9 as the largest “dimer” product. With the addition of nitric oxide (NO) into the chamber, the spectrum changed considerably, as expected. Dimer product signals decreased overall, but an increase in dimers with nitrate functionalities was seen, as a result of NO3 radical oxidation. The response of monomer signals to NO addition varied, and while nitrate-containing monomers increased, non-nitrate signals either increased or decreased, depending on the individual molecules. The addition of seed aerosol increased the condensation sink, which markedly decreased the signals of all low-volatility compounds. Larger molecules were seen to have a higher affinity for condensation, but a more detailed analysis showed that the uptake was controlled mainly by the number of oxygen atoms in each molecule. Nitrates required higher mass and higher oxygen content to condense at similar rates as the non-nitrate HOMs. We also tested two existing elemental-composition-based parameterisations for their ability to reproduce the condensation observed in our cyclohexene system. Both predicted higher volatilities than observed, most likely due to the number of oxygen atoms enhancing the product uptake more than the models would suggest.
Data‐driven forecasting of local PV generation for stochastic PV‐battery system management

Power systems face more uncertainty by increasing photovoltaic system installations on the roof of buildings. To optimally manage energy and available flexibility in a building, stochastic optimization is used to take an optimal decision under uncertainty and minimize the operational cost. In stochastic optimization, a scenario set is used as an input to represent the uncertainty in a random variable, PV energy generation in this case. In this paper, a data‐driven method is proposed to obtain the distribution of the random variable and later generate scenario sets representing the uncertainty on day‐ahead PV energy generation installed on the roof of a building. This method is only based on historical PV generation and it does not require any other external data such as weather forecasts. A machine learning‐based technique is applied to forecast the PV energy production following by generating a scenario set for day‐ahead decision‐making. Later, the day‐ahead PV‐battery system management problem is formulated as a two‐stage stochastic optimization while the generated scenario set is the input of this optimization. The proposed algorithm is tested in the day‐ahead scheduling of a PV‐battery system for a commercial building, informed by real‐life measurement data. The results show that the proposed algorithm is able to capture the uncertainty in PV system while providing a cost‐optimal and reliable solution to the application problem. Without using any weather data, the error of the proposed PV energy forecast method reaches to NRMSE = 11.89, while there is 5% reduction in the operational cost of the proposed two‐stage stochastic optimization. Moreover, the proposed algorithm is easy to implement in the energy management system of a building to manage PV‐battery system.
Column Flotation of Subbituminous Coal Using the Blend of Trimethyl Pentanediol Derivatives and Pico-Nano Bubbles

Patch-based Ensemble Learning Scheme for Heterogeneous Face Recognition by Cunjian Chen Doctor of Philosophy in Computer Science West Virginia University Arun Ross, Ph.D., Chair The problem of Heterogeneous Face Recognition (HFR) involves comparing and matching face images that look significantly different primarily due to variations in their photometric composition. Examples include matching face images acquired in different spectral bands (e.g., visible versus thermal spectrum), or before and after the application of makeup. In this dissertation, we develop and evaluate a robust face recognition method to address this challenge. The first part of the thesis deals with the topic of facial cosmetics. In this regard, we demonstrate the negative impact of facial cosmetics on existing face recognition as well as gender and age estimation systems. Next, we design a method that automatically detects makeup in face images. The proposed method extracts a feature vector that captures the shape, texture and color characteristics of the input face image, and employs a learning approach based on SVM/AdaBoost to determine the presence or absence of makeup. Finally, we design a patch-based ensemble learning method to perform makeup-invariant face recognition. In the proposed scheme, each face image is tessellated into patches and each patch is represented by a set of feature descriptors, viz., Local Gradient Gabor Pattern (LGGP), Histogramof Gabor Ordinal Ratio Measures (HGORM) and Densely Sampled Local Binary Pattern (DS-LBP). Then, a novel Semi Random Subspace Linear Discriminant Analysis (SRS-LDA) method is used to perform ensemble learning by sampling patches and constructing multiple common subspaces between before-makeup and after-makeup facial images. Finally, collaborative-based and sparse-based representation classifiers are used to compare feature vectors in this subspace and the resulting scores are combined via the sumrule. Extensive experimental analysis demonstrates the efficacy of the proposed method. The second part of the thesis deals with the topic of cross-spectral face recognition. Here, we design a method to compare input face images originating from the visible (VIS) and thermal (THM) spectrum. In the training phase of the proposed method, face images from VIS and THM are filtered and tessellated into patches. Each patch is represented using Pyramid Scale Invariant Feature Transform (PSIFT) or Histograms of Principal Oriented Gradients (HPOG). Then, a cascaded subspace learning process, consisting of whitening transformation, factor analysis, and common discriminant analysis, is used to construct multiple common subspaces between VIS and THM facial images. During the matching phase, the projected feature vectors from individual subspaces are concatenated to form a single feature vector. A Nearest Neighbor (NN) classifier is then used to compare feature
Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication

Heart failure (HF) and type 2 diabetes mellitus (T2DM) have a synergistic effect on cardiovascular (CV) morbidity and mortality in patients with established CV disease (CVD). The aim of this review is to summarize the knowledge regarding the discriminative abilities of conventional and novel biomarkers in T2DM patients with established HF or at higher risk of developing HF. While conventional biomarkers, such as natriuretic peptides and high-sensitivity troponins demonstrate high predictive ability in HF with reduced ejection fraction (HFrEF), this is not the case for HF with preserved ejection fraction (HFpEF). HFpEF is a heterogeneous disease with a high variability of CVD and conventional risk factors including T2DM, hypertension, renal disease, older age, and female sex; therefore, the extrapolation of predictive abilities of traditional biomarkers on this population is constrained. New biomarker-based approaches are disputed to be sufficient for improving risk stratification and the prediction of poor clinical outcomes in patients with HFpEF. Novel biomarkers of biomechanical stress, fibrosis, inflammation, oxidative stress, and collagen turn-over have shown potential benefits in determining prognosis in T2DM patients with HF regardless of natriuretic peptides, but their role in point-to-care and in routine practice requires elucidation in large clinical trials.
ASSOCIATION OF NURSES' EMPATHIC TENDENCY AND ATTITUDES TO AGEISM TOWARD OLDER ADULTS

Objective: This study aims to determine the correlation of nurses empathic tendency and ageism attitudes towards older adults Material and Method: A descriptive and correlational with a convenience sample of 132 Turkey nurses was conducted in April-June 2017. Structured question form, Scale of Empathic Tendency and Fraboni Scale of Ageism (FSA) were applied to nurses. Results: The regression model used in the study explains 16% of the factors affecting ageism in nurses. In this model, factors that remained negative attitude toward the elderly were female, obtaining information about the elderly from the media and willingness to care for the elderly (p<0.05). Conclusion: This study found that ageism and empathic tendencies of nurses were at moderate level. It is important to develop the empathic tendencies of nurses in preventing ageism. Accordingly, it can be suggested that nurses' knowledge on this subject should be improved with evidence-based practices.
Foam-Mat Drying of Muskmelon

Abstract The Box–Behnken design of experiments under response surface methodology (RSM) was used to optimize the foaming process for the development of foam mat-dried muskmelon powder. The independent variables were the concentration of egg albumen (EA), carboxymethyl cellulose (CMC) and whipping time (WT). The responses were foam density, foam drainage volume and foam expansion. The optimum conditions for foaming process were EA (11.59% w/w), CMC (0.59% w/w) and WT (3.97 min). The unfoamed muskmelon pulp took longer time to dry to the final moisture content of 2% d.b than foamed pulp. The effective diffusivity for the foamed muskmelon pulp was found to be higher than the unfoamed pulp. The unfoamed pulp resulted in a sticky and dark colored powder, whereas the foamed pulp had a free-flowing and light colored powder. The flaky and porous foam-dried product can be used as an ingredient in broad range of food products.
A Large-scale Static-dynamic Model Test System for Stability Study of Tunnel under Static-dynamic Load

In order to study the stability of tunnels under static-dynamic load, a new large-scale static-dynamic model test system was developed. The system is composed of a static loading unit, a dynamic loading unit, a data acquisition unit and a test bench, which are specially designed to overcome the technical problems of large-scale static-dynamic model test, such as dynamic loading and control, high-rate strain measurement and easy operation. The system uses a model of 3600 × 3600 × 2500 mm in size to study a larger load disturbance area than static tests. It can accurately apply three-dimensional gradient static stress and dynamic load with adjustable amplitude (or magnitude), frequency and waveform to simulate the true geo-stress field and disturbance field, and accurately and rapidly collect displacement, stress, strain, velocity, acceleration and other important parameters of the model. In addition, it is convenient to adjust the scale of the model, remove and retain the test model. The system was used to carry out a static load model test and a static-dynamic load model test with a tunnel as a prototype, and its reliability and accuracy were verified.
Commercial and potential applications of bacterial cellulose in Brazil: ten years review

Abstract In the last decade, bacterial cellulose (BC) has received considerable attention around the world, including in Brazil. The unique properties of BC, such as mechanical stability, tensile strength, thermostability, crystallinity, purity and biocompatibility make it a promising candidate for commercial applications in different areas. This article provides a comprehensive synthesis of commercial applications and studies related to BC around the world and shows the importance and development of Brazilian research during the last decade. In this review we present an overview of BC structure, biosynthesis and possible applications of BC mainly in the food, electronics, bioengineering, cosmetics and biomedical areas. The most significant contributions of Brazilian researchers using BC have been carried out in the biomedical area. Despite the increase in BC reserch, Brazil also needs to develop strategies to expand the use and commercialization of BC products, for which government financial support is extremely necessary.