Miracle-dz/UniFAR-FTUs huggingface dataset:
Sharing Clinical Notes in a Canadian Mental Health Setting: Recommendations from Applying the Consolidated Framework for Intervention Research

During the COVID-19 pandemic, the OpenNotes movement presents an optimal solution for virtual engagement through the sharing of clinical notes within mental health care settings. Therefore, we conducted interviews to discover how mental health clinicians interact with patients using OpenNotes. We integrated The Consolidated Framework for Intervention Research to establish implementation recommendations. As both challenges and opportunities were identified, future research should address challenges to foster patient and clinician engagement in sharing clinical notes.
A NEW ENVIRONMENTAL CHALLENGE: HOW TO IMPROVE DATA CENTER ENERGY EFFICIENCY

The introduction of Information and Communication Technology (ICT) as a common tool in almost all the companies has led to a significant increase of data centers energetic consumption. As a consequence, data centers management has become strategic from a budgetary point of view, both for costs and environmental impacts reduction and as a possibility to free up resources for other investments in the sector. This paper faces three main subjects: an analysis of the measurement methods for data centers energy consumption and the technological options to improve their efficiency, the economic impact of those improvements and, finally, the different initiatives developed in the E.U. and the U.S. to promote green IT solutions.
Modeling and Response Prediction in Performance-Based Seismic Evaluation: Case Studies of Instrumented Steel Moment-Frame Buildings

An evaluation of the four analytical methods recommended in FEMA-356 for the estimation of seismic demands is carried out on two instrumented steel buildings. Prior to conducting the FEMA-356 analyses, computer models of each structural system were calibrated to observed response data. One of the two buildings that experienced no visible damage during Northridge was modeled as a three-dimensional system, while the second building that suffered moderate damage during the Northridge earthquake was further tuned to yield a simplified two-dimensional model so that detailed inelastic evaluations could be performed on the model. Dozens of linear and nonlinear computer analyses under both static and seismic loads were carried out on each building, and typical results representing a summary of the findings are presented in this paper. Results of the evaluations provide insight into both modeling issues and the validity of the four analytical procedures outlined in FEMA-356. It is found that calibrating structural models to observed response is sensitive to mass and stiffness modeling assumptions and indirectly to the intensity of the ground motion that has an effect on the participation of nonstructural components. Linear and nonlinear static procedures do not consistently predict interstory drift estimates, a critical parameter in seismic evaluation and design. The linear static approach was found to be conservative in one case study while the findings from the second case study were less conclusive about the validity of each analytical procedure. This suggests that significant changes are warranted in the proposed FEMA-356 Prestandard if it is to be adopted in future design codes or in seismic evaluation studies of existing buildings.
Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network

This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN). This method processes the original input data by using a stack denoising autoencoder. Different from the traditional autoencoder, stack pruning sparse denoising autoencoder includes a fully connected autoencoding network, the features extracted from the front layer of the network are used for the operation of the subsequent layer, which means that some new connections will appear between the front and rear layers of the network, reduce the loss of information, and obtain more effective features. Firstly, a one-dimensional sliding window is introduced for data enhancement. In addition, transforming one-dimensional time-domain data into the two-dimensional gray image can further improve the deep learning (DL) ability of models. At the same time, pruning operation is introduced to improve the training efficiency and accuracy of the network. The convolutional neural network model with sPSDAE has a faster training speed, strong adaptability to noise interference signals, and can also suppress the over-fitting problem of the convolutional neural network to a certain extent. Actual experiments show that for the fault of unmanned aerial vehicle (UAV) blade damage, the sPSDAE-CNN model we use has better stability and reliable prediction accuracy than traditional convolutional neural networks. At the same time, For noise signals, better results can be obtained. The experimental results show that the sPSDAE-CNN model still has a good diagnostic accuracy rate in a high-noise environment. In the case of a signal-to-noise ratio of −4, it still has an accuracy rate of 90%.
The Cotton Breeding in Bulgaria

During 2011-2013 in comparative variety trial 11 new and two standard cotton varieties were tested. The varieties were obtained from two different targeted programs – for yield and for fiber quality with the application of different breeding methods. The results of the two-factor analysis (varieties-years) showed that the differences between the studied characters, except first picking, were significant, which means the varieties differed in them. Year conditions had the highest participation in the general variation of all characters (52.58 to 67.08%). The varieties Pearla-267 and Vicky had the highest seed cotton yield, exceeding the standard variety Chirpan-539 by 10.2 to 10.6%, average of three years. Best combination of length and lint percentage of the fiber was found for the varieties Darmi, Vega and Boyana. The studies are still focused on: creation of source material with diverse germplasm; maintenance and use of the genetic resources; heterosis based on male sterility; application of biotechnologies; drought resistance with reference to the selection of tolerant genotypes; resistance to Verticierum dahlia Kleb and seed production of regional varieties
Influence of dielectric loading on PIFA antennas in close proximity to user's body

The emerging trend for handset antenna design puts strong constraints on their volume occupation and user's proximity resilience. Several PIFA antenna configurations are investigated, studying the influence of substrate/superstrate dielectric loading on both absorption and mismatch loss at 900 MHz. By using high-permittivity materials and by leaving a small clearance area between the PIFA top plate and the back of the handset, both absorption and mismatch loss can be reduced at the expense of a narrower bandwidth.
FUZZY TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

This paper presents a novel design of a fuzzy control strategy (FCS) based on torque distribution for parallel hybrid electric vehicles (HEVs). An empirical load-regulating vehicle operation strategy is developed on the basis of analysis of the components efficiency map data and the overall energy conversion efficiency. The aim of the strategy is to optimize the fuel economy and balance the battery state-of-charge (SOC), while satisfying the vehicle performance and drivability requirements. In order to accomplish this strategy, a fuzzy inference engine with a rule-base extracted from the empirical strategy is designed, which works as the kernel of a fuzzy torque distribution controller to determine the optimal distribution of the driver torque request between the engine and the motor. Simulation results reveal that compared with the conventional strategy which uses precise threshold parameters the proposed FCS improves fuel economy as well as maintains better battery SOC within its operation range.
Solutions to a modified tool loading problem for a single FMM

Due to the common experience of changing production requirements, many flexible manufacturing machines (FMM) must be used to produce parts requiring more tools than the tool magazine can hold. Tool changes must then be made during jobs as well as between jobs. Many previously developed solutions to the tool loading problem are not capable of obtaining optimal schedules in this modified problem. This is remedied through a unique solution involving job splitting that provides better solutions to a generalized tool loading problem than previously obtained. By additionally allowing concurrent job and tool changing, a new optimal tooling policy is obtained that is useful either with previously developed job scheduling heuristics, or the algorithm presented in this paper.
Impact of Image Compression on the Performance of Steel Surface Defect Classification with a CNN

Machine vision is increasingly replacing manual steel surface inspection. The automatic inspection of steel surface defects makes it possible to ensure the quality of products in the steel industry with high accuracy. However, the optimization of inspection time presents a great challenge for the integration of machine vision in high-speed production lines. In this context, compressing the collected images before transmission is essential to save bandwidth and energy, and improve the latency of vision applications. The aim of this paper was to study the impact of quality degradation resulting from image compression on the classification performance of steel surface defects with a CNN. Image compression was applied to the Northeastern University (NEU) surface-defect database with various compression ratios. Three different models were trained and tested with these images to classify surface defects using three different approaches. The obtained results showed that trained and tested models on the same compression qualities maintained approximately the same classification performance for all used compression grades. In addition, the findings clearly indicated that the classification efficiency was affected when the training and test datasets were compressed using different parameters. This impact was more obvious when there was a large difference between these compression parameters, and for models that achieved very high accuracy. Finally, it was found that compression-based data augmentation significantly increased the classification precision to perfect scores (98–100%), and thus improved the generalization of models when tested on different compression qualities. The importance of this work lies in exploiting the obtained results to successfully integrate image compression into machine vision systems, and as appropriately as possible.
Development of Operational Performance and Decision Models for Arterial Bus Lanes

Bus lanes are a form of exclusive bus facility typically found along busy arterial streets. The use of bus lanes is justified because buses can carry more passengers than automobiles. However, a lane taken away from general-purpose traffic and designated as a bus-only lane can create congestion in other lanes, causing protests by motorists. An effort was undertaken to develop operational performance and decision models that can be used to justify and design bus lanes on arterial streets. The model considers the overall average person travel time under two treatments: with and without a bus-only lane. The CORSIM simulation model was used to estimate the bus and nonbus travel speeds under various scenarios of prevailing conditions, including bus volume, nonbus volume, right-turn volume, bus stop location, bus stop density, presence of bus bay, number of bus berths, mean dwell time, green ratio, cycle length, signal offset, and number of lanes. The simulated data were used as a substitute for field data in an empirical modeling of relationships between travel speeds and the prevailing conditions. The speed models allow person travel times to be estimated under different bus-lane designs and prevailing conditions. The models were found to produce speeds that closely approximate those reported in the literature for the given input conditions.
Analysis of Integration of the Indonesian Sharia Stock Index with Developing Countries

Globalization is rapidly causing an integration of economic and financial systems worldwide, resulting in shocks to the Islamic stock index and reducing the benefits of diversification for investors. Therefore, this study analyzes the integration, influence, response, and contribution of shocks to each developing country’s Islamic stock index. Specifically, analyzing the effect of developing country sharia stock index shocks on Indonesia's sharia stock index. The study uses monthly time series data for 2011-2021 with samples from Indonesia, Turkey, Malaysia, Pakistan, Kuwait, and India using the Vector Error Correction Model (VECM) method. The results showed cointegration or a long-term relationship in the developing countries’ sharia stock index. The Malaysian Islamic Stock Index and the Indian Islamic Stock Index influence the Indonesian Islamic Stock Index. Furthermore, the Indonesian Islamic Stock Index stabilized the fastest in response to the Turkish Islamic Stock Index shocks. However, the Malaysian Islamic Stock Index shock contributes the most to the Indonesian Islamic Stock Index. Developing countries could improve the infrastructure of the Islamic stock index and policy reforms. This would minimize the impact of international stock index shocks and accelerate integration. Investors should consider the dominant economic strength, geographical factors, and trade relations in determining portfolio diversification in global economic conditions.
Ecological Risk Assessment in the United States Environmental Protection Agency: A Historical Overview

ABSTRACT EDITOR'S NOTE: This is 1 of 4 papers from the US Environmental Protection Agency Science Advisory Board's Ecological Processes and Effects Committee workshop on the current and future practice of ecological risk assessment. The workshop was held in Washington, DC in February 2006. Risk assessment originated with the insurance industry and spread to the estimation of risks to people and property in other contexts, including the regulation of environmental contamination. Ecological assessment became an important component of environmental management in the United States with the legal mandate for environmental impact assessment in 1970. Risk assessment and ecological assessment merged in the 1980s to form ecological risk assessment (ERA). Since then, ERA has been institutionalized with the development of the US Environmental Protection Agency's (hereafter, USEPA or Agency) framework and guidance documents. Ecological risk assessment has been adapted by the Agency's program offices to fit their legal and policy contexts. The future of ERA will inevitably include the incorporation of more complex and demanding methods. However, the biggest challenge for future risk assessors will be to make ecological risks more compelling to decision makers.
Improving Physical Distribution Service Decisions with Trade‐off Analysis

The purpose of this paper is to discuss some recent developments in trade‐off analysis which may provide distribution managers some guidance in (1) evaluating the physical distribution service (PDS) mix, (2) setting service levels, and (3) determining which elements of the mix warrant the greatest attention. This article proceeds by briefly discussing the new approach, and then illustrating how it might be used; the paper ends with some suggestions concerning the future use of these methods.
Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here.
The Efficacy and Safety of Alzheimer's Disease Therapies: An Updated Umbrella Review.

BACKGROUND Evidence summaries for efficacy and safety of frequently employed treatments of Alzheimer's disease (AD) are sparse. OBJECTIVE We aimed to perform an updated umbrella review to identify an efficacious and safe treatment for AD patients. METHODS We conducted a search for meta-analyses and systematic reviews on the Embase, PubMed, The Cochrane Library, and Web of Science to address this knowledge gap. We examined the cognitive functions, behavioral symptoms, global clinical assessment, and Activities of Daily Living as efficacy endpoints, and the incidence of adverse events as safety profiles. RESULTS Sixteen eligible papers including 149 studies were included in the umbrella review. The results showed that AChE inhibitors (donepezil, galantamine, rivastigmine, Huperzine A), Ginkgo biloba, and cerebrolysin appear to be beneficial for cognitive, global performances, and activities of daily living in patients with AD. Furthermore, anti-Aβ agents are unlikely to have an important effect on slowing cognitive or functional impairment in mild to moderate AD. CONCLUSION Our study demonstrated that AChE inhibitors, Ginkgo biloba, and cerebrolysin are the optimum cognitive and activities of daily living medication for patients with AD.
Equivalent Circuit Models for Micromechanical Inertial Sensors

Numerical simulation models for capacitive, micromechanical inertial sensors are presented. These dynamic, nonlinear models include the contributions of electrical, mechanical, and fluidic energy domains by means of electrical equivalent circuits consisting of lumped, frequency-independent components. The mechanical resonance modes, gap capacitances, gas film damping forces and the electrostatic actuation forces are accounted for in the model. Special attention is paid to modelling the rarefied gas flow in narrow air-gaps, including the effect of gas-surface interaction. These gas-rarefaction effects are included in the effective gas viscosity. Novel, compact design equations are presented for the effective viscosity in narrow air-gaps, valid in viscous, transitional and molecular flow regions. Inertial sensor models for an accelerometer and an angular rate sensor have been built from electrical equivalent circuit blocks that consist of elementary voltage-controlled current and charge sources. The verification show very good agreement with the simulated and measured frequency responses over a wide pressure range. The gas-surface interaction model has been tested by extracting a numerical value of the surface accommodation coefficient. Compact equivalent circuit models allow both sensor and sensor system simulations with any general purpose circuit simulation program. The models derived have been implemented and simulated with the circuit simulation program APLAC.
Modified genetic algorithm to design arbitrary response filters for rectangular waveguides

In this study, we present an improved genetic algorithm (GA) to design user‐specified filters housed in a waveguide of arbitrary cross‐sectional dimensions. An edge‐based finite element method (FEM) is employed as the forward solver for the problem. Additionally, the structures generated via the GA are easily constructed using standard printed circuit board fabrication techniques. Two of the major improvements to the GA are: (1) a technique for enhancing the GA's ability to avoid local minima and (2) a fine‐tuning mechanism which allows the GA to more efficiently seek out a minimum once a low error has been obtained. Using the aforementioned techniques, numerical/experimental results are presented for notch, low pass, high pass, and bandpass filters. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.
Knowledge-based search for Earth Observation products

This diploma thesis considers both, theoretical and practical aspects of the semantic modelling approach for the EO domain. Firstly, it briefly introduces the EO data management at the DFD and then continues with the problem analysis based on real-life usecases used as a starting point to create a common high-level information model of the EO domain. Next, the common high-level information model can be formally speci�ed into an ontology for the EO domain that reuses common terminologies from earth sciences. Furthermore, the user access to the developed ontology is presented in the EOLOsearch web demonstrator. Finally, the feasibility of the semantic modelling approach for the EO domain is evaluated based on the developed ontology and its web demonstrator considering several quality criteria that apply for the EO domain.
Clogging and Water Quality Change Effects of Typical Metal Pollutants under Intermittent Managed Aquifer Recharge Using Urban Stormwater

Managed aquifer recharge (MAR) using urban stormwater facilitates relieving water supply pressure, restoring the ecological environment, and developing sustainable water resources. However, compared to conventional water sources, such as river water and lake water, MAR using urban stormwater is a typically intermittent recharge mode. In order to study the clogging and water quality change effects of Fe, Zn, and Pb, the typical mental pollutants in urban stormwater, a series of intermittent MAR column experiments were performed. The results show that the type of pollutant, the particle size of the medium and the intermittent recharge mode have significant impacts on the pollutant retention and release, which has led to different clogging and water quality change effects. The metals that are easily retained in porous media have greater potential for clogging and less potential for groundwater pollution. The fine medium easily becomes clogged, but it is beneficial in preventing groundwater contamination. There is a higher risk of groundwater contamination for a shallow buried aquifer under intermittent MAR than continuous MAR, mainly because of the de-clogging effect of porous media during the intermittent period.