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Research Summary: External representations, particularly visuals, are important in strategic decision‐making. However, their pervasiveness and impact are not well understood in the strategy literature. Based on cognitive science research, we identify four cognitive functions crucial to strategic decision‐making that benefit from using external representations. We also propose a conceptual model and propositions that explain how the quality of strategic decision‐making depends on the interactions among task environment, external representations, and managers. We show that external representations influence in predictable ways the boundedly rational process of searching for new strategies. Key determinants include the manager's representational capability and the usability and malleability of the external representation. We discuss implications for users, designers, and teachers of external representations in strategy, as well as suggest avenues for future research. Managerial Summary: This research points to the pivotal role of external representations, especially visuals, in strategic decision‐making. Drawing from cognitive science, this study identifies four critical cognitive functions that benefit from these external representations—working memory, long‐term memory, pattern recognition, and knowledge transfer. Further, the study highlights that external representations significantly influence the process of strategic decision‐making in predictable ways. Finally, we show that not all external representations are alike in their ease of use and a managers' ability to operate on an external representation, referred to as representational capability, greatly affects the decision‐making quality. The implications extend to users, designers, and educators of external representations, urging attention to the design and use of external representations for improved decision outcomes. [ABSTRACT FROM AUTHOR]

The indoor environment in most of the tropical coastal regions is associated with the residents' health and well-being. However, limited studies reported on quantifying the interrelation between indoor environmental quality, thermal comfort and residents' health risks in such climatic regions. Here, we evaluate indoor thermal comfort in coastal eastern India that falls under the ' Tropical Savanna ' climatic region. The perception survey was conducted to collect primary data in summer, monsoon, post-monsoon and winter across the year and the perception of warmth appeared to have a similar trend to direct and rational human thermal indices derived from a composite body heat exchange. Results indicate that indoor thermal stress significantly differed (p <.05) between seasons and across study locations. The maximum summer and monsoon days lay in a warm to very hot zone. Additionally, most of the heat-related stress or disorders significantly differed (p <.01) amongst different age groups of people. Finally, a principal component analysis using the human thermal indices yielded three groups of heat-related symptoms (a) physical fatigue and responses, (b) neural stressors and (c) hyperhidrosis disorder. Overall, results suggested that physical fatigue and responses arose predominantly from climate-induced thermal stress. The study recommends an effective bioclimatic design strategy following human-environment friendliness that can improve indoor thermal comfort in coastal eastern India. [ABSTRACT FROM AUTHOR]

Objective: Diagnostic errors in outpatient settings lead to significant consequences, especially in rare diseases such as systemic lupus erythematosus (SLE). A recent vignette‐based experimental study revealed that demographic factors influenced rheumatologists' diagnoses of SLE, raising concerns about potential diagnostic biases. We conducted a qualitative study to contextualize these results to generate insights about diagnostic challenges and biases, and root causes. Methods: We conducted 41 semistructured interviews among US rheumatologists. Transcripts were independently coded by at least two coders using a hybrid deductive–inductive approach and thematic analysis. A team of four researchers reviewed and defined themes collectively, and also resolved any discrepancies. Results: Participants were 66% women, and 49% had more than10 years of postfellowship experience. Five major themes were generated, including receiving training through the lens of race or sex, the role of the documented epidemiology of SLE, pattern recognition and test‐taking strategies, patient vignettes as an imperfect proxy for patient interactions, and varied consequences to patients from diagnostic bias. Participants noted that the consequences of diagnostic bias could include progressed disease from delayed diagnosis, unnecessary and inappropriate treatment due to missed diagnosis or misdiagnosis, and increased cost and harm. Conclusion: This study underscores the unique challenges of diagnosing SLE, with complex factors contributing to diagnosis bias and delays. Interventions during medical education could prevent downstream diagnostic biases. Future research should explore interventions to mitigate diagnostic bias and refine vignettes to better mirror real‐world clinical scenarios. Understanding diagnostic bias in SLE is crucial for improving patient outcomes and refining medical training practices. [ABSTRACT FROM AUTHOR]

This article is concerned with "all-or-nothing" approaches to female genital cosmetic surgeries, those that overemphasize either women's autonomy to defend total accessibility or the oppressive social context affecting women to defend the total banning of the procedures. By contrast, the author takes both phenomena into consideration. The author argues identifying patterns of false consciousness and weighing those against harm done to a patient provides a moral basis for a doctor to possibly deny their consent at face value. This also requires a shift in understanding the doctor–patient relationship as a first step toward a feminist model for women's healthcare. [ABSTRACT FROM AUTHOR]

This study investigates the cognitive mechanisms underlying vigilance and pattern recognition using a novel adaptation of Mackworth's Clock Test. We aimed to quantify the time it takes for temporal patterns detected unconsciously through implicit learning to surface in the conscious mind within a dynamic vigilance task environment. Forty-eight participants detected random and non-disclosed rhythmic anomalous clock hand movements in this setting. Our results indicate significant variability in detection accuracy, reaction times, and the ability to recognize the hidden pattern among participants. Notably, 23% of all participants and 56% of those who consciously reported the pattern exhibited statistically lower reaction times indicative of knowledge of the pattern 40 s before conscious identification. These findings provide valuable insights into the transition from unconscious to conscious detection, highlighting the complexity of sustained attention and pattern recognition. The study's implications extend to designing training programs and tasks for high-stakes professions requiring prolonged vigilance. Future research should further explore the cognitive and neural correlates of these processes and the impact of task complexity on performance. [ABSTRACT FROM AUTHOR]

In the era of big data, prediction has become a fundamental capability. Current prediction methods primarily focus on sequence elements; however, in multivariate time series forecasting, time is a critical factor that must not be overlooked. While some methods consider time, they often neglect the temporal distance between sequence elements and the predicted target time, a relationship essential for identifying patterns such as periodicity, trends, and other temporal dynamics. Moreover, the extraction of temporal features is often inadequate, and discussions on how to comprehensively leverage temporal data are limited. As a result, model performance can suffer, particularly in prediction tasks with specific time requirements. To address these challenges, we propose a new model, TE-LSTM, based on LSTM, which employs a temporal encoding method to fully extract temporal features. A temporal weighting strategy is also used to optimize the integration of temporal information, capturing the temporal relationship of each element relative to the target element, and integrating it into the LSTM. Additionally, this study examines the impact of different time granularities on the model. Using the Beijing International Airport station as the study area, we applied our method to temperature prediction. Compared to the baseline model, our model showed an improvement of 0.7552% without time granularity, 1.2047% with a time granularity of 3, and 0.0953% when addressing prediction tasks with specific time requirements. The final results demonstrate the superiority of the proposed method and highlight its effectiveness in overcoming the limitations of existing approaches. [ABSTRACT FROM AUTHOR]

Fuzzy sets (FSs) are a flexible and powerful tool for reasoning about uncertain situations that cannot be adequately expressed by classical sets. However, these sets fall short in two areas. The first is the reliability of this tool. Z-numbers are an extension of fuzzy numbers that improve the representation of uncertainty by combining two important components: restriction and reliability. The second is the problems that need to be solved simultaneously. Complex fuzzy sets (CFSs) overcome this problem by adding a second dimension to fuzzy numbers and simultaneously adding connected elements to the solution. However, they are insufficient when it comes to problems involving these two areas. We cannot express real-life problems that need to be solved at the same time and require the reliability of the information given with any set approach given in the literature. Therefore, in this study, we propose the complex fuzzy Z-number set (CFZNS), a generalization of Z-numbers and CFS, which fills this gap. We provide the operational laws of CFZNS along with some properties. Additionally, we define two essential aggregation operators called complex fuzzy Z-number weighted averaging (CFZNWA) and complex fuzzy Z-number weighted geometric (CFZNWG) operators. Then, we present an illustrative example to demonstrate the proficiency and superiority of the proposed approach. Thus, we process multiple fuzzy expressions simultaneously and take into account the reliability of these fuzzy expressions in applications. Furthermore, we compare the results with the existing set operations to confirm the advantages and demonstrate the efficiency of the proposed approach. Considering the simultaneous expression of fuzzy statements, this study can serve as a foundation for new aggregation operators and decision-making problems and can be extended to many new applications such as pattern recognition and clustering. [ABSTRACT FROM AUTHOR]

This article offers a sideways approach to analyzing market visualizations on trading screens as a part of the sensoryscapes of financial trading. Based on ethnographic research on the trading floor of a market-maker investment bank in Istanbul, I develop an anthropologically driven interdisciplinary lens built on social studies of finance (SSF) and film and media studies. Through this lens, I use 'haptic' (Marks, Laura U. 2000. The Skin of the Film: Intercultural Cinema, Embodiment, and Senses. Duke University Press) as a key analytic and argue that markets on screens generate a haptic visuality that shapes traders' bodily, sensuous, affective, and emotional engagements with markets. In this way, I offer an alternative reading of Beunza and Stark's conceptualization of pattern recognition and re-cognition by bringing it back to the works of Knorr Cetina and Bruegger. I show that traders' aspirations, intuition, and memories take part in pattern recognition and re-cognition, transforming these experiences into subjective and embodied knowledges and practices. Hence, I expand social scientific studies on financial trading by demonstrating that intersubjective relations with anonymous market actors require self-embedding into market visualizations, through which one can engage with the faces of markets as a face that touches back to the haptic visuality of markets on screens. [ABSTRACT FROM AUTHOR]

BACKGROUND: Patient-centered care (PCC) has been called for as a solution to improving care quality and patient outcomes. Patient experience, termed care experience, is a measurable aspect of PCC and aligns with coproduction. Identifying patterns of positivity and high performers is a Positive Deviance approach that can inform strategic improvement of the care experience. OBJECTIVE: To identify the characteristics of positive deviances from voluntary, unsolicited compliments from patients and family members about their care experiences. METHODS: The authors conducted a mixed-method analysis, including content and a thematic analysis of unsolicited comments from patients and families, submitted between January 2021 and January 2022. After removing duplicates and miscategorized comments, 213 compliments were included in the analysis using a single, blinded inductive coding to synthesize thematic statements. RESULTS: The main campus received the most compliments by location (89%); the most widely used patient sentiment was thankful (36.8%). Compassionate (26.8%), together with six others: competent (11.6%), communication (10.6%), cared for (8.5%), care team (8.0%), and supportive (8.0%), made up approximately 80% of drivers of care quality. Physicians (37.3%) and nurses (34.2%) were the most complimented personnel, although surgery (17.0%) were the most complimented services team. Similar characteristics were reported for exemplary individuals and their associated care teams. CONCLUSION: The results align with previously reported work by the Beryl Institute and CMS 5-star rating on key drivers of patient experience. This approach provides a method by which exemplars can be identified within health systems, and that information is used to guide improvement and organizational planning. [ABSTRACT FROM AUTHOR]

Good fortune can be attributed to many sources, including other people, personal efforts, and various theistic and nontheistic supernatural forces (e.g., God, karma). Four studies (total N = 4,579) of religiously diverse samples from the United States and the United Kingdom investigated the distinct emotional reactions to recalled positive experiences attributed to natural and supernatural benefactors. We found that the hallmarks of interpersonal gratitude (e.g., thankfulness, admiration, indebtedness) were reported when believers attributed their good fortune to a personal, benevolent God. However, a distinct emotional profile arose when participants attributed good fortune to the process of karmic payback, which was associated with relatively less gratitude but with higher scores for feelings of pride and deservingness. These results were partially explained by participants' attributions of positive experiences to an external agent (e.g., God) versus a universal law or internal factors as in the case of karma. We conclude that diverse spiritual beliefs influence causal attributions for good fortune, which, in turn, predict distinct emotional responses. [ABSTRACT FROM AUTHOR]

(1) Background: The basophil activation test (BAT) is a functional whole blood-based ex vivo assay to quantify basophil activation after allergen exposure by flow cytometry. One of the most important prerequisites for the use of the BAT in the routine clinical diagnosis of allergies is a reliable, standardized and reproducible data analysis workflow. (2) Methods: We re-analyzed a public mass cytometry dataset from peanut (PN) allergic patients (n = 6) and healthy controls (n = 3) with our binning approach "pattern recognition of immune cells" (PRI). Our approach enabled a comprehensive analysis of the dataset, evaluating 30 markers to achieve optimal basophil identification and activation through multi-parametric analysis and visualization. (3) Results: We found FcεRIα/CD32 (FcγRII) as a new marker couple to identify basophils and kept CD63 as an activation marker to establish a modified BAT in combination with our PRI analysis approach. Based on this, we developed an algorithm for automated raw data processing, which enables direct data analysis and the intuitive visualization of the test results including controls and allergen stimulations. Furthermore, we discovered that the expression pattern of CD32 correlated with FcεRIα, anticorrelated with CD63 and was detectable in both the re-analyzed public dataset and our own flow cytometric results. (4) Conclusions: Our improved BAT, combined with our PRI procedure (bin-BAT), provides a reliable test with a fully reproducible analysis. The advanced bin-BAT enabled the development of an automated workflow with an intuitive visualization to discriminate allergic patients from non-allergic individuals. [ABSTRACT FROM AUTHOR]

Neutrosophic topological spaces (NTS) offer a novel framework for uncertainty modeling by incorporating degrees of truth, indeterminacy, and falsity. This paper investigates the potential applications of NTS in computer science. We provide background on neutrosophic sets and their extension to topological spaces. We then explore how NTS could be used for uncertainty modeling in data analysis (e.g., handling noisy data in sensor networks), pattern recognition (e.g., improving image classification with imprecise features), and information retrieval (e.g., enhancing search results by considering relevance uncertainty). We discuss the challenges associated with applying NTS and highlight promising areas for future research, such as developing efficient algorithms for NTS operations. Overall, this paper aims to stimulate further exploration of how neutrosophic topological spaces can contribute to advancements in various computer science domains. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the ability of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and the International Ovarian Tumour Analysis (IOTA) two‐step strategy to predict malignancy in adnexal masses detected in an outpatient low‐risk setting, and to estimate the risk of complications in masses with benign ultrasound morphology managed using clinical and ultrasound follow‐up. Methods: This single‐center study was performed at Hospital Universitari Dexeus, Barcelona, Spain, using interim data from the ongoing prospective observational IOTA Phase‐5 (IOTA5) study. The primary aim of the IOTA5 study is to describe the cumulative incidence of complications during follow‐up of adnexal masses classified as benign on ultrasound examination. Consecutive patients with an adnexal mass detected between June 2012 and September 2016 in a private center offering screening for gynecological cancer were included and followed up until February 2020. Tumors were classified as benign or malignant based on histology (if patients underwent surgery) or the outcome of clinical and ultrasound follow‐up at 12 (range, 10–14) months. Multiple imputation was used when outcomes were uncertain. The ability of the ADNEX model without CA125 and of the IOTA two‐step strategy to distinguish benign from malignant masses was evaluated retrospectively using the prospectively collected data. We assessed performance with regard to discrimination (area under the receiver‐operating‐characteristics curve (AUC)), calibration, classification (sensitivity and specificity) and clinical utility (Net Benefit). In the group of patients with a mass judged to be benign who were selected for conservative management, we evaluated the occurrence of spontaneous resolution or any mass complication during the first 5 years of follow‐up by assessing the cumulative incidence of malignancy, torsion, cyst rupture and minor mass complications (inflammation, infection or adhesions) and the time to occurrence of an event. Results: A total of 2654 patients were recruited to the study. After application of exclusion criteria, 2039 patients with a newly detected mass were included for the model validation. Of those, 1684 (83%) masses were benign, 49 (2%) masses were malignant and, for 306 (15%) masses, the outcome was uncertain and therefore imputed. The AUC was 0.95 (95% CI, 0.89–0.98) for ADNEX without CA125 and 0.94 (95% CI, 0.88–0.97) for the two‐step strategy. Calibration performance could not be meaningfully interpreted because the small number of malignancies resulted in very wide confidence intervals. The two‐step strategy had better clinical utility than did the ADNEX model at malignancy risk thresholds < 3%. There were 1472 (72%) patients whose mass was judged to be benign based on pattern recognition by an experienced ultrasound examiner and were managed with clinical and ultrasound follow‐up. In this group, the 5‐year cumulative incidence was 66% (95% CI, 63–69%) for spontaneous resolution of the mass, 0% (95% CI, 0–0.2%) for torsion, 0.1% (95% CI, < 0.1–0.4%) for cyst rupture, 0.2% (95% CI, 0.1–0.6%) for a borderline tumor and 0.2% (95% CI, 0.1–0.6%) for invasive malignancy. Conclusions: The ADNEX model and IOTA two‐step strategy performed well to distinguish benign from malignant adnexal masses detected in a low‐risk population. Conservative management is safe for masses with a benign ultrasound appearance in this population. © 2024 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

The aim of this study was to replicate previous English-language musician advantage studies in Mandarin-speaking musicians and nonmusicians. Segregation of competing speech, melodic pitch perception, and spectro-temporal pattern perception were measured in normal-hearing native Mandarin-speaking musicians and nonmusicians. Speech recognition thresholds were measured in the presence of two-talker masker speech. The masker sex was either the same as or different from the target; target and masker speech were either co-located or spatially separated. Melodic pitch perception was tested using a melodic contour identification task. Spectro-temporal resolution was measured using a modified spectral ripple detection task. We hypothesized that, given musician advantages in pitch perception, musician effects would be larger when the target and masker sex was the same than when different. For all tests, performance was significantly better for musicians than for nonmusicians. Contrary to our expectation, larger musician effects were observed for segregation of competing speech when the target and masker sex was different. The results show that musician effects observed for non-tonal language speakers extend to tonal language speakers. The data also suggest that musician effects may depend on the difficulty of the listening task and may be reduced when listening tasks are too easy or too difficult. [ABSTRACT FROM AUTHOR]

The extraordinary success of convolutional neural networks (CNNs) in various computer vision tasks has revitalized the field of artificial intelligence. The out-sized expectations created by this extraordinary success have, however, been tempered by a recognition of CNNs' fragility. Importantly, the magnitude of the problem is unclear due to a lack of rigorous benchmark datasets. Here, we propose a solution to the benchmarking problem that reveals the extent of the vulnerabilities of CNNs and of the methods used to provide interpretability to their predictions. We employ cellular automata (CA) to generate images with rigorously controllable characteristics. CA allow for the definition of both extraordinarily simple and highly complex discrete functions and allow for the generation of boundless datasets of images without repeats. In this work, we systematically investigate the fragility and interpretability of the three popular CNN architectures using CA-generated datasets. We find a sharp transition from a learnable phase to an unlearnable phase as the latent space entropy of the discrete CA functions increases. Furthermore, we demonstrate that shortcut learning is an inherent trait of CNNs. Given a dataset with an easy-to-learn and strongly predictive pattern, CNN will consistently learn the shortcut even if the pattern occurs only on a small fraction of the image. Finally, we show that widely used attribution methods aiming to add interpretability to CNN outputs are strongly CNN-architecture specific and vary widely in their ability to identify input regions of high importance to the model. Our results provide significant insight into the limitations of both CNNs and the approaches developed to add interpretability to their predictions and raise concerns about the types of tasks that should be entrusted to them. [ABSTRACT FROM AUTHOR]

Neutrosophic Fuzzy Sets (NFS) expand upon classical fuzzy sets in the field of fuzzy set theory by including measures of truth, indeterminacy, and falsity. This paper thoroughly examines the creation and assessment of similarity measures for Single-Valued Neutrosophic Fuzzy Sets (SVNFS). The similarity measure is a crucial metric that quantifies the extent of similarity between two sets. It finds extensive application in various fields such as pattern recognition, medical diagnosis, and decision-making challenges. Nevertheless, the current similarity measures of Neutrosophic Fuzzy Sets(NFS) suffer from limited practicality and interpretation, and do not yield highly reliable outcomes. In order to tackle this issues, We provide a variety of new similarity measures, including the Hausdorff similarity measure, Membership-grade based similarity measure, and Trigonometric Hausdorff similarity measure specifically designed for Neutrosophic Fuzzy Sets(NFS). We conduct a comparison of their performance against existing measures. We verify the efficacy of these approaches by conducting thorough theoretical research and practical trials, showcasing their suitability in pattern recognition. The findings demonstrate substantial enhancements in precision and resilience, offering vital tools for academics and practitioners working with intricate and unpredictable data. The results of our research provide a foundation for future progress in the Neutrosophic Fuzzy Set theory and its practical use in several areas. [ABSTRACT FROM AUTHOR]

For humans to effectively work with robots, they must be able to predict the actions and behaviors of their robot teammates rather than merely react to them. While there are existing techniques enabling robots to adapt to human behavior, there is a demonstrated need for methods that explicitly improve humans' ability to understand and predict robot behavior at multi-task timescales. In this work, we propose a method leveraging the innate human propensity for pattern recognition in order to improve team dynamics in human–robot teams and to make robots more predictable to the humans that work with them. Patterns are a cognitive tool that humans use and rely on often, and the human brain is in many ways primed for pattern recognition and usage. We propose pattern-aware convention-setting for teaming (PACT), an entropy-based algorithm that identifies and imposes appropriate patterns over a robot's planner or policy over long time horizons. These patterns are autonomously generated and chosen via an algorithmic process that considers human-perceptible features and characteristics derived from the tasks to be completed, and as such, produces behavior that is easier for humans to identify and predict. Our evaluation shows that PACT contributes to significant improvements in team dynamics and teammate perceptions of the robot, as compared to robots that utilize traditionally 'optimal' plans and robots utilizing unoptimized patterns. [ABSTRACT FROM AUTHOR]

Featured Application: Increased cognitive demands during motor tasks emerged as a critical discriminator of functional patterns among older adults, shedding light on the intricate interplay between cognitive function and motor performance in fall prevention strategies in community-dwelling older adults. A wide range of outcomes makes identifying clinical and functional features distinguishing older persons who fall from non-fallers challenging, especially for professionals with less clinical experience. Thus, this study aimed to map a high-dimensional and complex clinical and functional dataset and determine which outcomes better discriminate older adults with and without self-reported falls. For this, clinical, functional, and cognitive outcomes of 60 community-dwelling older adults classified as fallers and non-fallers were selected based on self-report of a single fall in the last 12 months. An unsupervised intelligent algorithm (Self-Organizing Maps—SOM) was used to cluster and topographically represent the data studied. The SOM model mapped and identified two different groups (topographic error: 0.00; sensitivity: 0.77; precision: 0.42; accuracy: 0.53; F1-score: 0.55) based on self-report of a single fall. We concluded that although two distinct groups were mapped and clustered by the SOM, participants were not necessarily fallers or non-fallers. The increased cost of cognitive demands regarding a motor task (Timed Up and Go Test) and the effect of the Trail Making Test (TMT) Part B regarding TMT Part A could discriminate the functional and cognitive patterns in community-dwelling older adults. Therefore, in clinical practice, identifying patterns involving the interaction between cognition and motor skills, even in once-only faller older adults, can be an efficient approach to assessment and, consequently, to compound intervention programs to prevent falls in this population. [ABSTRACT FROM AUTHOR]

Background: Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions. Methods: We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback. Results: Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants. Conclusions: The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use. [ABSTRACT FROM AUTHOR]

Neuromorphic computers optimized to utilize artificial intelligence are currently receiving much attention. In this study, neural network circuit simulations are performed by varying the nonlinearity and learning rate to understand better the characteristics of the individual and parallel-connected synaptic devices. The simulation results show that pattern recognition accuracy tends to decrease with increasing nonlinearity, while the accuracy is achieved more gradually when the learning rate is reduced. By comparing the simulation results with actual experimental results, I find an ideal learning rate and investigate how to optimize neuromorphic computers. These findings will help design new neuromorphic computers to determine the appropriate learning rate. [ABSTRACT FROM AUTHOR]

A fundamental task for the brain is to generate predictions of future sensory inputs, and signal errors in these predictions. Many neurons have been shown to signal omitted stimuli during periodic stimulation, even in the retina. However, the mechanisms of this error signaling are unclear. Here we show that depressing inhibitory synapses shape the timing of the response to an omitted stimulus in the retina. While ganglion cells, the retinal output, responded to an omitted flash with a constant latency over many frequencies of the flash sequence, we found that this was not the case once inhibition was blocked. We built a simple circuit model and showed that depressing inhibitory synapses were a necessary component to reproduce our experimental findings. A new prediction of our model is that the accuracy of the constant latency requires a sufficient amount of flashes in the stimulus, which we could confirm experimentally. Depressing inhibitory synapses could thus be a key component to generate the predictive responses observed in the retina, and potentially in many brain areas. The retina is known to strongly respond to omitted stimuli in periodic patterns. Here the authors propose that depressing inhibitory synapses shape the timing of such a response and are key to perform temporal pattern recognition in neural networks. [ABSTRACT FROM AUTHOR]

Patent foramen ovale (PFO) is a common cardiac anomaly linked with cryptogenic strokes and migraine, particularly migraine with aura. This study aims to explore the spectrum of headache disorders in PFO patients, focusing on identifying patterns beyond the well-established migraine-PFO connection. A retrospective observational study was conducted on patients diagnosed with PFO. Headache types were classified using the International Classification of Headache Disorders, 3rd edition. The study analyzed headache prevalence and patterns in PFO patients, comparing those with and without a history of stroke. Of 177 participants, 63 (35.59%) reported headaches. Tension-type headache was the most common (15.25%), followed by migraine without aura (11.30%) and migraine with aura (8.47%). Notably, migraine without aura was more prevalent than migraine with aura, contrasting previous assumptions. No significant differences were found in headache types based on stroke history. The study reveals a diverse spectrum of headache types in PFO patients, with migraine without aura being more common than migraine with aura. These findings suggest a need for broader diagnostic perspective and individualized treatment approaches in PFO patients with headaches. [ABSTRACT FROM AUTHOR]

Commonsense knowledge is a broad and challenging area of research which investigates our understanding of the world as well as human assumptions about reality. Deriving directly from the subjective perception of the external world, it is intrinsically intertwined with embodied cognition. Commonsense reasoning is linked to human sense-making, pattern recognition and knowledge framing abilities. This work presents a new resource that formalizes the cognitive theory of image schemas. Image schemas are dynamic conceptual building blocks originating from our sensorimotor interactions with the physical world, and enable our sense-making cognitive activity to assign coherence and structure to entities, events and situations we experience everyday. ImageSchemaNet is an ontology that aligns pre-existing resources, such as FrameNet, VerbNet, WordNet and MetaNet from the Framester hub, to image schema theory. This article describes an empirical application of ImageSchemaNet, combined with semantic parsers, on the task of annotating natural language sentences with image schemas. [ABSTRACT FROM AUTHOR]

We presented a novel deep generative clustering model called Variational Deep Embedding based on Pairwise constraints and the Von Mises-Fisher mixture model (VDEPV). VDEPV consists of fully connected neural networks capable of learning latent representations from raw data and accurately predicting cluster assignments. Under the assumption of a genuinely non-informative prior, VDEPV adopted a von Mises-Fisher mixture model to depict the hyperspherical interpretation of the data. We defined and established pairwise constraints by employing a random sample mining strategy and applying data augmentation techniques. These constraints enhanced the compactness of intra-cluster samples in the spherical embedding space while improving inter-cluster samples' separability. By minimizing Kullback-Leibler divergence, we formulated a clustering loss function based on pairwise constraints, which regularized the joint probability distribution of latent variables and cluster labels. Comparative experiments with other deep clustering methods demonstrated the excellent performance of VDEPV. [ABSTRACT FROM AUTHOR]

The shape pattern recognition of building footprints stands as a pivotal concern within GIS spatial cognition. In this study, we introduce a novel approach for the shape recognition of building footprints, leveraging t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction visualization. First, the Canonical Time Warping (CTW) algorithm is employed to gauge the shape similarity distance of building footprints. Subsequently, the t-SNE model is utilized to map the building footprints, featuring varying numbers of coordinate vertices, onto points within the Cartesian coordinate system. The shape similarity distance serves as the input to the t-SNE model for parameter optimization. Lastly, building footprint shapes are identified through the inherent clustering patterns of points using a Gaussian Mixture Model (GMM). Experimental results demonstrate the method's robustness to the translation, rotation, scaling, and mirroring of geometric objects, while effectively measuring shape similarity between building footprints. Furthermore, diverse types of building footprints are discernible through natural clustering in low-dimensional spaces, aligning closely with human visual perception. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to verify if a single session on the NeuroTracker has predictive value in talent identification in ice hockey. Methods: Thirty-five male ice hockey players (aged 16-20) from the highest Canadian competition level for that age group participated in the study. A battery of tests (attention, working memory, time reproduction, pattern recognition, temporal equivalence, technical ability, and decision-making) was administered to verify the relation between various cognitive abilities, on-ice performance, and the baseline score on the NeuroTracker, which is claimed to solicit multiple cognitive functions. On-ice performance indicators were game-related statistics: games played, points (mean per game), on-ice goals differential, and draft rank. Results: Results show that the baseline score on the NeuroTracker is not associated with draft ranking, nor is it able to predict which players will perform best based on game-related statistics. However, the NeuroTracker baseline score does correlate with various tests involving working memory and attention. Conclusion: Currently, NeuroTracker is not specific enough to allow talent identification among same-level elite athletes in ice hockey. [ABSTRACT FROM AUTHOR]

The article discusses the concept of personalized law, which involves tailoring legal directives, privileges, and entitlements to align with individual citizens' unique characteristics, needs, and capacities. The author explores the political economy implications of personalized law, highlighting potential risks and benefits. They argue that uniform rules can protect minorities and facilitate the detection of injustice, while personalization undermines social enforcement of rights and undercuts legal activism. The author also raises concerns about the difficulty of rooting out bad personalized law and the potential for personalized law to degenerate into a social credit system. They discuss the intuitive appeal of personalized law to laypeople and the potential depersonalization of the law. The article concludes by suggesting that the case for personalized law may be weaker in constitutional law and calling for further research on the fairness perceptions of laypeople and strategies to offset the fairness gap. [Extracted from the article]

The article focuses on the integration of Artificial intelligence (AI) into service design, highlighting its role as a creative partner and offering guidance on effectively incorporating AI into the design process. Topics include understanding AI's capabilities, evolving communication and tools, and assigning specific roles to AI using the Analysis-Synthesis Bridge Model to enhance collaboration between humans and AI in service design.

In recent years, the mental health problems of athletes have become more and more serious, and due to the complexity and invisibility of mental illnesses, it is difficult to detect and intervene in the early stage of mental illnesses due to a number of reasons. With the explosion of biomedical data and the rapid development of pattern recognition technology, the problem of recognizing mental illnesses can be solved by using artificial intelligence related technology. This paper proposes a mental illness recognition model based on Roberta Text CNN. Based on Roberta Text CNN model, a text classification model is built for two types of labels, namely, mental illness and suicidal tendency, and a comparison experiment is carried out with several mainstream models. The experimental results show that the proposed method achieves competitive results. In addition, this paper also introduces the EEG signals to be organized into dimensionally consistent matrices using Mel's inverted spectral coefficients for scale change, and then inputs them into the convolutional neural network as image features for further extraction of high-dimensional features and serial fusion of EEG features and then inputs them into the long and short-term memory network, and the application of the pattern recognition technology provides a two-branch mental illness recognition model, which further enhances the capability of screening and preventing early mental illnesses in athletes. The application of pattern recognition technology provides a two-branch mental illness identification model, which further enhances the ability of screening and prevention of early mental illness in athletes. [ABSTRACT FROM AUTHOR]

Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices. [ABSTRACT FROM AUTHOR]

The q-rung orthopair fuzzy sets (q-ROFSs), a novel concept for processing vague information, offer a more potent and all-encompassing method compared to traditional fuzzy sets, intuitionistic fuzzy sets, and Pythagorean fuzzy sets. The inclusion of the parameter q allows for the q-rung orthopair fuzzy sets to capture a broader range of uncertainty of information. In this paper, we present two novel distance measures for q-ROFSs inspired by the Jensen–Shannon divergence, called D J S _ 2 D and D J S _ 3 D , and we analyze some properties they satisfy, such as non-degeneracy, symmetry, boundedness, and triangular inequality. Then, the normalized distance measures, called D J S _ 2 D ˜ and D J S _ 3 D ˜ , are proposed and we verify their rationality through numerical experiments. Finally, we apply the proposed distance measures to practical scenarios, including pattern recognition and multicriteria decision-making, and the results demonstrate the effectiveness of the proposed distance measures. [ABSTRACT FROM AUTHOR]

Similarity measures on trapezoidal-valued intuitionistic fuzzy numbers (TrVIFNs) are functions that measure the closeness between two TrVIFNs, which has a lot of applications in the area of pattern recognition, clustering, decision-making, etc. Researchers around the world are proposing various similarity measures on the generalizations of fuzzy sets. However, many such measures do not satisfy the condition that "the similarity between two fuzzy numbers is equal to 1 implies that both the fuzzy numbers are equal" and this gives a pathway for the researchers to introduce different similarity measures on various classes of fuzzy sets. Also, all of them try to find out the similarity by using a single function, and in the present study, we try to propose a combined similarity measure principle by using four functions (four similarity measures). Thus, the main aim of this work is to introduce a few sets of similarity measures on the class of TrVIFNs and propose a combined similarity measure principle on TrVIFNs based on the proposed similarity measures. To do this, in this paper, firstly, we propose four distance-based similarity measures on TrVIFNs using score functions on TrVIFNs and study their mathematical properties by establishing various propositions, theorems, and illustrations, which is achieved by using numerical examples. Secondly, we propose the idea of a combined similarity measure principle by using the four proposed similarity measures sequentially, which is a first in the literature. Thirdly, we compare our combined similarity measure principle with a few important similarity measures introduced on various classes of fuzzy numbers, which shows the need for and efficacy of the proposed similarity measures over the existing methods. Fourthly, we discuss the trapezoidal-valued intuitionistic fuzzy TOPSIS (TrVIF-TOPSIS) method, which uses the proposed combined similarity measure principle for solving a multi-criteria decision-making (MCDM) problem. Then, we discuss the applicability of the proposed modified TrVIF-TOPSIS method by solving a model problem. Finally, we discuss the sensitivity analysis of the proposed approaches by using various cases. [ABSTRACT FROM AUTHOR]

In nature, plants are constantly colonized by a massive diversity of microbes engaged in mutualistic, pathogenic or commensal relationships with the host. Molecular patterns present in these microbes activate pattern-triggered immunity (PTI), which detects microbes in the apoplast or at the tissue surface. Whether and how PTI distinguishes among soil-borne pathogens, opportunistic pathogens, and commensal microbes within the soil microbiota remains unclear. PTI is a multimodal series of molecular events initiated by pattern perception, such as Ca2+ influx, reactive oxygen burst, and extensive transcriptional and metabolic reprogramming. These short-term responses may manifest within minutes to hours, while the long-term consequences of chronic PTI activation persist for days to weeks. Chronic activation of PTI is detrimental to plant growth, so plants need to coordinate growth and defense depending on the surrounding biotic and abiotic environments. Recent studies have demonstrated that root-associated commensal microbes can activate or suppress immune responses to variable extents, clearly pointing to the role of PTI in root–microbiota interactions. However, the molecular mechanisms by which root commensals interfere with root immunity and root immunity modulates microbial behavior remain largely elusive. Here, with a focus on the difference between short-term and long-term PTI responses, we summarize what is known about microbial interference with host PTI, especially in the context of root microbiota. We emphasize some missing pieces that remain to be characterized to promote the ultimate understanding of the role of plant immunity in root–microbiota interactions. [ABSTRACT FROM AUTHOR]

Within the field of K-2 computer science (CS) education, unplugged computational thinking (CT) activities have been suggested as beneficial for younger students and shown to impact young students’ skills and motivation to learn about CS. This study sought to examine how children demonstrate CT competencies in unplugged sequencing tasks and how children use manipulatives to solve unplugged sequencing tasks. This case study approach examined two unplugged sequencing tasks for six children ranging from ages five to eight (prekindergarten to 2nd grade). Children showed evidence of several CT competencies during the sequencing tasks: (1) pattern recognition, (2) algorithms and procedures, (3) problem decomposition, and (4) debugging. The strategies and use of manipulatives to showcase CT competencies seemed to evolve in complexity based on age and developmental levels. Taking into account children’s abilities to demonstrate CT competencies, this study suggests that sequencing is a developmentally appropriate entry point for young children to begin engaging in other CT competencies. In addition, these unplugged sequencing tasks can also be easily integrated into other activities commonly experienced in early childhood classrooms. [ABSTRACT FROM AUTHOR]

The article discusses the need for confidential enquiries into airway complications in order to improve patient safety and outcomes. The National Audit Projects (NAPs) have published reports on anaesthesia-related complications since 2003, with the most recent study, NAP7, focusing on peri-operative cardiac arrest. The study found that airway and respiratory complications accounted for a significant percentage of cardiac arrests and deaths. While there have been improvements in airway management over the past decade, serious complications still occur. The article suggests that the implementation of confidential enquiries, similar to those used in other medical fields, could help identify contributing factors and improve patient care. [Extracted from the article]

Objective: Music has a complex structure consisting of auditory elements such as sound, rhythm and melody. The ability to perceive, understand and evaluate music is among the auditory skills. This study aims to investigate the effects of musical interest on temporal pattern recognition, auditory processing, and cognitive skills in young adults with normal hearing. Material and Methods: The study included sixty volunteer young adults with normal hearing between the ages of 18-30 ages. Young adults were divided into two groups, below (n=30) and above (n=30) average, according to their "Music Interest Scale" scores. "Frequency and Duration Pattern Tests" were applied to evaluate the participants' temporal pattern recognition skills. "STROOP Test" was used to evaluate selective attention, disruptive effect and memory skills among cognitive skills. The findings were analyzed statistically with the SPSS program, and the type 1 error level was determined as 0.05. Results: There were significant differences in STROOP subtests in terms of attention skills (p < 0.05). On the other hand, no statistically significant difference was obtained between the groups in terms of frequency and duration pattern recognition skills (p > 0.05). Conclusion: Young adults who were interested in music completed tests of selective attention, interference, and short-term memory in less time and performed better. The current study revealed that musical interest in young adults may provide positive effects on these cognitive skills. [ABSTRACT FROM AUTHOR]

Problem-solving skills are an ability that must be cultivated to equip students with the skills needed to deal with today's increasingly complex and volatile environment. Computational thinking represents a new paradigm in problem-solving skills. After Wing proposed Computational Thinking as problem-solving skills in 2006, other scholars investigated this topic; nevertheless, the link between Computational Thinking and problem-solving has not been clearly discussed in previous studies. To uncover evidence for the connection between Computational Thinking and problem-solving skills, we conduct a systematic literature review of 37 papers collected from Web of Science database. The results indicate that (a) problem-solving is discussed in the 37 articles in the context of Computational Thinking, (b) the most frequently employed Computational Thinking stages in problem-solving skills are decomposition, pattern recognition, abstraction, and algorithm, (c) Computational Thinking is closely linked to problem-solving, and (d) Computational Thinking and problem-solving stages serve the same functions in solving problems. The results of this study will encourage the development of education research, particularly in the application of CTas a problem-solving tool in various real-life scenarios. [ABSTRACT FROM AUTHOR]

Across five experiments (N = 1,714), we test whether people engage in wishful thinking to alleviate anxiety about adverse future outcomes. Participants perform pattern recognition tasks in which some patterns may result in an electric shock or a monetary loss. Diagnostic of wishful thinking, participants are less likely to correctly identify patterns that are associated with a shock or loss. Wishful thinking is more pronounced under more ambiguous signals and only reduced by higher accuracy incentives when participants' cognitive effort reduces ambiguity. Wishful thinking disappears in the domain of monetary gains, indicating that negative emotions are important drivers of the phenomenon. [ABSTRACT FROM AUTHOR]

Similarity measures (SMs) are fundamental in various applications, including identifying patterns within medical data and aiding pattern recognition (PR) by quantifying the likeness between different patterns. Moreover, they play a crucial role in real-world problems such as Multiple Criteria Decision Making (MCDM), where decision-makers assess and compare alternatives based on multiple criteria simultaneously. Moreover, Cosine similarity is a measurement that quantifies the similarity between two or more objects. This study presents a comprehensive exploration of Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measures (IV IFCSMs) as a novel technique for assessing the degree of association between objects in real-world applications. By extending traditional cosine similarity measures (CSM) to interval-valued intuitionistic fuzzy sets (IV IFS), the proposed IV IFCSMs offer an effective framework for handling uncertainty, ambiguity, and imprecision in decision-making processes. The research demonstrates the practical utility of IV IFCSMs in addressing complex issues in PR, medical diagnosis (MD), and MCDM. In contrast to established methods like Singh's, Xu's, and Luo's measures, our approach consistently generates higher similarity values, encompassing both membership (MF) and non-membership (NMF) with interval values. [ABSTRACT FROM AUTHOR]

Aiming to provide a feasible crawling motion analysis method for clinical application, this study introduced electromyography (EMG)-based motion intention recognition technology into the pattern recognition of interlimb coordination during human crawling for the first time. Eight inter-limb coordination modes (ILCMs) were defined. Ten adult participants were recruited, and each participant performed hands-knees crawling at low, medium, and fast speeds in self-selected ILCMs and the eight predefined ILCMs, respectively. EMG signals for pattern recognition were collected from 30 limbs and trunk muscles, and pressure signals for crawling cycle segmentation were collected from the left palm. The pattern recognition experiments were conducted in participantspecific, multi-participant, and participant-independent ways, respectively, adopting three different classifiers, including bidirectional long short-term memory (BiLSTM) network, support vector machine (SVM), and k-nearest neighbor (KNN). The experimental results show that EMG-based pattern recognition schemes could classify the eight ILCMs with high recognition rates, thereby confirming the feasibility of providing an EMG-based crawling motion analysis method for clinical doctors. Furthermore, based on the classification results of self-selected ILCMs at different speeds and the statistical results of stance duration, swing duration, and the duty factors of stance phase, the possible reasons why humans chose various ILCMs at different crawling speeds were discussed. The research results have potential application value for evaluating crawling function, understanding abnormal crawling control mechanisms, and designing rehabilitation robots. [ABSTRACT FROM AUTHOR]

This article explores the potential advantages of using botulinum toxin treatment in conjunction with robot-assisted therapy for stroke rehabilitation. The authors highlight how muscle overactivity can hinder the effectiveness of robot-assisted therapy and explain that botulinum toxin treatment has been shown to reduce spasticity and improve motor function in stroke survivors. They also suggest that this treatment may enhance muscle onset detection and classification in myoelectric pattern recognition. The article discusses the potential benefits of combining botulinum toxin treatment with myoelectric pattern recognition robotic training, emphasizing the potential for increased range of motion and improved conditions for enhanced myoelectric pattern recognition. The article provides information on author contributions, funding sources, conflicts of interest, and references to related research. [Extracted from the article]

Fermatean fuzzy sets (FFSs) serve as a nascent yet potent approach for coping with fuzziness, with their efficacy recently being demonstrated across a spectrum of practical contexts. Nevertheless, the scholarly literature remains limited in exploring the similarity and distance measures tailored for FFSs. The limited existing measures on FFSs sometimes yield counter-intuitive outcomes, which can obfuscate the accurate quantification of similarity and difference among FFSs. This paper introduces a suite of similarity and distance measures tailored for FFSs, drawing inspiration from the Tanimoto measure. We delve into the characteristics of these novel measures and offer some comparative studies with existing FFSs measures, highlighting their superior efficacy in the processing of fuzzy data from FFSs. Our proposed measures effectively rectify the counter-intuitive situations encountered with many existing measures and demonstrate a significant enhancement in differentiating between diverse FFSs. Moreover, we showcase the real-world applicability of our proposed measures through case studies in pattern recognition, medical diagnostics, and multi-attribute decision-making. [ABSTRACT FROM AUTHOR]

The pedagogical value and global relevance of heritage education regarding the safeguarding of global tradition and practice for future generations is indisputable. This research aims to investigate the efficacy of integrating a puzzle-based game, with scaffolding strategies, to enhance students' understanding and appreciation of cultural heritage content. In the activity, students are tasked with reconstructing art patterns from cultural heritage artifacts, a process which is designed to bolster their performance in recognizing and appreciating such patterns. A control group, utilizing a non-interactive instructional video with the same content, was also established to contrast and evaluate the benefits of the game-based approach. The evaluation of knowledge acquisition and transfer was conducted through pattern tests, with the participants' eye movements recorded during the testing process. The results show that the participants in the game group exhibited significant advantages in terms of efficiently recognizing fundamental content and effectively transferring pattern recognition. Furthermore, the examination of the visual behavior within the game reveals that the integration of the jigsaw puzzle (game mechanic) and scaffolding (instructional strategy) can enhance the efficacy and cognitive strategies employed by participants in comprehending heritage content. This study develops design methodologies for cultural heritage games that enhances learning and appreciation. [ABSTRACT FROM AUTHOR]

Memristor crossbar arrays are expected to achieve highly energy-efficient neuromorphic computing via implementing parallel vector–matrix multiplication (VMM) in situ. The similarities between memristors and neural synapses offer opportunities for realizing hardware-based brain-inspired computing, such as spike neural networks. However, the nonlinear I–V characteristics of the memristors limit the implementation of parallel VMM on passive memristor crossbar arrays. In our work, we propose to utilize differential conductance as a synaptic weight to implement linear VMM operations on a passive memristor array in parallel. We fabricated a TiO2/HfO2 memristor crossbar array, in which differential-conductance-based synaptic weight exhibits plasticity, nonvolatility, multi-states, and tunable ON/OFF ratio. The noise-dependent accuracy performance of VMM operations based on the proposed approach was evaluated, offering an optimization guideline. Furthermore, we demonstrated a spike neural network circuit capable of processing small spiking signals through the differential-conductance-based synapses. The experimental results showcase effective space-coded and time-coded spike pattern recognition. Importantly, our work opens up new possibilities for the development of passive memristor arrays, leading to increased energy and area efficiency in brain-inspired chips. [ABSTRACT FROM AUTHOR]