Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Antenna Arrays for Precision GNSS Receivers

Antenna arrays with adaptive filters are currently used to provide interference suppression capabilities for Global Navigation Satellite System (GNSS) receivers. An adaptive array allows greater performance over a single element antenna by providing beamforming/null steering in the directions of satellites and interference sources. Unfortunately, there are some important limitations to the GNSS adaptive arrays in use today. For example, in the process of suppressing interference, adaptive antennas may inadvertently distort the GNSS signal and introduce bias errors into the receiver’s position and time estimates. Furthermore, many systems produce suboptimal interference suppression performance, which degrades the accuracy of the navigation solution. To overcomes these limitations, this dissertation develops novel adaptive antenna algorithms and techniques suitable for precision GNSS receivers. Three primary contributions are made. First, it develops an approach for optimal suppression of interference. For a GNSS application, this corresponds to an adaptive filter that maximizes carrier-to-noise ratio (C/N0). The second contribution is the development of approaches for preventing the adaptive antenna array from introducing errors into the GNSS receiver measurements. These techniques take the form of a special adaptive filter algorithm and additional receiver logic that mathematically guarantee zero antenna-induced error even during interference suppression. Since
Effect of Air Gaps on Photovoltaic Operating Temperature and Heat Gain Penetration into Buildings

An installed photovoltaic (PV) module on a building envelope is beneficial not only for generating PV clean electricity, but also for reducing the cooling load of a building, provided that its installation is properly designed. Spacing of an air gap between the PV module and the exterior surface of the building envelope plays an important role in the PV modules’ performance and the heat gain penetration into the building, because it directly affects heat transfer from the PV module to the building envelope. PV module installations over a building envelope can be useful, especially in Thailand, where the climate is hot and humid, if it helps to minimize the heat gain penetration into the building, and at the same time, can also generate clean electricity from the PV modules. In this study, a simulation model based on one-dimensional transient heat transfer has been developed to investigate a proper air gap for a PV module installation, for fixing on a vertical wall facing southward. The accuracy of the developed model was verified with an outdoor experiment by a test rig under clear sky conditions in Bangkok, Thailand. The model’s validation showed a satisfactory result of predicted temperatures. The simulation results showed that an increase in the air gap’s spacing can improve the performance of the PV module, at the same time, it can also reduce the heat gain penetrating into the building. Although a PV module can be used as a shading device to reduce heat gain for a building envelope, it can also generate a large amount of heat gain penetration into the building envelope. This is a consequence of high thermal absorbtivity of the PV module, which results in intensively absorbing large amounts of heat within its body as it receives shortwave solar radiation from the sum and re-radiates it back to the building’s surface. Thus, a proper air gap space design is quite important for the installation. It was also found that the change of amounts of heat gain penetration into a building is much more sensitive than the change of electricity generation of the PV module performance, due to variations of the air gap. Thus, the judgment of a proper air gap spacing design should be given first priority on the reduction of the heat gain into the building. A minimum air gap of 50 mm is recommended for a single PV module, in vertical facing southward installations, for tropical climates.
EVALUASI DAYA DUKUNG TIANG PANCANG BERDASARKAN METODE DINAMIK

Pengujian tiang dinamik merupakan salah satu metode pengujian untuk mengevaluasi dayadukung tiang. Pengujian tiang metode dinamik (Pile Driving Analyzer/PDA) dilaksanakandengan memasang sensor sepanjang tiang untuk merekam parameter kecepatan dan gaya saattiang dipukul menggunakan palu dengan besaran energi tertentu. Pada penelitian ini, data ujiPDA untuk tiang pancang beton pracetak pada proyek pembangunan gedung Rumah SakitUniversitas Riau digunakan untuk dianalisis daya dukungnya. Metode yang digunakan untukmenganalisis daya dukung tiang adalah perhitungan manual Metode CASE, hasil keluaran ujiPDA, dan hasil keluaran software CAPWAP. Nilai daya dukung yang dihasilkan ketiga metodetersebut kemudian dibandingkan dan dievaluasi. Berdasarkan hasil analisis, nilai daya dukungyang diperoleh dari perhitungan manual Metode CASE adalah 98,8 ton, hasil keluaran uji PDAsebesar 98 ton, dan hasil keluaran software CAPWAP sebesar 98,3 ton. Hal ini menunjukkanbahwa ketiga metode tersebut menghasilkan daya dukung tiang yang relatif sama denganselisih kurang dari 1%. Oleh karena itu, ketiga metode tersebut dapat digunakan untukmenganalisis daya dukung tiang dengan tingkat keandalan yang baik. Kata kunci: CAPWAP, Metode CASE, Pengujian Dinamik, Pile Driving Analyzer (PDA).
MODELING AND SIMULATION OF A 5-AXIS RV-2AJ ROBOT USING SIMMECHANICS

This paper presents the reliability and accuracy of the developed model of 5-axis Mitsubishi RV-2AJ robot arm. The CAD model of the robot was developed by using SolidWorks while the multi-body simulation environment was demonstrated by using SimMechanics toolbox in MATLAB. The forward and inverse kinematics simulation results proposed that the established model resembles the real robot with accuracy of 98.99%.
On some limitations of probabilistic models for dimension‐reduction: Illustration in the case of probabilistic formulations of partial least squares

Partial least squares (PLS) refer to a class of dimension‐reduction techniques aiming at the identification of two sets of components with maximal covariance, to model the relationship between two sets of observed variables x∈ℝp and y∈ℝq , with p≥1,q≥1 . Probabilistic formulations have recently been proposed for several versions of the PLS. Focusing first on the probabilistic formulation of the PLS‐SVD proposed by el Bouhaddani et al., we establish that the constraints on their model parameters are too restrictive and define particular distributions for (x,y) , under which components with maximal covariance (solutions of PLS‐SVD) are also necessarily of respective maximal variances (solutions of principal components analyses of x and y, respectively). We propose an alternative probabilistic formulation of PLS‐SVD, no longer restricted to these particular distributions. We then present numerical illustrations of the limitation of the original model of el Bouhaddani et al. We also briefly discuss similar limitations in another latent variable model for dimension‐reduction.
Anxiety, Post–COVID-19 Syndrome-Related Depression, and Suicidal Thoughts and Behaviors in COVID-19 Survivors: Cross-sectional Study

Background Although the mental health impacts of COVID-19 on the general population have been well studied, studies of the long-term impacts of COVID-19 on infected individuals are relatively new. To date, depression, anxiety, and neurological symptoms associated with post–COVID-19 syndrome (PCS) have been observed in the months following COVID-19 recovery. Suicidal thoughts and behavior (STB) have also been preliminarily proposed as sequelae of COVID-19. Objective We asked 3 questions. First, do participants reporting a history of COVID-19 diagnosis or a close relative having severe COVID-19 symptoms score higher on depression (Patient Health Questionnaire-9 [PHQ-9]) or state anxiety (State Trait Anxiety Index) screens than those who do not? Second, do participants reporting a COVID-19 diagnosis score higher on PCS-related PHQ-9 items? Third, do participants reporting a COVID-19 diagnosis or a close relative having severe COVID-19 symptoms score higher in STB before, during, or after the first year of the pandemic? Methods This preliminary study analyzed responses to a COVID-19 and mental health questionnaire obtained from a US population sample, whose data were collected between February 2021 and March 2021. We used the Mann-Whitney U test to detect differences in the medians of the total PHQ-9 scores, PHQ-9 component scores, and several STB scores between participants claiming a past clinician diagnosis of COVID-19 and those denying one, as well as between participants claiming severe COVID-19 symptoms in a close relative and those denying them. Where significant differences existed, we created linear regression models to predict the scores based on COVID-19 response as well as demographics to identify potential confounding factors in the Mann-Whitney relationships. Moreover, for STB scores, which corresponded to 5 questions asking about 3 different time intervals (i.e., past 1 year or more, past 1 month to 1 year, and past 1 month), we developed repeated-measures ANOVAs to determine whether scores tended to vary over time. Results We found greater total depression (PHQ-9) and state anxiety (State Trait Anxiety Index) scores in those with COVID-19 history than those without (Bonferroni P=.001 and Bonferroni P=.004) despite a similar history of diagnosed depression and anxiety. Greater scores were noted for a subset of depression symptoms (PHQ-9 items) that overlapped with the symptoms of PCS (all Bonferroni Ps<.05). Moreover, we found greater overall STB scores in those with COVID-19 history, equally in time windows preceding, during, and proceeding infection (all Bonferroni Ps<.05). Conclusions We confirm previous studies linking depression and anxiety diagnoses to COVID-19 recovery. Moreover, our findings suggest that depression diagnoses associated with COVID-19 history relate to PCS symptoms, and that STB associated with COVID-19 in some cases precede infection.
Effect of Current and Deep Cryogenic Treated EDM Electrodes on Surface Roughness of AISI D3 Steel

The paper present the effect of current flow and Deep Cryogenic Treatment (DCT) on the Surface Roughness (SR) of AISI D3 steel machined using EDM. The EDM tool is modified using deep cryogenic treatment to obtain positive results. Cryogenic treatment is a add on process over conventional heat treatment processes which converts retained austenite to martensite along with formation of nano sized carbide. The treatment enhance the properties of materials like wear resistance, hardness, dimensional stability, thermal conductivity and tool consumption rate which are recognized as a important factors in industries. The results show that increase in current level enhance the surface roughness, while deep cryogenic treated electrodes produces less surface roughness as compared to that of non-cryogenic treated electrodes.
Design of aperture-coupled microstrip patch antenna array fed by SIW for 60 GHz band

This study deals with a design procedure and achieved parameters of a microstrip patch antenna array fed by a substrate integrated waveguide (SIW) operating at V-band. The antenna array consists of 6 × 8 microstrip patch antennas which are aperture coupled with an SIW-based feeding network. The antenna design is performed in a full-wave electromagnetic solver (ANSYS HFSS) for the operation frequency 60 GHz. The design procedure of the proposed antenna array comprises the analysis of an isolated radiating element, the synthesis of a linear array, and the development of an SIW-based power divider. At the operation frequency 60 GHz, a fabricated prototype has an impedance bandwidth of 1.8% for a reflection coefficient lower than -10 dB, a gain of 21.6 dBi, and a low sidelobe level below -23 dB in the E-plane and H-plane.
Prevalence of Salmonella and E. coli on Produce from Seattle Farmers Markets

Occupational & Environmental Exposure Sciences, MS Lyndsey Banks Characterization of Traffic‐Related Air Pollutants Near a Major Roadway in Albuquerque Using a Mobile Monitoring Approach Lea Duffin Validation of Training Concepts for Effective Ventilation Control for Welding Fumes in Confined Spaces Andrew Forbes Evaluation of Anti‐Vibration Gloves in a Manufacturing Setting Molly Halverson Evaluating Whole Body Vibration and Standing Balance Among Truck Drivers Abigail Sutphen PPE Effectiveness in Agricultural Settings Toxicology, MS Julie Park Toxicokinetics of Domoic Acid (DA) in Pregnant and Non‐pregnant Mice After Repeated Oral Administrations Jessica Youngblood Longitudinal Approaches for Metagenomic Characterization of the Puget Sound for Environmental Health Surveillance Environmental & Occupational Health, MPH Jonathan Childers Evaluating the Duwamish River Cleanup Health Impact Assessment
An Evaluation of Backpropagation Neural Network Modeling as an Alternative Methodology for Criterion Validation of Employee Selection Testing

Employee selection research identifies and makes use of associations between individual differences, such as those measured by psychological testing, and individual differences in job performance. Artificial neural networks are computer simulations of biological nerve systems that can be used to model unspecified relationships between sets of numbers. Thirty-five neural networks were trained to estimate normalized annual revenue produced by telephone sales agents based on personality and biographic predictors using concurrent validation data (N=1085). Accuracy of the neural estimates was compared to OLS regression and a proprietary nonlinear model used by the participating company to select agents.
Structural heterogeneity in silk fibers and its effects on failure mechanics and supercontraction

Spider dragline silk is a protein material that has evolved over millions of years to become one of the strongest and toughest natural fibers known. Silk features a heterogeneous structure that comprises P-sheet crystals embedded in an amorphous matrix. However, it is not fully understood how the heterogeneity of silk affects its mechanical properties. First, the origin of the nanoscale heterogeneity during the Nephila Clavipes dragline silk assembly is investigated. Using molecular dynamics simulations, a shear flow at natural pulling speeds is modelled and the secondary structure transitions as well as shear stresses in the silk protein chains are determined. It is shown that under shear stresses beyond the elastic regime, silk undergoes an a P-transition in the spinning duct. The stability of the assembled P-sheet structure seems to arise from a close proximity of the a-helices in the silk solution. The smallest molecule size that might give rise to a silk-like structure is determined to comprise four to six repeats of the silk sequence. Establishing the
A statistical investigation of the risk factors for tuberculosis

Tuberculosis (TB) is called a disease of poverty and is the main cause of death from infectious diseases among adults. In 1993 the World Health Organisation (WHO) declared TB to be a global emergency; however there were still approximately 1.4 million deaths due to TB in 2011. This thesis contains a detailed study of the existing literature regarding the global risk factors of TB. The risk factors identified from the literature review search which were also available from the NFHS-3 survey were then analysed to determine how well we could identify respondents who are at high risk of TB. We looked at the stigma and misconceptions people have regarding TB and include detailed reports from the existing literature of how a persons wealth, health, education, nutrition, and HIV status affect how likely the person is to have TB. The difference in the risk factor distribution for the TB and non-TB populations were examined and classification trees, nearest neighbours, and logistic regression models were trialled to determine if it was possible for respondents who were at high risk of TB to be identified. Finally gender-specific statistically likely directed acyclic graphs were created to visualise the most likely associations between the variables. [This page intentionally left blank]
Risk-standardized sepsis mortality map of the United States

Objective Sepsis is the leading cause of in-hospital mortality in the United States (US). Quality improvement initiatives for improving sepsis care depend on accurate estimates of sepsis mortality. While hospital 30-day risk-standardized mortality rates have been published for patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, risk-standardized mortality rates for sepsis have not been well characterized. We aimed to construct a sepsis risk-standardized mortality rate map for the United States, to illustrate disparities in sepsis care across the country. Methods This cross-sectional study included adults from the US Nationwide Inpatient Sample who were hospitalized with sepsis between 1 January 2010 and 30 December 2011. Hospital-level risk-standardized mortality rates were calculated using hierarchical logistic modelling, and were risk-adjusted with predicted mortality derived from (1) the Sepsis Risk Prediction Score, a logistic regression model, and (2) gradient-boosted decision trees, a supervised machine learning (ML) algorithm. Results Among 1,739,033 adults hospitalized with sepsis, 50% were female, and the median age was 71 years (interquartile range: 58–81). The national median risk-standardized mortality rate for sepsis was 18.4% (interquartile range: 17.0, 21.0) by the boosted tree model, which had better discrimination than the Sepsis Risk Prediction Score model (C-statistic 0.87 and 0.78, respectively). The highest risk-standardized mortality rates were found in Wyoming, North Dakota, and Mississippi, while the lowest were found in Arizona, Colorado, and Michigan. Conclusions Wide variation exists in sepsis risk-standardized mortality rates across states, representing opportunities for improvement in sepsis care. This represents the first map of state-level variation of risk-standardized mortality rates in sepsis.
Using Stanford Parser Method For Assessing The Competencies Of It Professionals

The paper deals with the presentation of the Stanford Parser method and its implementation into a competency assessment tool. The competency assessment tool is designed for appraising the performance that IT professionals have. Using the Stanford Parser package, the opinions of technical employees can be measured and their level of specific competencies can be revealed. The present research tries to provide evidence on the competencies that IT Romanian professionals have. The research is conducted on implementing the Stanford Parser into a web based assessment tool. This software application represents an efficient and transparent method to evaluate the competencies of technical professionals.Acknowledgment This papers was co-financed from the European Social Fund, through the Sectorial Operational Programme Human Resources Development 2007-2013, project number POSDRU/159/1.5/S/138907 "Excellence in scientific interdisciplinary research, doctoral and postdoctoral, in the economic, social and medical fields -EXCELIS", coordinator The Bucharest University of Economic Studies.
Creativity in Advertising: How to Test for Highly Creative Individuals, How to Generate Alternative Creative Ideas, and How to Pretest Them

Abstract This article addresses the three biggest issues surrounding creativity in advertising: the need for a new test to identify highly creative individuals, the need to provide a better creative idea generation procedure, and the need to pretest ideas for effectiveness. I critically review what has already been done and then draw upon my own extensive work in the field of creativity ability testing and my work with Larry Percy on creative idea generation and pretesting to show how these procedures can be carried out more effectively. Creative ability is shown to be best measured with a multiple-item measure of divergent thinking that uses familiar cue objects, with responses scored only for originality. Creative ideas for use in advertising can be most efficiently generated by recruiting high creative ability individuals and using the I-G-I method of brainstorming. The most promising creative ideas can then be pretested in rough ad form by using the Remote Conveyor Test, which requires that the creative idea be attention-getting, novel for the product category, correctly understood with the key benefit quickly evident, and executed to be free of conflicting associations.
Objective assessment of image quality (OAIQ) in fluorescence-enhanced optical imaging

Objective Assessment of Image Quality (OAIQ) in Fluorescence-Enhanced Optical Imaging. (December 2006) Amit K. Sahu, B. Tech., Indian Institute of Technology, Roorkee, India Co-Chairs of Advisory Committee: Dr. Eva Marie Sevick-Muraca Dr. Mahmoud El-Halwagi The statistical evaluation of molecular imaging approaches for detecting, diagnosing, and monitoring molecular response to treatment are required prior to their adoption. The assessment of fluorescence-enhanced optical imaging is particularly challenging since neither instrument nor agent has been established. Small animal imaging does not address the depth of penetration issues adequately and the risk of administering molecular optical imaging agents into patients remains unknown. Herein, we focus upon the development of a framework for OAIQ which includes a lumpy-object model to simulate natural anatomical tissue structure as well as the non-specific distribution of fluorescent contrast agents. This work is required for adoption of fluorescence-enhanced optical imaging in the clinic. Herein, the imaging system is simulated by the diffusion approximation of the time-dependent radiative transfer equation, which describes near infra-red light propagation through clinically relevant volumes. We predict the time-dependent light propagation within a 200 cc breast interrogated with 25 points of excitation illumination and 128 points of fluorescent light collection. We simulate the fluorescence generation from Cardio-Green at tissue target concentrations of 1, 0.5, and 0.25 μM with
Investigation on Feeding Techniques for Rectangular Dielectric Resonator Antenna in Higher-Order Mode for 5G Applications

This paper presents the investigation on rectangular dielectric resonator antenna (RDRA) using three different feeding techniques that are microstrip slot aperture (MSA), microstrip line (ML) and open-end coplanar waveguide (OECPW). In order to increase the RDRA size and gain for 5G applications, the higher-order mode was used to excite the RDRA at the frequency resonant 28 GHz. Duroid dielectric substrate was used with a thickness of 0.254 mm, a permittivity of 2.2 and a loss tangent of 0.0009. Verification of the comparison was done by simulation using Ansoft HFSS. The simulated result for reflection coefficients, bandwidth, gain, radiation pattern and E-field strength was analyzed and compared.
Design and construction of an Industrial Control System testbed

Industrial Control Systems (ICS) are used through- out critical national infrastructure. This has the potential to attract malicious attackers with a variety of motivations e.g. political, financial, reputational. There has been development and publication of highly technical security research, but also basic scripted attacks, which can be executed with the most limited of resources. This paper focuses on the development (design and construction) of an ICS testbed, with the view to further research in two key areas, namely security and resilience. Included is a survey detailing the approaches taken by other institutes/organisations when designing ICS testbeds. From this the key design requirements of flexibility, reliability, and credibility are drawn. A design of our testbed is provided, alongside details of future work, not only within our testbed, but also the wider context of ICS security.