Miracle-dz/UniFAR-FTUs huggingface dataset:
A Body-Blockage Analysis and Comparison Between Humans and a Full-Body Phantom: Using measurements at 28 GHz

Human blockage is an important phenomenon that should be considered in millimeter-wave (mm-wave) communication systems. Realizing that blockage behavior could be very valuable in mm-wave link simulations, several blockage models have been proposed in previous works based on knife-edge diffraction (KED), high-frequency simulations, and measurements. This article investigates blockage using a full-body phantom and proves that a body phantom is accurate enough to replace a human subject in blockage studies for both data and talk modes. A campaign of measurements for these two modes with 17 human subjects of different heights and genders is conducted. The effect of different types of clothing on body blockage is studied for a body phantom and a human subject. The impact of the relative antenna-user position on the body-blockage intensity is investigated as accurate measurements are difficult to achieve with human subjects.
Automatic Fruit Detection and Counting System Using Neural Network

In the field of agriculture, Identification and counting the number of fruits from the image helps the farmers in crop estimation. At present manual counting of fruits present in many places. The current practice of yield estimation based on the manual counting of fruits has many drawbacks as it is time consuming and expensive process. while considering the progress of fruit detection, estimating proper and accurate fruit counts from images in real-world scenarios such as orchards is still a challenging problem. The focus of this paper is on the web application of fruit yield estimation. This web application helps the farmers to count the number of fruits easily. This system provides an automated and efficient fruit counting system using computer vision techniques. This paper provides the progress towards in-field fruit counting using neural network object detection methods. So this process is done by recognizing each fruit in the image and taking the count. In the neural network, we have used YOLO architecture for recognizing the fruits.
Effects of Mixing Weights and Predictor Distributions on Regression Mixture Models

ABSTRACT Regression mixture models (RMMs) can be used to specifically test for and model differential effects in heterogeneous populations. Based on the results of the Aim 1 simulation study, enumeration conducted with constrained predictor means appears to be advantageous. Furthermore, researchers should estimate the K and K+1 unconditional models (chosen during initial enumeration), adding the C on X paths, to investigate the potential for model instability as well as the possibility that the models are misspecified because the underlying populations contain predictor variance differences in the subgroups. The Aim 2 simulation study explored the extent to which RMMs are robust to predictor variance differences. Although the coverage rates for the simulation conditions where the predictor variances differed across classes were not the nominal rate, parameter estimates were not biased even in the presence of moderate violations of this assumption.
An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired seismic data. Nevertheless, the strong nonlinear relationship between the seismic data and the subsurface model will cause nonconvergence and unstable problems in practice. To divide the nonlinear inversion into some more linear steps, a 2D AI inversion imaging method is proposed to estimate the broadband AI model based on a broadband reflectivity. Firstly, a novel scheme based on Gaussian beam migration (GBM) is proposed to produce the point spread function (PSF) and conventional image of the subsurface. Then, the broadband reflectivity can be obtained by implementing deconvolution on the image with respect to the calculated PSF. Assuming that the low-wavenumber part of the AI model can be deduced by the background velocity, we implemented the AI inversion imaging scheme by merging the obtained broadband reflectivity as the high-wavenumber part of the AI model and produced a broadband AI result. The developed broadband migration based on GBM as the computational hotspot of the proposed 2D AI inversion imaging includes only two GBM and one Gaussian beam demigraton (Born modeling) processes. Hence, the developed broadband GBM is more efficient than the broadband imaging using the least-squares migrations (LSMs) that require multiple iterations (every iteration includes one Born modeling and one migration process) to minimize the objective function of data residuals. Numerical examples of both synthetic data and field data have demonstrated the validity and application potential of the proposed method.
Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.
Characteristics of NixFe1−xOy Electrocatalyst on Hematite as Photoanode for Solar Hydrogen Production

The use of hematite as the photoanode for photoelectrochemical hydrogen production by solar energy has been actively studied due to its abundance, stability, and adequate optical properties. Deposition of an electrocatalyst overlayer on the hematite may increase kinetics and lower the onset potential for water splitting. NixFe1−xOy is one of the most effective electrocatalysts reported for this purpose. However, the condition and results of the previous reports vary significantly, and a comprehensive model for NixFe1−xOy/hematite is lacking. Here, we report a simple and novel chemical bath deposition method for depositing low-onset-potential NixFe1−xOy electrocatalyst on hematite. With a Ni percentage of 80% and an immersion time of 2 min, the as-prepared NixFe1−xOy overlayer raised the photovoltage from 0.2 V to 0.7 V, leading to a cathodic shift of the onset potential by 400 mV, while maintaining the same level of current density. The dependence of the electrochemical and photoelectrochemical characteristics of the photoanode on the condition of the electrocatalyst was studied systematically and explained based on energy level diagrams and kinetics.
Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners

Applicability of SLAM (simultaneous localization and mapping) technology for mine surveys and subsequent 3D modelling of post-extracted surfaces is assessed. The resulting surface geometry is validated via terrestrial laser scanner (TLS) acquired reference data. Typical discrepancies remained within 2 and 5 cm in horizontal and vertical directions, respectively. Discrepancies between TLS, SLAM-enabled handheld scanner and conventional surveying results are small and fully satisfy the contemporary accuracy requirements, yet evidence that the conventional mine survey results are affected by the subjectivity of the surveyors. The SLAM-enabled laser scanning hence appears to be the most suitable method for underground mining surveys.
Rapid detection of tulathromycin in pure milk and honey with an immunochromatographic test strip

ABSTRACT In this paper, we describe the development of a rapid, simple, specific, and sensitive immunochromatographic strip test based on an antibody–antigen reaction for detection of tulathromycin (TULA) in samples of 0.01 M PBS pH 7.4, pure milk and honey. The monoclonal antibodies specifically recognized the corresponding antigens produced in our laboratory. Under optimized conditions, the cut-off limits of the test strips for TULA in 0.01 M PBS pH 7.4 was found to be 2.5 ng/mL. Meanwhile, with simple preparation for detection in pure milk and honey samples, the cut-off limits for TULA were found to be 5 and 10 ng/mL, respectively. Each test can be evaluated in 5 min. In summary, this lateral-flow device provides a sensitive, effective, specific, and rapid method for detection of TULA residues in food matrix.
The Role of Pneumococcal Pneumonia among Community-Acquired Pneumonia in Adult Turkish Population: TurkCAP Study.

OBJECTIVE To evaluate the rate of pneumococcal pneumonia (PP) among patients with community-acquired pneumonia (CAP) in Turkey and to investigate and compare features of PP and non-PP CAP patients. MATERIAL AND METHODS This multicenter, non-interventional, prospective, observational study included adult CAP patients (age ≥ 18 years). Diagnosis of PP was based on the presence of at least 1 positive laboratory test result for Streptococcus pneumoniae (blood culture or sputum culture or urinary antigen test [UAT]) in patients with radiographic findings of pneumonia. RESULTS Four hundred sixty-five patients were diagnosed with CAP, of whom 59 (12.7%) had PP. The most common comorbidity was chronic obstructive pulmonary disease (30.1%). The mean age, smoking history, presence of chronic neurological disease, and CURB-65 score were significantly higher in PP patients, when compared to non-PP patients. In PP patients, 84.8% were diagnosed based ony on the UAT. The overall rate of PP patients among CAP was calculated as 22.8% considering the UAT sensitivity ratio of 63% (95% confidence interval: 45-81). The rate of intensive care treatment was higher in PP patients (P = .007). While no PP patients were vaccinated for pneumococcus, 3.8% of the non-PP patients were vaccinated (P = .235). Antibiotic use in the preceding 48 hours was higher in the non-PP group than in the PP group (31.8% vs. 11.1%, P = .002). The CURB-65 score and the rate of patients requiring inpatient treatment according to this score were higher in the PP group. CONCLUSION The facts that PP patients were older and required intensive care treatment more frequently as compared to non-PP patients underline the burden of PP.
State-and-Evolution Detection Models: A Framework for Continuously Monitoring Landscape Pattern Change

Detecting the evolution of large-area landscape patterns using long-term remote-sensing images is helpful in supporting research on the relationship between landscape patterns and ecological processes, as well as the development of ecological process simulations and spatiotemporal interaction models. However, detection methods have generally been developed as separate applications, each with a separate type of landscape pattern change; remote-sensing images are acquired at epochal timesteps. Consequently, in practical applications, many omission changes for some types of pattern changes and inaccurate evolution time are presented in the detected map. In this article, state-and-evolution detection models (SEDMs) are promoted to obtain complete information about the evolution of landscape patterns based on yearly land cover data. In the proposed framework, we first define the major categories of landscape pattern changes to comprehensively reveal the characteristics of landscape pattern changes associated with real change cases. Next, a morphological rule-based pattern recognition approach is proposed for quantitative discrimination among these categories. This approach is then applied in annual land cover data to continuously detect landscape pattern evolution processes and evolution time. Finally, the detected evolution time in different evolution processes is applied to measure the timestep between two disparate types. The performances of the SEDMs are presented by Landsat-derived land cover evolution in Shanxi, China. The detected results are indirectly verified by the land cover conversion matrix and connect index, indicating strong robustness and generalization ability of the SEDMs.
“The quiet revolution”: convenience voting, vote centers, and turnout in Texas elections

ABSTRACT Polling place consolidation makes administering voting easier but scholars question the effect on turnout. Vote centers (which consolidate polling locations) are theoretically less expensive to administer and convenient for many voters, but less is known about the impacts on specific racial communities or across election cycles. Using Texas’ registered voters’ list from the Secretary of State’s Voting Division, this paper uses Geographic Information Systems (GIS) to calculate estimated driving distances from each registered voter’s residence to a vote center location. The results show that the displacement of traditional precinct-level voting and the increase in distance between polling locations takes a greater toll on voter turnout for voters in midterm (but not presidential) elections and rural counties with more Latino voters. The implications demonstrate that vote centers can boost turnout in some elections but that the location of vote centers significantly effects turnout among ethnic minorities.
Reducing the Evaporative Demand Improves Photosynthesis and Water Use Efficiency of Indoor Cultivated Lettuce

Currently, climate change is affecting considerably the availability of freshwater for agriculture, increasing the need for the optimization of crop water use efficiency. Attempts to use VPD (vapor pressure deficit) modulation to reduce water consumption have been made. However, the effects of VPD on leaf stomatal and hydraulic traits, and on possible tradeoffs between photosynthetic carbon gain and transpiration, are rarely reported. We analyzed photosynthesis (gas-exchange, photochemistry) stomatal and hydraulic-related traits of green (G) and red (R) butterhead lettuce (Lactuca sativa L.) grown under low and high VPD (LV, HV) in a controlled environment. Our results showed that plants developed a higher number of small stomata under LV, allowing better regulation over opening/closing mechanisms and thus increasing net photosynthesis by 18%. LV plants also achieved better performance of the photosystem II and a more efficient water use (increments in ΦPSII and iWUE by 3% and 49%), resulting in enhanced plant growth and reduced need for irrigation. Significant differences between G and R plants were limited to a few traits, and the physiological response under the two VPDs did not show cultivar-specific response. We discuss the role of VPD management as necessary to maximize crop water use by harmonizing photosynthesis and transpiration.
Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers

We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.
Effect of Ionic Wind Induced by DC Electric Field on Biogas/Air Turbulent Premixed Flame Structure

ABSTRACT Effect of DC electric field on lean premixed biogas turbulent flame structure at low and medium turbulence intensity was studied experimentally using OH-PLIF. OH signal distribution, turbulent burning velocity and flame structure geometric parameters were derived. Results show that downward electric field could broaden the downstream burned gas and push the flame tip down. The flame base is closer to the burner rim in downward electric field for the modification of thermal diffusion and flow modification caused by ionic wind. The effect on flame tip height reduction is more obvious for biogas flame with a higher CO2 ratio. According to the flame structure analysis, the turbulent burning velocity of biogas is increased slightly due to the modification of flow field by ionic wind generated by downward electric field, and the increment decreases with the increase of turbulence intensity. In addition, there’s a decrease in mean flame surface density and an increase in centerline flame brush thickness. The flame surface curvature PDF distribution is nearly not affected by the application of DC electric field. For turbulent flames, the effect of electric field is non-uniform and inconstant, which induces extra flow perturbation. This perturbation is different from turbulence, and the combined effects lead to the variation of flame structure.
SMART WARNING SYSTEM USING EDGE COMPUTING

In recent years, the occurrence of road accidents increases drastically. There are many factors influencing the road accidents. Nowadays usage of mobile phones while driving is common but it leads to the distraction in driving and subsequently causes accidents. Though jurisdictions are made that usage of electronic devices such as mobile phones, tablets, laptops while driving are illegal, no one is aware of that. The drivers who use mobile gadgets while driving not only risk their lives but also the people around them. Most of the road users are well aware of the general rules and safety measures. However due to human errors, accidents occur. To overcome from the human errors this paper focuses on developing a security alert system for drivers using edge computing paradigm.
Analysis and Implementation of a Frequency Control DC–DC Converter for Light Electric Vehicle Applications

In order to realize emission-free solutions and clean transportation alternatives, this paper presents a new DC converter with pulse frequency control for a battery charger in electric vehicles (EVs) or light electric vehicles (LEVs). The circuit configuration includes a resonant tank on the high-voltage side and two variable winding sets on the output side to achieve wide output voltage operation for a universal LEV battery charger. The input terminal of the presented converter is a from DC microgrid with voltage levels of 380, 760, or 1500 V for house, industry plant, or DC transportation vehicle demands, respectively. To reduce voltage stresses on active devices, a cascade circuit structure with less voltage rating on power semiconductors is used on the primary side. Two resonant capacitors were selected on the resonant tank, not only to achieve the two input voltage balance problem but also to realize the resonant operation to control load voltage. By using the variable switching frequency approach to regulate load voltage, active switches are turned on with soft switching operation to improve converter efficiency. In order to achieve wide output voltage capability for universal battery charger demands such as scooters, electric motorbikes, Li-ion e-trikes, golf carts, luxury golf cars, and quad applications, two variable winding sets were selected to have a wide voltage output (50~160 V). Finally, experiments with a 1 kW rated prototype were demonstrated to validate the performance and benefits of presented converter.
ENTREPRENEURIAL INTENTION AMONG INFORMATION SYSTEMS STUDENTS: INVESTIGATING THE ROLE OF FAMILY BACKGROUND, SELF-EMPLOYMENT, AND GENDER IN UNDERGRADUATE EDUCATION

This research aims to investigate family background, self-employment, and gender in undergraduate students of information systems. Regarding the approach to the problem, this research is characterized as quantitative and as for the objectives, there is exploratory research. The technical procedure adopted was the case study in information systems students (number of respondents equal to 100). The main results that most students intend to become entrepreneurs in the future: Students who have a family background tend to support entrepreneurship more; and high entrepreneurial behavior in females concerning males. The importance of this study is to indicate how such factors (family background, self-employment, and gender) affect undergraduate students, contributing to the theoretical and managerial aspects of studies in entrepreneurial intention in the context of the Amazonia region.
Differential Evolution Based Machine Learning Scheme for Secure Cooperative Spectrum Sensing System

The secondary users (SUs) in cognitive radio networks (CRNs) can obtain reliable spectrum sensing information of the primary user (PU) channel using cooperative spectrum sensing (CSS). Multiple SUs share their sensing observations in the CSS system to tackle fading and shadowing conditions. The presence of malicious users (MUs) may pose threats to the performance of CSS due to the reporting of falsified sensing data to the fusion center (FC). Different categories of MUs, such as always yes, always no, always opposite, and random opposite, are widely investigated by researchers. To this end, this paper proposes a hybrid boosted tree algorithm (HBTA)-based solution that combines the differential evolution (DE) and boosted tree algorithm (BTA) to mitigate the effects of MUs in the CSS systems, leading to reliable sensing results. An optimized threshold and coefficient vector, determined against the SUs employing DE, is utilized to train the BTA. The BTA is a robust ensembling machine learning (ML) technique gaining attention in spectrum sensing operations. To show the effectiveness of the proposed scheme, extensive simulations are performed at different levels of signal-to-noise-ratios (SNRs) and with different sensing samples, iteration levels, and population sizes. The simulation results show that more reliable spectrum decisions can be achieved compared to the individual utilization of DE and BTA schemes. Furthermore, the obtained results show the minimum sensing error to be exhibited by the proposed HBTA employing a DE-based solution to train the BTA. Additionally, the proposed scheme is compared with several other CSS schemes such as simple DE, simple BTA, maximum gain combination (MGC), particle swarm optimization (PSO), genetic algorithm (GA), and K-nearest neighbor (KNN) algorithm-based soft decision fusion (SDF) schemes to validate its effectiveness.