Pattern Map Research Articles

Hyperbolic Sine Function Control-Based Finite-Time Bipartite Synchronization of Fractional-Order Spatiotemporal Networks and Its Application in Image Encryption

This work is devoted to the hyperbolic sine function (HSF) control-based finite-time bipartite synchronization of fractional-order spatiotemporal networks and its application in image encryption. Initially, the addressed networks adequately take into account the nature of anisotropic diffusion, i.e., the diffusion matrix can be not only non-diagonal but also non-square, without the conservative requirements in plenty of the existing literature. Next, an equation transformation and an inequality estimate for the anisotropic diffusion term are established, which are fundamental for analyzing the diffusion phenomenon in network dynamics. Subsequently, three control laws are devised to offer a detailed discussion for HSF control law’s outstanding performances, including the swifter convergence rate, the tighter bound of the settling time and the suppression of chattering. Following this, by a designed chaotic system with multi-scroll chaotic attractors tested with bifurcation diagrams, Poincaré map, and Turing pattern, several simulations are pvorided to attest the correctness of our developed findings. Finally, a formulated image encryption algorithm, which is evaluated through imperative security tests, reveals the effectiveness and superiority of the obtained results.

Mapping patterns of thought onto brain activity during movie-watching.

Movie-watching is a central aspect of our lives and an important paradigm for understanding the brain mechanisms behind cognition as it occurs in daily life. Contemporary views of ongoing thought argue that the ability to make sense of events in the 'here and now' depend on the neural processing of incoming sensory information by auditory and visual cortex, which are kept in check by systems in association cortex. However, we currently lack an understanding of how patterns of ongoing thoughts map onto the different brain systems when we watch a film, partly because methods of sampling experience disrupt the dynamics of brain activity and the experience of movie-watching. Our study established a novel method for mapping thought patterns onto the brain activity that occurs at different moments of a film, which does not disrupt the time course of brain activity or the movie-watching experience. We found moments when experience sampling highlighted engagement with multi-sensory features of the film or highlighted thoughts with episodic features, regions of sensory cortex were more active and subsequent memory for events in the movie was better-on the other hand, periods of intrusive distraction emerged when activity in regions of association cortex within the frontoparietal system was reduced. These results highlight the critical role sensory systems play in the multi-modal experience of movie-watching and provide evidence for the role of association cortex in reducing distraction when we watch films.

Accounting for functionality in the identification of global conservation priorities: promises and pitfalls.

Whereas preventing species extinctions remains a central objective of conservation efforts, it must be complemented by the long-term preservation of functional ecosystems and of the benefits humans derive from them. Here, I review recent approaches that explicitly account for functionality in setting large-scale conservation priorities, discussing their promise while highlighting challenges and pitfalls. Crossing data on species' distributions and ecological traits has enabled the mapping of global patterns of functional diversity and functional rarity and the identification of species that stand out for their functional distinctiveness. However, the priorities identified through these general indices do not directly address ecosystem functionality, instead, they are methods for ensuring the representation of individual functional traits as intrinsically valuable biodiversity elements. Three other approaches integrate functionality into large-scale priorities by taking into account the specific context of each ecosystem, site or species: the International Union for Conservation of Nature's Red List of Ecosystems, Key Biodiversity Areas and the Green Status of Species. Currently at various stages of development, testing and implementation, these approaches are playing an increasingly important role in the definition, implementation and monitoring of global- and national-scale conservation strategies to ensure the long-term persistence of ecosystem functions and associated ecosystem services.This article is part of the discussion meeting issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.

Exploring genetic mapping and co-expression patterns to illuminate significance of Tbx20 in cardiac biology.

The transcription factor Tbx20 is integral to heart development and plays a significant role in various cardiac diseases. Despite its established importance, the regulatory mechanisms and functional significance of Tbx20 remain incompletely understood. To elucidate these mechanisms, we initially conducted eQTL mapping to identify genetic loci associated with Tbx20 expression in heart tissue from BXD mice. Co-expression and enrichment analyses revealed pathways linked to Tbx20, including dilated cardiomyopathy, hypertrophic cardiomyopathy, and FoxO signaling. Additionally, protein-protein interaction studies identified essential cardiac proteins, such as Myl2 and Myl7, along with upstream regulators like Mef2c. To validate our bioinformatic findings, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to assess the relative mRNA expression levels of TBX20 and Mef2c in the heart tissues of BXD mice compared to their parental strains (B6 and D2). Our results demonstrated significant up-regulation of both TBX20 and Mef2c in the BXD group relative to the parental strains. Conversely, both genes were down-regulated in B6, D2, Control, and Treatment groups when compared to BXD mice. These findings confirm the predicted regulatory roles of TBX20 and Mef2c in cardiac development as suggested by our initial analyses.This study not only reinforces the critical role of Tbx20 in cardiac gene regulation but also highlights its potential as a therapeutic target for cardiovascular disorders. Further investigations into Tbx20 and its interactions will enhance our understanding of heart biology and contribute to the development of targeted therapies for heart diseases.

Shade watch: Mapping citywide shade dynamics through ray tracing and LiDAR data in Hong Kong's complex 3-D built environment

The importance of urban shade in city planning has been underscored by its various associations with thermal comfort, living environment quality, human health and well-being. However, existing methods for urban shade mapping are often limited to street-level scopes or singular sampling observational times, leaving citywide shade mapping, its spatiotemporal patterns, and controlling factors largely unexplored. To fill these knowledge gaps, this study proposed a novel protocol that enables seamless mapping of spatiotemporal urban shade patterns over the entire city by fusing look-up-table (LUT)-based ray-tracing approach with Google Earth Engine cloud computing technology, as well as high-resolution digital surface model (DSM) derived from LiDAR data. This protocol was applied to Hong Kong, a city characterized by a complex 3-D built environment, and then delved into the spatiotemporal patterns of urban shade and the associated controlling factors. Validation results indicate that the proposed method can accurately quantify urban shade compared to ground-based photo references and high-resolution satellite imagery. The analysis uncovers that: (1) Shade exhibits spatial heterogeneity across different 3-D built environments while maintaining a consistent "U-shape" diurnal shade fraction during the day; (2) Solar geometry, regardless of solar zenith and azimuth angle, is positively associated with aggregated metrics of shade landscape and negatively associated with fragmented metrics. Moreover, solar zenith angles exert stronger controls over the citywide shade landscape patterns; and (3) Persistent shade significantly reduces the sunlight hours (i.e., the accumulated sunshine hours per day) across administrative districts, local climate zones (LCZs), and seasons. Due to shade effects originating from 3-D urban structures, Hong Kong typically experiences an average of 4-8 sunlight hours per day within a one-year cycle, which is substantially lower than the 11-14 hours of natural daylength. This study offers a practical protocol for large-scale urban shade mapping, enhancing our understanding of urban shade, its spatiotemporal dynamics, and benefits in a broader spatiotemporal context. The associated datasets and findings from this study can inform urban planners and policymakers in developing effective strategies to create healthier and sustainable urban environments in Hong Kong.

COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartments with Epigenomic Information.

Chromatin compartmentalization and epigenomic modification are crucial in cell differentiation and diseases development. However, precise mapping of chromatin compartmental patterns requires Hi-C or Micro-C data at high sequencing depth. Exploring the systematic relationship between epigenomic modifications and compartmental patterns remains challenging. To address these issues, we present COCOA, a deep neural network framework using convolution and attention mechanisms to infer fine-scale chromatin compartment patterns from six histone modification signals. COCOA extracts 1-D track features through bi-directional feature reconstruction after resolution-specific binning epigenomic signals. These track features are then cross-fused with contact features using an attention mechanism and transformed into chromatin compartment patterns through residual feature reduction. COCOA demonstrates accurate inference of chromatin compartmentalization at a fine-scale resolution and exhibits stable performance on test sets. Additionally, we explored the impact of histone modifications on chromatin compartmentalization prediction through in silico epigenomic perturbation experiments. Unlike obscure compartments observed with 1 kb resolution high-depth experimental data, COCOA generates clear and detailed compartmental patterns, highlighting its superior performance. Finally, we demonstrated that COCOA enables cell-type-specific prediction of unrevealed chromatin compartment patterns in various biological processes, making it an effective tool for gaining chromatin compartmentalization insights from epigenomics in diverse biological scenarios. The COCOA python code is publicly available at https://github.com/onlybugs/COCOA.

Annual Cropping Intensity Dynamics in China from 2001 to 2023

Spatial and temporal information about cropping patterns of single and multiple crops is important for monitoring crop production and land-use intensity. We used time-series MODIS NDVI 8-day composite data to develop annual cropping pattern products at a 250 m spatial resolution for China, covering the period from 2001 to 2023. To address the potential impacts of varying parameters in both data pre-processing and the peak detection algorithm on the accuracy of cropping pattern mapping, we employed a grid-search method to fine-tune these parameters. This process focused on optimizing the Savitzky–Golay smoothing window size and the peak width parameters using a calibration dataset. The results highlighted that an optimal combination of a five to seven MODIS composite window size in Savitzky–Golay smoothing and a peak width of four MODIS composites achieved good overall mapping accuracy. Pixel-wise accuracy assessments were conducted for the selected mapping years of 2001, 2011, and 2021. Overall accuracies were between 89.7% and 92.0%, with F1 scores ranging from 0.921 to 0.943. Nationally, this study observed a fluctuating trend in multiple cropping percentages, with a notable increase after 2013, suggesting shifts toward more intensive agricultural practices in recent years. At a finer spatial scale, the combination of Mann–Kendall and Sen’s slope analyses revealed that approximately 12.9% of 3 km analytical windows exhibited significant changes in cropping intensity. We observed spatial clusters of increasing and decreasing crop intensity trends across provinces such as Hebei, Shandong, Shaanxi, and Gansu. This study underscores the importance of data smoothing and peak detection methods in analyzing high temporal resolution remote sensing data. The generation of annual single/multiple cropping pattern maps at a 250 m spatial resolution enhances our comprehension of agricultural dynamics through time and across different regions.

Geospatial mapping of terrain dynamics and rainfall patterns for hazard mitigation in Sikkim

Geospatial mapping of terrain dynamics and rainfall patterns for hazard mitigation in Sikkim

Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts.

Cerebrospinal Fluid Flow within Ventricles and Subarachnoid Space Evaluated by Velocity Selective Spin Labeling MRI.

This study aims to evaluate cerebrospinal fluid (CSF) flow dynamics within ventricles, and the subarachnoid space (SAS) using the velocity selective spin labeling (VSSL) MRI method with Fourier-transform-based velocity selective inversion preparation. The study included healthy volunteers who underwent MRI scanning with specific VSSL parameters optimized for CSF flow quantification. The VSSL sequence was calibrated against phase-contrast MRI (PC-MRI) to ensure accurate flow velocity measurements. The CSF flow patterns observed in the ventricles were consistent with those obtained using 3D amplified MRI and other advanced MRI techniques, verifying the reliability of the VSSL method. The VSSL method successfully measured CSF flow in the SAS along major arteries, including the middle cerebral artery (MCA), anterior cerebral artery (ACA), and posterior cerebral artery (PCA), with an average flow velocity of 0.339 ± 0.117 cm / s . The diffusion component was well suppressed by flow-compensated gradients, enabling comprehensive mapping of the rapid CSF flow pattern in the SAS system and ventricles. The flow pattern in the SAS system closely resembles the recently discovered perivascular subarachnoid space (PVSAS) system. CSF flow around the MCA, PCA, and ACA arteries in the SAS exhibited a weak orientation dependency. CSF flow in the ventricles was also measured, with an average flow velocity of 0.309 ± 0.116 cm / s , and the highest velocity observed along the superior-inferior direction. This study underscores the potential of VSSL MRI as a non-invasive tool for investigating CSF dynamics in both SAS and ventricles.

Anatomy of trade disputes: mapping patterns and trends of WTO dispute initiations (1995–2023)

Anatomy of trade disputes: mapping patterns and trends of WTO dispute initiations (1995–2023)

Bicriteria Shapes: Hierarchical Grouping and Aggregation of Polygons with an Efficient Graph-Cut Approach

An important task of pattern recognition and map generalization is to partition a set of disjoint polygons into groups and to aggregate the polygons within each group to a representative output polygon. We introduce a new method for this task called bicriteria shapes. Following a classical approach, we define the output polygons by merging the input polygons with a set of triangles that we select from a conforming Delaunay triangulation of the input polygons’ exterior. The innovation is that we control the selection of triangles with a bicriteria optimization model that is efficiently solved via graph cuts. In particular, we minimize a weighted sum that combines the total area of the output polygons and their total perimeter. In a basic problem, we ask for a single solution that is optimal for a preset parameter value. In a second problem, we ask for a set containing an optimal solution for every possible value of the parameter. We discuss how this set can be approximated with few solutions and show that it is hierarchically nested. Hence, the output is a hierarchical clustering that corresponds to multiple levels of detail. An evaluation with building footprints as input and a comparison with α -shapes that are based on the same underlying triangulation conclude the article. An advantage of bicriteria shapes compared to α -shapes is that the sequence of solutions for decreasing values of the parameter is monotone with respect to the total perimeter of the output polygons, resulting in a monotonically decreasing visual complexity.

Social4Science: Mapping Trends and Patterns in Discussions of Scientific Publications on Social Media

With the growing use of social media, it has become increasingly important to understand how scientific publications are disseminated and discussed on these online platforms. Analyzing this data on the interaction and circulation of scientific research has been investigated in altmetrics studies and can provide valuable information on how science is perceived and shared by the general public. This work aims to propose a platform for collecting and analyzing social data related to scientific publications, with a focus on the video-sharing platform YouTube. By collecting data from YouTube, the platform seeks to understand how scientific publications are disseminated and discussed on social media. In the solution developed, called Social4Science, it is possible to obtain social data from YouTube and correlate it with scientific data from publications. This approach makes it possible to identify trends and patterns in discussions about scientific publications on social media. The results obtained show that the proposed platform is very promising in providing a deeper understanding of the interaction between science and the public, and also opens up possibilities for future studies on this subject.

Short- and Medium-Range Predictability of Warm-Season Derechos. Part I: Operational Predictability and Composites

Abstract This study analyzes the operational predictability of warm-season (May–August) progressive derechos. A subset of 47 derechos occurring between 2010 and 2022 were selected based on NCEI Storm Data and radar imagery. The Storm Prediction Center Convective Outlooks issued from 5 days before each derecho to the day of the event were used to determine whether the severe weather and derecho predictability was low, moderate, or high, for each case. The cases were also categorized based on the synoptic-scale midlevel flow pattern. Composite maps of synoptic patterns associated with the derecho cases and distributions of severe weather parameters in the derecho inflow region were generated. About 72% of the 47 derechos selected for this study were associated with low predictability, and the remainder were categorized with moderate predictability. None of the cases had high predictability. Most derechos occurring under northwest- and zonal-flow regimes (80% and 85%, respectively) had low predictability. Composites of low- and moderate-predictability derechos indicate that derechos formed in the equatorward entrance region of the upper-level jet, where low-level warm advection exists in conjunction with an equivalent potential temperature maximum. A midlevel trough is located upstream of the derecho initiation point in many of the moderate-predictability composites but is absent in the low-predictability composites, which indicates weaker synoptic-scale forcing for ascent in low-predictability cases. The distributions of severe weather parameters for low- and moderate-predictability derechos are similar, suggesting that these parameters alone are not particularly useful as a discriminator of derecho predictability. Significance Statement Derechos are characterized by swaths of damaging wind gusts caused by an organized group of long-lived convective storms. These events are more frequent in the summer and are often difficult to predict far in advance. This study analyzes a subset of derecho cases to better understand the differences between cases with lower and higher predictability. About 72% of the 47 derechos examined in this study had low predictability. In these cases, the development and maintenance of storms are typically associated with larger uncertainty, but if persistent storms do form, there is potential to produce a derecho. These findings are part of ongoing work that examines ways to improve derecho forecasts with greater lead time.

Enhancing No Reference Laparoscopic Video Quality Assessment with Evolutionary ANFIS

Abstract Distortions in laparoscopic videos affect surgeon visibility and surgical precision, underscoring the need for sustained high video quality. This study presents a real-time laparoscopic video quality assessment algorithm independent of reference content availability. Statistical parameters derived from luminance, local binary pattern and motion-vector maps of video frames are observed to effectively discern distortion types and severities. These parameters are used to train an evolutionary adaptive neuro-fuzzy inference system (ANFIS) end-to-end with subjective score labels. Training and validation loss curves saturate at the 85th epoch, demonstrating the model’s efficient data fitting capability. Performance comparison with other state-of-the-art methods reveals superior results, with high correlation scores of 0.9989 and 0.9446 for experts and 0.9956 and 0.9847 for non-experts, alongside low root mean square errors of 0.0828 and 0.1685 for expert and non-experts, respectively. The model accurately replicates the expert and non-expert perceptual opinions, encouraging future research in stereoscopic, augmented, and virtual reality data.

Distinctive pattern mapping between cardiometabolic multimorbidity and neuropsychiatric disturbance in multi‐regional‐ethnical older adults

Distinctive pattern mapping between cardiometabolic multimorbidity and neuropsychiatric disturbance in multi‐regional‐ethnical older adults

Assessment Pattern Mapping in NANDA-I Nursing Diagnoses Framework by BERT Approach

Nurses play a crucial role in healthcare, directly influencing patient care quality. With a global nursing shortage, enhancing nursing efficiency and care quality is urgently needed. This foundational study explores the advantages of text and data processing techniques to determine NANDA-I nursing diagnoses using both subjective and objective patient data recorded by nurses. By employing text data similarity analysis and a prototype of the predictive model, our research aims to rene the nursing assessment process and facilitate the automation of nursing diagnoses. This work highlights the accuracy of BERT-based assessment pattern matching to support nursing practices and sets a platform for future research to address the nursing shortage effectively.

Analysis of CO2 bubble growth detachment kinetics in direct methanol fuel cell flow channels

CO2 bubbles flow in the anode flow channel is an important issue in the commercialization process of direct methanol fuel cells (DMFC). A T-channel model is built in COMSOL using the phase field method to investigate the CO2 bubbles flow at the anode side of the DMFC. The factors and mechanisms of single bubble detachment are discussed by analyzing the methanol inlet velocity (Ul), CO2 inlet velocity (Ug), contact angle of the diffusion layer, and the Weber number during bubble detachment. The research findings indicate that increasing the liquid flow rate leads to the generation of smaller bubbles that detach more rapidly due to the increases of drag force (FD) and the shear-life force (FSL) to overcome the surface tension on the bubble. The CO2 inlet velocity can promote the bubble detachment due to the increase in FSL, but also leads to a larger detachment diameter. Compared to hydrophobic surfaces, hydrophilic surfaces are more conducive to bubble detachment and removal. In all case We (Weber number) is significantly less than 0.6, indicating that liquid momentum dominated the bubble detachment process. Once the ratio of the gas momentum to the liquid one is greater than 1, the bubble is hard to detach. The contour map of bubble flow patterns and the bubble detachment diameters distribute with the ratio of Ug/Ul can further indicate that the bubble detachment is connected with the ratio of Ug/Ul closely, which will have guiding significance for the selection of inlet velocity of DMFC.

A depthwise convolutional neural network model based on active contour for multi-defect wafer map pattern classification

As semiconductor manufacturing processes continue to witness increased integration density and design complexity, semiconductor wafers are experiencing a growing diversity and complexity of defects. While previous research in wafer map classification using deep learning has made significant advancements in dealing with single defect patterns, the classification of mixed-type defects has received less attention due to their considerably higher difficulty level compared to single defects. This research addresses this critical gap, emphasizing the need for improved methods to classify mixed-type defects, which are more complex and challenging. To tackle this challenge, this paper introduces the active contour-based lightweight depthwise network (AC-LDN) model for the classification of multi-defect wafer map patterns. Initially, multi-defect features are extracted using an active contour-based segmentation model. Subsequently, the learning model employs a depthwise convolutional neural network (CNN) architecture that combines separable CNN and dilated CNN techniques. This unique approach optimizes the model in the separable segment while effectively addressing defect complexity in the depthwise segments. Consequently, AC-LDN outperforms other state-of-the-art models, offering a balance between lightweight characteristics and high accuracy. The proposed method demonstrates its superiority over previous models when evaluated on the extsdsensive multi-wafer map dataset, achieving an average classification accuracy exceeding 98% and a confusion matrix coefficient surpassing 0.97.

Disaggregation of climatic variables using a novel stochastic approach and its application in building performance simulation studies

Daily weather data, both observed and synthesized, are available for most locations worldwide but not at the sub-daily temporal scale, which is desirable for building performance analysis. In this context, we introduce a novel stochastic methodology based on pattern mapping for disaggregation of daily data into sub-daily (hourly) time resolution. The methodology has been tested and validated for three weather variables required for energy simulation (temperature, relative humidity and solar radiation). This approach yields better results than existing methodologies for the selected climate variables. Process efficiency is maintained even with the limited input data. Its stochastic nature enables time-invariant pattern mapping to generate future weather files. The annual ‘space conditioning energy’ was simulated to assess building performance accuracy using weather files for the hottest, coldest, average and test reference years (TRY) from disaggregated and observed datasets. Results show that the pattern mapping disaggregation methodology accurately downscales daily to sub-daily data.