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Early intensive behavioral intervention (EIBI) is a treatment designed to increase adaptive behavior and decrease maladaptive behaviors for children with autism spectrum disorder (ASD). EIBI service providers typically collect data using pen‐and‐paper. Participants were four service providers employed at a large community‐based EIBI program. Differences in accuracy between collecting discrete‐trial‐teaching (DTT) data and challenging behavior data using pen‐and‐paper and an eHealth electronic data collection (EDC) application were assessed. The social validity of both methods of data collection was also examined. Pen‐and‐paper and EDC were equally accurate, but participants preferred using pen‐and‐paper. Our accuracy findings agreed with previous comparisons of EDC and pen‐and‐paper. Both methods of data collection are viable for an EIBI program; however, social validity considerations will determine the ease of EIBI programs transitioning to using an eHealth tool for data collection. [ABSTRACT FROM AUTHOR]

In recent years, the development of high‐speed railways in China has been rapid. High‐speed railway bridges play an essential role whenever the railway intersects with existing facilities in three dimensions. With the increasing speed of high‐speed railways and increasingly complex geographical locations, the span of bridges is constantly increasing. Based on their advantages, steel structures in large‐span bridges are beginning to replace traditional concrete structures. Taking the steel cover beam technology applied in the Fuqiangbao Tremendous Bridge as a starting point, this paper uses Rhino parameterized modeling and Abaqus software to conduct finite element analysis on the stress of steel cover beams. It compares and analyzes stress under different conditions. The results show that the steel cover beam's most unfavorable and unstable position in this paper is at the support on the stress side. This provides a reference for monitoring the steel cover beam after its application in the Fuqiangbao Tremendous Bridge and technical accumulation for the application of steel cover beam technology. [ABSTRACT FROM AUTHOR]

The accuracy of the fundamental electric quadrupolar constitutive relation used in the paper by Yaghjian and Silveirinha (2016, https://doi.org/10.1002/2016RS006066) is determined and used to improve upon the derivation of one of the boundary conditions for electric quadrupolar media. In addition, three typographical errors in the aforementioned paper are noted. Key Points: The accuracy of expressions in the previous paper is determinedThe derivation of one of the boundary conditions in the previous paper is improvedTypographical errors in the previous paper are noted [ABSTRACT FROM AUTHOR]

Abstract: As the primary interface between test developers and multiple educational stakeholders, score reports are a critical component to the success (or failure) of any assessment program. The purpose of this review is to document recent research on individual‐level score reporting to advance the research and practice of score reporting. We conducted a search for research studies published or presented between 2005 and 2015, examining 60 scholarly works for (1) the research focus, (2) stated or implied theoretical frameworks of communication, and (3) the characteristics of data sets employed in the studies. Results show that research on score properties, especially subscores, and score report design/layout are well‐represented in the literature base. The predominant approach to score reporting has been through a cybernetics tradition of communication. Data sets were often small or localized to a single context. We present example research questions from novel communication frameworks, and encourage our colleagues to adopt new roles in their relationships to stakeholders to advance score reporting research and practice. [ABSTRACT FROM AUTHOR]

This paper presents an UHD Active Barrier auto stereoscopic 3D module, including its base panel, 3D technology, 3D design, structure, and performance estimation. In order to achieve an optimized 3D image, two kinds of 3D mapping designs were compared. With the different module structure, different manufacture process was also studied, especially how the alignment accuracy between base panel and Active barrier affect the 3D image quality. Finally, the UHD Active Barrier 3D module was estimated to ensure it has the potential for home application. [ABSTRACT FROM AUTHOR]

This paper reports the results of an investigation into the use of magnetic sensors for measuring AC currents and subsequently, estimating AC current phasors in low‐ and medium voltage AC power systems. Tunnelling magnetoresistive (TMR) sensor of high sensitivity and a wide range was used for the magnetic field measurement around AC conductor. The sensor was calibrated to overcome the inequality in the sensed magnetic field due to various aspects such as the distance from the source, minute structural variations, the magnitude of the source current, and presence of harmonics. Performance was tested for accuracy at lower frequencies such as 1, 2, 5 and 10 Hz as well as at higher frequencies such as 2nd, 3rd, 4th and 5th harmonics of the fundamental frequency. The percentage total vector error (TVE) was calculated for current phasors with input current magnitudes varying from 5 to 1500 A of various frequencies and was compared with the actual current as well as with the outputs of a high accuracy conventional core‐wound donut type current transformer (CT). The measurement accuracy corresponding to magnitude, phase and TVE during laboratory and field applications validated the suitability of TMR sensor for contactless and non‐invasive AC current measurement. [ABSTRACT FROM AUTHOR]

Although the smart microgrid improves the power quality of the power system, it still has some security risks. So, this paper proposes a network security risk detection method of an intelligent microgrid monitoring system based on the artificial immune algorithm. By analyzing the structure of a smart microgrid monitoring system and introducing Policy Protection Detection Response (P2DR) dynamic network security model and ale static network security analysis method, the network security risk of the microgrid monitoring system is evaluated. The theoretical model is analyzed by the overall framework of the artificial immune algorithm, and its term definition is given. The mutation self is detected by self‐dynamic description and immune cell evolution process with the network structure brought into and the risk of the computing network facing different attack subjects is analyzed. This method has a high detection accuracy for more than 10 kinds of network attack modes, the highest detection accuracy is 98%, and the average time is only 0.8 s. Compared with other methods, this method has higher detection accuracy and faster detection speed. It is universal in various network attack modes and can detect the possible network security risks of the intelligent microgrid monitoring system and protect its safe and stable operation. [ABSTRACT FROM AUTHOR]

Previous analyses identified research on environmental tobacco smoke to be subject to strong fluctuations as measured by both quantitative and qualitative indicators. The evolution of search algorithms (based on the Web of Science and Web of Knowledge database platforms) was used to show the impact of errors of omission and commission in the outcomes of scientometric research. Optimization of the search algorithm led to the complete reassessment of previously published findings on the performance of environmental tobacco smoke research. Instead of strong continuous growth, the field of environmental tobacco smoke research was shown to experience stagnation or slow growth since mid-1990s when evaluated quantitatively. Qualitative analysis revealed steady but slow increase in the citation rate and decrease in uncitedness. Country analysis revealed the North- European countries as leaders in environmental tobacco smoke research (when the normalized results were evaluated both quantitatively and qualitatively), whereas the United States ranked first only when assessing the total number of papers produced. Scientometric research artifacts, including both errors of omission and commission, were shown to be capable of completely obscuring the real output of the chosen research field. [ABSTRACT FROM AUTHOR]

The paper-based pedigree is the current standard for family health history (FHH) documentation in genetic counseling. Several tools for electronic capture of family health data have been developed to improve re-use and accessibility, data quality and standardization, ease of updating, and integration with electronic medical records. One such tool, the tablet-based Proband application, provides a flexible approach to data capture in dynamic and diverse clinical settings. This study compared Proband FHH collection to paper-based methods and investigated the usability of Proband in a clinical setting. After one use by 23 genetic counselors and students, Proband had 91% accuracy with a FHH audio scenario, which was significantly less (p < 0.001) than paper’s 96% accuracy. These differences were attributed to incorrect or missing ages of grandparents (p < 0.001) and great-aunts/uncles (p = 0.012) and missing documentation of consanguinity (p < 0.001). Possible explanations for these differences include greater experience with paper FHH documentation and pre-populated prompts for consanguinity on the paper template used. Proband’s perceived usability increased with use, with individual System Usability Scores increasing between first and last use (p = 0.033). We conclude that tools for dynamic, provider-driven FHH documentation such as Proband show promise for improving risk assessment accuracy and quality patient care. [ABSTRACT FROM AUTHOR]

Summary: Text classification is one of the most important tasks to extract information from the Internet and identifying the best text representation settings. With the increase of data volume on the world wide web, the significance of text classification increases. This situation requires huge human efforts to understand and classify the digital data available on the Internet. Text classification is classifying the number of text files into different classes. The data or text available on the Internet is in an unstructured form which increases the difficulty to understand and classify it for useful purposes. This paper proposes a context‐aware text classification system to improve text quality. We use a content‐aware recommendation system to extract the data from well‐known news databases. Text preprocessing techniques like tokenization, stemming, and stop words removal are studied in detail. Furthermore, unigram, bigram, and trigram attributes are also being tested. Attribute selection methods are also examined and their impact on the text classification results. To carry out a detailed investigation, 11 versions are created of each dataset to save the time in experimentation process and applied the different preprocessing techniques to understand the impact of each technique on classification results. The proposed system is compared with the existing approach to check the accuracy where the proposed system achieved better performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Microscopy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The advent of computer‐guided surgery removed the need for complex surgical interventions such as extensive flap elevations, second stage implant exposure, and complications usually associated with conventional protocols. Purpose (a) Analyze available literature reporting on applicability, accuracy, clinical outcome of flapless surgery with or without computer guidance. (b) Evaluate quality of studies, in terms of scientific level of evidence and ethical committee approval. Materials and methods: A PUBMED search was performed in July 2018. A first search was based on a general search string limited to "Dental Implants" and "flapless surgery." A second search focused on accuracy of computer‐guided surgery using search string "Surgery, Computer‐Assisted" or "guided surgery," and "Dental implants." The following inclusion criteria were applied: (a) studies in English; (b) human studies (excluding cadaver); (c) systematic reviews; (d) systematic reviews with meta‐analysis. Reviews not mentioning accuracy were excluded in search 2. Results: Nine reviews included in total. Implant survival ranged between 89% and 100%. Early surgical and prosthetic complications reported in 9.1% to 36.4% of reviewed papers. Tooth‐supported guides show more accuracy than bone or mucosa‐supported guides. Fully guided surgery yields higher accuracy, with lower values for horizontal coronal, horizontal apical and angular deviation (1.00, 1.23, and 3.13°mm, respectively) than those placed with half guided surgery (1.44, 1.91, and 4.30 mm, respectively). Thirty‐four of 71 human studies included in nine reviews, mentioned ethical committee approval or compliance with Declaration of Helsinki. Conclusions: Guided flapless surgery is comparable to free‐hand surgery in terms of implant survival, marginal bone remodeling, and peri‐implant variables. Clinicians advised to take care in all steps of the protocol, and include safety margins around virtually planned implants. Regarding compliance with research ethics, we should question whether scientific reports of clinical trials performed without an ethical umbrella are trustworthy. Compliance of ethics standards is imperative for submitted research papers. [ABSTRACT FROM AUTHOR]

ABSTRACT Cardiac arrhythmia refers to irregular heartbeats caused by anomalies in electrical transmission in the heart muscle, and it is an important threat to cardiovascular health. Conventional monitoring and diagnosis still depend on the laborious visual examination of electrocardiogram (ECG) devices, even though ECG signals are dynamic and complex. This paper discusses the need for an automated system to assist clinicians in efficiently recognizing arrhythmias. The existing machine‐learning (ML) algorithms have extensive training cycles and require manual feature selection; to eliminate this, we present a novel deep learning (DL) architecture. Our research introduces a novel approach to ECG classification by combining the vision transformer (ViT) and the capsule network (CapsNet) into a hybrid model named ViT‐Cap. We conduct necessary preprocessing operations, including noise removal and signal‐to‐image conversion using short‐time Fourier transform (SIFT) and continuous wavelet transform (CWT) algorithms, on both normal and abnormal ECG data obtained from the MIT‐BIH database. The proposed model intelligently focuses on crucial features by leveraging global and local attention to explore spectrogram and scalogram image data. Initially, the model divides the images into smaller patches and linearly embeds each patch. Features are then extracted using a transformer encoder, followed by classification using the capsule module with feature vectors from the ViT module. Comparisons with existing conventional models show that our proposed model outperforms the original ViT and CapsNet in terms of classification accuracy for both binary and multi‐class ECG classification. The experimental findings demonstrate an accuracy of 99% on both scalogram and spectrogram images. Comparative analysis with state‐of‐the‐art methodologies confirms the superiority of our framework. Additionally, we configure a field‐programmable gate array (FPGA) to implement the proposed model for real‐time arrhythmia classification, aiming to enhance user‐friendliness and speed. Despite numerous suggestions for high‐performance FPGA accelerators in the literature, our FPGA‐based accelerator utilizes optimization of loop parallelization, FP data, and multiply accumulation (MAC) unit. Our accelerator architecture achieves a 57% reduction in processing time and utilizes fewer resources compared to a floating‐point (FlP) design. [ABSTRACT FROM AUTHOR]

The paper deals with the modern methods of testing and diagnostics of existing, mainly steel, bridges. The focus is on the DIC method (digital image correlation) and its application for the static measurements and dynamic measurements of the bridge deflection at high speeds and on the detection of the behavior of bridge details, such as pins and fatigue cracks. The sources of potential related error values are identified and possible strategies for the minimization of errors corresponding to the DIC field measurements are developed and described in the paper. The proposed DIC methods were successfully applied on the deflection measurement on long distances and on the measurement of the strain on details in‐situ. Relevant applications were done in the Czech Republic as a part of the bridge load testing and the results are discussed in the paper. [ABSTRACT FROM AUTHOR]

Consideration of collectives raises important questions about human rationality. This has long been known for questions about preferences, but it holds also with respect to beliefs. For one, there are contexts (such as voting) where we might care as much, or more, about the rationality of a collective than the rationality of the individuals it comprises. Here, a given standard may yield competing assessments at the individual and the collective level, thus giving rise to important normative questions. At the same time, seemingly rational strategies of individuals may have surprising consequences, or even fail, when exercised by individuals within collectives. This paper will illustrate these considerations with examples, provide an overview of different formal frameworks for understanding and assessing the beliefs of collectives, and it will illustrate how such frameworks can combine with simulations in order to elucidate epistemic norms. The paper examines the accuracy and rationality of beliefs of individuals in collectives and of the collectives themselves. [ABSTRACT FROM AUTHOR]

This paper applies the Random Forest (RF) method for the robust modelling of credit default prediction. This technique has been proven as an efficient classifier and can provide better interpretability in comparison to other classifiers. Using Chines micro‐enterprise credit data set, this study emphasizes the multidimensional analysis of credit risk, such as the whole sample, subsample, and the incremental effect of the group of predictors. To that end, relative variable importance (RVIs) has been presented for all predictors according to the contribution in the prediction accuracy so that to ensure interpretability of the model. The empirical findings confirm that RF technique is reliable and efficient across all of the criteria used in this study. In addition, the examined experimental analysis indicates that non‐traditional variables have a significant effect on the classification accuracy. Thus, this paper recommends some alternative predictors like the legal representative's basic information, internal non‐financial factors, along with traditional financial variables for sustainable model development. The performance is compared from the perspective of five different performance measures. This modelling algorithm can be used by different financial markets participants to measure systematically credit default prediction of individual and institutional customers. [ABSTRACT FROM AUTHOR]

Child and family social workers routinely make professional judgements involving significant legal and moral questions (e.g. whether a child has been abused) and more 'everyday' issues (e.g. will the child be re‐referred again if we close the case now?) Yet the world is capricious, and we rarely know with certainty what is going to happen in future or the likely impact of our different choices. Given the consequences of their judgements and decisions, it is imperative that social workers are provided with the best possible support. This paper reports a proof‐of‐concept study of a set of interventions to improve the judgemental accuracy of social workers: (i) a survey to identify respondents with above‐average existing abilities, (ii) training sessions on cognitive debiasing and (iii) structured group working and (iv) three methods for aggregating individual judgements. Findings indicate that it is possible to measure the accuracy of social work judgements in relation to case‐study materials and retrospective questions, while the feedback about the training was largely positive. Any future studies should aim to recruit a more diverse set of respondents, test judgemental accuracy in relation to prospective judgements and explore what types of questions would be most helpful for real‐world decision‐making. [ABSTRACT FROM AUTHOR]

Partial discharge identification is of great significance in monitoring power equipment. Although substantial work has been done in partial discharge pattern recognition, most studies have been performed in noise‐free environments. To improve the accuracy of partial discharge pattern recognition, this paper introduces a partial discharge image feature extraction method based on upright speeded‐up robust features. Each group of phase‐resolved pulse sequence data is converted into grayscale images to construct a sample library of partial discharge defects. Upright speeded‐up robust features is used to extract features in grayscale images. The improved support vector machine is used to achieve the best classification of partial discharge sources. The sensitivity of local features to different phase resolutions and different image resolutions of partial discharge phase‐resolved pulse sequence patterns was investigated, and the optimal phase resolution and image resolution were used to construct phase‐resolved pulse sequence patterns. In addition, the recognition effect of this feature extraction method under different noise conditions was also analysed. The results prove that upright speeded‐up robust features can effectively deal with noisy data. The results of this research can provide references for the storage setting and recognition of three‐dimensional images of on‐site partial discharge data. [ABSTRACT FROM AUTHOR]

Day‐ahead forecasting for campus‐level load is important for better energy management, but especially difficult to be accurate, compared to large‐scale loads such as cities or regions. This is because irregular and unpredictable behavior of individual loads in small‐scale loads cannot be fully smoothed out and thus pose a negative impact on forecasting accuracy. This paper presents an online refinement strategy for day‐ahead forecasting using intraday data for a campus‐level load, focusing on self‐adapting correction of forecasting considering load pattern. First, according to the load pattern forecasted by Classification and Regression Trees (CART) one day ahead, the correction parameter group is assigned to the correction model. If the correction conditions are met, the forecasting results for the rest of the day will be updated. After all‐day load data is achieved, the actual load pattern can be identified and used to train CART model. To confirm the effectiveness of the proposed strategy, the model is evaluated by two datasets, a real‐life dataset of an industrial park in Shanghai China and a public dataset of an institute campus in Delhi, India. The results demonstrate that the proposed strategy can improve short‐term forecasting accuracy. [ABSTRACT FROM AUTHOR]

Insulators of the electrical power grid are usually installed outdoors, so they suffer from environmental stresses, such as the presence of contamination. Contamination can increase surface conductivity, which can lead to system failures, reducing the reliability of the network. The identification of insulators that have their properties compromised is important so that there are no discharges through its insulating body. To perform the classification of contaminated insulators, this paper presents computer vision techniques for the extraction of contamination characteristics, and a neural network (NN) model for the classification of this condition. Specifically, the Sobel edge detector, Canny edge detection, binarization with threshold, adaptive binarization with threshold, threshold with Otsu and Riddler–Calvard techniques will be evaluated. The results show that it is possible to have an accuracy of up to 97.50% for the classification of contaminated insulators from the extraction of characteristics with computer vision using the NN for the classification. The proposed model is more accurate than well‐established models such as support‐vector machine (SVM), k‐nearest neighbor (k‐NN), and ensemble learning methods. This showed that optimizing the model's parameters can make it superior to solve the problem in question. [ABSTRACT FROM AUTHOR]

Time‐of‐use (TOU) electricity prices are implemented based on the response characteristics of users. TOU prices guide the electricity consumption behaviour of users and make them actively respond to peak shaving and valley filling. In this paper, the conventional psychology response model of consumers is improved based on the history data of customer response to the TOU price. The paper defines four types of response load curves and proposes the response reliability indices. Additionally, it establishes the probability models of customer response. According to the characteristics of customer response, the sensitivity analysis of influencing factors and the risk analysis of customers are carried out according to the probability model of response. For instance, risk and characteristics analyses of the customer response were performed using the load data of residential areas in Nanjing city. The results indicate that the probability distribution of the customer response is approximate to the trapezoid distribution. The sequence of sensitivity coefficients of customer response factors is as follows: customer income, type of customer, and electrical equipment. The proposed method provides a scientific basis for electricity price implementers to accurately grasp the response behaviour of the user and establish a reasonable demand response strategy. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) is the process of identifying and analysing activities performed by a person (or persons). This paper proposes an efficient HAR system based on wearable sensors that uses deep learning techniques. The proposed HAR takes the advantage of staking Convolutional Neural Network and Long Short‐Term (LSTM), for extracting the high‐level features of the sensors data and for learning the time‐series behaviour of the abstracted data, respectively. This paper proposed a Fuzzy Soft‐max classifier for the dense layer which classifies the output of LSTM Blocks to the associated activity classes. The authors' decision for proposing this classifier was because sensor data related to the resembling human activities, such as walking and running or opening door and closing door, are often very similar to each other. For this reason, the authors expect that adding fuzzy inference power to the standard Soft‐max classifier will increase its accuracy for distinguishing between similar activities. The authors were also interested in considering a post‐processing module that considers activity classification over a longer period. Using the proposed Fuzzy Soft‐max classifier and by the post‐processing technique, the authors were able to reach the 97.03 and 85.1 rates of accuracy for the PAMAP2 and Opportunity dataset, respectively. [ABSTRACT FROM AUTHOR]

This paper presents an improved finite‐control‐set model predictive control (MPC) strategy for the doubly fed induction generator (DFIG). The MPC‐based controller's main drawback is high computational burden, which requires a long time to perform online optimization. In this regard, real‐time implementation becomes impossible in some cases. Thereupon, this paper suggests a variable sampling time in the prediction process to achieve a balance between the prediction horizon and computational burden. In other words, compared to the conventional MPC‐based techniques, longer horizon is predicted with fewer samples. In this method, there are two sampling times including a fixed sampling time for the system discretization structure, and a variable sampling time used in the prediction process. The proposed technique also utilizes a comparative incremental algorithm to prevent checking all possible control inputs over the objective function in order to reduce the computational time further. In addition, a penalty term, which includes the number of switch changes, is added to the cost function to reduce the switching frequency. Both the simulation and experimental results show that the proposed MPC can significantly improve the DFIG control system's performance by increasing the accuracy of tracking response and reducing the computational time. The experimental test is carried out by the hardware in loop system by using TMS320F28338 and PC‐Lab card. The computational delay caused by the digital signal processor and other influential factors are compensated by using the prediction plus prediction approach. The results confirm that the proposed controller has a lower overshoot and power ripple compared to the vector controller and direct torque controller. It also reduces the computational time up to 40% and reduces the average switching frequency by 12% in comparison to the conventional MPC. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) are becoming increasingly popular in modern remote sensing image classification tasks and have exhibited excellent results. For the existing CNN‐based hyperspectral image (HSI) classification methods, most of which extract spatial or spectral features separately by convolution. But nearly all of these methods ignore the fact that the weighted summation of convolution may lead to appear new features in another dimension. To address this issue, a novel multiscale spectral‐spatial cross‐extraction network (MSSCEN) is proposed for HSI classification. Specifically, the proposed MSSCEN introduces spectral‐spatial features cross extraction module (SSCEM), which fed extracted features from previous layer into spatial and spectral extraction branches separately again, so that the changes that occurred in the other domain after each convolution can be fully utilized. In addition, a new independent data augmentation module based on U‐Net is designed to mitigate the problem of limited labelled samples. The paper conducts experiments on three classic hyperspectral datasets and the results demonstrate that the proposed method achieves the best classification accuracy than other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

In this paper, multiple signal classification and estimation of signal parameters via rotational invariance technique (ESPRIT) algorithms are proposed to estimate the direction of arrival (DOA) of multiple incident signals under known mutual coupling among elements of the antenna array. These algorithms use the same subspace based techniques to achieve the separation of the eigenvectors into two subspaces: the signal space and the noise space. However, ESPRIT only uses the signal space. The performance of subspace based DOA estimation algorithm is evaluated using uniform linear arrays of dipole elements. The simulation results show the exact number of samples and elements, signal‐to‐noise ratio and spacing between the antennas elements used are the most important parameters in the algorithms, to sustain the accuracy of the DOA of the incident signals. The problem of calculating mutual coupling coefficient of the array elements is formed into a matrix has been considered. It is shown from the simulation results that the performance of the DOA estimation techniques considering the mutual coupling effect can be improved by the proposed compensation method. The different results obtained are in good agreement with those of the literature. Plain Language Summary: In this paper, the direction of arrival (DOA) estimation of linear half wavelength dipole array with known mutual coupling has been investigated. The analysis has been based on basic electromagnetic concepts, multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance technique (ESPRIT) algorithms. Computer simulations were performed to demonstrate the effect of various parameters on the performance of the MUSIC and ESPRIT algorithms and their ability to resolve incoming signals accurately and efficiently. The simulation results show that the performance of DOA improves with more elements in the array, with large snapshots of signals and greater angular separation between the signals. These improvements are seen in form of the sharper peaks and a smaller error in angle detection. The MUSIC algorithm is accurate as compared to ESPRIT algorithm. The result indicates, the MUSIC algorithm has successfully estimated the DOA of the incidents signals impinging on the antenna array. The known mutual coupling between elements may seriously distorts the array output signal and degrades the performance of high‐resolution direction finding techniques such as MUSIC, and ESPRIT algorithms. Key Points: Direction of arrival estimationKnown mutual couplingMultiple signal classification and estimation of signal parameters via rotational invariance technique [ABSTRACT FROM AUTHOR]

The traditional method of operating photovoltaic (PV) systems on the right side of the maximum power point (MPP) may cause the voltage to be close to the open‐circuit voltage of the PV panel, resulting in a large oscillation of the PV power. In contrast, the change in dP/dU value of the curve on the left of the MPP is small, which is more conducive to adjusting the output power of the PV. A frequency regulation control strategy is proposed for a single‐stage PV power plant (PVPP), maintaining the PV string operating on the left side of the MPP. This paper proposes an optimal step‐size calculation method based on the flexible power tracking evaluation function. This calculation method is suitable for PV operating on the left side of the MPP and can improve the dynamic and steady‐state performance of the PV power tracking process. In addition, the system response is analyzed by establishing a small‐signal model with PV operating on the left side of the MPP. Finally, simulations and experiments verify the correctness and accuracy of the proposed primary frequency regulation strategy and step‐size calculation method. [ABSTRACT FROM AUTHOR]

Objective To study any change in reference accuracy in the Australasian emergency medicine journal (now known as Emergency Medicine Australasia), in the 10 years since a previous analysis was undertaken. Method A sample of 100 randomly selected references was compared with the results from the previous analysis. Results There was a significant reduction in the number of citations with errors and in the total number of errors. Conclusion This paper demonstrates a significant improvement in the accuracy of references in the journal Emergency Medicine Australasia over the past decade. This paper did not identify the reasons for this improvement, but potential causes might include the introduction of a web-based peer review and manuscript submission process, as well as improved reference bibliography systems by authors. In addition, an increase in the overall standard of paper submitted to the journal might have contributed to this improvement. [ABSTRACT FROM AUTHOR]

The article discusses the protocol for the simultaneous quantification of chlorophyll (Chl) a and b described by D. I. Arnon in 1949. Topics covered include the citations received by Arnon's study, the imprecision of Arnon's equations, and an unusual bimodal pattern revealed in a chronological analysis of the citations of Arnon's paper.

ABSTRACT Medical image processing plays an important role in brain tissue detection and segmentation. In this paper, a computer aided detection of brain tissue compression based on the estimation of the location of the brain tumor. The proposed system detects and segments the brain tissues and brain tumor using mathematical morphological operations. Further, the brain tissue with tumor is compressed using lossless compression technique and the brain tissue without tumor is compressed using lossy compression technique. The proposed method achieves 96.46% sensitivity, 99.20% specificity and 98.73% accuracy for the segmentation of white matter regions from the brain. The proposed method achieves 98.16% sensitivity, 99.36% specificity and 98.78% accuracy for the segmentation of cerebrospinal fluid (CSF) regions from the brain and also achieves 93.07% sensitivity, 98.79% specificity and 97.63% accuracy for the segmentation of grey matter regions from the brain. This paper focus the brain tissue compression based on the location of brain tumor. The grey matter of the brain is applied to lossless compression due to the presence of the tumor in grey matter of the brain. The proposed system achieves 29.23% of compression ratio for compressing the grey matter of the brain region. The white matter and CSF regions of the brain are applied to lossy compression due to the non-presence of the tumor. The proposed system achieves 39.13% of compression ratio for compressing the white matter and also achieves 37.5% of compression ratio for compressing the CSF tissue. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 237-242, 2016 [ABSTRACT FROM AUTHOR]

Copyright of Journal of Microscopy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Training on shooting technique can effectively improve the score in football games. This paper constructed a kinetic model of the football during its flight based on the Newton's second law after analyzing the force during flying and proposed to solve the model with numerical simulation before drawing the simulation trajectories of a flying football. The results showed that the kinetic model effectively simulated the flight trajectory of the football. The further the distance of the shot was, the lower the average hit rate of the football was, and the more the football flight trajectory deviated from the reference trajectory; however, after the training assisted by the kinetic model, not only the average hit rate was improved, but also the average deviation of the flight trajectory was reduced. This paper analyzed the force of the flying football ball, constructed the kinetic model of its flight process, and proposed to solve the model with numerical iteration to draw the simulation trajectory of the football ball flight. Finally, the validity of the kinetic model was verified in a case analysis, and the effect of the kinetic model in improving the player's shooting technique was tested. [ABSTRACT FROM AUTHOR]

The capability to design and manufacture metal lattice structures is today one of the most promising targets of powder bed fusion technologies. Not only additively manufactured lattices offer great lightweighting possibilities but they also open the way to tailored and graded mechanical response. To best capitalize on this opportunity, research effort is first needed to assess the feasibility of reticular structures and to quantify the expected deviations from the nominal geometry, as a function of the cell topology and dimensions. Notwithstanding the inherent suitability of additive processes to complex shapes, this paper proposes a more exact definition of the technological boundaries for body‐centered cubic lattices, showing to what extent specific dimensional ratios, as well as a self‐supporting cell structure, can be favorable to minimize the deviation from the nominal reticulum in terms of dimensions, density, and presence of defects. [ABSTRACT FROM AUTHOR]

Biomonitoring studies of vulnerable populations in low‐ and middle‐income countries are limited because traditional sampling methods are challenging to implement in low‐resource settings. The present study examined the feasibility, precision, and accuracy of dried blood spots (DBS) for human biomonitoring of nonessential elements (cadmium [Cd], mercury [Hg], and lead [Pb]) in an area of northern Tanzania with artisanal and small‐scale gold mining activities. Pregnant women (n = 44) were recruited in Geita during antenatal clinic visits, and DBS from capillary blood were collected on filter paper. As a gold‐standard comparison, venous blood was sampled from the same participants and compared with the DBS. Venous blood, DBS, and quality control samples were analyzed for chemical elements by inductively coupled plasma mass spectrometry. Field blanks were very clean for most elements, generally only twice as high as corresponding laboratory filter blanks. No significant differences were found between duplicate DBS samples taken from the same participants, with near perfect intraclass correlation coefficients (0.99) for Cd, Hg, and Pb, indicating excellent reliability. Moreover, correlation was strong (r2 > 0.9) and significant (p < 0.0001) between DBS and the quantitative venous blood, with regression line slopes close to 1.0 (0.847, 0.976, and 0.969 for Cd, Hg, and Pb, respectively), indicating high accuracy of the DBS method compared with the gold‐standard approach. The DBS method is minimally invasive and was a feasible, precise, and accurate means of measuring exposure to Cd, Hg, and Pb in pregnant women in a low‐resource setting. Environ Toxicol Chem 2019;38:1285–1293. © 2019 SETAC [ABSTRACT FROM AUTHOR]

Numerical methods have been extensively applied to fracture mechanics, while they cannot simulate the problems without the mechanical models or constitutive equations. Artificial neural networks (ANNs) can be utilized to predict complex fracture problems, but these approaches require large amounts of data for the training. Therefore, in this paper, to combine the advantages of the numerical methods and the ANNs, an improved back propagation neural network (BPNN) is proposed by introducing the enrichment used in the numerical methods into the activation function utilized in the neural networks. The enrichment is able to represent the crack tip field which can accelerate the convergence. At the field near the crack tip, the improved BP solution can converge to the analytical solutions which validate the high accuracy of the proposed method. Without sufficient data, especially the data are missing in the near field of the crack tip, the improved BP method can also achieve high accuracy and convergence, while the conventional BP method may not converge to the predetermined error bound. Numerical simulations of the quasi‐static and fatigue crack problems demonstrate that the improved BP method can accurately predict the crack propagation and its growth rate with relatively little data. [ABSTRACT FROM AUTHOR]

Based on fieldwork carried out at the Early Drug Development Service of a world‐leading cancer institution, our study sheds lights on decision‐making processes at the stage where decisions are made about which clinical trial to pursue and thus which experimental drugs will feed the growing pipeline of molecularly guided therapies and therapeutic strategies available to treating physicians. The paper shows how such collective decision‐making practices by a translational research unit employ formal tools and ad hoc valuation strategies that interweave technical‐scientific matters of concern with patient‐oriented clinical ones, as part of the institutional assetization of biomedical knowledge production. In the process, decision‐making practices in part define the conditions of possibility for the provision of care in what is increasingly becoming a 'clinic of variants.' They do so by reconfiguring on an evolving basis the socio‐material ecosystem through which precision oncology is enacted as a rapidly evolving assemblage of patients, physicians, research and support staff, protocols, molecular markers, drugs and administrative components. [ABSTRACT FROM AUTHOR]

This paper deals with the numerical solution of the integral equations of linear second kind Volterra–Fredholm. These integral equations are commonly used in engineering and mathematical physics to solve many of the problems. A hybrid of Bernstein and improved block‐pulse functions method is introduced and used where the key point is to transform linear second‐type Volterra–Fredholm integral equations into an algebraic equation structure that can be solved using classical methods. Numeric examples are given which demonstrate the related features of the process. [ABSTRACT FROM AUTHOR]

This paper presents 2‐channel time‐multiplexed chopper instrument amplifier for better gain accuracy and mismatch. Mostly, the current‐attenuated feedback resistor with a duty‐cycled resistor is adopted for boosting the RC time constant. It can achieve the high pass corner frequencies between 10 Hz and 320 Hz by a 3‐bit digitally controlled resistor bank of the attenuator. As a result, the instrument amplifier achieves 0.6% gain accuracy between two time‐multiplexed channels, 100 dB CMRR, ‐40 dB crosstalk, and noise densities of 50nV/√Hz. It is implemented in a 180‐nm CMOS technology. It occupies a 0.6 mm*0.6 mm chip area and consumes 1.5μA current consumption from a 1.8 V supply for each channel. [ABSTRACT FROM AUTHOR]

Summary: Recently, many researches have been conducted to exploit the compressive sensing (CS) theory in wireless sensor networks (WSNs). One of the most important goals in CS is to prolong the lifetime of WSNs. But CS may suffer from some errors during the reconstruction phase. In addition, an adaptive version of CS named Bayesian compressive sensing has been studied to improve the reconstruction accuracy in WSNs. This paper investigates these adaptive methods and identifies their associated problems. Finally, a distributed and semi‐adaptive CS‐based data collection method is proposed. The proposed method tackles the aforementioned problems. Simulation results show that considering both lifetime and accuracy factors as a compound metric, the proposed method yields a 200% improvement compared with the Bayesian compressive sensing‐based method and outperforms other compared methods in the literature. [ABSTRACT FROM AUTHOR]

In this study, a new multi-criterion based fuzzy logic model has been proposed to determine the overall health index of transformers. The method relies on the concentrations of individual dissolved gasses, significant diagnostic test results of transformer oil and paper insulation. Real field data of 200 working transformers of a state owned power utility have been tested to validate the accuracy and reliability of the proposed fuzzy logic condition assessment model of transformers. Results obtained from the present proposed model have been compared with the previously proposed condition monitoring models. Comparison of the results shows that the output of present proposed model is more reliable and accurate. Integration of multi-criterion analysis in the fuzzy logic model has overcome the shortcomings of the previous fuzzy models requiring higher number of inputs and large set of rules. The proposed fuzzy model is flexible, accurate and easy to implement for determining the overall health index of the working transformers. It shall prove to be very useful to the utility managers and power utilities. [ABSTRACT FROM AUTHOR]

Infrared imaging technology is widely used in the fault diagnosis of power equipment. The power equipment images obtained by infrared thermal cameras generally have the characteristics of low resolution, low contrast, similar geometric features, and complex image backgrounds. The traditional infrared image detection methods have problems of low detection accuracy, low real‐time performance, and missed and fault detection, all of which will lead to great difficulties in the automatic identification process of infrared images of power equipment. This paper adopted an algorithm based on You Only Look Once (YOLO) v4 to realize the detection of electrical equipment under infrared images. First of all, the authors utilized the Fast Super‐Resolution Convolutional Neural Network (FSRCNN) algorithm to perform super‐resolution reconstruction on the infrared images of power equipment to achieve image enhancement. Moreover, the lightweight network Mobilenetv3 was selected as the backbone network of YOLOv4 to improve the running speed by reducing network parameters. Finally, the performance of the YOLOv4 algorithm was compared to verify the effectiveness of the algorithm proposed in this paper. The experimental results showed that the proposed method had strong generalization ability and reliable detection of infrared targets of various types of power equipment, with the mean average precision (mAP) reaching 91.3%. [ABSTRACT FROM AUTHOR]

The Standard Test Condition (STC) correction procedures are algorithms used for transforming the Photovoltaic (PV) module current‐voltage (I‐V) data measured at arbitrary conditions back to STC. The PV module Temperature Coefficients are used as inputs by various STC correction procedures and can significantly influence their accuracy. This paper presents the sensitivity analysis of accuracy of six STC correction procedures (IEC 60891‐Procedure 1, Procedure 2, Modified IEC 60891‐Procedure 1, IEC 60891‐Procedure 4 (Voltage Dependent Temperature Coefficient (VDTC) Procedure), Anderson Procedure, and Standard Irradiance and Desired Temperature Procedure) to the errors in the temperature coefficients of Pmax, Voc, and Isc. Experimentally measured as well as simulated I‐V curves of six multi c‐Si PV modules were used in this study. IEC 60891‐Procedure 4 (Voltage Dependent Temperature Coefficient) that uses a Voltage Dependent Temperature Coefficient which can be calculated using a single measured I‐V curve was found to be most robust against errors in the temperature coefficients. [ABSTRACT FROM AUTHOR]

Analysis of blood phenylalanine is central to the monitoring of patients with phenylketonuria (PKU) and age‐related phenylalanine target treatment‐ranges (0‐12 years; 120‐360 μmol/L, and >12 years; 120‐600 μmol/L) are recommended in order to prevent adverse neurological outcomes. These target treatment‐ranges are based upon plasma phenylalanine concentrations. However, patients are routinely monitored using dried bloodspot (DBS) specimens due to the convenience of collection. Significant differences exist between phenylalanine concentrations in plasma and DBS, with phenylalanine concentrations in DBS specimens analyzed by flow‐injection analysis tandem mass spectrometry reported to be 18% to 28% lower than paired plasma concentrations analyzed using ion‐exchange chromatography. DBS specimens with phenylalanine concentrations of 360 and 600 μmol/L, at the critical upper‐target treatment‐range thresholds would be plasma equivalents of 461 and 768 μmol/L, respectively, when a reported difference of 28% is taken into account. Furthermore, analytical test imprecision and bias in conjunction with pre‐analytical factors such as volume and quality of blood applied to filter paper collection devices to produce DBS specimens affect the final test results. Reporting of inaccurate patient results when comparing DBS results to target treatment‐ranges based on plasma concentrations, together with inter‐laboratory imprecision could have a significant impact on patient management resulting in inappropriate dietary change and potentially adverse patient outcomes. This review is intended to provide perspective on the issues related to the measurement of phenylalanine in blood specimens and to provide direction for the future needs of PKU patients to ensure reliable monitoring of metabolic control using the target treatment‐ranges. [ABSTRACT FROM AUTHOR]

The invasive intra‐arterial approach is the gold standard for measuring blood pressure in intensive care units where accuracy is crucial. However, invasive procedures increase the risk of infections and mortality. This evidence‐based review aimed to determine whether continuous non‐invasive blood pressure (CNIBP) monitoring, using Radial Artery Applanation Tonometry (RAAT) devices, is as accurate as invasive methods. Six papers were included: three prospective cohort studies and three comparative studies. Most studies showed that mean arterial pressure is accurately recorded through RAAT monitoring; however, more research is needed to assess the accuracy of non‐invasive readings of systolic and diastolic blood pressures, as data are not always concordant. [ABSTRACT FROM AUTHOR]

Background: Arabic is recognised as diglossic; one manifestation of diglossia is the co‐existence of two varieties of the language used in different social settings: standard (or literary) Arabic (StA) and spoken Arabic (SpA). The study investigated the impact of lexical–phonological distance in Arabic (identical, cognate, unique, which are different types of words) on reading accuracy and fluency across grade levels. Methods: The participants were 180 native Arabic‐speaking children first and second graders. Participants were asked to read three‐word lists comprising words of three different lexical distance from spoken Arabic (identical, cognates and unique) and three text selections representing similar lexical distance (mostly identical, mostly cognates and mostly unique). Results: The findings showed a main effect of the word and text lexical distance fluency and accuracy reading scores. There was also a main effect of grade level on both scores. In addition, a significant interaction between the word and type words text lexical distance and grade level was found. The results revealed a more significant gap between the identical and the other two lexical distance categories (cognates and unique) in the second grade than in the first grade. Similarly, a more significant disparity was observed between the mostly identical type words and mostly cognate type word texts compared to unique type word texts in the second grade than in the first grade. Conclusions: The results highlight the importance of StA knowledge and the centrality of diglossia lexical–phonological distance categories in reading, especially the contribution of reading StA words to text reading. The higher performance levels when reading identical compared to unique and cognate words and texts are discussed in light of the theoretical approaches to diglossic lexical–phonological distance. The results point to the centrality of diglossia lexical–phonological distance categories in reading. Highlights: What is already known about this topic: The Arabic language may challenge children's linguistic abilities.The linguistic distance in diglossic Arabic was found to impact the acquisition of linguistic skills in StA. Diglossia is the discrepancy between the spoken Arabic form and standard Arabic form, which are used in different social settings.Diglossia affects word reading development. What this paper adds: It highlights the gap between the identical and the other two lexical distance categories (cognates and unique) in the second grade than in the first grade. It also reveals the disparity between the mostly identical type words and mostly cognate type words texts compared to unique type words texts in the second grade than in the first grade.It highlights the impact of Arabic diglossia, specially relating to specific text types and word types (identical, cognates and unique) and its impact on reading. Implications for theory, policy or practice: The study highlights the importance of diglossic lexical–phonological distance in understanding the reading process in Arabic.Future work may want to examine whether promoting the lexical phonological representations of StA words helps learning to read in Arabic. This may include exposing children to standard Arabic words, such as cognate and unique words, from the early stages of reading development. [ABSTRACT FROM AUTHOR]

The advancement in technology has led to increase in cyber‐attacks. Hackers have become more skilled at finding the loopholes in the system and can penetrate easily on to host network. The rate of cybercrimes is increasing exponentially with the growth of digital era. Phishing is considered as one of the top cybercrimes that has impacted the society at large. As per Kaspersky reports 2021, around 22% attacks were phishing attacks. This paper explores methods for detecting phishing uniform resource locator (URLs) by analyzing various features using Machine Learning techniques. Various data mining algorithms are used to learn data patterns that can identify and differentiate between benign and phishing websites using phishing website data set. The best results are shown by an XGBoost Model that provides more than 90% accuracy on the balanced class dataset. [ABSTRACT FROM AUTHOR]

In this paper a novel technique is presented for fault location in distribution networks in the presence of distributed generators (DGs). The proposed approach uses recorded voltage and current at the beginning of feeder and DG terminals. This method is based on transient state data, and due to presence of high frequencies in this state, to achieve high accuracy it utilizes the distributed time‐domain (DTD) line model. The fault location problem is formulated as an optimization problem and is solved using Genetic Algorithm (GA). Through modal transformation, in addition to locate the symmetrical three‐phase to ground (3LG) faults, the location of asymmetrical faults including single line to ground (SLG) and double line (LL) faults can also be determined. The effects of fault distance, fault resistance and fault inception angle on the accuracy of the proposed algorithm have also been investigated. A solution is also proposed to overcome the multi‐response problem. The simulations are conducted and the results are obtained via PSCAD and MATLAB softwares, respectively. The effectiveness of the proposed approach has been verified using the modified IEEE 34‐node test feeder under different fault conditions. The simulation results demonstrate the high efficiency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

The recognition of the preceding vehicle lane‐changing manoeuvre (LCM) is essential for improving the rationality and safety of the decision‐making of driverless vehicles. However, traditional recognition researches for preceding vehicle LCM are generally characterized by problems such as low recognition accuracy and poor research foundation. In response to these problems, this paper carried out naturalistic driving study (NDS) on the highway, from which a large amount of on‐road data consisting of lane‐keeping (LK) and lane‐changing left and right (LCL, LCR) manoeuvres were collected. A stacking‐based ensemble learning method for the recognition of the LCM of the preceding vehicle, which integrates the random forest, support vector machine, long and short‐term memory network (LSTM), and bi‐directional LSTM based on attention mechanism (AT‐Bi‐LSTM) algorithms, is proposed. Compared with traditional machine learning methods, the proposed method exhibits greater advantages in terms of its recognition accuracy. It is particularly of note that the recognition accuracy of the model at 0.4 s and 0.8 s after the LCM reached 90.77% and 95.54%, respectively. The study reported is of great significance for the construction of more intelligent vehicle‐vehicle collaboration and the promotion of the industrial applications of intelligent vehicle technology. [ABSTRACT FROM AUTHOR]

Low‐light enhancement is a crucial task in computer vision because of the limited dynamic range of digital imaging devices in poor lighting conditions. Images taken under low‐light conditions often suffer from insufficient brightness and severe noise. At present, many models based on convolutional neural networks have been proposed to enhance low‐light images. However, most models treat the features on different channels equally, which is not conducive to models learning hierarchical features. Consequently, the method proposed a channel splitting attention network (CSAN) that divides the shallow features into two branches, the residual and dense branches, transmitting different information. Residual branching facilitates feature reuse, while dense branching promotes the exploration of new features. In addition, CSAN uses merge‐and‐run mappings to assist information integration between different branches and distinguishes the information contained in different branch features through an attention module designed in this paper. Multiple experiment results show that the method proposed is superior to state‐of‐the‐art methods in qualitative and quantitative evaluation. Furthermore, CSAN can better suppress chromaticity aberration while enhancing low‐light images. [ABSTRACT FROM AUTHOR]

This paper presents a novel, sequentially executed supervised machine learning‐based electric theft detection framework using a Jaya‐optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre‐processing phase. An oversampling algorithm based on the Robust‐SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into "Honest" and "Fraudster," the ensemble machine learning‐based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re‐trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research. [ABSTRACT FROM AUTHOR]

This paper proposes a high‐speed and accurate method for extracting the arrival times (ATs) of traveling waves (TWs) in the power system that can be used for fault location applications and transmission line protection. The proposed method is based on the expression of the traveling wave as the exponentially damped component superimposed on the sinusoidal wave in a small time window. Also the sine component is removed by using approximation and consecutive differences of the input signal samples (the modal components of three‐phase voltages or currents) from the resulting wave. Due to the fact that the estimation of traveling wave arrival times (TWATs) is done applying basic mathematical operations in the time domain, the proposed method, in addition to the simplicity of implementation, has the appropriate accuracy and speed for applications such as online fault location and protection purposes. In order to evaluate the performance of the proposed approach in fault locating, a 230 kV transmission line is modelled in EMTP/ATP. Then, the fault location under various conditions of faults such as changes in location and type of fault, fault conditions as well as variations in the measurement noise level and sampling frequency are studied using MATLAB. [ABSTRACT FROM AUTHOR]