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Analysing data from educational tests allows governments to make decisions for improving the quality of life of individuals in a society. One of the key responsibilities of statisticians is to develop models that provide decision-makers with pertinent information about the latent process that educational tests seek to represent. Mixtures of t $$ t $$ factor analysers (MtFA) have emerged as a powerful device for model-based clustering and classification of high-dimensional data containing one or several groups of observations with fatter tails or anomalous outliers. This paper considers an extension of MtFA for robust clustering of censored data, referred to as the MtFAC model, by incorporating external covariates. The enhanced flexibility of including covariates in MtFAC enables cluster-specific multivariate regression analysis of dependent variables with censored responses arising from upper and/or lower detection limits of experimental equipment. An alternating expectation conditional maximization (AECM) algorithm is developed for maximum likelihood estimation of the proposed model. Two simulation experiments are conducted to examine the effectiveness of the techniques presented. Furthermore, the proposed methodology is applied to Peruvian data from the 2007 Early Grade Reading Assessment, and the results obtained from the analysis provide new insights regarding the reading skills of Peruvian students., (© 2023 British Psychological Society.)

The term irritant-induced (occupational) asthma (IIA) has been used to denote various clinical forms of asthma related to irritant exposure at work. The causal relationship between irritant exposure(s) and the development of asthma can be substantiated by the temporal association between the onset of asthma symptoms and a single or multiple high-level exposure(s) to irritants, whereas this relationship can only be inferred from epidemiological data for workers chronically exposed to moderate levels of irritants. Accordingly, the following clinical phenotypes should be distinguished within the wide spectrum of irritant-related asthma: (i) definite IIA, that is acute-onset IIA characterized by the rapid onset of asthma within a few hours after a single exposure to very high levels of irritant substances; (ii) probable IIA, that is asthma that develops in workers with multiple symptomatic high-level exposures to irritants; and (iii) possible IIA, that is asthma occurring with a delayed-onset after chronic exposure to moderate levels of irritants. This document prepared by a panel of experts summarizes our current knowledge on the diagnostic approach, epidemiology, pathophysiology, and management of the various phenotypes of IIA., (© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed., (© 2023 John Wiley & Sons Ltd.)

The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions. [ABSTRACT FROM AUTHOR]

Document forgeries that involve modification of the materials used, such as ink and paper, provide evidence of any malpractices being performed. Forensic specialists use different techniques to identify and classify these samples; however, the most preferred method is to use nondestructive techniques to avoid any potential damage to the original specimen under investigation. Hyperspectral imaging has already been explored in several application domains and used as a powerful method in forensic investigations to extract information about the materials under examination. To precisely classify the material information and utilize the hyperspectral imaging technique's potential, we probed the potential of some hybrid spectral similarity measures to classify different commonly used paper samples. A comparison of these methods is quantitatively presented in this article. Hybrid spectral similarity algorithms are tested on forensic analysis of paper data. We compared the classification capabilities of various hybrid spectral similarity algorithms on hyperspectral data of 40 different paper samples. The overall accuracy (OA), kappa K̂, Z‐score of kappa (ZK̂), and the 95% confidence interval of kappa (CI(K̂)) are used for comparison. The SID‐SAM and SID‐SCA produced an overall accuracy of 88% and 87%, respectively, which is highest among the hybrid spectral similarity measures tested. [ABSTRACT FROM AUTHOR]

Rationale The recent development of isotope ratio infrared spectroscopy (IRIS) was quickly followed by the addition of online extraction and analysis systems, making it faster and easier to measure soil and plant water isotopes. However, memory and sample size effects limit the efficiency and accuracy of these new setups. In response, this study presents a scheme dedicated to estimating and eliminating these two effects. Methods Memory effect was determined by injecting two standard waters alternately. Each standard was injected nine times in a row and analyzed using induction module cavity ring-down spectroscopy (IM-CRDS). Memory coefficients were calculated using a new 'multistage jump' algorithm. Sample size effects were evaluated by injecting water volumes ranging from 1 μL to 6 μL. Finally, the influence of cellulose filter paper on the isotopic measurements, the memory, and the sample size effect was evaluated by comparing it with glass filter paper. Results Memory effects were detected for both δ18O and δ2H values, with the latter being stronger. Isotopic differences between replicates of the same plant or soil sample showed a clear decrease after memory correction. A small water volume effect was found only when the injected water volume was larger than 3 μL. However, while the correction method performed well for laboratory-made samples, it did not for field samples, due to the heterogeneity of the isotopic composition of the samples. Stronger memory and water volume effects were found for cellulose filter paper. Conclusions The memory coefficients and the water volume-isotope relationship improved the consistency and accuracy of both laboratory and field data. Our results indicate that cellulose filter paper may not be a suitable medium to measure standard waters and evaluate memory and water volume effects. Finally, a detailed correction and calibration protocol is suggested, along with notes on best practices to obtain good-quality IM-CRDS data. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The D50 CIELAB gamut volume was standardized as a suitable and unified gamut size measurement methodology. The color gamut volume is visualized using proportionate gamut rings in a two‐dimensional diagram. Gamut ring intersection assists the visual comparison of the size and shape of the color gamut coverage between a display and a reference. This paper introduces a desktop application with an intuitive graphical interface for visualizing the gamut rings and describes the algorithm of the gamut ring intersection. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algorithm which can eliminate abnormal detection data of our OLED panels caused by damaged detection TFTs. During the manufacture of oversize OLED TVs, particles bring many serious problems especially when they make detection TFTs be damaged and these TFTs will cause one or even several columns of detection data to be abnormal. The new "abnormal detection data forbidden algorithm" will replace the adjacent normal column detection data for the column with obvious abnormal detection data and the new "mosaic algorithm" will scatter the detection data of these columns that the detection data is not obviously abnormal but they are thin lines visible to the human eyes when the panel is working. To sum up, the new integration algorithm is very important for improving the image quality and stable operation of our products. [ABSTRACT FROM AUTHOR]

This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient-specific supervised-machine-learning (SML) analysis of glucose level based on a continuous-glucose-monitoring system (CGM) that needs no human intervention, and minimises false-positive alerts. The CGM data over 7 to 50 non-consecutive days from 11 type-1 diabetic patients aged 18 to 39 with a mean HbA1C of 7.5% ± 1.2% were analysed using four SML models. The algorithm was constructed to choose the best-fit model for each patient. Several statistical parameters were calculated to aggregate the magnitudes of the prediction errors. The personalised solutions provided by the algorithm were effective in predicting glucose levels 30 minutes after the last measurement. The average root-mean-square-error was 20.48 mg/dL and the average absolute-mean-error was 15.36 mg/dL when the best-fit model was selected for each patient. Using the best-fit-model, the true-positive-hypoglycemia-prediction-rate was 64%, whereas the false-positive- rate was 4.0%, and the false-negative-rate was 0.015%. Similar results were found even when only CGM samples below 70 were considered. The true-positive-hyperglycemia-prediction-rate was 61%. State-of-the-art SML tools are effective in predicting the glucose level values of patients with type-1diabetes and notifying these patients of future hypoglycemic and hyperglycemic events, thus improving glycemic control. The algorithm can be used to improve the calculation of the basal insulin rate and bolus insulin, and suitable for a closed loop "artificial pancreas" system. The algorithm provides a personalised medical solution that can successfully identify the best-fit method for each patient., (© 2020 John Wiley & Sons Ltd.)

Local dimming LCDs with a matrix backlight unit (BLU) are favored by many car manufacturers for high quality and low power displays. Automotive application sets high requirement like safety standard, whereas the temperature range and lifespan are wide. The contents are mostly HMIs with high contrast and circular structures. Thus, the two key components, BLU and the local dimming algorithm need to be adopted to automotive needs/demands and shall be jointly optimized. In this paper, the BLU design aspects and specification are described. Specific features relevant for automotive, like black corners and containment against LED outage, are introduced. The tradeoff between performance and cost is lifted. With a BLU with 384 LEDs, the contrast ratio may be enhanced by a factor of 20, while an average power saving ratio of 50% can be achieved. [ABSTRACT FROM AUTHOR]

The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications for costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. In the above list, biological activities have never been used as an independent variable towards the prediction of clinical trial outcomes. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the "Pass" class. "Pass" refers to one of the two classes (Pass/Fail) of all clinical trials, and the model performed well in predicting the "Pass" category. Through the analysis of feature contributions to predictive capability, we have demonstrated that bioactivity plays a statistically significant role in predicting clinical trial outcome. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. Cleaned, organized, integrated data and code to map these entities, created as a part of this work, are available open-source. This reproducibility and the freely available code ensure that researchers with access to deep curated and proprietary clinical trial databases (we only use open-source data in this study) can further expand the scope of the results., (© 2022 John Wiley & Sons Ltd.)

Keeping up with rapidly growing research fields, especially when there are multiple interdisciplinary sources, requires substantial effort for researchers, program managers, or venture capital investors. Current theories and tools are directed at finding a paper or website, not gaining an understanding of the key papers, authors, controversies, and hypotheses. This report presents an effort to integrate statistics, text analytics, and visualization in a multiple coordinated window environment that supports exploration. Our prototype system, Action Science Explorer ( ASE), provides an environment for demonstrating principles of coordination and conducting iterative usability tests of them with interested and knowledgeable users. We developed an understanding of the value of reference management, statistics, citation text extraction, natural language summarization for single and multiple documents, filters to interactively select key papers, and network visualization to see citation patterns and identify clusters. A three-phase usability study guided our revisions to ASE and led us to improve the testing methods. [ABSTRACT FROM AUTHOR]

A full‐panel circuit model of the on‐cell touch AMOLED is introduced to simulate the mutual interference between touch and display operations. The proposed panel model successfully reproduced nonlinear behaviors of the display noise observed in real systems. Also, the reverse‐directional interference, i.e., touch‐to‐display coupling, was simulated, showing that touch stimulation incurs the fluctuation of display charging voltages, possibly causing perceivable line defects. The proposed panel model can be used for various touch/display algorithms to reduce the interference effects and to determine key design parameters for new panels. [ABSTRACT FROM AUTHOR]

Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables' contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. To provide an example of the different algorithms' function, they were used on a completed cross‐sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods. [ABSTRACT FROM AUTHOR]

We present the results of running 4 different papers through the automated filtering system used by the open access preprint server 'arXiv' to classify papers and implement quality control barriers. The exercise was carried out in order to assess whether these highly sophisticated, state-of-the-art filters can distinguish between papers that are controversial or have gone past their 'sell-by date,' and otherwise normal papers. We conclude that not even the arXiv filters, which are otherwise successful in filtering fringe-topic papers, can fully acquire 'Domain-Specific Discrimination' and thus distinguish technical papers that are taken seriously by an expert community from those that are not. Finally, we discuss the implications this has for citizen and policy-maker engagement with the Primary Source Knowledge of a technical domain. [ABSTRACT FROM AUTHOR]

This study conducts a bibliometric analysis and systematic review of cryptocurrency trading algorithms to identify existing gaps in the area. From our standpoint, this is the first study to carry out a deep analysis of price forecasts and portfolio management in cryptocurrencies in addition to analyzing the most relevant studies and authors, trend topics of the area, and identifying countries with the most published studies. During our research, we identified some gaps that can be used for further research. Currently, there are approximately 16,000 cryptocurrencies; however, in majority of the papers, the authors have only used the top 10 ranking market capitalization cryptocurrencies, leaving aside potential minor cryptocurrencies. Thus, trading strategies using Big Data can be a potential research topic, considering the greater number of emerging cryptocurrencies. [ABSTRACT FROM AUTHOR]

For each vehicle in a trusted clustering model, its movement is analyzed by vehicle network mobility routing protocol with identified speed and position. The fifth paper entitled "Ant colony optimization-induced route optimization for enhancing driving range of electric vehicles" by Gunasekaran Manogaran et al. proposed a fitness-ant colony optimization (FACO)-based route optimization for improving the driving range of electric vehicles (EVs). The second paper entitled "A SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic" by Liuwei Huo et al. proposed a novel accurate and software defined networking (SDN) based fine-grained traffic measurement approach to obtain comprehensive traffic in VCN. [Extracted from the article]

Context: Although many medical schools seek to improve diversity, they grapple with the challenge of how to weight the scores of different admission methods to achieve a balance between obtaining high predictiveness and ensuring diversity in the selected student pool. Yet, in large-scale employment settings, substantial progress has been made on this front: Pareto-optimization has been introduced as an elegant statistical tool to assist decision makers in determining the weights assigned to selection methods in advance (before the selection has taken place) so that a selection system is designed to achieve an optimal balance as reflected by the trade-off that one outcome (e.g., predictiveness) cannot be improved without harm to the other outcome (e.g., diversity)., Aims: This paper reviews the theory and research evidence about Pareto-optimization and explains how Pareto-optimization permits medical schools to better balance predictiveness and diversity in medical admission systems., Methods: After reviewing common weighting schemes (unit, regression-based and ad hoc weighting) and their drawbacks, we introduce the theory and logic of Pareto-optimization for better balancing predictiveness and diversity. To this end, we also offer an illustrative example. Next, we review the mathematical basis and available research evidence regarding Pareto-optimization. Finally, we discuss potential criticisms (i.e., complexity and legal concerns)., Conclusions: Compared to traditional unit weighting, regression-based weighting and ad hoc weighting, Pareto-optimization leads to substantial increases in diversity intake (up to three times more), while keeping the predictiveness of the selection methods at the same level. Moreover, the Pareto-optimization is robust to sampling variability and variability of the input selection parameters., (© 2021 Association for the Study of Medical Education and John Wiley & Sons Ltd.)

Summary: This paper presents the authors' response to the discussion by Dean J. Maxam and Kumar K. Tamma of the paper titled "Development of a family of unconditionally stable explicit direct integration algorithms with controllable numerical energy dissipation." [ABSTRACT FROM AUTHOR]

The author presents comments on a paper on the use of machine learning to support the system dynamics method. Topics discussed include its interpretation of simulation models and explanation of policy analysis, and the emerging view whereby dynamic problems from endogenous feedback structures can be tackled via wider tools and methodological approaches. Also noted is the resulting potential for greater insights into the modelling process.

A novel algorithm has been developed and validated to isolate individual papermaking fibres in micro-computed tomographic images of paper handsheets as a first step to characterize the structure of the paper. The three-step fibre segmentation algorithm segments the papermaking fibres by (i) tracking the hollow inside the fibres via a modified connected component methodology, (ii) extracting the fibre walls using a distance transform and (iii) labelling the fibres through collapsed sections by a final refinement step. Furthermore, postprocessing algorithms have been developed to calculate the length and coarseness of the segmented fibres. The fibre segmentation algorithm is the first ever reported method for the automated segmentation of the tortuous three-dimensional morphology of papermaking fibres within microstructural images of paper handsheets. The method is not limited to papermaking fibres, but can be applied to any material consisting of tortuous and hollow fibres. [ABSTRACT FROM AUTHOR]

License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

This paper mainly introduces the development of 4K HDR "Stacked‐Panel" TV based on Dual‐Cell LCD. Dual‐Cell technology enables significant improvement in display contrast, but the drop of transmittance limits the increase of peak luminance. In this work, we innovatively designed the pixel control algorithm for Dual‐Cell LCD. Moreover, the peak luminance and color gamut were further improved using direct‐lit backlight and QD technology, leading to the outstanding HDR display effect. [ABSTRACT FROM AUTHOR]

The question of which type of computer science (CS) publication-0conference or journal-0is likely to result in more citations for a published paper is addressed. A series of data sets are examined and joined in order to analyze the citations of over 195,000 conference papers and 108,000 journal papers. Two means of evaluating the citations of journals and conferences are explored: h5 and average citations per paper; it was found that h5 has certain biases that make it a difficult measure to use (despite it being the main measure used by Google Scholar). Results from the analysis show that CS, as a discipline, values conferences as a publication venue more highly than any other academic field of study. The analysis also shows that a small number of elite CS conferences have the highest average paper citation rate of any publication type, although overall, citation rates in conferences are no higher than in journals. It is also shown that the length of a paper is correlated with citation rate. [ABSTRACT FROM AUTHOR]

This study proposes and evaluates a diagnostic classification model framework for multiple-choice items. Models in the proposed framework have a two-level nested structure which allows for binary scoring (for correctness) and polytomous scoring (for distractors) at the same time. One advantage of these models is that they can provide distractor information while maintaining the statistical properties of the correct response option. We evaluated parameter recovery through a simulation study using Hamiltonian Monte Carlo algorithms in Stan. We also discussed three approaches to implementing the proposed modelling framework for different purposes and testing scenarios. We illustrated those approaches and compared them with a binary model and a traditional nominal model through an operational study., (© 2020 British Psychological Society.)

Two high dynamic range displays: dual‐cell LCD and mini‐LED backlit LCD, are investigated. In a dual‐panel display, the parallax error at large viewing angles is analyzed and mitigated by a newly proposed splitting algorithm. Besides, the image qualities of mini‐LED backlit LCD are compared with OLED under different display brightness, ambient reflectance, and ambient light illuminance. The halo effect of mini‐LED based LCD becomes less noticeable as the ambient light brightness increases. [ABSTRACT FROM AUTHOR]

In order to solve the problem of lack of detail information and serious damage to the texture of the light source area when the dark channel prior dehazing algorithm processes fog images at night, this paper proposes a dehazing algorithm for nighttime fog images using the dark‐state point light source model. By introducing the dark state point light source model, the dark channel credibility weight factor and the pseudo dehazing image, combined with the nighttime image imaging model, the improved transmittance distribution is obtained, and the fog image at night is dehazed. The experimental results show that the image processed by the algorithm in this paper has little loss of texture details, high image definition, and the contrast between the light and dark of the image is better stretched, which can effectively dehaze the fog image at night. [ABSTRACT FROM AUTHOR]

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses article on topics including tree algorithms to generate test sequence and Coincidental correctness.

Using Louis' formula, it is possible to obtain the observed information matrix and the corresponding large-sample standard error estimates after the expectation-maximization (EM) algorithm has converged. However, Louis' formula is commonly de-emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis' formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis' formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis' formula with previously existing methods. Results from these presentations suggest that Louis' formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility., (© 2020 The British Psychological Society.)

Often it is assumed that electronic recording by observers necessarily provides better quality data than pen-and-paper methods. Fifteen novice observers recorded rates of responding from 10 role-played video samples using one of three continuous recording input formats: keyboard (laptop), touchscreen (personal digital assistants), or pen-and-paper. We evaluated the quality of the observers' data compared with criterion records using calibration and interobserver agreement algorithms. Results of the calibration analysis revealed that observers in the touchscreen group produced the most consistently accurate and precise data, the keyboard group observers showed wide variation in precision and accuracy, and the pen-and-paper group observers were significantly less precise than the touchscreen group. We conclude that although electronic recording has the potential to be as accurate as, and more precise than, pen-and-paper methods, this is far from guaranteed. Analyses of observers' errors advise recommendations for improving data accuracy and precision when using each method. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the colour vision severity classification standard 'CIE 143:2001 International recommendations for colour vision requirements in transport' (CIE 143:2001), which has become out of date because of the lack of commercial availability of required colour vision tests., Methods: One-hundred-five subjects had colour vision tested and colour vision severity classified according to a modified CIE 143:2001 algorithm that included pseudoisochromatic plates (Ishihara's test and Hardy Rand Rittler (HRR) 4th edition), Optec 900 lantern and Farnsworth D-15. Subject's results and colour vision severity classification were compared to performance and colour vision severity classification on the computerized 'Colour Assessment and Diagnosis' (CAD) test., Results: According to CIE 143:2001, using Ishihara's test, Optec lantern and Farnsworth D 15, 11 subjects (10%) were category I (normal), 16 (15%) were category II (mild), 48 (46%) were category III (poor), and 30 (29%) were category IV (severe). Classified by CAD score, 10 (10%) were category I, 11 (10%) were category II, 41 (39%) were category III, and 43 (41%) were category IV. The correlation between the two estimates of the severity of colour vision loss (i.e. CIE 143:2001 and CAD) was high, with a Kendall's Tau test of 0.81 (τ = 0.81 p < 0.001). A suggested CIE 143:2001 classification including new CAD score limits improves the classification correlation to 0.90 (τ = 0.90 p < 0.001) for all diagnoses., Conclusion: The colour vision severity classification standard 'CIE 143:2001 International recommendations for colour vision requirements in transport', has not implemented new diagnostic tools with better accuracy. We propose three possible revisions to the CIE 143:2001 algorithm, based on the availability of CAD: (1) Replacing the current CIE 143:2001 algorithm using new CAD threshold limits, (2) Use of CAD as a secondary test to Ishihara's test and HRR or (3) Revising the current CIE 143:2001 algorithm using Ishihara's test, HRR, Optec 900 and FD15., (© 2020 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.)

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, Mutual Rank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. Mutual Rank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that Mutual Rank greatly outperforms the state-of-the-art competitors, including Page Rank, HITS, Co Rank, Future Rank, and P- Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by Mutual Rank are also quite reasonable. [ABSTRACT FROM AUTHOR]

This paper proposes a multitasking Demura algorithm based on backlight modulation and liquid crystal voltage adjustment that significantly increase the luminance uniformity and improves the details of high gray image without saturation issues. The multitasking Demura benefit was experimentally validated in a 55‐inch splicing module display, and the experimental results show that the multitasking Demura can eliminate severe mura, increasing the module's luminance uniformity from 34%, 60% and 88% to 83%, 95% and 98% at L25, L128 and L240 gray levels. [ABSTRACT FROM AUTHOR]

High resolution and high refresh rate increase the pressure of wide transmission. Usually, video compression is used to solve this problem. However, traditional video compression methods cannot be performed in bit‐plane, which will increase the cost of storage area. For this reason, this paper first introduces a bit just noticeable difference (BJND) model based on bit‐plane. By analyzing the relationship between the bit‐plane, frequency and eccentricity, the bit just noticeable difference threshold of viewpoint is calculated. Finally, the just noticeable difference model applied to bit‐plane is obtained. Then this paper proposes a video compression scheme based on bit motion estimation algorithm, which optimizes the search range of motion estimation into two small rhombuses of time dimension and gray‐scale dimension. According to human visual system and probability statistical analysis, supplementary matching blocks are added to replace residual data, so that the compression ratio will be constant. The experimental results show that the proposed scheme has the best comprehensive effect on the compression of the lower five‐bit plane. The compression ratio is 1.385, the data transmission is constant, and there is no obvious difference between the restored image and the original image, which conforms to the intuitive perception of the human eye. [ABSTRACT FROM AUTHOR]

Complex simulator-based models with non-standard sampling distributions require sophisticated design choices for reliable approximate parameter inference. We introduce a fast, end-to-end approach for approximate Bayesian computation (ABC) based on fully convolutional neural networks. The method enables users of ABC to derive simultaneously the posterior mean and variance of multidimensional posterior distributions directly from raw simulated data. Once trained on simulated data, the convolutional neural network is able to map real data samples of variable size to the first two posterior moments of the relevant parameter's distributions. Thus, in contrast to other machine learning approaches to ABC, our approach allows us to generate reusable models that can be applied by different researchers employing the same model. We verify the utility of our method on two common statistical models (i.e., a multivariate normal distribution and a multiple regression scenario), for which the posterior parameter distributions can be derived analytically. We then apply our method to recover the parameters of the leaky competing accumulator (LCA) model and we reference our results to the current state-of-the-art technique, which is the probability density estimation (PDA). Results show that our method exhibits a lower approximation error compared with other machine learning approaches to ABC. It also performs similarly to PDA in recovering the parameters of the LCA model., (© 2019 The British Psychological Society.)

Dual‐layer LCDs have the advantage of high contrast ratio, but due to the physical distance between the two LC panels, parallax error may appear when viewed off‐axis, affecting the display quality. In this paper, we propose a convolutional neural network‐based algorithm to mitigate parallax error and improve the display quality. First, a suitable training data set is selected and the input image is processed by a convolutional neural network to output a front image and a rear image. These two images are staggered and multiplied to obtain reproduced images at different angles. The reproduced image is compared with the original image by means of a loss function, and the parameters in the convolution are continuously updated to achieve the best display quality. The algorithm proposed in this paper can effectively mitigate the parallax error phenomenon of a dual‐layer LCD and improve the display quality, and the computational efficiency is also higher than the traditional algorithms. [ABSTRACT FROM AUTHOR]

The vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults. [ABSTRACT FROM AUTHOR]

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The estimation of time‐lapse time shifts between two, or several, repeated seismic surveys has become increasingly popular over the past eighteen years. These time shifts are a reliable and informative seismic attribute that can relate to reservoir production. Correction for these time shifts or the underlying velocity perturbations and/or subsurface displacement in an imaging sense also permits accurate evaluation of time‐lapse amplitudes by attempting to decouple the kinematic component. To date, there are approximately thirty methods for time‐shift estimation described in the literature. We can group these methods into three main families of mathematical development, together with several miscellaneous techniques. Here we detail the underlying bases for these methods, and the acknowledged benefits and weaknesses of each class of method highlighted. We illustrate this review with a number of time‐lapse seismic examples from producing fields. No method is necessarily superior to the others, as its selection depends on ease of implementation, noise characteristics of the field data, and whether the inherent assumptions suit the case in question. However, cross‐correlation stands out as the algorithm of choice based on the Pareto principle and waveform inversion the algorithm delivering best resolution. This is a companion study to the previous review of time‐shift magnitudes and a discussion of their rock physics basis. [ABSTRACT FROM AUTHOR]

Letter to the Editor concerning "Describing center of pressure movement in stabilometry by ellipse area approximation" from Agnieszka Golab. Reply to "Describing center of pressure movement in stabilometry by ellipse area approximation" from Agnieszka Golab concerning the paper "A Review of Center of Pressure (COP) Variables to Quantify Standing Balance in Elderly People: Algorithms and Open Access Code" Our choice was actually to present the formula of the prediction ellipse area in the article, as it indeed does not strongly depend on the sample size as the confidence ellipse area does. [Extracted from the article]

Aiming at the problem of motion blur in the inspection image of wind power generation equipment, an improved fast motion blur removal method based on deblurganv2 is proposed in this paper. Firstly, according to the characteristics of disparate local motion blur degrees and different global motion blur directions of the wind power inspection image, the real wind power fuzzy image data set is collected and produced. Secondly, according to the characteristics of the single background of the wind power inspection image and the requirements of fast and effective processing, the lightweight network Ghostnet is redesigned as the backbone network of the Deblurganv2 generator to reduce the number of network parameters and calculations. Finally, five SE channel attention mechanism layers are added to GhostNet to strengthen feature extraction, and the upsampling process is optimized through bilinear interpolation, to improve the deblurring performance of the wind power inspection image. The experimental results show that compared with other algorithms, the proposed algorithm has a higher peak signal‐to‐noise ratio and structural similarity, the network model parameters are compressed to 6.1 MB, and the reasoning speed is improved to 0.42 s. [ABSTRACT FROM AUTHOR]

Summary: Fire is a serious threat to human life and property, and effective prediction of fire can reduce the harm of fire to human. In the research of fire alarm automation, the fire alarm system based on wireless sensor network is a common method. When using wireless sensor to monitor the fire alarm information, it is necessary to ensure the safety coverage of the network in the monitoring area. Therefore, it is very necessary to study the network coverage in the fire alarm monitoring, which can ensure the effective work of the monitoring network, and good coverage control can reduce the energy consumption of the network and extend the service life of the network. Based on this, this paper designs a network coverage control algorithm based on multiobjective genetic algorithm optimization considering the coverage and energy consumption of wireless network. In this algorithm, the probability perception model is used to design the network coverage control model for the monitoring area with different coverage requirements, taking the maximum area coverage and the minimum network energy consumption rate as the optimization goal. Then the genetic algorithm is used to solve the model. In addition, because the multiobjective genetic algorithm is easy to fall into the local optimal solution when solving the optimization problem, this paper introduces the cross and variation coefficients of self‐adaptive adjustment to improve the genetic algorithm. Through the simulation analysis, it is shown that when solving the multiobjective optimization problem, the introduction of self‐adaptive adjustment of cross and variation coefficients can well overcome the problem that genetic algorithm falls into the local optimal solution, whereas the network coverage control algorithm designed in this paper based on the multiobjective genetic algorithm optimization can achieve greater coverage and lower energy consumption. Compared with the network coverage control algorithm before optimization, the optimized algorithm improves greatly in coverage, network residual energy, and number of nodes, which can well extend the service life of wireless sensor network, and is conducive to the good application of wireless sensor in fire prediction. [ABSTRACT FROM AUTHOR]

Communication security has become particularly crucial with the rapid development of the Internet of Things (IoT). Frequency hopping spread spectrum (FHSS) technology, a prevalent method in wireless communication, has a wide range of applications in the Internet of Things. Enhancing the security of frequency hopping sequences is an essential means to improve the security of frequency hopping communication in the Internet of Things, as the performance of frequency hopping sequences plays a crucial role in frequency hopping systems. This paper proposes constructing secure adaptive frequency hopping sequence sets based on the advanced encryption standard (AES) algorithm. As a block cipher algorithm with superior security, the AES algorithm can provide a fundamental guarantee for the security of the proposed frequency hopping sequences. The mapping methods from ciphertext sequences to frequency hopping sequences proposed in this paper can achieve the construction of frequency hopping sequences of any frequency set size to meet the requirements of adaptive frequency hopping. In addition, we also model and analyse the problem of overlapping spectrum band of the IoT groups in the industrial, scientific, and medical (ISM) band, aiming to achieve better packet transmission performance by adjusting the frequency set size. [ABSTRACT FROM AUTHOR]

The YOLO series of algorithms have made substantial contributions to the detection of insulator defects in power transmission line operations. However, existing target detection algorithms for the small target detection and low‐quality insulator images encounter difficulties in effectively capturing relevant features, resulting in a higher probability of target loss. To identify and classify defects in the operational state of insulators, an improved YOLOv8 target identification algorithm called DGW‐YOLOv8 is proposed in this paper. The deformable attention backbone of the DGW‐YOLOv8 target identification algorithm is designed by adding the deformable ConvNets v2 module and the global attention mechanism. This addition reduces the feature loss caused by the network feature processing, enhances the sensitivity of the algorithm to small‐scale targets, and reduces the impact caused by the different global positions of the targets. Additionally, to address the problem of low quality of captured images, WIoU v3 is used to replace CIoU in the original YOLOv8 target identification algorithm to optimize the loss function, reduce the degrees of freedom, and improve the network robustness. Experimental results demonstrate that the enhanced YOLOv8 algorithm can achieve an improvement of 2.4% and 5.5% in mAP and mAP50‐95, respectively, compared with the original algorithm. [ABSTRACT FROM AUTHOR]

This paper describes the author's attempt to design assignments that engage preservice elementary teachers in original mathematical thinking. In particular, the choice of integer operations as the focus of a structured writing assignment that takes students two weeks to complete is explained and justified. Exemplary student work is quoted. [ABSTRACT FROM AUTHOR]

Modern graphics processing units (GPUs) are flexibly programmable and have peak computational throughput significantly faster than conventional CPUs. Herein, we describe the design and implementation of PAPER, an open-source implementation of Gaussian molecular shape overlay for NVIDIA GPUs. We demonstrate one to two order-of-magnitude speedups on high-end commodity GPU hardware relative to a reference CPU implementation of the shape overlay algorithm and speedups of over one order of magnitude relative to the commercial OpenEye ROCS package. In addition, we describe errors incurred by approximations used in common implementations of the algorithm. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010 [ABSTRACT FROM AUTHOR]

Key of development for OLED devices is compatibility between organic materials. Therefore, the construction of the materials' properties DB should be followed in order to advance characterization and prediction of the OLED device. In particular, it is necessary to build a number of organic materials DB unlike the conventional Si‐based homo‐junction and alloy changing only heterojunction. So, it should be built based on the organic material parameters, SIMDB (Simulation DB), it is possible to predict and optimize driving voltage and efficiency characteristics for OLED devices. However, the skilled Engineer gets to decide on the simple repeat operation and time consumption for calibrating the material parameter/structure (4~5 layer device based on 20,000 times or more). In order to solve such problems, this paper presents an automatic optimization for electro‐optical characteristics of OLED device. The proposed method and tool are based on a unique heuristic algorithm, which is obtained from accumulated manual calibration experience. In other words, the convergence know‐how is applied random search algorithm and heuristic algorithm were constructed in‐house optimizer enables automated extraction of OLED properties. Finally, by utilizing the built SIMDB is expected to various factors affecting the OLED elements is optimized to be analyzed and prior prediction. [ABSTRACT FROM AUTHOR]

In recent years, nighttime images collected under haze weather often have problems such as low contrast and serious texture loss. The previous algorithms still have some limitations for nighttime dehazing. In this paper, we proposed a novel nighttime dehazing algorithm and compare our results with existing daytime and nighttime dehazing algorithms� results. The experimental results show that the proposed algorithm has a better dehazing performance and shorter runtime. This algorithm can effectively address image distortion, texture loss and low brightness of the dehazing images. [ABSTRACT FROM AUTHOR]