Stand-alone Application Research Articles

A low ripple current rejection method to improve the lifetime of solar photo voltaic integrated GaN converter system

ABSTRACT The performance up-gradation that enhances the power output of the solar photovoltaic (SPV) panel along with life term is the latest demand in any standalone applications. The current ripple rejected to the SPV panel has both electrical and thermal effects. Lower the ripple, lower will be the power loss and the rise in temperature. Here, a method for an additional ripple current reduction at the source is demonstrated with a 300 W, 500 kHz, gallium nitride device-based panel integrated three-phase synchronous boost converter. A simple procedure of listing the switching states at each ON and OFF of polyphase periods in the analytical method for ripple estimation is proposed. A novel input current ripple reduction method, in addition to that of conventional operations, which leads the converter operations with minimum ripple rejection implemented. The factors that enhance the life of the SPV system, accomplishment of a typical improvement of 7% with a reduction of 3.5 deg. C rise in temperature, for all electrical reflections of the converter, is evaluated. Additionally, the electrical performance of 96.4% efficiency and 3.90 × 106 W/m3 power density was achieved. An SPV module integrated converter for a LED street-lighting application, implemented with the proposed method is detailed.

An ensemble data assimilation approach to improve farm-scale actual evapotranspiration estimation

Estimation of actual evapotranspiration (ET) is key to irrigation water application and basin-scale agricultural water demand assessment. While modelers and water managers rely on stand-alone ET estimation model application in their planning and management, several uncertainties including model structure, parameter set, and initial condition exist, cascading in ET calculation leading to inaccurate results. In this study, an ensemble data assimilation approach is employed to explore the benefit of remotely sensed actual ET to improve the simulations of the widely used Priestley-Taylor ET model while accounting for uncertainties. The study is conducted at farm-scale for three different crops (corn, cotton, and soybean) in the Mobile River basin in Deep South United States, which has experienced severe droughts during the cropping seasons in the past. Prior to employing data assimilation, the Priestley-Taylor model is modified for each crop to simulate actual ET instead of reference ET. Following which the model is calibrated over 320,000 farms in the river basin for identifying the optimal parameters. The calibrated model is later used for the Open-Loop simulation, as well as in the development and implementation of data assimilation. The simulated and observed actual ET is used to calculate the Kalman gain and update the model initialization every time step during the assimilation period. The findings of the study showed that assimilating the actual ET observation into the Priestley-Taylor model results in more accurate and reliable model initialization and also posterior ET estimates at farm-scale compared to open-loop simulation. These results highlight, the importance of digital agricultural tools in robust agricultural planning and management and open door for further research.

Challenges and Opportunities for Applications of Advanced X-ray Spectroscopy in Catalysis Research

X-ray spectroscopy has had a significant and continually growing impact on catalysis research for nearly 50 years. In particular, the ability to obtain element selective electronic and geometric structural information via the X-ray absorption (XAS) edge and extended X-ray absorption fine structure regions, respectively, has been a major asset for catalysis research. In the last two decades, the development of dedicated synchrotron-based X-ray emission spectrometers has greatly expanded the range of possible experiments, enabling both nonresonant and resonant X-ray emission spectroscopy experiments that can provide greater selectivity and more detailed electronic and structural information. Herein, we briefly review the range of hard X-ray photon-in, photon-out experiments that are presently possible, and highlight their recent applications in catalysis research. We also discuss the ongoing need for conventional XAS applications, either in standalone applications or in combination with more advanced approaches. The open opportunities and ongoing challenges for applying these methods, and ultimately for analyzing and interpreting the data, are also discussed.

A Novel J-Shape Antenna Array for Simultaneous MR-PET or MR-SPECT Imaging.

Simultaneous MR-PET/-SPECT is an emerging technology that capitalises on the invaluable advantages of both modalities, allowing access to numerous sensitive tracers and superior soft-tissue contrast alongside versatile functional imaging capabilities. However, to optimise these capabilities, concurrent acquisitions require the MRI antenna located inside the PET/SPECT field-of-view to be operated without compromising any aspects of system performance or image quality compared to the stand-alone instrumentation. Here, we report a novel gamma-radiation-transparent antenna concept. The end-fed J-shape antenna is particularly adept for hybrid ultra-high field MR-PET/-SPECT applications as it enables all highly attenuating materials to be placed outside the imaging field-of-view. Furthermore, this unique configuration also provides advantages in stand-alone MR applications by reducing the amount of coupling between the cables and the antenna elements, and by lowering the potential specific absorption rate burden. The use of this new design was experimentally verified according to the important features for both ultra-high field MRI and the 511 keV transmission scan. The reconstructed attenuation maps evidently showed much lower attenuation ( ∼15 %) for the proposed array when compared to the conventional dipole antenna array since there were no high-density components. In MR, it was observed that the signal-to-noise ratio from the whole volume obtained using the proposed array was comparable to that acquired by the conventional array which was also in agreement with the simulation results. The unique feature, J-shape array, would enable simultaneous MR-PET/-SPECT experiments to be conducted without unduly compromising any aspects of system performance and image quality compared to the stand-alone instrumentation.

Forensic Face Sketch Construction and Recognition

Abstract: In forensic science, it is seen that hand-drawn face sketches are still very limited and time consuming when it comes to using them with the latest technologies used for recognition and identification of criminals. In this paper, we present a standalone application which would allow users to create composites face sketch of the suspect without the help of forensic artists using drag and drop feature in the application and can automatically match the drawn composite face sketch with the police database much faster and efficiently using deep learning and cloud infrastructure. Keywords: Forensic Face Sketch, Face Sketch Construction, Face Recognition, Criminal Identification, Deep Learning, Machine Locking, Two Step Verification.

Recent Trends in Power Electronics for Renewable energy Systems

The complete world is focused on renewable power to reduce the global energy issue. Power electronic based energy conversion is being used extensively to improve the efficiency of the renewable energy conversion. It has a significant impact on the control and interface of renewable energy systems with both the network and stand-alone applications. As a result, increasing attention is being placed on the design and implementation of power converters. This study discusses the renewable energy systems (wind and solar) and the features of their energy conversion. The fundamental principles underlying their operations are discussed, as well as their recent technological advancements. It is a fact that power electronics is critical for interfacing and thus improving the system capacity.

An Open-Source Iterative Python Module for the Automated Identification of Photopeaks in Photon Spectra

The UK, and other countries worldwide, have benefited from nuclear energy to provide a low-carbon power source to fuel their increasing populations and industrial growth. In support of the extensive end-of-life decommissioning activities ongoing globally, as well as to enable accident clean-up and nuclear security/monitoring provisions; systems are necessary to rapidly and accurately detect and attribute the nature of any nuclear and/or radioactive materials. To facilitate the utilisation of the increasing suite of miniaturised radiation sensor systems for a range of largely robotic (whether aerial, underwater or ground-based) deployment applications, without the issue of being ’tethered’ to a specific vendor or system, an open-source and compact python module has been developed. Within this readily integrable code-base designed for incorporation into wider software architectures (such as the Robotic Operating System, or ROS), gamma-ray spectroscopy data are recorded in real-time and processed with a peak identification procedure once sufficient data has been recorded. Iterative peak-fitting is applied to determine the isotopic compositions of the incident radiation. The stand-alone application comprises two connected components: a small detector-specific module (or wrapper) that translates a detector’s serial output into the desired format, ahead of the main analysis function. Second, a photopeak identification is performed through an algorithm which uses the second derivative of the spectrum. The peaks identified are subsequently labelled by the program, utilizing the properties of all the mathematically detected/derived peaks, and finally output in a user-defined format for subsequent usage.

Design and Implementation of a Cardiovascular Disease Detection System Using Artificial Neural Network

Cardiovascular diseases are a major cause of death globally. The early detection of these diseases and continuous supervision by health personnel can significantly reduce the mortality rate. However, accurate detection of cardiovascular diseases in all cases and supervision of a patient round-the-clock by a doctor is not feasible since it requires more sapience, time and expertise. Therefore, the aim of this work is to design and implement a cardiovascular disease detection system using artificial neural network. For the accurate detection of cardiovascular diseases, an efficient artificial neural network technique known as Multilayer Perception (MLP) neural network was used to build the predictive model. The system requires patients’ heartrelated medical records to detect the presence or absence of any cardiovascular disease and it will serve as an aid for medical personnel to diagnose the disease in a more efficient way. In the project’s prototype implementation, system logic implementation was achieved using Java while user interfaces were built using the JavaFX framework. The development of the system followed the waterfall model which helped in breaking down the development process into smaller sub-processes that were followed sequentially to achieve a fairly efficient cardiovascular disease detection system. The system is a stand-alone desktop application and tested using WEKA and a laptop. In order to ascertain the accuracy, validation and performance measure of the developed system, a comparative analysis of the developed system against existing cardiovascular disease detection system was done, and the developed system yielded an accurate result of about 91.10%

Contractility analysis of human engineered 3D heart tissues by an automatic tracking technique using a standalone application.

The use of Engineered Heart Tissues (EHT) as in vitro model for disease modeling and drug screening has increased, as they provide important insight into the genetic mechanisms, cardiac toxicity or drug responses. Consequently, this has highlighted the need for a standardized, unbiased, robust and automatic way to analyze hallmark physiological features of EHTs. In this study we described and validated a standalone application to analyze physiological features of EHTs in an automatic, robust, and unbiased way, using low computational time. The standalone application “EHT Analysis” contains two analysis modes (automatic and manual) to analyzes the contractile properties and the contraction kinetics of EHTs from high speed bright field videos. As output data, the graphs of displacement, contraction force and contraction kinetics per file will be generated together with the raw data. Additionally, it also generates a summary file containing all the data from the analyzed files, which facilitates and speeds up the post analysis. From our study we highlight the importance of analyzing the axial stress which is the force per surface area (μN/mm2). This allows to have a readout overtime of tissue compaction, axial stress and leave the option to calculate at the end point of an experiment the physiological cross-section area (PSCA). We demonstrated the utility of this tool by analyzing contractile properties and compaction over time of EHTs made out of a double reporter human pluripotent stem cell (hPSC) line (NKX2.5EGFP/+-COUP-TFIImCherry/+) and different ratios of human adult cardiac fibroblasts (HCF). Our standalone application “EHT Analysis” can be applied for different studies where the physiological features of EHTs needs to be analyzed under the effect of a drug compound or in a disease model.

Dual Battery Storage Technique for Remote, Location-Based Solar PV System and Standalone Applications

Nowadays, the usage of renewable energy resources (RER) is growing rapidly, but at the same time, the effective utilization of RER is also a challenging task. For the better usage of RER and the reduction of loss, the dual battery storage is proposed. The main aim of this work is to focus on the design and implementation of a reliable and renewable power generating system under a robust situation, along with a battery storage system. The perturb and observe (P&O) maximum power point tracking (MPPT) technique has been applied to improve the solar photovoltaic power production. In addition, the dual battery storage system is being introduced to improve the life cycle of the primary storage system. The proposed dual storage system is highly preferable for remote, location-based application systems, space applications and military operations. In the dual battery storage system, the batteries are working effectively with a good lifespan, when compared with the existing methods. To determine the state of charge (SOC) and depth of discharge (DOC), those batteries’ input charging and discharging levels were monitored closely. MATLAB Simulink (R2013) is used for simulation; finally, a real-time, three-phase inverter was designed and validated. Under this dual battery storage mode, the life time of battery is improved.

Enhancement of wind energy harvesting scheme over wide wind velocity using induction generator and variable speed drive

ABSTRACT The conventional Squirrel Cage Induction Generator (SCIG)-based Wind Energy Conversion Systems (WECS) connected to the grid is meant for nearly fixed wind speed operation. Since the grid is maintained at a fixed frequency, whenever the wind speed falls below the rated speed, the SCIG-based WECS units are forced to be in the parked condition. In the proposed system, a Variable Voltage Variable Frequency (VVVF) converter is used between the SCIG terminals and the battery on the DC link. With the inclusion of the VVVF converter, it becomes possible to harvest wind energy even when the wind speed falls below the rated level. The proposed idea has been validated using relevant simulations in MATLAB SIMULINK environment. An experimental prototype has also been setup to examine and validate the proposed idea. The functionality of the proposed system, as confirmed by the simulations and experimental verifications, is promising and is suitable for medium power standalone applications.

GuideMaker: Software to design CRISPR-Cas guide RNA pools in non-model genomes.

CRISPR-Cas systems have expanded the possibilities for gene editing in bacteria and eukaryotes. There are many excellent tools for designing CRISPR-Cas guide RNAs (gRNAs) for model organisms with standard Cas enzymes. GuideMaker is intended as a fast and easy-to-use design tool for challenging projects with (i) non-standard Cas enzymes, (ii) non-model organisms, or (iii) projects that need to design a panel of gRNA for genome-wide screens. GuideMaker can rapidly design gRNAs for gene targets across the genome using a degenerate protospacer-adjacent motif (PAM) and a genome. The tool applies hierarchical navigable small world graphs to speed up the comparison of guide RNAs and optionally provides on-target and off-target scoring. This allows the user to design effective gRNAs targeting all genes in a typical bacterial genome in ∼1-2 minutes. GuideMaker enables the rapid design of genome-wide gRNA for any CRISPR-Cas enzyme in non-model organisms. While GuideMaker is designed with prokaryotic genomes in mind, it can efficiently process eukaryotic genomes as well. GuideMaker is available as command-line software, a stand-alone web application, and a tool in the CyCverse Discovery Environment. All versions are available under a Creative Commons CC0 1.0 Universal Public Domain Dedication.

Classifying urban functional regions by integrating buildings and points-of-interest using a stacking ensemble method

The automatic classification of urban functional regions is vital for urban planning and governance. The current methods mainly rely on single remote sensing image data or social sensing data. However, these imagery-based methods have the disadvantage of capturing high-level socioeconomic features, whereas the information from social sensing data alone rarely contains the morphological features. To overcome these limitations, it is necessary to combine multisource data for sensing urban functionalities. This study presents an ensemble classification method that combines vector-based buildings and points-of-interest (POIs). For each block, we constructed an improved graph convolutional neural network (GCNN) to extract morphological features from the constituent buildings. The ‘Word2Vec’ model was used to obtain the socioeconomic characteristics of POIs. On this basis, a stacking ensemble model was designed to combine morphological and socioeconomic features for classifying the functionality of each block. The proposed method was trained and tested in Nanshan District, Shenzhen, China. The results showed a classification accuracy of 86.83%, which was 12.2%–16.1% higher than standalone applications based on single-source data. The trained models were also applied to two other districts, namely Futian and Guangming, achieving accuracies of 85.32% and 68.37%, respectively, which were 3.68%–7.79% and 3.69%–8.94% higher than those obtained using single-sourced data. Moreover, the classification accuracies of the proposed method showed improvements of 2.41%–9.76%, compared with the existing multisource data integration method in the three study areas. These results suggest that our ensemble method can effectively integrate features from different data sources and provide an alternative, higher-accuracy solution for classifying urban functional regions.

Economic Evaluation of the Dr. Bart Application in Individuals With Knee and/or Hip Osteoarthritis.

ObjectiveTo evaluate the cost‐utility and cost‐effectiveness of the dr. Bart app compared to usual care in people with osteoarthritis (OA) of the knees and hips, applying a health care payer perspective.MethodsThis economic evaluation was conducted alongside a 6‐month randomized controlled trial that included 427 participants. The dr. Bart app is a stand‐alone eHealth application that invites users to select pre‐formulated goals (i.e., “tiny habits”) and triggers for a healthier lifestyle. Self‐reported outcome measures were health care costs, quality‐adjusted life years (QALYs) according to the EuroQol 5‐dimension 3‐level (EQ‐5D‐3L) descriptive system, the EuroQol visual analog scale (QALY VAS), patient activation measure 13 (PAM‐13), and 5 subscales of the Knee Injury and Osteoarthritis Outcome Score/Hip Disability and Osteoarthritis Outcome Score. Missing data were multiply imputed, and bootstrapping was used to estimate statistical uncertainty.ResultsThe mean ± SD age of the study participants was 62.1 ± 7.3 years, and the majority of participants were female (72%). Health care costs were lower in the intervention group compared to the group who received usual care (€−22 [95% confidence interval €−36, −3]). For QALY and QALY VAS, the probability of the dr. Bart app being cost‐effective compared to usual care was 0.71 and 0.67, respectively, at a willingness‐to‐pay (WTP) of €10,000 and 0.64 and 0.56, respectively, at a WTP of €80.000. For self‐management behavior, symptoms, pain, and activities of daily living, the probability that the dr. Bart app was cost‐effective was >0.82, and the probability that the dr. Bart app was cost‐effective in the areas of activities and quality of life was <0.40, regardless of WTP thresholds.ConclusionThis economic evaluation showed that costs were lower for the dr. Bart app group compared to the group who received usual care. Given the noninvasive nature of the intervention and the moderate probability of it being cost‐effective for the majority of outcomes, the dr. Bart app has the potential to serve as a tool to provide education and goal setting in OA and its treatment options.

Fabrication of ceramic bioscaffolds from fly ash cenosphere by susceptor-assisted microwave sintering

Cenospheres (CS) are ceramic hollow microspheres and have been used to prepare composite foams for applications such as medical implants. However, its potential standalone application in the biomedical field is not fully explored. Herein, a susceptor-assisted microwave (SMW) sintering approach was used for producing CS foam scaffolds. Owing to the hybrid heating mechanism offered by the SMW process, sintering of the low-dielectric cenospheres was realized. We found that sintering was initiated at a lower temperature (1100 °C) compared to conventional heating (1250 °C) as reported in the literature, probably due to the lower activation energy required by SMW sintering. The physical and compositional properties of the sintered CS specimens were examined, and in vitro studies were performed. The as-fabricated CS foam possessed minimal effect on cell viability. Cells migrated and adhered well within the pores of the specimens, which indicates the potential of the CS as scaffold materials for cell engineering applications.

Demonstratingsolarpilot’s Python Application Programmable Interface Through Heliostat Optimal Aimpoint Strategy Use Case

Abstractsolarpilot is a software package that generates solar field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. solarpilot was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL’s System Advisor Model (SAM) in a simplified format. Prior means for user interaction with solarpilot have included the application’s graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called “LK.” This article presents a new, full-featured, python-based application programmable interface (API) for solarpilot, which we hereafter refer to as CoPylot. CoPylot enables python users to perform detailed CSP tower analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. CoPylot’s enables CSP researchers to perform analysis that was previously not possible through solarpilot’s existing interfaces. This article discusses the capabilities of CoPylot and presents a use case wherein we populate a model that obtains optimal solar field aiming strategies.

Pneumatic Cylinders Controlled by Two Different Controllers, Arduino and MyRIO: an Educational Approach

Nowadays, new pneumatic equipment is becoming attrac-tive to many industries that are beginning to accept the idea of replacing their hydraulic equipment with pneumatic one. Beginner engineers and students have a hard time under-standing the differences between different controllers in the market and their applicability. This article provides in-formation to support the understanding of the way the elec-tronic control of stand-alone pneumatic systems works. This comparative study provides future specialists with the core knowledge concerning the influence that controllers have on the operation of pneumatic systems, as well as the principles of the controllers' utilization. The steps to take in controllers' utilization list the construction of the block diagram of an electronically controlled pneumatic system, followed by the simulation of the pneumatic system using modern software tools and ended by the assembly of the physical system and its programming by means of different classes controllers. If the control by pneumatic devices was still accepted, thus maintaining full-pneumatic systems on the market, the use of industrial electronic controllers would become indispensable both for more precise control of the systems and for increased industrial integrability. The compared results of a double-acting pneumatic cylin-der control using the Arduino Uno and MyRIO-1900 con-trollers (academic equipment and for stand-alone applica-tions) are presented. This way, the problem of pneumatic installations that requires equal forces to move the load in both directions is solved. The study performed a compari-son of the times and cycles of the piston rod, variables that are defined in many applications. These results are also compared with those obtained by simulation using Automa-tion Studio software (AS). The study was conducted to assess the interchangeability of both controllers in this common architecture. The research results are a step forward to-wards the implementation of electronic control in pneu-matic systems using industrial controllers and then towards the harmonization of the structures thus established with systems in Industry 4.0.

HDfleX: Software for Flexible High Structural Resolution of Hydrogen/Deuterium-Exchange Mass Spectrometry Data.

Hydrogen/deuterium-exchange mass spectrometry (HDX-MS) experiments on protein structures can be performed at three levels: (1) by enzymatically digesting labeled proteins and analyzing the peptides (bottom-up), (2) by further fragmenting peptides following digestion (middle-down), and (3) by fragmenting the intact labeled protein (top-down) using soft gas-phase fragmentation methods, such as electron transfer dissociation (ETD). However, to the best of our knowledge, the software packages currently available for the analysis of HDX-MS data do not enable the peptide- and ETD-levels to be combined; they can only be analyzed separately. Thus, we developed HDfleX, a standalone application for the analysis of flexible high structural resolution of HDX-MS data, which allows data at any level of structural resolution (intact protein, peptide, fragment) to be merged. HDfleX features rapid experimental data fitting, robust statistical significance analyses, and optional methods for theoretical intrinsic calculations and a novel empirical correction for comparison between solution conditions.

Reconsideration of solar array simulator based on Thévenin equivalent circuit for low-power applications

The installation of photovoltaic (PV) plants has increased considerably, reaching high-power plants, residential consumers and low-power stand-alone applications. Along with the new PV panel technologies, power electronics have been developed to connect and control these systems, such as PV inverters and charge controllers. The testing and certification of these devices requires controlled power and voltage conditions, which led to the development of solar array simulators (SAS). This work reconsiders the use of the Thévenin equivalent circuit (TEC) for solar array simulator realization. The approach is based on taps to change the value of voltage and resistance for different solar irradiance conditions, while keeping the total error of power and voltage within acceptable limits. The TEC-SAS is compared with a commercial emulator, which shows worse behavior than the TEC-SAS at low-power conditions. Also, the results show that the proposed TEC-SAS can achieve less than 10% error for the range of irradiance from 0 to 1000W/m2 by using 16 resistance taps.

Probabilistic Analysis of Solar Cell Performance Using Gaussian Processes

This article investigates the application of machine learning-based probabilistic prediction methodologies to estimate the performance of silicon-based solar cells. The concept of confidence-bound regions is introduced and the advantages of this concept are discussed in detail. The results show that the optical and electrical performance of a photovoltaic device can be accurately estimated using Gaussian processes with accurate knowledge of the uncertainty in the prediction values. It is also shown that cell design parameters can be estimated for a desired performance metric and trained machine learning models can be deployed as a standalone application.