Miracle-dz/UniFAR-FTUs huggingface dataset:
BIFM: Big-Data Driven Intelligent Forecasting Model for COVID-19

Ever since the pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China, it has been recognized as a global threat and several studies have been carried out nationally and globally to predict the outbreak with varying levels of dependability and accuracy. Also, the mobility restrictions have had a widespread impact on people’s behavior such as fear of using public transportation (traveling with unknown passengers in the closed area). Securing an appropriate level of safety during the pandemic situation is a highly problematic issue that resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The autoregressive integrated moving average (ARIMA) machine learning model is used to develop the best model for twenty-one worst-affected states of India and six worst-hit countries of the world including India. The best ARIMA models are used for predicting the daily-confirmed cases for 90 days future values of six worst-hit countries of the world and six high incidence states of India. The goodness-of-fit measures for the model achieved 85% MAPE for all the countries and all states of India. The above computational analysis will be able to throw some light on the planning and management of healthcare systems and infrastructure.
Interoperable Models for Dynamics and Shape Tracking of Soft Fingers

A lot of works highlight the soft material properties, used in grasping tasks by soft fingers. However, the kinematics as well as the dynamics of these soft fingers are strongly influenced by the material’s properties along their soft structure. Due to their compliance, the soft grippers are more often used to achieve form closure grasping which is safer than force closure grasping. One main issue related to soft grasping concerns the shape control of the finger, allowing obtaining perfect compliance when attempting a form closure grasping. In this work, we propose interoperable models based on Pythagorean Hodograph and Euler-Bernoulli’s beam dynamics to model both dynamics and shape of Fluidic Elastomeric soft fingers. This makes a relationship between the physical actuators and the virtual control points of the parametric Pythagorean Hodograph (PH) curve which drive the finger shape. The PH curve with its finite control points is used to model and control the kinematics of the shape of the soft fingers, characterized by an infinite degree of freedom (DoF). This modeling will allow controlling the position of the virtual control points of soft fingers. The results of this modeling are validated numerically and experimentally with a soft finger made up of Fluidic Elastomeric Actuators (FEA).
Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

Scientists try to design experiments that will yield maximal information. For instance, given the available evidence and a limitation on the number of variables that can be observed simultaneously, it may be more informative to intervene on variable <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> and observe the response of variable <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> than to intervene on <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> and observe <inline-formula> <tex-math notation="LaTeX">$Z$ </tex-math></inline-formula>; in other situations, the opposite may be true. Scientists must often make these decisions without primary data. To address this problem, in previous work, we created software for annotating aggregate statistics in the literature and deriving consistent causal explanations, expressed as causal graphs. This meta-analytic pipeline is useful not only for synthesizing evidence but also for planning experiments: one can use it strategically to select experiments that could further eliminate causal graphs from consideration. In this paper, we introduce interpretable policies for selecting experiments in the context of piecemeal causal discovery, a common setting in biological sciences in which each experiment can measure not an entire system but rather a strict subset of its variables. The limits of this piecemeal approach are only beginning to be fully characterized, with crucial theoretical work published recently. With simulations, we show that our experiment-selection policies identify causal structures more efficiently than random experiment selection. Unlike methods that require primary data, our meta-analytic approach offers a flexible alternative for those seeking to incorporate qualitative domain knowledge into their search for causal mechanisms. We also present a method that categorizes hypotheses with respect to their utility for identifying a system’s causal structure. Although this categorization is usually infeasible to perform manually, it is critical for conducting research efficiently.
DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar

We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton. edu/dales3d.
Online Downlink Multi-User Channel Estimation for mmWave Systems Using Bayesian Neural Network

We propose a Bayesian deep learning framework for model driven online sparse channel estimation task in Multi-user MIMO systems. Tools from Bayesian neural network and stochastic variational Bayesian Inference are utilized to capture aleatoric and epistemic uncertainty estimates. We treat the network prediction as an auxiliary variable to allow inference performance to be unaffected by the stage of training of the network. In addition to providing uncertainty estimates, being Bayesian, the framework enables us the possibility to marginalize over penalty parameters and is well suited for online scenario with changing environments. Our simulations show that the framework is robust to model mismatch, and efficiently captures uncertainty in the predictions.
Manifestation of Flexural Vibration Modes of Rails by the Phase-Based Magnification Method

With the recent development of high performance photography devices, vibration measurements using digital image processing can be utilized for the condition monitoring and the analysis of generation mechanisms. High-quality image recording provides very dense spatial information unlike physical sensors with a limited number of point-oriented measurements and output channels such as accelerometers. Moreover, image acquisition is a non-contact technique and ensures that the installed sensors do not affect the mass or stiffness. In this study, the vibration mode of rails is investigated using the phase-based video magnification method. An extensive amount of information that cannot be detected by human eyes, can be obtained with this imaging motion magnification technique by amplifying and visualising the minute vibrational movements. Flexural vibration modes in rails generate rolling noise, especially at high frequencies where the displacement amplitudes are small. The motion magnification method is applied using a high-speed camera to measure the rail mode shapes. The measured mode shapes are compared to the numerical results obtained from the waveguide finite element method. This method helps in understanding the vibration generation and the transfer mechanisms in the waveguide structures with a uniform cross-section in the lengthwise direction such as a railway track.
Computational Study on Redox-Switchable Second-Order Nonlinear Optical Properties of Totally Inorganic Keggin-Type Polyoxometalate Complexes

The electronic structure and second-order nonlinear optical (NLO) property of a series of monoruthenium-substituted Keggin-type polyoxometalates (POMs) have been studied by using density functional theory (DFT) and time-dependent (TD)DFT calculations. The DFT calculation supports that these POM complexes possess donor-π-conjugated bridge-acceptor (D-π-A) structure, which the ruthenium atom acts as the role of electron donor and the three vanadium atoms in cap region of Keggin-type POM act as the role of electron acceptor. It is well-known that D-π-A structure, as a simple molecular scheme, has been successfully used in the development of organic NLO materials. However, the totally inorganic molecule having D-π-A structure is very rare. Thereby the second-order NLO properties of these POM complexes have been analyzed in this work. According to the calculations, introduction of the electron acceptor leads to a substantial enhancement on the second-order NLO response. The calculated βHRS(−2ω; ω, ω) value of .
Reduction of the Pollutant Emissions from a Diesel Engine by the Application of Dimethyl Ether (DME) and the Control of the Intake Oxygen Flow Rate

The aim of this study was to investigate the effects of varying the global equivalence ratio and intake oxygen flow-rate on the combustion and exhaust emission characteristics of dimethyl ether (DME)-fueled combustion engine. This study dealt with the reduction characteristics of HC and CO emissions, as well as a simultaneous decrease of NOx and soot in a DME-fueled single-cylinder combustion engine. Our findings demonstrate that increasing the global equivalence ratio while holding the intake oxygen flow-rate constant caused the premixed combustion duration (CA10-CA50) to increase. Additionally, it was observed that decreasing the intake oxygen flow-rate while holding the injection quantity constant shortens the duration of CA10-CA50. In this investigation, simultaneous reduction of soot and indicated specific (IS)-NOx emissions from a diesel engine can be achieved by fueling with DME and setting a high EGR rate (low intake oxygen flow-rate). Further, applying high equivalence ratio together with a high .
Risk factors for acute kidney injury in critically ill patients with torso injury

Supplemental Digital Content is available in the text Abstract Acute kidney injury (AKI) is common in trauma patients and associated with poor outcomes. Identifying AKI risk factors in trauma patients is important for risk stratification and provision of optimal intensive care unit (ICU) treatment. This study identified AKI risk factors in patients admitted to critical care after sustaining torso injuries. We performed a retrospective chart review involving 380 patients who sustained torso injuries from January 2016 to December 2019. Patients were included if they were aged >15 years, admitted to an ICU, survived for >48 hours, and had thoracic and/or abdominal injuries and no end-stage renal disease. AKI was defined according to the Kidney Disease Improving Global Outcomes definition and staging system. Clinical and laboratory variables were compared between the AKI and non-AKI groups (n = 72 and 308, respectively). AKI risk factors were assessed using multivariate logistic regression analysis. AKI occurred in 72 (18.9%) patients and was associated with higher mortality than non-AKI patients (26% vs 4%, P < .001). Multivariate logistic regression analysis identified bowel injury, cumulative fluid balance >2.5 L for 24 hours, lactate levels, and vasopressor use (adjusted odds ratio: 2.953, 2.058, 1.170, and 2.910; 95% confidence interval: 1.410–6.181, 1.017–4.164, 1.019–1.343, and 1.414–5.987; P = .004, .045, .026, and .004, respectively) as independent risk factors for AKI. AKI in patients admitted to the ICU with torso injury had a substantial mortality. Recognizing risk factors at an early stage could aid risk stratification and provision of optimal ICU care.
International Journal of Social Sciences and Management Review

Sustainable development goal 8 targets to promote inclusive and sustainable economic growth, employment and decent work for youths and adults. This could be achieved through investment in human capital development. Hence, this paper discussed the roles human capital development plays in achieving sustainable development goal 8 in Nigeria. It also identified the strategies for developing human capital to achieve sustainable development goal 8 in Nigeria. The paper highlighted among others that investing in human capital development increases the number of individuals in the society who are able and willing to start-up small and medium scale business ventures and effectively manage the venture. The paper also emphasised that improving the relevance of general education to economic activities, promoting entrepreneurship education and improving the quality of adult and nonformal education for youths and adults among others are effective strategies for human capital development towards achieving sustainable economic growth, employment and decent work in Nigeria.
Probabilistic Graphical Models , Spring 2012 Problem Set 4 : Structure learning & exact inference

For the following questions, you may use the programming language of your choice. You are allowed to use basic graph packages (e.g., for representing and working with directed or undirected graphs), but are not permitted to use any graphical model or probabilistic inference packages. E-mail a zip file of your full assignment, including a PDF file called " solutions.pdf " with your written solutions, separate output files (see question 2), and all of the code that you wrote, to Li. 1. The Alarm Bayesian network [1], shown in Figure 1, was an alarm message system for patient monitoring that was designed in 1989 as a case study for applying Bayesian networks to medical diagnosis. The Alarm Bayesian network is provided in the file " alarm.bif " (the format should be self-explanatory). Since writing code to parse this input file can be time-consuming, we also provide Python and Matlab code that you can (optionally) use to load the Bayesian network. This question will explore variable elimination as applied to the Alarm BN. Implement the sum-product variable elimination algorithm from class (also described in Section 9.3 of Koller & Friedman). Do not implement pruning of inactive variables. Use the min-fill heuristic (see page 314) to choose an elimination ordering. Note that you do not need to make this query-specific, i.e. it should be performed only once on the whole graph and then this elimination order should be used for all queries. (a) What is the elimination ordering found by the min-fill heuristic? How many fill edges were added? (b) What is the induced width of the graph with respect to the ordering found by min-fill? List the variables in the largest clique. Figure 1: ALARM (" A Logical Alarm Reduction Mechanism ") Bayesian network. [1]
Cloning and characterization of a putative fructosyltransferase and two putative invertase genes from the temperate grass Lolium temulentum

The invertases of Lolium temulentum have been characterized at the enzyme level. However, studies on the expression of the genes coding for these enzymes have been lacking. To elucidate the role of acid `invertase-like' genes in sucrose metabolism and carbon partitioning in Gramineae further, three `invertase-like' homologous clones were isolated from L. temulentum cDNA expression libraries based on leaf tissue, using maize soluble invertase probes. The effect of developmental stage and alterations in carbohydrate status on the expression and tissue distribution of these genes was investigated. The three highly homologous genes (Inv 1:2, Inv 1:4, and FT 2:2) show different patterns of expression and different tissue distribution. Inv 1:2 was predominantly expressed in root tissue. Expression increased during the dark in root and tiller base tissue. Minimal variations in gene expression were observed in leaf tissue following changes in carbohydrate status. Inv 1:4 was predominantly expressed in tiller bases, leaf sheath, and leaf base, with increased expression in tissue samples in the dark period. FT 2:2 was also predominantly expressed in tiller bases, leaf sheath, and leaf base. Higher expression was observed in leaf tissue following increases in carbohydrate content, in a manner that paralleled the regulation and spatial occurrence of fructan in the leaf tissue. Whilst invertases and fructosyltransferases are dif®cult to distinguish at the level of the whole sequence, analysis of 5¢ sequence and speci®c amino acids allows discrimination which correlates with patterns of expression within the tissue. Based on expression patterns and sequence characteristics, it is proposed that Inv 1:2 and Inv 1:4 code for soluble acid invertases, whilst FT 2:2 codes for a fructosyltransferase.
Changes in quality of grass carp tablets during storage between refrigeration and partial freezing

The quality variation of grass carp tablets(Ctenpharyngodon idellus)stored in two different conditions with temperature of 4 ℃ and -3 ℃,were evaluated by sensory assessment,pH,volatile basic nitrogen(TVBN),aerobic plate count,2-thiobarbituric acid value(TBA) and K value.The sensory acceptability limit was 10 days at 4 ℃ and 35 days at -3 ℃. The aerobic plate count of grass carp stored for 60 days at -3 ℃ was 6.95 log cfu/ g,however,at 4 ℃ on the 8th day it was 7.06 logcfu/g;At -3 ℃ stored for 30 days the value of TVB-N was 12.75 mg/100 g,and the value of 4 ℃ on the 6th day was 12.84 mg/100 g;The TBA value was 0.79 mg/100 g on the 60th day at -3 ℃,while on the 14th day at 4 ℃ it was 2.05 mg/100 g;When stored for 30 days pH value was still falling at -3℃,it was already the minimum on the 8th day at 4 ℃;The K value stored for 40 day at -3 ℃ and for 8 days at 4 ℃ were 60.08% and 61.23% respectively.Taking all indicators of changes,the shelf life is 8 days at 4 ℃ and 35 days at -3 ℃. It can be concluded that partial freezing temperatures increase the shelf life markedly.
Induced representations of quantum kinematical algebras and quantum mechanics

Unitary representations of kinematical symmetry groups of quantum systems are fundamental in quantum theory. In this paper we propose their generalization to quantum kinematical groups. Using the method, proposed by us in a recent paper (Arratia O and del Olmo M A 2001 Preprint math.QA/0110275) to induce representations of quantum bicrossproduct algebras, we construct the representations of the family of standard quantum inhomogeneous algebras Uλ(isoω(2)). This family contains the quantum Euclidean, Galilei and Poincare algebras, all of them in (1+1) dimensions. As byproducts we obtain the actions of these quantum algebras on regular co-spaces that are an algebraic generalization of the homogeneous spaces and q-Casimir equations which play the role of q-Schrodinger equations.
Layered mesoporous Mg(OH)2/GO nanosheet composite for efficient removal of water contaminants

A layered magnesium hydroxide (Mg(OH)2) nanosheet/graphene oxide (GO) composite was synthesized through laser ablation of the Mg target in an aqueous solution with GO. Its mesoporous structure and application as an adsorbent for the removal of methylene blue (MB) and heavy metal ions from water were investigated. Mg(OH)2 nanosheets were organized in situ from the strong reaction between the laser-ablated Mg species and water molecules. The GO nanosheet served as a heterogeneous nucleation and growth site for sheet-like Mg(OH)2 nanocrystals. The resulting porous Mg(OH)2/GO nanosheet composite had a high specific surface area of 310.8 m2 g−1 and a pore volume of 1.031 cm3 g−1. These characteristics show that the composite could be an excellent adsorbent. The composite exhibited a maximum adsorption capacity of 532 mg g−1 at 298 K for typical contaminants of MB, and over 300 mg g−1 for heavy metal ions Zn2+ and Pb2+.
Ozone-Depleting Substances, Economic Growth, and Energy Consumption in Latin America and the Caribbean: An Analysis Applying a Panel Vector Autoregression (PVAR)

After the Montreal Protocol in 1989, countries agreed to reduce their ozone-depleting substances (ODS) emissions to improve environmental conditions by reducing UV radiation from our planet's surface. The policies to control ODS have influenced how countries' industries produce, ergo affecting their energy consumption. This study explores the causal relationship between economic growth, energy consumption, and ODS emissions. Retrieving data from Latin America and the Caribbean from 1998 to 2018, a panel VAR is built and analyzed using GMM robust estimates. The main results show a bidirectional causality between GDP and energy consumption, a unidirectional causality from GDP to ODS emissions, and an Environmental Kuznets Curve (EKC) for ODS emissions. Policies and actionable campaigns are discussed.
The Effect of Education on the Relationship between Genetics, Early-Life Disadvantages, and Later-Life Ses

This paper investigates whether education weakens the relationship between early-life disadvantages and later-life SES. We use three proxies for advantage that we show are independently associated with SES in middle-age. Besides early, favorable family and neighborhood conditions, we argue that the genes a child inherits also represent a source of advantages. Using a regression discontinuity design and data for over 110,000 individuals, we study a compulsory schooling reform in the UK that generated exogenous variation in schooling. While the reform succeeded in reducing educational disparities, it did not weaken the relationship between early-life disadvantages and wages. This implies that advantaged children had higher returns to schooling. We exploit family-based random genetic variation and find no evidence that these higher returns were driven by genetically-influenced individual characteristics such as innate ability or skills.
Estimating error and uncertainty in change detection analyses of historical aerial photographs

By gathering, rectifying, interpreting, and digitizing historical aerial photography (from 1938 to 1998) we computed the rate of change of back-barrier land cover types and used GIS spatial analysis tools to compute the degree of fragmentation of marshes through time and place. To quantify the significance of this historical change, a series of tests were designed and conducted to describe the amount of spatial variability and accuracy of the rectified photographs, the digitized polygons, and the quantification of change. A digitizing accuracy assessment was conducted where randomly chosen locations were identified on the aerial photographs and compared with the digitized data. The accuracy assessment resulted in greater than 80 percent accurate which is acceptable. Second, the digitized polygons were tested for degree of crenulation, or curviness, and also line generalization tests were conducted which indicated that the interpretation of the photographs was not a factor in the results. Third, we incorporated a fuzziness test (using derived epsilon bands) into the GIS data to identify and quantify true changes in the marsh habitats versus positional changes, or sliver polygons. Results indicate that rectification of aerial photography (although an RMS error of less than 1), interpretation, and digitizing can lead to erroneous results however by using fuzziness techniques we can minimize the errors and predict which areas are changing through time.