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Rarely any study investigates how information gatekeeping through the solutions and services enabled by algorithms, hereafter referred to as algorithmic technologies (AT), creates negative consequences for the users. To fill this gap, this state‐of‐the‐art review analyzes 229 relevant articles from diverse academic disciplines. We employed thematic analysis to identify, analyze, classify, and reveal the chain reactions among the negative consequences. We found that the gatekeeping of information (text, audio, video, and graphics) through AT like artificial intelligence (e.g., chatbots, large language models, machine learning, robots), decision support systems (used by banks, grocery stores, police, etc.), hashtags, online gaming platforms, search technologies (e.g., voice assistants, ChatGPT), and Web 3.0 (e.g., Internet of Things, non‐fungible tokens) creates or reinforces cognitive vulnerability, economic divide and financial vulnerability, information divide, physical vulnerability, psychological vulnerability, and social divide virtually and in the offline world. Theoretical implications include the hierarchical depiction of the chain reactions among the primary, secondary, and tertiary divides and vulnerabilities. To mitigate these negative consequences, we call for concerted efforts using top‐down strategies for governments, organizations, and technology experts to attain more transparency, accountability, ethical behavior, and moral practices, and bottom‐up strategies for users to be more alert, discerning, critical, and proactive. [ABSTRACT FROM AUTHOR] more...

Explainability is central to trust and accountability in artificial intelligence (AI) applications. The field of human‐centered explainable AI (HCXAI) arose as a response to mainstream explainable AI (XAI) which was focused on algorithmic perspectives and technical challenges, and less on the needs and contexts of the non‐expert, lay user. HCXAI is characterized by putting humans at the center of AI explainability. Taking a sociotechnical perspective, HCXAI prioritizes user and situational contexts, preferences reflection over acquiescence, and promotes the actionability of explanations. This review identifies the foundational ideas of HCXAI, how those concepts are operationalized in system design, how legislation and regulations might normalize its objectives, and the challenges that HCXAI must address as it matures as a field. [ABSTRACT FROM AUTHOR] more...

This article evaluates the predictions of an algorithmic-level distributed associative memory model as it introduces, propagates, and resolves ambiguity, and compares it to the predictions of computational-level parallel parsing models in which ambiguous analyses are accounted separately in discrete distributions. By superposing activation patterns that serve as cues to other activation patterns, the model is able to maintain multiple syntactically complex analyses superposed in a finite working memory, propagate this ambiguity through multiple intervening words, then resolve this ambiguity in a way that produces a measurable predictor that is proportional to the log conditional probability of the disambiguating word given its context, marginalizing over all remaining analyses. The results are indeed consistent in cases of complex structural ambiguity with computational-level parallel parsing models producing this same probability as a predictor, which have been shown reliably to predict human reading times., (© 2024 The Author(s). Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).) more...

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.) more...

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] more...

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.) more...

Background and Aims: This study prospectively compared the diagnostic accuracies of the improved Attenuation Measurement (iATT) algorithm and the Controlled Attenuation Parameter (CAP) and assessed the interchangeability of iATT with magnetic resonance imaging-derived proton density fat fraction (MRI-derived PDFF)., Methods: Patients with chronic liver disease were prospectively enrolled and underwent iATT, CAP and MRI-derived PDFF measurements for hepatic steatosis evaluation. According to MRI-derived PDFF values, steatosis grades were categorised as steatosis (S)0 (< 5.2%), S1 (≥ 5.2%, < 11.3%), S2 (≥ 11.3%, < 17.1%) and S3 (≥ 17.1%). Correlation coefficients (CCs) were determined, diagnostic performances were compared by the area under the receiver operating characteristic curve (AUROC) and agreement was evaluated using the calculated percentage error (PE) and expected limit of agreement (LOA)., Results: A total of 414 patients (median age 64 years, 203 females) were evaluated. The CC between iATT and MRI-derived PDFF was 0.727 (95% confidence interval [CI] 0.678-0.770), which was higher than that between CAP and MRI-derived PDFF at 0.615 (95% CI 0.551-0.672) (p < 0.001). The AUROCs of iATT for ≥ S1, ≥ S2 and ≥ S3 were 0.901 (95% CI 0.870-0.931), 0.878 (95% CI 0.846-0.910) and 0.839 (95% CI 0.794-0.883), respectively. The diagnostic performances of iATT for ≥ S1 and ≥ S2 showed significantly higher AUROCs than those of CAP (p < 0.001, p = 0.036, respectively). The calculated PE and the expected LOA for CAP and iATT were 38.94% and 22.66% and 32.94% and 30.03%, respectively., Conclusions: iATT was superior to CAP and comparable to MRI-derived PDFF in assessing hepatic steatosis., Trial Registration: This study was registered in the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN000047411)., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.) more...

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR] more...

Background & Aims: There is an unmet need for a reliable and reproducible non-invasive measure of fatty liver content (FLC) for monitoring steatotic liver disease in clinical practice. Sonographic FLC assessment is qualitative and operator-dependent, and the dynamic quantification range of algorithms based on a single ultrasound (US) parameter is unsatisfactory. This study aims to develop and validate a new multiparametric algorithm based on B-mode images to quantify FLC using Magnetic Resonance (MR) values as standard reference., Methods: Patients with elevated liver enzymes and/or bright liver at US (N = 195) underwent FLC evaluation by MR and by US. Five US-derived quantitative features [attenuation rate(AR), hepatic renal-ratio(HR), diaphragm visualization(DV), hepatic-portal-vein-ratio(HPV), portal-vein-wall(PVW)] were combined by mixed linear/exponential regression in a multiparametric model (Steatoscore2.0). One hundred and thirty-four subjects were used for training and 61 for independent validations; score-computation underwent an inter-operator reproducibility analysis., Results: The model is based on a mixed linear/exponential combination of 3 US parameters (AR, HR, DV), modelled by 2 equations according to AR values. The computation of FLC by Steatoscore2.0 (mean ± std, 7.91% ± 8.69) and MR (mean ± std, 8.10% ± 10.31) is highly correlated with a low root mean square error in both training/validation cohorts, respectively (R = 0.92/0.86 and RMSE = 5.15/4.62, p < .001). Steatoscore2.0 identified patients with MR-FLC≥5%/≥10% with sensitivity = 93.2%/89.4%, specificity = 86.1%/95.8%, AUROC = 0.958/0.975, respectively and correlated with MR (R = 0.92) significantly (p < .001) better than CAP (R = 0.73)., Conclusions: Multiparametric Steatoscore2.0 measures FLC providing values highly comparable with MR. It is reliable, inexpensive, easy to use with any US equipment and qualifies to be tested in larger, prospective studies as new tool for the non-invasive screening and monitoring of FLC., (© 2024 The Author(s). Liver International published by John Wiley & Sons Ltd.) more...

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.) more...

Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design. [ABSTRACT FROM AUTHOR] more...

In order to improve the effect of intelligent automatic test paper composition, this paper combines the hybrid fuzzy clustering algorithm to study the computer automatic test paper composition algorithm. In this paper, a computer automatic test paper composition system based on hybrid fuzzy clustering algorithm is constructed. Moreover, the hybrid fuzzy clustering method used in this paper is used as the basic algorithm of the system, and the algorithm is improved according to the actual needs of intelligent paper composition. In addition, this paper uses an intelligent algorithm to input the relevant constraint parameters and combines the original parameters to select the most suitable test questions from the database and combine them into test papers. Finally, this paper constructs the system structure based on the requirements of intelligent test paper composition. The experimental research shows that the computer automatic test paper composition system based on the hybrid fuzzy clustering algorithm proposed in this paper has a good test paper composition function, which can effectively promote the progress of the intelligent examination mode in colleges and universities. [ABSTRACT FROM AUTHOR] more...

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] more...

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] more...

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] more...

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] more...

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] more...

We have developed a "Kirameki display" that can represent a real texture of materials. ("Kirameki" means a shining/glittering sense in Japanese.) In addition, we have investigated an improvement of "texture representation" by optimization of both an optical design and a software algorithm. Lastly, we discuss the future technical fields for texture representation on displays. [ABSTRACT FROM AUTHOR] more...

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.) more...

This special issue is based on the program area "Decision support" of the EURO 2021 conference held online and on-site in Athens, Greece, on July 11-14, 2021. It contains papers presented at the conference and later submitted for this special issue, as well as additional papers submitted in response to an open call for papers after the conference. [Extracted from the article] more...

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] more...

In order to improve the teaching effect of sports dance, this paper analyzes the traditional dance teaching motion capture, uses sensor motion perception algorithms to capture sports dance motion perception, and designs an intelligent sensor system that can be used for sports dance motion capture. Moreover, this paper combines the digital filter algorithm to design the hardware system structure of the sports dance motion capture system and builds a motion capture system for sports dance teaching based on the digital filter algorithm according to actual needs. In addition, this paper combines the simulation test to evaluate the performance of the system designed in this paper. The research results show that the motion capture system for sports dance teaching based on the digital filtering algorithm proposed in this paper can play an important role in sports dance teaching and effectively improve the efficiency of sports dance teaching. [ABSTRACT FROM AUTHOR] more...

At present, the monitoring of concrete cracks is still mainly carried out by engineering personnel using simple mechanical monitoring instruments. The human inspection will undoubtedly be interfered by the individual's psychological, physical, and external conditions, and there may also be unobjective emotions, so it is impossible to ensure that the quality of the detection is up to standard and accurate. This paper combines digital image processing technology and multiscale feature analysis automatic detection algorithm to construct an intelligent building structure crack detection system. Moreover, this paper proposes an enrichment scheme for the unknown partially entangled states of building microparticles and utilizes the entanglement exchange process based on the Raman interaction of two building microparticles. The experimental results show that the automatic detection method of building cracks based on digital image processing technology and multiscale feature analysis has a good effect. [ABSTRACT FROM AUTHOR] more...

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] more...

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] more...

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] more...

With the development of the times, figurative expressions no longer meet the creative needs of artists and the aesthetic demands of the people. In order to express art in a more profound way, the perfect use of abstract graphics plays a crucial role in the success of the work. In recent years, there has been a surge in the creation of digital art, but there is relatively little theoretical literature on the combination of abstract graphics as a visual language and digital art. In addition, research on the theoretical aspects of digital art design is also relatively weak, so it is essential to analyse the formal aesthetics and innovative applications. In fact, digital art design has a very important role to play in promoting the development of creative cultural industries. In other words, the healthy development of digital art design can influence the future prospects of a country's creative and cultural industries. Digital art design is an integrated and complex production process and labour outcome. In addition to its human, aesthetic, and social value, digital art also has an economic value. Digital art is a new art form that combines digital technology and artistic aesthetics. As such, digital art is characterised by high technology, diverse forms, popularised art, and the advantages of high communication, interactivity, and influence, which can provide more assistance for the innovation and application of abstract graphics. Digital art is multifaceted and has an artistic expression that cannot be matched by other forms of technology. Abstract graphics, driven by digital art, are full of novelty and interest and can greatly enrich people's emotions and senses. Abstract graphics bring the experience of digital art to its fullest potential. The combination of digital art and abstract graphics offers more innovation and possibilities for the development of art and will bring great prosperity to art communication. With the widespread use of computer and network technology, the Internet has developed rapidly. In this context, digital art, as art created in a digital way and concept, has gained widespread attention. As a result, how to integrate existing computer resources in the new environment to build a model of digital art design effectiveness will cause a direct influence on the quality of digital art design with digital content innovation as the core. At the same time, as digital art becomes more and more popular, the demand for digital talents becomes very urgent. As a result, the cultivation of high-quality digital talents has become a major concern for society. Therefore, in order to explore the success of digital art design and the cultivation of digital art talents, and to better serve the innovation of digital art, this paper proposes a digital art effectiveness model based on the K-medoids algorithm. This model can provide a deeper and more comprehensive understanding of digital art and abstract graphics and provide theoretical support for professional design creation. [ABSTRACT FROM AUTHOR] more...

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.) more...

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] more...

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] more...

An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechanical characterisation, which has traditionally been slow manual work. The algorithm classifies the objects in images based on their morphology, and detects the proper grasp points from the individual fibres by applying given geometrical constraints. The authors test the ability of the algorithm to detect the individual fibres with 35 images containing more than 500 fibres in total, and also compare the graspability analysis and the calculated grasp points with the results of an experienced human operator with 15 images that contain a total of almost 200 fibres. The detection results are outstanding, with fewer than 1% of fibres missed. The graspability analysis gives sensitivity of 0.83 and specificity of 0.92, and the average distance between the grasp points of the human and the algorithm is 220 µm. Also, the choices made by the algorithm are much more consistent than the human choices. [ABSTRACT FROM AUTHOR] more...

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.) more...

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] more...

Background and Objective: Vitiligo is a chronic skin damage disease, triggered by differential melanocyte death. Vitiligo (0.5%–1% of the population) is one of the most severe skin conditions. In general, the foundation of the condition of vitiligo remains gradual patchy loss of skin pigmentation, overlying blood, and sometimes mucus. This paper provides a systematic review of the relevant publications and conference papers based on the subject of vitiligo diagnosis and confirmation through computer‐aided machine learning (ML) techniques. Materials and Methods: A search was conducted using a predetermined set of keywords across three databases, namely, Science Direct, PubMed, and IEEE Xplore. The selection process involved the application of eligibility criteria, which led to the inclusion of research published in reputable journals and conference proceedings up until June 2024. These selected papers were then subjected to full‐text screening for additional analysis. Research publications that involved application of ML techniques with targeted population of vitiligo were selected for further systematic review. Results: Ten selected and screened studies are included in this systematic review after applying eligibility criteria along with inclusion and exclusion criteria applied on initial search result which was 244 studies based on vitiligo. Priority is given to those studies only which use ML techniques to perform detection and diagnosis on vitiligo‐targeted population. Data analysis was carried out only from the selected and screened research articles that were published in authentic journals and conference proceedings. Conclusion: The importance of applying ML techniques in the clinical diagnosis of vitiligo can give more accurate results and at the same also eliminate the need of biased human judgement. Based on a comprehensive examination of the research, encompassing the methodologies employed and the metrics utilized to assess outcomes, it was determined that there is a need for further research and investigation regarding the application of ML algorithm for the detection and diagnosis of vitiligo with different datasets and more feature extraction. [ABSTRACT FROM AUTHOR] more...

The current bolt detection for overhead work robots used for transmission lines faces the problems of lightweight algorithms and high accuracy of target detection. To address these challenges, this paper proposes a lightweight bolt detection algorithm based on improved YOLOv8 (you only look once v8) model. Firstly, the C2f module in the feature extraction network is integrated with the self‐calibrated convolution module, and the model is streamlined by reducing spatial and channel redundancies of the network through the SRU and CUR mechanisms in the module. Secondly, the P2 small object detection layer is introduced into the neck structure and the BiFPN network structure is incorporated to enhance the bidirectional connection paths, thereby promoting the upward and downward propagation of features. It improves the accuracy of the network for bolt‐small target detection. The experimental results show that, compared to the original YOLOv8 model, the proposed algorithm demonstrates superior performance on a self‐collected dataset. The mAP accuracy is improved in this paper by 9.9%, while the number of model parameters and the model size is reduced by 0.973 × 106 and 1.7 MB, respectively. The improved algorithm improves the accuracy of the bolt detection while reducing the computation complexity to achieve more lightweight model. [ABSTRACT FROM AUTHOR] more...

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] more...

This paper proposes a smart grid distributed security architecture based on blockchain technology and SDN cluster structure, referred to as ClusterBlock model, which combines the advantages of two emerging technologies, blockchain and SDN. The blockchain technology allows for distributed peer-to-peer networks, where the network can ensure the trusted interaction of untrusted nodes in the network. At the same time, this article adopts the design of an SDN controller distributed cluster to avoid single point of failure and balance the load between equipment and the controller. A cluster head was selected in each SDN cluster, and it was used as a blockchain node to construct an SDN cluster head blockchain. By combining blockchain technology, the security and privacy of the SDN communication network can be enhanced. At the same time, this paper designs a distributed control strategy and network attack detection algorithm based on blockchain consensus and introduces the Jaccard similarity coefficient to detect the network attacks. Finally, this paper evaluates the ClusterBlock model and the existing model based on the OpenFlow protocol through simulation experiments and compares the security performance. The evaluation results show that the ClusterBlock model has more stable bandwidth and stronger security performance in the face of DDoS attacks of the same scale. [ABSTRACT FROM AUTHOR] more...

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] more...

In the face of this increasingly intelligent society and home life, it is necessary to research and design more intelligent music systems to meet people's needs. This paper uses music speech recognition algorithms when building a music system, and combines artificial intelligence technology to apply cloud‐computing technology to the processing and transmission of music data. The system is formed by the fusion of various technologies. At the same time, using embedded technology as the core can handle multiple tasks at the same time, thereby effectively improving the efficiency of the system. In addition, based on speech recognition technology, this paper combines artificial intelligence to develop music systems. Through experimental analysis, it can be seen that the music system based on speech recognition and artificial intelligence proposed in this paper has good results. [ABSTRACT FROM AUTHOR] more...

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. more...

Visual SLAM algorithms can obtain a large amount of texture information from the environment and usually perform very well in static scenes, but there are a large number of irregular dynamic points when running in dynamic scenes, which can lead to increased error in SLAM feature point matching and loss of tracking localization. To address this challenge this paper proposes a SLAM system (YVG‐SLAM) that adapts to dynamic scenes. YVG‐SLAM is an improvement on the ORB‐SLAM3 algorithm, integrating the view geometry algorithm and the current powerful YOLOv5 algorithm, while proposing a dynamic feature removal strategy without a priori assumptions to reduce the influence of dynamic targets. In this paper, we first lighten the YOLOv5 algorithm, then process the image frame to get the bounding box, then use the view geometry algorithm to determine the dynamic feature points on the image frame, and finally remove the moving objects in the image frame according to the dynamic feature recognition strategy proposed in this paper. The performance of the algorithm is tested on the TUM RGB‐D and BONN RGB‐D datasets. The experimental results show that the robustness of the proposed algorithm on most video sequences is better than the existing SLAM algorithms dealing with dynamic scenes such as Detect‐SLAM and DynaSLAM, and the RMSE index of the absolute trajectory error measured on high‐speed dynamic video sequences can be significantly reduced by more than 93% compared to ORB‐SLAM3. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR] more...

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] more...

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] more...

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.) more...

SUMMARY The present study deals with a comprehensive thermodynamic modeling of a combined heat and power (CHP) system in a paper mill, which provides 50 MW of electric power and 100 ton h−1 saturated steam at 13 bars. This CHP plant is composed of air compressor, combustion chamber (CC), Air Preheater, Gas Turbine (GT) and a Heat Recovery Heat Exchanger. The design parameters of this cycle are compressor pressure ratio ( rAC), compressor isentropic efficiency ( ηAC), GT isentropic efficiency ( ηGT), CC inlet temperature ( T3), and turbine inlet temperature ( T4). In the multi-objective optimization three objective functions, including CHP exergy efficiency, total cost rate of the system products, and CO2 emission of the whole plant, are considered. The exergoenvironmental objective function is minimized whereas power plant exergy efficiency is maximized using a Genetic algorithm. To have a good insight into this study, a sensitivity analysis of the results to the interest rate as well as fuel cost is performed. The results show that at the lower exergetic efficiency, in which the weight of exergoenvironmental objective is higher, the sensitivity of the optimal solutions to the fuel cost is much higher than the location of the Pareto Frontier with the lower weight of exergoenvironmental objective. In addition, with increasing exergy efficiency, the purchase cost of equipment in the plant is increased as the cost rate of the plant increases. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] more...

Many primary or secondary diseases of the myocardium are accompanied with complex remodelling of the cardiac tissue that results in increased heart mass, often identified as cardiac ‘hypertrophy’. Although there have been numerous attempts at defining such ‘hypertrophy’, the present paper delineates the reasons as to why current definitions of cardiac hypertrophy remain unsatisfying. Based on a brief review of the underlying pathophysiology and tissue and cellular events driving myocardial remodelling with or without changes in heart dimensions, as well as current techniques to detect such changes, we propose to restrict the use of the currently popular term ‘hypertrophy’ to cardiac myocytes that may or may not accompany the more complex tissue rearrangements leading to changes in shape or size of the ventricles, more broadly referred to as ‘remodelling’. We also discuss the great potential of genetically modified (mouse) models as tools to define the molecular pathways leading to the different forms of left ventricle remodelling. Finally, we present an algorithm for the stepwise assessment of myocardial phenotypes applicable to animal models using well-established imaging techniques and propose a list of parameters most suited for a critical evaluation of such pathophysiological phenomena in mouse models. We believe that this effort is the first step towards a much auspicated unification of the terminology between the experimental and the clinical cardiologists. [ABSTRACT FROM AUTHOR] more...

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] more...

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] more...