Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Evaluation Tools for Dietitians of Canada’s Practice-based Evidence in Nutrition (PEN) Service

Background: Evidence-based practice (EBP) uses the best available research evidence, in combination with clinical expertise and client values, to guide practice decisions (1). Dietitians recognize the benefits of using an evidence-based approach, but do not routinely incorporate it into their practice (2, 3). Lack of time and the skills needed to find and critically evaluate the published research are cited as barriers to adopting evidence-based practice (3). The knowledge translation and transfer (KTT) process can assist in bridging the gap between evidence and practice (4). In September 2005, Dietitians of Canada (DC) launched Practice-based Evidence in Nutrition (PEN), an online evidence-based knowledge translation/transfer (KTT) service for dietetic practice. PEN must be evaluated to determine if the service meets the needs of users and to assess its effectiveness as a KTT tool for incorporating evidence-based knowledge into dietetic practice. Objective: The aim of this research was to develop two evaluation tools for PEN: a webbased questionnaire to collect demographic and quality assurance data (PEN Evaluation Questionnaire) and a set of interview questions to evaluate PEN as medium for KTT and its impact on dietetic practice (PEN Evaluation Interview Guide). Methods: The questionnaire was developed using a structured four-step process. An advisory panel (n=14) validated the researcher-developed questionnaire and guided the development of its online format. The resulting questionnaire was pilot tested with a purposive sample (n=19) of PEN subscribers and non-subscribers. The interview guide was developed with the assistance of an expert panel (n=7) using a three round modified Delphi process. Results: The questionnaire development process resulted in a 46 item web-based questionnaire to collect demographic and quality assurance data on the PEN service. The findings confirm that successful questionnaire development requires the use of a systematic approach including a comprehensive review of face and content validity. The Delphi process was successful in bringing together a diverse group of experts with extensive knowledge in KTT and EBP to produce an interview guide containing open-ended questions focusing on respondents’ understanding of evidence-based practice and PEN, the use of PEN by dietitians and other disciplines, the perception of the quality/usefulness of PEN and barriers and facilitators to PEN use. Conclusions: Quantitative and qualitative evaluation tools, validated by experts in the KTT and dietetics fields, will provide a comprehensive evaluation of PEN’s impact on practice and its effectiveness as a KTT tool and ensure the quality and relevance of the data collected. This research adds to the limited body of knowledge regarding the formal evaluation of online evidence systems and KTT strategies.
Time-Efficient Integrated Electrothermal Model for a 60-kW Three-Phase Bidirectional Synchronous DC–DC Converter

This article introduces an accurate and fast electrothermal model to calculate power loss and junction temperature of power electronic devices in a three-phase dc–dc converter, and to simulate converter dynamics during a simulated drive cycle. The proposed electrothermal model calculates power loss and junction temperature by representing the three-phase dc–dc converter dynamics within a drive cycle using a three-phase state-space average model. The three-phase state-space average model is modified to represent converter dynamics when one or two phases are off, in addition to the phase transition dynamics. The existing electrothermal models can either represent converter dynamics but are too slow for long simulations like drive cycles, or they reduce the simulation time at the expense of junction temperature accuracy. Furthermore, the existing electrothermal models in the literature are for inverters and single-phase dc–dc converters. The proposed electrothermal model combines the advantages of fast simulation time with accurate estimation of junction temperature. The power loss calculated by the proposed method is verified by a switching Simulink/PLECS model, and the mosfet temperature estimation is validated experimentally using a 60-kW three-phase bidirectional boost converter under steady-state conditions and a custom drive cycle. The efficiency of the three-phase converter is measured experimentally and compared with the efficiency calculations from the proposed electrothermal model.
Smart Battery Pack for Electric Vehicles Based on Active Balancing with Wireless Communication Feedback

In this paper, the concept of smart battery pack is introduced. The smart battery pack is based on wireless feedback from individual battery cells and is capable to be applied to electric vehicle applications. The proposed solution increases the usable capacity and prolongs the life cycle of the batteries by directly integrating the battery management system in the battery pack. The battery cells are connected through half-bridge chopper circuits, which allow either the insertion or the bypass of a single cell depending on the current states of charge. This consequently leads to the balancing of the whole pack during both the typical charging and discharging time of an electric vehicle and enables the fault-tolerant operation of the pack. A wireless feedback for implementing the balancing method is proposed. This solution reduces the need for cabling and simplifies the assembling of the battery pack, making also possible a direct off-board diagnosis. The paper validates the proposed smart battery pack and the wireless feedback through simulations and experimental results by adopting a battery cell emulator.
Nonlinear Moving Horizon Estimation for Large-Scale Urban Road Networks

Perimeter control schemes proposed to alleviate congestion in large-scale urban networks usually assume perfect knowledge of the accumulation state together with current and future inflow demands, requiring information about the origins and destinations (OD) of drivers. Such assumptions are problematic for practice due to: (i) Measurement noise, (ii) difficulty of measuring OD-based accumulation states and inflow demands. To address these, we propose a nonlinear moving horizon estimation (MHE) scheme for large-scale urban road networks with dynamics described via macroscopic fundamental diagram. Furthermore, we consider various measurement configurations likely to be encountered in practice, such as measurements on regional accumulations and transfer flows without OD information, and provide results of their observability tests. Simulation studies, considering joint operation of the MHE with a model predictive perimeter control scheme, indicate substantial potential towards practical implementation of MFD-based perimeter control.
Control of Thermo-Mechanical Wafer Deformations in EUV Lithography Using an Active Wafer Clamp

This paper deals with the modeling and control of thermo-mechanical deformations of a wafer, resulting from absorbed exposure power, in EUV lithography. To achieve correction of the induced deformations, an active wafer clamp concept is proposed. In this concept, between 1,000 and 30,000 actuators are incorporated into the electrostatic wafer clamp, such that forces can be applied directly onto the wafer. Due to dimensionality of the control problem, a distributed feedforward control approach is proposed which acts locally around the exposure slit, and reduces the number of design inputs by approximately 80%. The results obtained with this controller show an average reduction of a factor 10, of the thermo-mechanical deformations at the edge of the wafer as well as more towards the center of the wafer.
Leak detection on water pipelines in unsaturated ground by discrete fibre optic sensing

The article describes a study using discrete fibre optic sensing as a means of leak detection on water distribution pipes installed in unsaturated ground. A short length of pipe fitted with artificial leak points was installed, to which a fibre optic cable with fibre Bragg gratings was attached. An optical fibre with fibre Bragg gratings was also installed in the ground parallel to but separate from the pipe. Thermistors were installed at selected locations to measure temperature changes independent of strain. It was found that a simulated water leak resulted in clearly detectable temperature changes and thermally induced fibre Bragg grating wavelength changes in the ground around the pipe. However, significantly larger deformation-induced fibre Bragg grating wavelength changes were measured on the pipe walls and also in the initially unsaturated ground in response to leaks. A wetting front originating from a water leak propagating through unsaturated soil is associated with significant effective stress changes because the infiltrating water alters the ambient matric suction in the soil. This effective stress change is associated with significant ground deformation resulting in a fibre Bragg grating response which significantly exceeds the thermal response associated with (usually) colder water leaking into unsaturated soil. The study illustrates advantages of measuring ground deformation-induced fibre Bragg grating wavelength changes over pure temperature changes as an efficient means of leak detection in unsaturated ground. However, due to the limited number of fibre Bragg gratings that can be monitored along a single optical fibre, a leak detection system suitable for practical implementation should be based on distributed fibre optic strain sensing, an aspect requiring further research.
Robust Repetitive Control of Power Inverters for Standalone Operation in DG Systems

Standalone operation of an inverted-based distributed generation unit is performed in voltage control mode. In this mode, it is important that the controller provides tracking and rejection of periodic signals. Strategies like Repetitive Control (RC) are well suited in the voltage control of power inverters. However, the robustness of repetitive controllers is importantly affected by the connected loads. The reason is that the load modifies the dynamics of the system where the design of the RC relies on. Having a low bandwidth action can reduce the problem but with the cost of degrading the voltage waveform and transient response. As an alternative, an $H_{\infty }$ design is proposed for the RC of voltage source inverters. In this proposal the load is directly treated as an unstructured uncertainty. With this approach, the design procedure directly maximizes the robust stability margin of the system against load variations/uncertainty. Additionally, the analysis provides some insight about the design of the inverter LC filter. Experimental results show that a remarkable Total Harmonic Distortion and good transient response are obtained for a large variety of loads.
Sunlight-Driven Photothermal Effect of Composite Eggshell Membrane Coated with Graphene Oxide and Gold Nanoparticles

Eggshell membrane (ESM), which consists of unique interwoven shell membrane fibers, provides a unique supporting platform for functional nanoparticles in catalysis and sensing. This work reports a novel strategy for fabricating sunlight-driven photothermal conversion composite membranes by loading graphene oxide (GO) and gold nanoparticles (AuNPs) on the three-dimension (3D) network structured eggshell membrane. Surface morphologies and chemical elements were characterized by scanning electron microscopy and X-ray photoelectron spectroscopy. High photothermal conversion under simulated sunlight irradiation, which may be caused by the synergistic effect of GO and AuNPs, was achieved by coating both GO and AuNPs onto ESM. The temperature of ESM modified with AuNPs, and then GO increased from 26.0 °C to 49.0 °C after 10 min of light irradiation. Furthermore, the nanoscaled GO and AuNPs could add benefit to the heating localization of the obtained composite membrane. It is expected this biocompatible ESM modified with GO and AuNPs would have great potential in drug release and photothermal therapy applications.
Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles.
Optimization of wireless power transfer using artificial neural network: A review

Wireless power transfer (WPT) is widely explored and applied nowadays because of its simplicity in transferring power without using wire, easy maintenance, and equipment mobility. Due to mobility and compatibility attributes, WPT is utilized in powering biomedical devices, small electronic equipment, wireless sensor, mobile phones, and high voltage applications (eg, electric vehicles). The implementation of artificial neural network (ANN) in WPT has emerged as a powerful/prominent tool for estimating the performance parameters due to its learning and significant features. Such implementation can minimize design complexity and time‐consuming calculations. An early application of ANN employs the information derived from the collectively measured processes for training the ANN algorithm. After a suitable training process, the network output can be considered in place of computationally thorough representations to speed up the result search. To obtain precise result and optimize the parameters in WPT, several popular ANN algorithms have been used by researchers. This review paper highlighted the latest research specifically regarding the implementation of ANN in WPT, which included the types of ANN implemented in WPT, current WPT problem investigation that used ANN, and a comparison between the techniques. Moreover, the challenges and constraints of ANN techniques were elucidated at the end of this paper.
PCRB and IMM for Target Tracking in the Presence of Specular Multipath

This article considers the general problem of tracking a noncooperative target in the presence of specular multipath. We revisit a novel posterior Cramér–Rao bound (PCRB) methodology for this problem [1] and develop a robust interacting multiple model (IMM) extended Kalman filter (EKF) implementation. Both the PCRB and the IMM-EKF augment the target state to include the multipath parameters. The approaches are demonstrated via a simulated scenario, in which an airborne radar tracks a low-altitude airborne target. It is shown that the PCRB increases rapidly subsequent to the multipath effect occurring, indicating that tracking performance is significantly degraded at such times. Furthermore, it is shown that the IMM-EKF offers near-optimal performance, with target state estimation errors consistently within 15% of the PCRB. The IMM-EKF also consistently provides credible estimates. It is concluded that the PCRB methodology and the IMM-EKF tracker provide powerful tools for performance assessment and target state estimation in challenging multipath scenarios.
Metronidazole-Loaded Porous Matrices for Local Periodontitis Treatment: In Vitro Evaluation and In Vivo Pilot Study

Periodontal therapy focuses on thorough removal of subgingival calculus and plaque products followed by the smoothing out of root surfaces. However, such conventional mechanotherapeutic approaches are inefficient with regard to microbial biofilm elimination from the space between the root and deep periodontal pockets. Therefore, local chemotherapeutic agents need to be applied. Local antimicrobial treatment is also considered a safer treatment, as it avoids systemic complications related to drug application. In this study, porous matrices consisting of gelatin (GE) and cellulose derivatives (carboxymethylcellulose (CMC) and hydroxyethyl cellulose (HEC)) were loaded with antimicrobial drug metronidazole (MTZ). The matrices’ structural morphology, physiochemical properties, swelling and degradation ratio, mechanical properties, and MTZ release from the matrices were analyzed. Additionally, cytotoxicity tests for fibroblast and osteoblast cell cultures (L929 and U2-OS, respectively) and antimicrobial activity assessments of MTZ-loaded matrices against anaerobic Bacteroides sp. Bacteria were performed. Finally, clinical application of HEC matrices into periodontal pockets was conducted. The applied matrices showed a high antibacterial efficacy and a moderate cytotoxicity in vitro. The clinical application of HEC dressings corresponded with the decrease of periodontal pockets’ depth and bleeding observed 1 month after a single application. The presented results show that intra-pocket application of metronidazole using manufactured matrices may serve not only as a support for a standard treatment in periodontal practice but also as an alternative to systemic drug administration in this setting. Clinical data were analyzed using a nonparametric Friedman’s ANOVA for dependent trials.
Exploiting Redundancy for Reliability Analysis of Sensor Perception in Automated Driving Vehicles

For automated driving, the perception provided by lidar, radar, and camera sensors is safety-critical. Validating sensor perception reliability with standard empirical tests is impractical, owing to the large required test effort and the need for a reference truth to identify sensor errors. To address these challenges, we investigate the possibility of estimating sensor perception reliability without a reference truth. In particular, we propose a framework to learn sensor perception reliability solely by exploiting sensor redundancies. We formulate a likelihood function for redundant binary sensor data without a reference truth and propose a Gaussian copula to model dependent sensor errors. Synthetic numerical experiments show that under an adequate dependence model, correct sensor perception reliabilities can be estimated without a reference truth. Because the selection of an adequate dependence model is challenging without a reference truth, we also investigate how inadequate dependence models influence the estimation. The proposed framework is a step toward the validation of sensor perception reliability because it could enable the learning of reliabilities from a fleet of driver-controlled vehicles equipped with series sensors.
P Dalga Dispersiyonunun İnfarkt İlişkili Aater Açıklığını Saptamadaki Prediktif Değeri

Introduction: The value of P-wave dispersion (PWD) on the surface electrocardiogram (ECG) has been shown to increase in coronary ischemia. Previous studies demonstrated that PWD decreased with repefusion therapy in patients with STEMI. The aim of current study is to investigate whether PWD is able to be used as an additional parameter to predict successful reperfusion and infarct related artery patency in patients with STEMI who were admitted in the first 12 hours of myocardial infarction and received thrombolytic therapy.Patients and Methods: A total of 150 patients who referred to Dokuz Eylul University Cardiology Department with STEMI between 01 January 2012 and 01 December 2013 and received thrombolytic therapy in the first 12 hours of the symptoms were included in our study. Hospital records of each patient were evaluated retrospectively. Demographical features were noted. Standart 12-lead surface ECG of each patient which were recorded before and at 30, 60, 90 and 120 minutes after the start of thrombolytic therapy were obtained. Maximum, minimum P-wave duration and PWD were calculated on each electrocardiogram and ST segment resolution was determined. Coronary angiography images were also evaluated to determine TIMI flow grade and TIMI frame count of the infarct related artery. The relationship between PWD and ST segment resolution and infarct related artery patency was investigated.Results: While PWD at the start of the thrombolytic therapy was 50.79±14.12 ms, PWD at 90th minute of thrombolytic therapy was calculated as 48.34±15.60 and PWD at 120th minute of thrombolytic therapy was calculated as 47.85±10.87 ms; the difference was not statistically significant (p= 0.07). The comparison of PWD values with ST segment resolution groups demonstrated that PWD at 120th minute of thrombolytic therapy was found to be significantly lower in complete resolution group than incomplete and inadequate resolution groups (42.10±9.55, 49.65±10.60, 56.08±7.44 ms respectively; p
Adaptive Natural Gradient Method for Learning of Stochastic Neural Networks in Mini-Batch Mode

Gradient descent method is an essential algorithm for learning of neural networks. Among diverse variations of gradient descent method that have been developed for accelerating learning speed, the natural gradient learning is based on the theory of information geometry on stochastic neuromanifold, and is known to have ideal convergence properties. Despite its theoretical advantages, the pure natural gradient has some limitations that prevent its practical usage. In order to get the explicit value of the natural gradient, it is required to know true probability distribution of input variables, and to calculate inverse of a matrix with the square size of the number of parameters. Though an adaptive estimation of the natural gradient has been proposed as a solution, it was originally developed for online learning mode, which is computationally inefficient for the learning of large data set. In this paper, we propose a novel adaptive natural gradient estimation for mini-batch learning mode, which is commonly adopted for big data analysis. For two representative stochastic neural network models, we present explicit rules of parameter updates and learning algorithm. Through experiments on three benchmark problems, we confirm that the proposed method has superior convergence properties to the conventional methods.
Additive Manufacturing (AM) Capacitive Acoustic and Ultrasonic Transducers Using a Commercial Direct Light Processing (DLP) Printer

In recent years, there has been increasing interest in using additive manufacturing (3D printing) technology to fabricate sensors and actuators due to rapid prototyping, low-cost manufacturing processes, customized features and the ability to create complex geometries at micrometre scale. State of the art additive manufactured acoustic and ultrasonic transducers show limitations in miniaturization, repeatability (defects) and sensitivity. This new work encompasses the development of a capacitive acoustic and ultrasonic transducer, including its fabrication process using a commercial digital light processing printer and output signal characterization with a custom-made amplification circuit. A set of capacitive acoustic and ultrasonic transducers was fabricated and tested using different diaphragm diameters from 1.8 – 2.2 mm, for comparison, with central operating frequency between 19 – 54 kHz, respectively. This capacitive transducer design has a receiving sensitivity of up to ${0.4} ~\textit {mV}/\textit {Pa}$ at its resonant frequency, and a comparison with a commercial reference microphone is provided.