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Water on land is essential for all societal, ecosystem, and planetary health aspects and conditions, and all life as we know it. Many disciplines consider and model similar terrestrial water phenomena and processes, but comparisons and consistent validations are lacking for the datasets used by various science communities for different world parts, scales, and applications. Here, we present a new global data synthesis that includes and harmonises four comparative datasets for main terrestrial water fluxes and storage changes, and the catchment-wise water balance closure they imply for the 30-year period 1980–2010 in 1561 non-overlapping hydrological catchments around the world. This can be used to identify essential agreements and disagreements of the comparative datasets for spatial variations and temporal changes of runoff, evapotranspiration, water storage, and associated water-balances around the global land area, e.g., for pattern recognition and hypothesis/model testing. The facilitated direct dataset comparison can advance a more coherent, realistic cross-disciplinary understanding of Earth's water states and changes across regions and scales, from local and up to continental and global. [ABSTRACT FROM AUTHOR]

Person Re-identification (ReID) is an important research direction in the field of pattern recognition, which aims to retrieve the same pedestrian in different cameras. The combination of deep learning and attention mechanism greatly improves the accuracy of image retrieval, but previous researchers usually use on-channel or spatial convolution to learn attention, ignoring the connection between attention feature nodes. In this article, we first improve a bottleneck attention module (BAM) to make the learned attention map faster. Secondly, to capture the relevance of each feature node in the global attentional feature map, we design a self-relevant attention module (SRA), which models the global scope structure information and is used to capture the connection between the feature node positions to make the obtained attentional map more robust. Finally, we propose a method to strengthen the attention features, so that the higher attention features around the position also get higher feature values, so that the obtained feature map is more robust. The effectiveness of the model is confirmed in several mainstream pedestrian re-identification datasets, and the proposed model outperforms most state-of-the-art methods. [ABSTRACT FROM AUTHOR]

One of the commonly used visualization techniques for multivariate data is the parallel coordinates plot. It provides users with a visual overview of multivariate data and the possibility to interactively explore it. While pattern recognition is a strength of the human visual system, it is also a strength of the auditory system. Inspired by the integration of the visual and auditory perception in everyday life, we introduce an audio-visual analytics design named Parallel Chords combining both visual and auditory displays. Parallel Chords lets users explore multivariate data using both visualization and sonification through the interaction with the axes of a parallel coordinates plot. To illustrate the potential of the design, we present (1) prototypical data patterns where the sonification helps with the identification of correlations, clusters, and outliers, (2) a usage scenario showing the sonification of data from non-adjacent axes, and (3) a controlled experiment on the sensitivity thresholds of participants when distinguishing the strength of correlations. During this controlled experiment, 35 participants used three different display types, the visualization, the sonification, and the combination of these, to identify the strongest out of three correlations. The results show that all three display types enabled the participants to identify the strongest correlation — with visualization resulting in the best sensitivity. The sonification resulted in sensitivities that were independent from the type of displayed correlation, and the combination resulted in increased enjoyability during usage. [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]

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]

Cancer has become a leading cause of death and disease burden worldwide, closely related to rapid socioeconomic development. However, the fundamental reason is the lack of comprehensive understanding of the mechanism of cancer, accurate identification of preclinical cancer, and effective treatment of the disease. Therefore, it is particularly urgent to study specific mechanisms of cancer and develop effective prediction and treatment methods. Long Pentraxin PTX3 is a soluble pattern recognition molecule produced by various cells in inflammatory sites, which plays a role as a promoter or suppressor of cancer in multiple tumors through participating in innate immune response, neovascularization, energy metabolism, invasion, and metastasis mechanisms. Based on this, this article mainly reviews the role of PTX3 in various cancers. [ABSTRACT FROM AUTHOR]

In this paper, two new distance measures are introduced, which are used in various application problems in decision-making, pattern recognition, and clustering. Intuitionistic fuzzy sets are sources of information that contain both the membership and non-membership degrees of the elements in the set. As such, distance measures based on geometric concepts are sometimes misleading. Hence, six parameters are identified to construct the distance measures. These parameters are membership information dissimilarity, non-membership information dissimilarity, hesitancy information dissimilarity, product cross-information dissimilarity, maximum cross-information dissimilarity, and minimum cross-information dissimilarity. Among all the parameters, the product cross-information dissimilarity is newly introduced in this work. Compensations for the proposed distance measures are established by various counter-intuitive problems in decision-making and pattern recognition. Finally, validation of the proposed distance measures is established by diverse problems of applications in decision-making, pattern recognition, and clustering problems. [ABSTRACT FROM AUTHOR]

Term extraction from textbooks is the cornerstone of many different intelligent natural language processing systems, especially those that support learners and educators in the education system. This paper proposes a novel unsupervised domain-independent model that automatically extracts relevant and domain-related key terms from a single PDF textbook, without relying on a statistical technique or external knowledge base. It only relies on the basic linguistic techniques of the natural language processing: pattern recognition, sentence tokenization, part-of-speech tagging, and chunking. The model takes a PDF textbook as an input and produces a list of key terms as an output. Furthermore, the model proposes a novel classification of sentences from which the concept of defining sentences is proposed. The defining sentences are the main textual units that the model revolves around to identify the key terms. The architecture of the proposed work consists of 21 processes distributed across three phases. The first phase consists of five processes for extracting text from a PDF textbook and cleaning it for the next phases. The second phase consists of eight processes for identifying the defining sentences and extracting them from all the textbook's sentences. The last phase consists of eight processes for identifying and extracting the key terms from every defining sentence. The proposed work was evaluated by two experiments in which two PDF textbooks from different fields are used. The experimental evaluation showed that the results were promising. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach aimed at enhancing multi-attribute decision-making through the utilization of fuzzy pattern recognition, with a specific emphasis on engaging decision-makers more effectively. The methodology establishes a multi-attribute fuzzy pattern recognition model within a hybrid information system framework. It categorizes attributes into natural and abstract groups, standardizes them, and employs membership functions to transform them into degrees of membership. This adaptable approach permits the derivation of various decision criteria from the hybrid system. Subsequently, a testing set is generated from this system, and a suitable fuzzy operator is selected. The optimal solution is determined by assessing the similarity between the standard and testing sets. To underscore its effectiveness, a practical example is provided. Crucially, in the realm of multi-attribute decision-making, our method simplifies the process by reducing computational steps in contrast to the conventional TOPSIS model, while maintaining consistent outcomes. This streamlines the decision-making process and reduces complexity. We also demonstrate its applicability in multi-objective decision-making through a case study evaluating exemplary educators, thereby highlighting its adaptability and effectiveness. This method exhibits significant promise for enhancing multi-attribute decision-making and offers practical applications. [ABSTRACT FROM AUTHOR]

The patterns of communication among different chondrocyte subtypes in human cartilage degeneration and regeneration help us understand the microenvironment of osteoarthritis and optimize cell-targeted therapies. Here, a single-cell transcriptome dataset of chondrocytes is used to explore the synergistic and communicative patterns of different chondrocyte subtypes. We collected 1600 chondrocytes from 10 patients with osteoarthritis and analyzed the active communication patterns for the first time based on network analysis and pattern recognition at the single-cell level. Manifold learning and quantitative contrasts were performed to analyze conserved and specific communication pathways. We found that ProCs (Proliferative chondrocytes), ECs (Effector chondrocytes), preHTCs (Prehypertrophic chondrocytes), HTCs (Hypertrophic chondrocytes), and FCs (Fibrocartilage chondrocytes) are more active in incoming and outgoing signaling patterns, which is consistent with studies on their close functional cooperation. Among them, preHTCs play multiple roles in chondrocyte communication, and ProCs and preHTCs have many overlapping pathways. These two subtypes are the most active among all chondrocyte subtypes. Interestingly, ECs and FCs are a pair of "mutually exclusive" subtypes, of which ECs are predominant in incoming patterns and FCs in outgoing patterns. The active signaling pathways of ECs and FCs largely do not overlap. COLLAGEN and LAMININ are the main pivotal pathways, which means they are very important in the repair and expansion of joint homeostasis. Notably, only preHTCs assume multiple roles (including sender, receiver, mediator, and influencer) and are involved in multiple communication pathways. We have examined their communication patterns from the perspective of cellular interactions, revealed the relationships among different chondrocyte subtypes, and, in particular, identified a number of active subtypes and pathways that are important for targeted therapy in the osteoarthritic microenvironment. Our findings provide a new research paradigm and new insights into understanding chondrocyte activity patterns in the osteoarthritic microenvironment. [ABSTRACT FROM AUTHOR]

Inspired by Fechner's law, we propose a Fechner multiscale local descriptor (FMLD) for feature extraction and face recognition. Fechner's law is a well-known law in psychology, which states that a human perception is proportional to the logarithm of the intensity of the corresponding significant differences physical quantity. FMLD uses the significant difference between pixels to simulate the pattern perception of human beings to the changes of surroundings. The first round of feature extraction is performed in two local domains of different sizes to capture the structural features of the facial images, resulting in four facial feature images. In the second round of feature extraction, two binary patterns are used to extract local features on the obtained magnitude and direction feature images, and four corresponding feature maps are output. Finally, all feature maps are fused to form an overall histogram feature. Different from the existing descriptors, the FMLD's magnitude and direction features are not isolated. They are derived from the "perceived intensity", thus there is a close relationship between them, which further facilitates the feature representation. In the experiments, we evaluated the performance of FMLD in multiple face databases and compared it with the leading edge approaches. The results show that the proposed FMLD performs well in recognizing images with illumination, pose, expression and occlusion changes. The results also indicate that the feature images produced by FMLD significantly improve the performance of convolutional neural network (CNN), and the combination of FMLD and CNN exhibits better performance than other advanced descriptors. [ABSTRACT FROM AUTHOR]

Background: Whether the false memory dysfunction in schizophrenia is at the early (encoding, perceptual) or later (retrieval) stages and how delusions affect it remains unclear. Method: In this study, we examined the perception and memory biases in schizophrenia patients with (Sch/D; n = 31) and without delusions (Sch/Nd; n = 32) and compared them with healthy controls (HCs; n = 35). We used the Deese Roediger McDermott (DRM) Paradigm and the Noise Task to measure the false memory and illusory pattern perception (IPP) biases, respectively. Results: We found that the patient groups performed lower in both the recall and recognition phases for the DRM and the Noise tasks and made more errors compared to the HC group. Additionally, the performance of the Sch/D group was remarkably lower than the Sch/Nd and HC groups. Conclusions: Our results indicated that the information-processing problem in schizophrenia exists in both the encoding and retrieval stages. Also found significant relationship between the presence of delusions and the increase in cognitive deficits. [ABSTRACT FROM AUTHOR]

Distance and similarity measures are popular due to various applications across different fields, including clustering, classification, information retrieval, decision-making, and image and pattern recognition. Pythagorean fuzzy sets (PFSs) are more efficient than fuzzy sets (FSs) and intuitionist fuzzy sets (IFSs) in dealing with all kinds of uncertain and incomplete information related to real life. Since PFSs and interval-valued fuzzy sets (IVFs) are isomorphic to each other, so the interval values can be used to represent the distance between two PFSs uniquely. Therefore, in this article, we utilize the concept of Pythagorean fuzzy interval values to construct a new distance between two PFSs based on Lp metric. Furthermore, the suggested distance is used to construct several similarity measures between PFSs using simple and reasonable functions. Newly established distance and similarity measures between Pythagorean fuzzy sets satisfy all the required axioms. To show the reasonability, comparison analysis is conducted with existing one in an application to pattern recognition. The numerical comparison results reveal that our proposed method works better than the existing method. To reveal practical applicability and usefulness, we put forwarded an algorithm Pythagorean Vlsekriterijumsko Kompromisno Rangiranje in Serbian, means multicriteria optimization and compromise solution (P-VIKOR) based on our suggested method and applied it to solve daily life issues involving complex multicriteria decision-making (MCDM) process. Finally, we utilize our proposed similarity measure to establish Pythagorean clustering. Numerical results and practical applications demonstrate that the given approaches are practically applicable, reasonable, and reliable in dealing with a variety of complex problems carrying uncertainty and vague information in everyday life. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy measures have been widely used in multi-attribute decision-making. However, in the complex decision-making process, only a few distance measures take individual tendencies into account. In this paper, we propose an intuitionistic fuzzy distance measure based on the tendency of decision makers, and give a novel emergency decision-making method using the distance. Firstly, according to the function transformation, we construct a novel distance measure that includes different tendency coefficients of intuitionistic indexes. These indexes are the membership, non-membership and hesitancy of the intuitionistic fuzzy set. Secondly, the proposed distance has been proved that it satisfies the axiomatic characteristics of distance measure. Then using a few examples to illustrate the novel distance in pattern recognition and fuzzy multi-attribute decision-making, while could adapt to decision-making problems more flexible by adjusting tendency coefficients of each intuitionistic index. Finally, applying the proposed distance measure to a real case of online public opinion in the proposed emergency decision-making in this paper, compared with other methods, our distance measure shows more reasonable, effective and superior results in the decision model. [ABSTRACT FROM AUTHOR]

Printed fabrics often have rich colors and variable patterns in different sizes and shapes, which make it difficult to achieve accurate pattern recognition and color measurement using traditional spectrophotometers and digital cameras. This paper develops a grid-based density peaks clustering (GDPC) algorithm to automatically recognize patterns and extract colors of multispectral images of printed fabrics. The multispectral images captured by a self-developed multispectral imaging system is firstly converted into color images in CIELAB color space and three principal components are calculated by applying principal component analysis to reduce the dimensions of the multispectral images. During the multispectral image processing, the noise pixels are removed by calculating the local stability of each pixel, and then the remaining stable pixels are clustered using proposed GDPC algorithm based on three CIELAB color channels and three principal components. Compared with widely-used color clustering algorithms, the proposed GDPC algorithm can recognize the color patterns from more intricate multispectral printed fabric images with higher accuracy and less computational time. [ABSTRACT FROM AUTHOR]

Previous research indicates that irrational beliefs (Paranormal beliefs & conspiracy theory endorsement) are associated with the perception of patterns in noise, but the previous findings do not conclusively describe this relationship. This study aims to disentangle the underlying parameters of this association by applying a signal detection theory approach, thus allowing to distinguish illusory pattern perception (false alarms) from perceptual sensitivity and response tendencies—while also taking base rate information into account. Results from a large sample (N = 723) indicate that paranormal beliefs relate to a more liberal response bias and a lower perceptual sensitivity, and that this relationship is driven by illusory pattern perception. Such a clear pattern could not be observed for conspiracy beliefs, for which the increase in false alarm rates was moderated by the base rate. The associations between irrational beliefs and illusory pattern perception were however less substantial compared to other sources of variance. Implications are discussed. [ABSTRACT FROM AUTHOR]

A single-node failure recovery mechanism designed for the failed node and a failure recovery mechanism under multi-node fault and associated fault are proposed. Liu et al. and Zhong et al. aim to optimize the continuous location-based query processing mechanism by developing a variety of light-weighted indexing structures and stream data processing algorithms. This special issue contains 20 research articles that present solid and novel fundamental research studies in the area of spatiotemporal data analytics for recommender systems, and 2 survey articles that investigate related studies of relevant research fields. [Extracted from the article]

Introduction and Hypothesis: This study is aimed at characterizing patients seen at a new combined pelvic floor clinic (CPFC) established at a tertiary women’s hospital in Singapore. Study goals include identifying patterns of symptom clusters and reviewing management strategies compared with international care standards, improving understanding of complex female pelvic floor dysfunction, and guiding future development of clinical and ancillary support.This is a single-center, retrospective cohort study of medical case record review for all patients seen at the CPFC between 1 September 2020 and 31 August 2021. All patients seen at CPFC during this study period were included for analysis. Data were deidentified and aggregated, with analysis performed for descriptive and summary statistics. Institutional Review Board approval was obtained with waiver of informed consent.Sixty-six referrals were made to the CPFC, with 57 patients seen in 11 clinic sessions across the 12-month period. The most common reasons for referral were fecal or flatus incontinence (30, 52.6%), severe constipation or defecatory dysfunction (21.1%), and rectal prolapse (17.5%). Most patients (78.9%) had multiple symptoms.Our patient demographic profile aligns with known risk factors for pelvic floor dysfunction, presenting with gastrointestinal symptoms—similar to PFCs worldwide. The new workflow and services were well positioned to address patients’ needs, including those left previously untreated, or received uncoordinated care. Conservative approaches were first employed, like with other global PFCs. The combined multidisciplinary model of care resulted in good outcomes for patients managed both conservatively and surgically. Future studies to analyze patient satisfaction are planned.Methods: This study is aimed at characterizing patients seen at a new combined pelvic floor clinic (CPFC) established at a tertiary women’s hospital in Singapore. Study goals include identifying patterns of symptom clusters and reviewing management strategies compared with international care standards, improving understanding of complex female pelvic floor dysfunction, and guiding future development of clinical and ancillary support.This is a single-center, retrospective cohort study of medical case record review for all patients seen at the CPFC between 1 September 2020 and 31 August 2021. All patients seen at CPFC during this study period were included for analysis. Data were deidentified and aggregated, with analysis performed for descriptive and summary statistics. Institutional Review Board approval was obtained with waiver of informed consent.Sixty-six referrals were made to the CPFC, with 57 patients seen in 11 clinic sessions across the 12-month period. The most common reasons for referral were fecal or flatus incontinence (30, 52.6%), severe constipation or defecatory dysfunction (21.1%), and rectal prolapse (17.5%). Most patients (78.9%) had multiple symptoms.Our patient demographic profile aligns with known risk factors for pelvic floor dysfunction, presenting with gastrointestinal symptoms—similar to PFCs worldwide. The new workflow and services were well positioned to address patients’ needs, including those left previously untreated, or received uncoordinated care. Conservative approaches were first employed, like with other global PFCs. The combined multidisciplinary model of care resulted in good outcomes for patients managed both conservatively and surgically. Future studies to analyze patient satisfaction are planned.Results: This study is aimed at characterizing patients seen at a new combined pelvic floor clinic (CPFC) established at a tertiary women’s hospital in Singapore. Study goals include identifying patterns of symptom clusters and reviewing management strategies compared with international care standards, improving understanding of complex female pelvic floor dysfunction, and guiding future development of clinical and ancillary support.This is a single-center, retrospective cohort study of medical case record review for all patients seen at the CPFC between 1 September 2020 and 31 August 2021. All patients seen at CPFC during this study period were included for analysis. Data were deidentified and aggregated, with analysis performed for descriptive and summary statistics. Institutional Review Board approval was obtained with waiver of informed consent.Sixty-six referrals were made to the CPFC, with 57 patients seen in 11 clinic sessions across the 12-month period. The most common reasons for referral were fecal or flatus incontinence (30, 52.6%), severe constipation or defecatory dysfunction (21.1%), and rectal prolapse (17.5%). Most patients (78.9%) had multiple symptoms.Our patient demographic profile aligns with known risk factors for pelvic floor dysfunction, presenting with gastrointestinal symptoms—similar to PFCs worldwide. The new workflow and services were well positioned to address patients’ needs, including those left previously untreated, or received uncoordinated care. Conservative approaches were first employed, like with other global PFCs. The combined multidisciplinary model of care resulted in good outcomes for patients managed both conservatively and surgically. Future studies to analyze patient satisfaction are planned.Conclusions: This study is aimed at characterizing patients seen at a new combined pelvic floor clinic (CPFC) established at a tertiary women’s hospital in Singapore. Study goals include identifying patterns of symptom clusters and reviewing management strategies compared with international care standards, improving understanding of complex female pelvic floor dysfunction, and guiding future development of clinical and ancillary support.This is a single-center, retrospective cohort study of medical case record review for all patients seen at the CPFC between 1 September 2020 and 31 August 2021. All patients seen at CPFC during this study period were included for analysis. Data were deidentified and aggregated, with analysis performed for descriptive and summary statistics. Institutional Review Board approval was obtained with waiver of informed consent.Sixty-six referrals were made to the CPFC, with 57 patients seen in 11 clinic sessions across the 12-month period. The most common reasons for referral were fecal or flatus incontinence (30, 52.6%), severe constipation or defecatory dysfunction (21.1%), and rectal prolapse (17.5%). Most patients (78.9%) had multiple symptoms.Our patient demographic profile aligns with known risk factors for pelvic floor dysfunction, presenting with gastrointestinal symptoms—similar to PFCs worldwide. The new workflow and services were well positioned to address patients’ needs, including those left previously untreated, or received uncoordinated care. Conservative approaches were first employed, like with other global PFCs. The combined multidisciplinary model of care resulted in good outcomes for patients managed both conservatively and surgically. Future studies to analyze patient satisfaction are planned. [ABSTRACT FROM AUTHOR]

Pattern recognition is an important skill of Computational Thinking and is one of the most important competences for solving a problem that involves finding similarities or patterns in small problems to solve more complex ones. In this work, we present the mobile application software Patrony. The main contribution of this work is to promote the learning of Computational Thinking, especially pattern recognition, in specific sectors of education in Mexico through the simple use of a software application. To evaluate the effectiveness of the mobile application, tests were carried out in two elementary schools with a total of 43 students, which were divided into 2 groups: a control group and an experimental group. The results of the tests showed that the learning gain (M = 6.50 in postest compared to M = 4.94 on pretest) of the students who used our mobile application produces a significant difference with respect to students who learned using a traditional method of classroom teaching. The results also infer that computational thinking applications can be used as effective learning tools within some important Mathematics topics in public and private schools in Mexico. [ABSTRACT FROM AUTHOR]

The humidity sensor is an essential sensing node in medical diagnosis and industrial processing control. To date, most of the reported relative humidity sensors have a long response time of several seconds or even hundreds of seconds, which would limit their real application for certain critical areas with fast-varying signals. In this paper, we propose a flexible and low-cost humidity sensor using vertically aligned carbon nanotubes (VACNTs) as electrodes, a PDMS-Parylene C double layer as the flexible substrate, and graphene oxide as the sensing material. The humidity sensor has an ultrafast response of ~20 ms, which is more than two orders faster than most of the previously reported flexible humidity sensors. Moreover, the sensor has a high sensitivity (16.7 pF/% RH), low hysteresis (<0.44%), high repeatability (2.7%), good long-term stability, and outstanding flexibility. Benefiting from these advantages, especially the fast response, the device has been demonstrated in precise human respiration monitoring (fast breathing, normal breathing, deep breathing, asthma, choking, and apnea), noncontact electrical safety warning for bare hand and wet gloves, and noncontact pipe leakage detection. In addition, the facile fabrication of the flexible platform with the PDMS-Parylene C double layer can be easily integrated with multisensing functions such as pH sensing, ammonium ion sensing, and temperature sensing, all of which are useful for more pattern recognition of human activity. [ABSTRACT FROM AUTHOR]

Individuals with upper extremity (UE) amputation abandon prostheses due to challenges with significant device weight—particularly among myoelectric prostheses—and limited device dexterity, durability, and reliability among both myoelectric and body-powered prostheses. The Modular Prosthetic Limb (MPL) system couples an advanced UE prosthesis with a pattern recognition paradigm for intuitive, non-invasive prosthetic control. Pattern recognition accuracy and functional assessment—Box & Blocks (BB), Jebsen-Taylor Hand Function Test (JHFT), and Assessment of Capacity for Myoelectric Control (ACMC)—scores comprised the main outcomes. 10 participants were included in analyses, including seven individuals with traumatic amputation, two individuals with congenital limb absence, and one with amputation secondary to malignancy. The average (SD) time since limb loss, excluding congenital participants, was 85.9 (59.5) months. Participants controlled an average of eight motion classes compared to three with their conventional prostheses. All participants made continuous improvements in motion classifier accuracy, pathway completion efficiency, and MPL manipulation. BB and JHFT improvements were not statistically significant. ACMC performance improved for all participants, with mean (SD) scores of 162.6 (105.3), 213.4 (196.2), and 383.2 (154.3), p = 0.02 between the baseline, midpoint, and exit assessments, respectively. Feedback included lengthening the training period to further improve motion classifier accuracy and MPL control. The MPL has potential to restore functionality to individuals with acquired or congenital UE loss. [ABSTRACT FROM AUTHOR]

The automatic creation of a repository of the building's floor plan helps a lot to the architects to reuse them. The basic approach is to extract and recognize texts, symbols or graphics to retrieve the information of the floor plan from the images. This paper proposes a floor plan information retrieval algorithm. The proposed algorithm is based on shape extraction and room identification. α -shape is used for finding an accurate shape. From the detected shapes, actual areas of rooms are calculated. Later, a regression model-based binary room classification model is proposed to classify them into room-type, i.e., bedroom, drawing room, kitchen, and non-room-type, i.e., parking porch, bathroom, study room and prayer room. The proposed model is tested on the CVC-FP dataset with an average room detection accuracy of 85.71% and room recognition accuracy of 88%. [ABSTRACT FROM AUTHOR]

The application of Poincaré plot analysis to characterize inter-beat interval dynamics has been successfully proposed in the scientific literature for the assessment of humans' physiological states and related aberrations. In this study, we proposed novel descriptors to trace the evolution of Poincaré plot shape over the lags. Their reliability in ultra-short cardiovascular series analysis was validated on synthetic inter-beat series generated through a physiologically plausible integral pulse frequency modulation model. Furthermore, we used the proposed approach for the investigation of the direct relationship between autonomic nervous system (ANS) dynamics and hedonic olfactory elicitation, in a group of 30 healthy subjects. Participants with a similar olfactory threshold were selected, and were asked to score 5-s stimuli in terms of arousal and valence levels according to the Russell's circumflex model of affect. Their ANS response was investigated in 35-s windows after the elicitation. Experimental results showed a gender-specific, high discriminant power of the proposed approach, discerning between pleasant and unpleasant odorants with an accuracy of 83.33% and 73.33% for men and for women, respectively. Graphical Abstract Olfaction plays a crucial role in our life and is strictly related to the Autonomic Nervous System (ANS) activity, which can be monitored studying Heart Rate Variability. We used the Lagged Poincare Plot approach to recognize gender-specific ANS response in 35-second windows after the elicitation through pleasant/unpleasant odorants. [ABSTRACT FROM AUTHOR]

To yield well-suited image segmentation results, conventional clustering algorithms depend on customized hand-crafted features as well as an appropriate initialization process. This latter aspect is a mandatory pre-requisite for convergence of the algorithm, in other words, its efficiency impacts the quality of the result. In this work, we introduce an Intuitionistic Fuzzy C-Means clustering algorithm enhanced by means of Robust Statistics, which develops an outstanding image segmentation based on a basic feature such as the color information, and it requires a reduced iteration number to converge. The non-parametric Lorentzian Redescending M-estimator is used both at initialization and iterative stages of the clustering algorithm; since, it behaves such as a robust location estimator when the centroid vector is computed, and as a weighting when the membership matrix is updated. With the fusion of both techniques, we can guarantee that the introduced clustering algorithm can efficiently develop the task of segmentation of color images and pattern recognition processes. The robustness and effectiveness of this proposal is verified by experiments on the natural color images BSDS500 dataset, as well as a simulated dataset corrupted with atypical data. [ABSTRACT FROM AUTHOR]

Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins' set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands. [ABSTRACT FROM AUTHOR]

Although Artificial Intelligence (AI) has become a buzzword for self-organizing IT applications, its relevance to software engineering has hardly been analyzed systematically. This study combines a systematic review of previous research in the field and five qualitative interviews with software developers who use or want to use AI tools in their daily work routines, to assess the status of development, future development potentials and equally the risks of AI application to software engineering. The study classifies the insights in the software development life cycle. The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e.g. for debugging and documentation, b) the structured analysis of big data pools to discover patterns and novel information clusters and c) the systematic evaluation of these data in neural networks. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains. AI to date depends on man-made structures and is mainly reproductive, but the automation of software engineering routines entails a major advantage: Human developers multiply their creative potential when using AI tools effectively. [ABSTRACT FROM AUTHOR]

In recent past, there has been a steep increase in the use of online platforms for the search of desired products. Real estate industry is no exception and has started initiating rent/sale of houses through online platforms. In this paper, we propose a deep neural network framework to facilitate automatic search of homes based on their floor plans. The salient features of this framework are that the query can be either an image (existing floor plan) or a sketch through a sketch pad interface. Our proposed framework automatically determines the type of query (image or sketch) and retrieves similar floor plan images from the database. The critical contributions of our proposed approach are: (1) a novel unified floor plan retrieval framework using multimodal query, i.e., an intuitive and convenient sketch query mode as well as a query by example mode ; (2) a conjunction of autoencoder, Cyclic GAN and CNN for the task of domain mapping and floor plan image retrieval. We have reported results of extensive experimentation and comparison with baseline results to establish the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Very few published studies have reported auditory speech perception in Hindi children with pre-lingual hearing loss. The study is aimed at comparing the speech perception skills of Hindi speaking children with pre-lingual severe to profound hearing loss using hearing aids and cochlear implants. Forty-three 6 to 8-year old children were included as participants, of which 22 were bilateral behind-the-ear hearing aid (HA) users and 21 were unilateral cochlear implant (CI) users. Speech perception was assessed through a forced-choice, picture-pointing task using recorded stimuli presented at 70 dB HL in the sound field. The skills assessed include: (a) pattern perception, (b) bisyllabic word identification, (c) monosyllabic word identification, (d) sentence identification and (e) minimal pair identification. Children using CI consistently performed significantly better than those with HA on all tasks. For the skills assessed, best performance was seen in pattern perception and poorest performance was seen in monosyllabic word identification. One participant from the CI group obtained ceiling scores for pattern perception and bisyllabic word identification. There was no statistically significant difference in the performance of 6 to 7 and 7 to 8-year-old children for any of the tasks. Children fitted with CI have better access to the cues important for perception of speech and hence perform consistently better than those using hearing aids. Recorded speech perception test can be used with children using cochlear implants and hearing aids. [ABSTRACT FROM AUTHOR]

This paper aims at comparing robustness of local features for camera-based document image retrieval and spotting system. We present a literature review of the state of the art of local features extraction that includes keypoint detectors and keypoint descriptors. We also present a dataset and evaluation protocol for camera-based document image retrieval and spotting systems. This dataset is composed of three subparts: The first dataset represents the images with textual content only; the second dataset represents images with graphical content mainly; the third dataset contains text plus graphical elements. Along with the datasets, we present the protocol that describes measurements to evaluate the accuracy and processing time of camera-based document image retrieval and spotting systems. The latter is employed for presenting a detailed evaluation of local features from the literature. [ABSTRACT FROM AUTHOR]

Beef meat freshness was evaluated using artificial vision technique and pattern recognition algorithms. Color and texture features were extracted from the saturation images. The wavelet transform was used to characterize texture and a range of features was used to better characterize color. Two classes of beef meat samples were obtained from the projection of color, texture, and color associated with texture datasets using Principal Component Analysis (PCA) method. The first class corresponds to fresh beef meat samples that have undergone 6 days of cold storage and the second class presents spoiled meat. Probabilistic Neural Network (PNN) and Linear Discriminant Analysis (LDA) algorithms were used to classify and predict beef meat samples into fresh or spoiled samples. Results show that the classification and identification rates obtained by PNN are superior to LDA algorithm using the datasets of color, texture, and color associated with texture. In addition, results show that texture features associated with color features give the best classification and identification rates. An implementation of all proposed algorithms was carried out on a real time embedded system. [ABSTRACT FROM AUTHOR]

Purpose: Inflammatory FDG uptake in the lung (PET-pneumonitis) following curative-intent radiotherapy (RT)/chemo-RT (CRT) in non-small cell lung cancer (NSCLC) can pose a challenge in FDG-PET/CT response assessment. The aim of this study is to describe different patterns of PET-pneumonitis to guide the interpretation of FDG-PET/CT and investigate its association with tumor response and overall survival (OS). Methods: Retrospective analysis was performed on 87 NSCLC patients in three prospective trials who were treated with radical RT (n = 7) or CRT (n = 80), with baseline and post-treatment FDG-PET/CT. Visual criteria were performed for post-treatment FDG-PET/CT response assessment. The grading of PET-pneumonitis was based on relative lung uptake intensity compared to organs of reference and classified as per Deauville score from grade 1–5. Distribution patterns of PET-pneumonitis were defined as follows: A) patchy/sub-pleural; B) diffuse (involving more than a segment); and C) peripheral (diffusely surrounding a photopenic region). Results: Follow-up FDG-PET/CT scans were performed approximately 3 months (median, 89 days; interquartile range, 79–93) after RT. Overall, PET-pneumonitis was present in 62/87 (71%) of patients, with Deauville 2 or 3 in 12/62 (19%) and 4 or 5 in 50/62 (81%) of patients. The frequency of patterns A, B and C of PET-pneumonitis was 19/62 (31%), 20/62 (32%) and 23/62 (37%), respectively. No association was found between grade or pattern of PET-pneumonitis and overall response at follow-up PET/CT (p = 0.27 and p = 0.56, respectively). There was also no significant association between PET-pneumonitis and OS (hazard ratio [HR], 1.3; 95% confidence interval [CI], 0.6–2.5; p = 0.45). Early FDG-PET/CT response assessment, however, was prognostic for OS (HR, 1.7; 95% CI, 1.2–2.2; p < 0.001). Conclusion: PET-pneumonitis is common in early post-CRT/RT, but pattern recognition may assist in response assessment by FDG-PET/CT. While FDG-PET/CT is a powerful tool for response assessment and prognostication, PET-pneumonitis does not appear to confound early response assessment or to independently predict OS. [ABSTRACT FROM AUTHOR]

This work presents a classification performance comparison between different frameworks for functional connectivity evaluation and complex network feature extraction aiming to distinguish motor imagery classes in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The analysis was performed in two online datasets: (1) a classical benchmark-the BCI competition IV dataset 2a-allowing a comparison with a representative set of strategies previously employed in this BCI paradigm and (2) a statistically representative dataset for signal processing technique comparisons over 52 subjects. Besides exploring three classical similarity measures-Pearson correlation, Spearman correlation, and mean phase coherence-this work also proposes a recurrence-based alternative for estimating EEG brain functional connectivity, which takes into account the recurrence density between pairwise electrodes over a time window. These strategies were followed by graph feature evaluation considering clustering coefficient, degree, betweenness centrality, and eigenvector centrality. The features were selected by Fisher's discriminating ratio and classification was performed by a least squares classifier in agreement with classical and online BCI processing strategies. The results revealed that the recurrence-based approach for functional connectivity evaluation was significantly better than the other frameworks, which is probably associated with the use of higher order statistics underlying the electrode joint probability estimation and a higher capability of capturing nonlinear inter-relations. There were no significant differences in performance among the evaluated graph features, but the eigenvector centrality was the best feature regarding processing time. Finally, the best ranked graph-based attributes were found in classical EEG motor cortex positions for the subjects with best performances, relating functional organization and motor activity. Graphical Abstract Evaluating functional connectivity based on Space-Time Recurrence Counting for motor imagery classification in brain-computer interfaces. Recurrences are evaluated between electrodes over a time window, and, after a density threshold, the electrodes adjacency matrix is stablish, leading to a graph. Graph-based topological measures are used for motor imagery classification. [ABSTRACT FROM AUTHOR]

A fast algorithm is proposed for choosing the coefficients of blur coefficients for kernel functions in a nonparametric estimate of the separating surface equation for a two-alternative pattern recognition problem. The algorithm is based on the results of a study of the asymptotic properties of nonparametric estimates of the decision function in the recognition problem for patterns and the probability densities of the distribution of random variables in classes. We compare the proposed algorithm with the traditional approach based on minimizing the estimated probability of a classification error. [ABSTRACT FROM AUTHOR]

Current researches of acoustic emission (AE) mainly put the focus on fault diagnosis of traditional isotropic materials machining process or analysis of fiber-reinforced composite (FRC) tensile or bending strength, while there are merely studies on AE research of FRC grinding process. Numerous kinds of damage occur during FRC grinding process owing to their complicated structure. The main purpose of this paper is to extract proper index to estimate AE signals of FRC grinding and to recognize damage patterns, thus realizing FRC processing on-line detection. AE signals are obtained by single-grain grinding experiments of quartz fiber–reinforced silicon dioxide matrix composite (SiO2/SiO2). The AE signal features are discussed, and the outstanding character of frequency components is proposed. The frequency of each damage pattern is analyzed and verified. AE effective voltage value (EVV) and event number percentage (ENP) of peak frequency (PF) of AE signals with processing parameters are researched. The results show that for the same kind of FRCs, frequency components of AE signals are only affected by damage patterns rather than processing parameters or grinding directions, thus being a proper estimate index. There are four main frequency bands during SiO2/SiO2 grinding. The frequency 6.4–9.8 KHz corresponds to fiber fracture, 14.8–17.9 KHz is fiber debonding, 23.6–26.4 KHz is debris rubbing with workpiece and tool, and 34–35.5 KHz is matrix crack. EVV has a similar changing trend to grinding force with machining parameters. AE ENP of PF that the maximum peak amplitude (PA) corresponds to could quantitatively confirm the main damage modes under each processing condition. [ABSTRACT FROM AUTHOR]

The speech signal is inherently rich, and this reflects complexities of speech articulation. During spoken-word recognition, listeners must process time-dependent perceptual cues, and the role that these cues play varies depending on the phonological status of the sounds across languages. For example, Canadian French has both phonologically nasal vowels (i.e., contrastive) and coarticulatorily nasalized vowels, as opposed to English, which only has coarticulatorily nasalized vowels. We investigated how vowel nasalization duration, a time-dependent phonetic cue to the French nasal contrast, affects spoken-word recognition. Using eye tracking in two visual world paradigm experiments, the results show that fine-grained phonetic information is important for lexical recognition, and that lexical access is dependent on small variations in the signal. The results also show gradient interpretation of ambiguous vowel nasalization despite the phonemic distinction between phonological nasal vowels and coarticulatorily nasalized vowels in Canadian French. Gradience was found when words were ambiguous, and interpretation was more categorical when words were unambiguous. These results support the hypothesis of gradient interpretation of phonetic cues for ambiguously produced stimuli and the storage of coarticulatory information in phono-lexical representations for a language that has a phonological contrast for nasality (i.e., French). [ABSTRACT FROM AUTHOR]

Purpose: Diagnosis of the depth of invasion is crucial in the endoscopic management of early colorectal cancer. Image-enhanced endoscopy (IEE) is a method for easily evaluating the depth of invasion. Linked colour imaging (LCI) is an IEE method that enables clearer identification of neoplastic lesions and mucosal inflammation. The aim of this experimental study was to explore the efficacy of LCI in vessel and pit pattern recognition when used in magnifying chromoendoscopy with crystal violet staining for superficial colorectal neoplasms. Methods: This was an experimental study. Colour difference (CD) values between the surrounding mucosa and vessels and pits were measured on white light (WLI), blue laser (BLI), and LCI images. The CD values of 10 neoplastic lesions were calculated and compared between WLI and the other techniques. Results: The CD value was 9.8 (interquartile range, 7.3–12.4) for WLI, 9.7 (6.7–13.4) for LCI, and 6.8 (5.1–9.3) for BLI. The CD value was statistically different between WLI and BLI but not between WLI and LCI. With regard to vessel description, the CD value was 7.5 (4.0–11.0) for WLI, 15.6 (11.6–23.9) for LCI, and 23.3 (15.8–30.4) for BLI. Conclusions: LCI provides more diagnostic information than other light modes. Further, it is superior to the other techniques in terms of vessel visibility and is comparable to them in terms of pit recognition. These unique features of LCI may lead to its use as an alternative to WLI and BLI for pit and vessel pattern evaluation in the future. [ABSTRACT FROM AUTHOR]

Emotions play an important role in human decision-making process, and consequently, they should be embedded into the reasoning process in our efforts to model human reactions. Adnan Khashman et al. have proposed an emotional backpropagation (EmBP) learning algorithm and have successfully applied it to several practical pattern recognition tasks. However, the design of the emotional input values to the EmBP is not reasonable and may thus cause the failure of its entire implementation. Aimed at improving this weakness, we propose a novel self-organizing map-based emotional neural network (EmSOM) learning algorithm. In contrast to EmBP, the emotional input values of EmSOM are determined based upon its correspondingly associated SOM blocks, and moreover, the network hierarchy has been taken into account in its design, thus improving the deficiencies of EmBP to a certain extent. Furthermore, we incorporate a sparse online SOM (SOR-SOM) algorithm into our emotional neural network learning algorithm and establish a hybrid sparse online relational SOM-based emotional neural network (Em-SOR-SOM) model, so that those advantages of SOR-SOM can be exploited to further boost the recognition performance of the model. The EmSOM and Em-SOR-SOM algorithms have been compared with SBP and EmBP, and several other recent algorithms, and their effectiveness and efficiency have been numerically confirmed by the experiments we presented on the ORL face database and three benchmark credit datasets. [ABSTRACT FROM AUTHOR]

Object recognition in complex backgrounds has challenged the fields of pattern recognition for years. It is even harder when the targets in images are of different poses. Current methods use descriptors of characteristic vectors and machine learning algorithms to produce classifiers for object recognition. However, the generalization ability of these methods relies on the quality of the training phase and cannot find the precise boundaries of the targets. The geometric features of objects are the most stable and consistent features, so the recognition method based on shapes can be more intuitive than those based on color and texture features. This paper proposes a novel method that uses the images represented by line segments. The recognition mission becomes to effectively filter and combine them. The contour fragments after combinations are expected to satisfy the given model, or certain parts of it. In this way, object recognition can be viewed as a combinatorial optimization problem. This paper develops a genetic algorithm-based method to solve this problem. The experimental results show that this method can solve the combinatorial optimization problem effectively and can accurately distinguish the contour of the target object from the background. This method, which is based on geometric features, may contribute to the development of explicit principals for the description of object structure and recognition method based on symbolic reasoning. [ABSTRACT FROM AUTHOR]

In order to deal with the pseudo-Gibbs phenomenon in the process of hyperspectral remote sensing image enhancement, a novel image enhancement method based on nonsubsampled shearlet transform (NSST) is proposed in this paper. The main motivation of this study is to adjust the coefficient of remote sensing image enhancement as a pattern recognition task. Firstly, the input image is decomposed into a low-frequency component and some high-frequency components by NSST decomposition; Secondly, the guided filter is applied to process the low-frequency component to improve the contrast, and the improved fuzzy contrast is used to suppress the noise of the high-frequency components; Thirdly, the processed coefficients of low-frequency and high-frequency are reconstructed by inverse nonsubsampled shearlet transform (INSST), and the final enhanced image is obtained. The experimental results demonstrate that the proposed approach has obvious advantages in terms of objective data and subjective vision. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation is a crucial process in several applications such as feature extraction and pattern recognition. In this paper, a novel Masi entropy-based criterion for color satellite image multilevel thresholding is proposed. The proposed algorithm is based on Masi entropy which can deal with the additive/non-extensive information through the aid of a concordant entropic parameter 'r' which is extended in favor of multilevel based color satellite image segmentation. In addition, a comparative study between proposed Masi entropy-based color image multilevel thresholding and well known state-of-the-art entropies such as Kapur's, Renyi's and Tsallis entropy is presented. The simulation results of the proposed Masi entropy-based algorithm illustrate better performance for normal and color satellite image segmentation. Trials are conducted on various color test images to concrete the efficiency of the proposed algorithm. For segmentation purpose numerous fidelity parameters are computed such as structural similarity index (SSIM), feature similarity index (FSIM), misclassification error (ME), mean square error (MSE) and peak signal to noise ratio (PSNR). [ABSTRACT FROM AUTHOR]

In the area of machine learning and pattern recognition, object classification is getting an attraction due to its range of applications such as visual surveillance. In recent times, numerous deep learning-based methods are presented for object classification but still, set of problems/concerns exists which reduce the overall classification accuracy. Complex background, congest situtaions, and similarity among different objects are few challenging issues. To tackle such problems, we propose a technique by using deep convolutional neural network (DCNN) and scale invariant features transform (SIFT). First, an improved saliency method is implemented, and the point features are extracted. Then, DCNN features are extracted from two deep CNN models like VGG and AlexNet. Thereafter, Reyni entropy-controlled method is implemented on DCNN pooling and the SIFT point matrix to select the robust features. Finally, the selected robust features are fused in a matrix by a serial approach, which is later fed to ensemble classifier for recognition. The proposed method is evaluated on three publically available datasets including Caltech101, Barkley 3D, and Pascal 3D and obtained classification accuracy of 93.8%, 99%, and 88.6% - clearly showing the exceptional performance compared to existing methods. [ABSTRACT FROM AUTHOR]

Pollination syndromes evolved under the reciprocal selection of pollinators and plants (coevolution). Here, the two main methods are reviewed which are applied to prove such selection. (i) The indirect method is a cross-lineage approach using phylogenetical trees to understand the phylogeny. Thus, features of single origin can be distinguished from those with multiple origins. Nearly all pollination modes originate in multiple evolutionary ways. (ii) The most frequent pollinators cause the strongest selection because they are responsible for the plant's most successful reproduction. The European sexually deceptive orchid genus Ophrys provides an example of a more direct way to prove selection because the attraction of a pollinator is species specific. Most members of the genus have remarkably variable flowers. The variability of the signals given off by the flowers enables the deceived pollinator males to learn individual flower patterns. They thus avoid already visited Ophrys flowers, interpreting them as females rejecting them. As the males will not return to these individually recognizable flowers, the pollinators´ learning behavior causes cross-pollination and prevents the orchid's self-pollination. [ABSTRACT FROM AUTHOR]

Research in cognitive psychology has focused mainly on the visual modality as the input interface for mental processes. We suggest that integrating studies from different modalities can aid in resolving theoretical controversies. We demonstrate this in the case of subitizing. Subitizing, the quick and accurate enumeration of small quantities, has been studied since the 19th century. Nevertheless, to date, the underlying mechanism is still debated. Two mechanisms have been suggested: a domain-general mechanism—attention, and a domain-specific mechanism—pattern recognition. Here, we review pivotal studies in the visual, tactile, and auditory modalities. The accumulative findings shed light on the theoretical debate. Accordingly, we suggest that subitizing is a subprocess of counting that occurs in the presence of facilitating factors, such as attentional resources and familiar patterns. [ABSTRACT FROM AUTHOR]

The "facies" is a frequently used term for describing sedimentary units. In the literature, this term has been used for different purposes, as depositional environment, rock type, lithofacies, etc. In subsurface geology, the core samples are essential for facies studies. While lacking cored intervals, the well logs provide precious subsurface information, but the complexity of well log responses leads most of the time to the complexity of interpretations. In this paper, a method is proposed to study the facies types through well logs. The case study is a carbonate platform system, deposited in the upper Cretaceous, named Sarvak Formation, in one of Iranian onshore oilfields, located in the Abadan Plain. For this purpose, parametric and non-parametric (k-nearest neighbor) classifiers were applied to the dataset. Detailed petrography, assisted by core descriptions, led to 37 microfacies, grouped into three main lithofacies, four carbonate rock types and six environmental facies. Classifiers could not identify the microfacies due to a limited number of observations and high variations. However, the environmental facies were truly classified. In addition, lithofacies classification and Dunham carbonate rock typing were carried out correctly. It is discussed that k-nearest neighbor is clearly the outperformed classifier, and the classical parametric models were inadequate due to the nature of the input well logs (dependency of well logs and may be their distribution), and the overlapping of the input feature space. [ABSTRACT FROM AUTHOR]

The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processing and pattern recognition technology can easily classify these images, identify the subcellular location of proteins, and subsequently carry out related work such as analysis and investigation of protein function. Here, based on a fluorescence microscopy 2D image dataset of HeLa cells, the CapsNet network model was used to classify ten types of images of proteins in different subcellular compartments. Capsules in the CapsNet network model were trained to capture the possibility of certain features and variants rather than to capture the characteristics of a specific variant. The capsule at the same level predicted the instantiation parameters of the higher level capsule through the transformation matrix, and the higher level capsule became active when multiple dynamic routing forecasts were consistent. Experiments show that using the CapsNet network model to classify 2D HeLa datasets can achieve higher accuracy. Graphical abstract ᅟ. [ABSTRACT FROM AUTHOR]

It is well known that high impedance contrasts between salt bodies and their surroundings yield blurriness in seismic images which does not allow adequate interpretation; some of the artifacts generated by this process are multiple reflections and diffractions. In this work we propose a novel method of detection and attenuation of multiples from seismic gathers which is based on pattern recognition and fuzzy logic. In order to corroborate the effectiveness of this new approach, we use synthetic models which mimic a region intensely affected by salt tectonics in deep-water Gulf of Mexico. We obtained synthetic seismic sections by simulating wave propagation with an FDTD–CPML formulation. Detection of seismic events is performed through the mapping of parabolic shapes in shot gathers that approximate the recorded wavefronts. Undesired multiple events are then surgically attenuated by using fuzzy threshold criteria. Results show an optimal attenuation of multiple events, even when they are not directly related to salt structures. [ABSTRACT FROM AUTHOR]

The artificial neural networks (ANNs) are increasingly being used to solve the problem of pattern recognition, but it is an arduous task for their designer to obtain the optimal topology to be used for ANN training since this is considered a very difficult problem. Even after several tests, the optimized topology may not be reached. A possible solution for this problem is the use of a hybrid intelligent system; an optimization technique is used together with the ANN in order to search for an optimized topology. This paper applies this concept, using the genetic algorithms for the optimization of the topology of a multilayer perceptron, used for the classification of wrist orientation, muscle contraction levels and subjective parameters of the voice. The data were preprocessed with wavelet packet transform. The tool presents promising results above 96% all the way up to 99% of total hits, with 98% and 90% reliability. [ABSTRACT FROM AUTHOR]

A Pythagorean fuzzy set is one of the successful extensions of the intuitionistic fuzzy set to handle the uncertain and fuzzy information in a more wider way. In this paper, some new exponential similarity measures (SMs) for measuring the similarities between objects are proposed. For it, we used the exponential function for the membership and the non-membership degrees and hence defined some series of the SMs for PFSs. The various desirable properties and their relations are examined. Several counter-intuitive cases are given to show the effectiveness of the proposed measures with the existing SMs. Furthermore, examples to classify the pattern recognition and the decision-making problems are presented and compared with the existing approaches. [ABSTRACT FROM AUTHOR]