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Traditionally, physical models of associative memory assume conditions of equilibrium. Here, we consider a prototypical oscillator model of associative memory and study how active noise sources that drive the system out of equilibrium, as well as nonlinearities in the interactions between the oscillators, affect the associative memory properties of the system. Our simulations show that pattern retrieval under active noise is more robust to the number of learned patterns and noise intensity than under passive noise. To understand this phenomenon, we analytically derive an effective energy correction due to the temporal correlations of active noise in the limit of short correlation decay time. We find that active noise deepens the energy wells corresponding to the patterns by strengthening the oscillator couplings, where the more nonlinear interactions are preferentially enhanced. Using replica theory, we demonstrate qualitative agreement between this effective picture and the retrieval simulations. Our work suggests that the nonlinearity in the oscillator couplings can improve memory under nonequilibrium conditions. [ABSTRACT FROM AUTHOR]

Pre-chamber spark ignition system enhances the combustion stability and efficiency of lean burn engines. Pre-chamber spark ignition system performance directly relies on the characteristics of the turbulent flame jet, which are governed by the pre-chamber geometrical parameters and nozzle hole configurations. Based on the existing literatures, limited research on nozzle hole numbers and patterns is noted with passive pre-chamber, and thus, the impact of these nozzle hole configurations on the combustion and flame development characteristics for hydrogen-methane-air mixtures is investigated in a constant volume combustion chamber. In the current study, single-layer nozzle hole configurations with three, four, six, and eight nozzle holes and double-layer nozzle hole configurations with six and eight nozzle holes are designed and tested in an optically accessible combustion chamber under various equivalence ratio conditions. The results indicated that the combustion characteristics deteriorated with an increase in nozzle holes, except with the single-layer four nozzle hole configuration. Compared to the single-layer eight nozzle hole configuration, the ignition delay and combustion duration for hydrogen-methane-air mixtures with single-layer four nozzle hole configuration was reduced by 31.9% and 30.7% under lean conditions. A reduction in flame jet penetration length, area and velocity and increased adjacent flame jet interaction was noticed for the single-layer configuration with more nozzle holes, and thus, a different nozzle hole pattern was studied in the current study. The double-layer configurations with more nozzle holes showed better combustion and flame development characteristics than single-layer configurations due to their unique flame structure, uniform and faster flame jet distribution, and reduced adjacent flame jet interaction. The ignition delay and combustion duration for hydrogen-methane-air mixtures with the double layer eight nozzle hole configuration were reduced by 30.5% and 40.7% compared to the single-layer eight nozzle hole configuration. [Display omitted] • Combustion characteristics deteriorated with an increase in pre-chamber nozzle holes. • SL-H4 configuration exhibited better combustion and flame development characteristics. • Increased jet interaction and reduced jet penetration was noted for more nozzle holes. • DL-H8 configuration improved the combustion process with a unique flame structure. [ABSTRACT FROM AUTHOR]

Background: Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results: We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions: The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes. [ABSTRACT FROM AUTHOR]

We prove formulae for the F 2 -Rasmussen invariant of satellite knots of patterns with wrapping number 2, using the multicurve technology for Khovanov and Bar-Natan homology developed by Kotelskiy, Watson, and the second author. A new concordance homomorphism, which is independent of the Rasmussen invariant, plays a central role in these formulae. We also explore whether similar formulae hold for the Ozsváth–Szabó invariant 휏. [ABSTRACT FROM AUTHOR]

Lizard species of wide distribution can show differences in morphology and many other characteristics, in particular in the scutelation. In this study, we analyzed the variation of 22 meristic characters between sexes and among populations of Sceloporus variabilis from the state of Hidalgo in central Mexico. The variables such as dorsal scales, scales around the body, ventral scales, femoral pores, interfemoral scales, and lamellae of the fourth digit of the hindlimbs were different among populations; the population of San Pablo Tetlapayac presented the highest average values in these variables. A positive and significant relationship between snout–vent length and the number of ventral scales was found only in the females of San Pablo Tetlapayac and Santa Catarina. The variations recorded in the present study indicate an effect of environmental variation in the pattern of the number of scales, and suggest that both environmental conditions, such as precipitation and temperature, and ecological conditions, such as substrate or microhabitats, may be influencing differences in the number and size of scales among populations. Further studies on physiology and ecomorphology are necessary to determine the direction of variations in scutelation between sexes and populations. [ABSTRACT FROM AUTHOR]

Sparse matrix–matrix multiplication (SpMM) is essential for deep learning models and scientific computing. Recently, Tensor Cores (TCs) on GPUs, originally designed for dense matrix multiplication with mixed precision, have gained prominence. However, utilizing TCs for SpMM is challenging due to irregular memory access patterns and a varying number of non-zero elements in a sparse matrix. To improve data locality, previous studies have proposed reordering sparse matrices before multiplication, but this adds computational overhead. In this paper, we propose Tensor Core-Adapted SpMM (TCA-SpMM), which leverages TCs without requiring matrix reordering and uses the compressed sparse row (CSR) format. To optimize TC usage, the SpMM algorithm's dot product operation is transformed into a blocked matrix–matrix multiplication. Addressing load imbalance and minimizing data movement are critical to optimizing the SpMM kernel. Our TCA-SpMM dynamically allocates thread blocks to process multiple rows simultaneously and efficiently uses shared memory to reduce data movement. Performance results on sparse matrices from the Deep Learning Matrix Collection public dataset demonstrate that TCA-SpMM achieves up to 29.58× speedup over state-of-the-art SpMM implementations optimized with TCs. [ABSTRACT FROM AUTHOR]

The penetration rate of water supply via water supply facilities in the Republic of Korea has reached 99%, with 94% of the energy for operation consumed by pumps transporting water. Consequently, developing efficient pump operation techniques is crucial for reducing energy costs in water supply systems. This study employs real-coded genetic algorithm techniques to compute optimized demand patterns, considering the utilization of water tanks within networks. Hydraulic appropriateness is verified by evaluating pressure within nodes determined by derived patterns through numerical analysis simulations. Furthermore, after calculating flows supplied to the networks, pump power is determined, and resultant energy costs are estimated to evaluate economic feasibility. Results indicate that pressure distribution in networks with optimal patterns is hydraulically appropriate, meeting hydrodynamic pressure conditions suggested in water supply design standards. Additionally, this study demonstrates a 9% reduction in network energy costs compared to existing patterns. The model presented herein offers a means to efficiently operate water supply systems through water tanks, thereby reducing energy costs. [ABSTRACT FROM AUTHOR]

Integrating Industry 4.0 and Quality 4.0 optimises manufacturing through IoT and ML, improving processes and product quality. The primary challenge involves identifying patterns in computer numerical control (CNC) machining time‐series data to boost manufacturing quality control. The proposed solution involves an experimental study comparing one‐class and binary classification algorithms. This study aims to classify time‐series data from CNC turning machines, offering insight into monitoring and adjusting tool wear to maintain product quality. The methodology entails extracting spectral features from time‐series data to train both one‐class and binary classification algorithms, assessing their effectiveness and computational efficiency. Although certain models consistently outperform others, determining the best performing is not possible, as a trade‐off between classification and computational performance is observed, with gradient boosting standing out for effectively balancing both aspects. Thus, the choice between one‐class and binary classification ultimately relies on dataset's features and task objectives. [ABSTRACT FROM AUTHOR]

This paper investigates a Cahn–Hilliard–Swift–Hohenberg system, focusing on a three-species chemical mixture subject to physical constraints on volume fractions. The resulting system leads to complex patterns involving a separation into phases as typical of the Cahn–Hilliard equation and small scale stripes and dots as seen in the Swift–Hohenberg equation. We introduce singular potentials of logarithmic type to enhance the model's accuracy in adhering to essential physical constraints. The paper establishes the existence and uniqueness of weak solutions within this extended framework. The insights gained contribute to a deeper understanding of phase separation in complex systems, with potential applications in materials science and related fields. We introduce a stable finite element approximation based on an obstacle formulation. Subsequent numerical simulations demonstrate that the model allows for complex structures as seen in pigment patterns of animals and in porous polymeric materials. [ABSTRACT FROM AUTHOR]

Mathematical foundations and algorithms for recognizing bichromatic two-dimensional graphic codes, regardless of their type (QR codes, DataMatrix, GridMatrix, etc.) are presented. The stages of achieving the result include detecting the code, localizing it within an arbitrary quadrilateral, transforming the quadrilateral to a canonical square, constructing a grid of elements (modules) of the square code, and filling it with a sequence of bits. It is shown that perspective transformation formulas make it possible to transform localized quadrangular regions to canonical squares with an acceptable error level for further processing. A flat grid of square code elements is formed based on the search for extrema of the derivatives of the pixel intensity distribution of the square image along the axes x and y. The algorithm for filling grid cells (code modules) with a sequence of zeros and ones uses information about the average intensity of each such cell. At the end of the paper, the algorithms are tested on a variety of real images of two-dimensional codes, and the limitations of the proposed algorithms are examined. [ABSTRACT FROM AUTHOR]

The present work investigates whether financial abilities can be associated with numerical abilities and with general cognitive abilities. We compared performance on numerical and financial tests, and on tests routinely used to measure general cognitive performance, in healthy controls and in a group of people with heterogeneous pathological conditions including mild cognitive impairment, amyotrophic lateral sclerosis, traumatic brain injury, and schizophrenia. Patients showed lower performances in both numerical and financial abilities compared to controls. Numerical and financial skills were positively correlated in both groups, but they correlated poorly with measures of general cognitive functioning. Crucially, only basic financial tasks -such as counting currencies- but not advanced ones -like financial judgments- were associated with numerical or general cognitive functioning in logistic regression analyses. Conversely, advanced financial abilities, but not basic ones, were associated with abstract reasoning. At a qualitative analysis, we found that deficits in numerical and financial abilities might double dissociate. Similarly, we observed double dissociations between difficulties in financial abilities and cognitive deficits. In conclusion, financial abilities may be independent of numerical skills, and financial deficits are not always related to the presence of cognitive difficulties. These findings are important for both clinical and legal practice. [ABSTRACT FROM AUTHOR]

In this paper, the structural performances of steel plate shear walls employing web plates with rectangular, triangular (zigzag), trapezoidal, and sinusoidal corrugation patterns under monotonic loading are investigated. Several significant structural response parameters including buckling stability, stiffness, strength, serviceability, and energy dissipation capability are considered in this research endeavor. The results of this study provide crucial insights into the behavior of corrugated-web steel shear walls with varying infill plate shapes and thicknesses. It is shown that corrugated-web shear walls predominantly undergo local and/or interactive buckling modes, while flat-web systems exhibit more of a global buckling behavior. Additionally, corrugated infill plates substantially enhance the overall capacity of the system, with the rectangular pattern exhibiting the highest strength, surpassing the flat-web panels by approximately 11 times for a 2-mm web-plate thickness. Assessment of the energy absorption capacity reveals the superior performance of the rectangular pattern for web thicknesses between 2.0 and 3.0 mm. From the load–displacement responses, it is found that increasing of the web thickness significantly enhances the strength and stiffness performances and the base shear capacity is improved by 42% while increasing the thickness from 1.5 to 3.0 mm. These results demonstrate the importance of proper design and detailing in ensuring the desirable and high performance of corrugated-web steel shear walls as promising lateral force-resisting systems. [ABSTRACT FROM AUTHOR]

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Prior studies have failed to adequately address intangible characteristics and lacked a comprehensive quantification of cultural dimensions. Additionally, such works have not merged supervised and unsupervised classification methodologies. To address these gaps, this study employed multidimensional numerical techniques for precise spatial pattern recognition and urban morphology classification at the block scale. By examining building density, mean floor numbers, functional compositions, and street block mixed-use intensities, alongside historical and contemporary cultural assets within blocks—with assigned weights and entropy calculations from road networks, building vectors, and POI data—a hierarchical categorization of high, medium, and low groups was established. As a consequence, cluster analysis revealed seven distinctive morphology classifications within the studied area, each with unique spatial configurations and evolutionary tendencies. Key findings include the dominance of high-density, mixed-use blocks in the urban core, the persistence of historical morphologies in certain areas, and the emergence of new, high-rise clusters in recently developed zones. The investigation further elucidated the spatial configurations and evolutionary tendencies of each morphology category. These insights lay the groundwork for forthcoming studies to devise morphology-specific management strategies, thereby advancing towards a more scientifically grounded, rational, and precision-focused approach to urban morphology governance. [ABSTRACT FROM AUTHOR]

This study took a person-centered approach to delinquency trajectories to identify unobserved heterogeneity among Korean male adolescents. A series of latent class growth analyses were fit to five-wave panel data from the Korean Children and Youth Panel Survey – from the sixth grade of elementary school through grade 10 of high school. Three trajectory classes were obtained from the data: Early-onset and decreasing (4.3%); late peak (4.6%); and rare offender (91.1%). The conditional application of the group-based models included self-domains, motivations, and constrains variables as time-specific predictors within the framework of Agnew’s (2005) general theory of crime and delinquency. The overall findings revealed that the link between life domains and class membership was fully mediated by motivations and constraints. [ABSTRACT FROM AUTHOR]

Effective exploration of the pattern dynamic behaviors of reaction-diffusion models is a popular but difficult topic. The Schnakenberg model is a famous reaction-diffusion system that has been widely used in many fields, such as physics, chemistry, and biology. Herein, we explore the stability, Turing instability, and weakly non-linear analysis of the Schnakenberg model; further, the pattern dynamics of the fractional-in-space Schnakenberg model was simulated numerically based on the Fourier spectral method. The patterns under different parameters, initial conditions, and perturbations are shown, including the target, bar, and dot patterns. It was found that the pattern not only splits and spreads from the bar to spot pattern but also forms a bar pattern from the broken connections of the dot pattern. The effects of the fractional Laplacian operator on the pattern are also shown. In most cases, the diffusion rate of the fractional model was higher than that of the integer model. By comparing with different methods in literature, it was found that the simulated patterns were consistent with the results obtained with other numerical methods in literature, indicating that the Fourier spectral method can be used to effectively explore the dynamic behaviors of the fractional Schnakenberg model. Some novel pattern dynamics behaviors of the fractional-in-space Schnakenberg model are also demonstrated. [ABSTRACT FROM AUTHOR]

The tensile failure of complex branching cracks will cause irreversible damage to the structures. In this paper, the effects of the crack orientations, the local branch lengths, and the different load forms on the crack propagation pattern and the bearing capacity of structures are investigated based on the phase field method and experimental research method. The results show that the bearing capacity and propagation patterns of the specimens change significantly with the change of the orientation of the Y-shaped crack under tensile load. The local branch length of the crack only affects the sequence in which the regional crack reaches the final failure and the bearing capacity of the specimen. Under the dynamic impact load, the bifurcation phenomenon occurs, and with the increase of the dynamic impact load, the number of bifurcations increases. The crack propagation pattern in the numerical simulation is completely consistent with the experiment, and the difference in the maximum reaction force is 2.5%. [ABSTRACT FROM AUTHOR]

Layered rocks were prepared to conduct Brazilian splitting tests with different laminar inclinations. Cracks on the surface are identified and quantitatively characterized based on digital images. The discrete element numerical software PFC3D was utilized to model the Brazilian splitting of laminated rocks with different inclinations. The effect of cohesion strength, tensile strength, and joint roughness coefficient (JRC) of smooth joints on tensile strength was investigated. The findings reveal that with increasing the inclination angle (β), the properties of the laminar surface gradually play a dominant role in the specimen damage, demonstrating that the peak load decreases continuously. The variation in the fractal dimension of the cracks primarily undergoes three stages: continuous increase, sharp decrease, and increase again. The tensile strength and failure patterns of the numerical simulations correlate well with the test. When β = 0°, the tensile strength of the specimen is independent of the joint properties; As β increases, the influence becomes apparent. Specifically, the tensile strength gradually increases with the increase in joint cohesion. Except when β = 90°, the tensile strength shows initially increasing and then leveling off with increasing the tensile strength of joints. Whereas, under the influence of the bedding plane roughness, it exhibits a continuous decrease at low inclination and an increase at high inclination with increasing JRC. [ABSTRACT FROM AUTHOR]

Stochastic delayed modeling has a significant non-pharmaceutical intervention to control transmission dynamics of infectious diseases and its results are close to the reality of nature. The covid-19 has been controlled globally but there is still a threat and appears in different variants like omicron and SARS-CoV-2 etc. globally. This article, considered pattern a mathematical model based on Susceptible, Infected, and recovered populations with highly nonlinear incidence rates. we studied the dynamics of the coronavirus model; a newly proposed version is a stochastic delayed model that is based on nonlinear stochastic delayed differential equations (SDDEs). Transition probabilities and parametric perturbation methods were used for the construction of the stochastic delayed model. The fundamental properties like positivity, boundedness, existence and uniqueness, and stability results of equilibria of the model with certain conditions of reproduction number are studied regularly. Also, the extinction and persistence of disease are studied with the help of well-known theorems. The numerical methods used to find a visualization of results due to the complexity of stochastic delayed differential equations. Furthermore, for computational analysis, we implemented existing methods in the literature and compared their results with the proposed method like nonstandard finite difference for stochastic delayed model. The proposed method restores all dynamical properties of the model with a free choice of time steps. [ABSTRACT FROM AUTHOR]

Social interactions are often analyzed by scoring segments of predefined behavior and then statistically assessing numerical and sequential patterns to identify the structure of the encounters. However, this approach can miss the dynamics of the animals' relationship over the course of the encounter, one that often involves invariant bonds, say a nose-to-nose orientation, with many different movements performed by both partners acting to counteract each other's attempts to break or maintain the relationship. Moreover, these invariant bonds can switch from one configuration to another during an interaction, leading from one stable configuration to another. It is this stepwise sequence of configurational stabilities that lead to functional outcomes, such as mating, aggression, or predation. By focusing on the sequence of invariant relational configurations, the deep structure of interactions can be discerned. This deep structure can then be used to differentiate between compensatory movements, no matter how seemingly stereotyped they may appear, from movement patterns which are restricted to a particular form when more than one option is available. A dynamic perspective requires suitable tools for analysis, and such tools are highlighted as needed in describing particular interactions. [ABSTRACT FROM AUTHOR]

Animals use a small number of morphogens to pattern tissues, but it is unclear how evolution modulates morphogen signaling range to match tissues of varying sizes. Here, we used single-molecule imaging in reconstituted morphogen gradients and in tissue explants to determine that Hedgehog diffused extracellularly as a monomer, and rapidly transitioned between membrane-confined and -unconfined states. Unexpectedly, the vertebrate-specific protein SCUBE1 expanded Hedgehog gradients by accelerating the transition rates between states without affecting the relative abundance of molecules in each state. This observation could not be explained under existing models of morphogen diffusion. Instead, we developed a topology-limited diffusion model in which cell-cell gaps create diffusion barriers, which morphogens can only overcome by passing through a membrane-unconfined state. Under this model, SCUBE1 promoted Hedgehog secretion and diffusion by allowing it to transiently overcome diffusion barriers. This multiscale understanding of morphogen gradient formation unified prior models and identified knobs that nature can use to tune morphogen gradient sizes across tissues and organisms. [ABSTRACT FROM AUTHOR]

This article discusses the mathematical communities that Jim Simons supported throughout his life. Simons was a renowned mathematician, a successful investor, and a generous philanthropist. He founded the Simons Foundation in 1994, which supports research in mathematics and the basic sciences. Simons also contributed to various organizations and initiatives, such as the Institute for Advanced Study, the Math for America program, the Simons Center for Geometry and Physics, and the National Museum of Mathematics. His philanthropic efforts aimed to foster collaboration and advance knowledge in mathematics and science. [Extracted from the article]

The ability to recognize different driving styles of surrounding vehicles is crucial to determine the safest and most efficient driving decisions, prevent accidents, and analyze the causes of traffic accidents. Understanding if the surrounding vehicle is aggressive or cautious can greatly assist in the decision making of vehicles in terms of whether and when it is appropriate to make particular maneuvers. A driver's driving style usually changes with the environment, which brings great challenges to the current research. To this end, a dynamic driving-style analysis framework, in which drivers' interactions with other vehicles are considered, is proposed in this article. First, by analyzing common traffic scenarios, five surrounding vehicles are selected as the environmental vehicles to be considered. Time headway (THW) and time to collision (TTC) that can consider the relative speed and position with the ego vehicle are selected as the clustering indicators. Then, a Bayesian nonparametric learning method based on a hierarchical Dirichlet-process hidden semi-Markov model (HDP-HSMM) is introduced to extract primitive driving patterns from time-series driving data without prior knowledge of the number of these patterns. Then the driving pattern is scored according to the risk degree. A driver's aggressiveness is scored and drivers are divided into different styles based on the frequency distribution of driving patterns. The effectiveness of the proposed method is demonstrated on a real-world vehicle trajectory data set where results show that driving pattern switches and more complex driving behaviors can be better captured and understood semantically. [ABSTRACT FROM AUTHOR]

The experimental study presented in this paper aimed to investigate the effects of bolt number and their pattern on the failure time of minimally fire-protected glulam beam bolted connections. Four full-size glulam beam-end connection configurations reinforced perpendicular to wood grain with self-tapping screws (STS) were experimentally tested under exposure to elevated temperatures that followed the CAN/ULC S101 (Standard methods of fire endurance tests of building construction and materials. Underwriters Laboratories of Canada, Fifth edition, Ottawa, Canada, 2019) standard fire time–temperature curve. All metal connecting components (i.e., bolt heads and nuts and steel plate edges) were fire protected using wood plugs and strips, respectively. Throughout the duration of fire tests, all specimens were subjected to monotonic load causing bending moment equivalent to the maximum moment design capacity of the weakest unreinforced connection configuration. Fire test results show that the failure time of all four proposed wood–steel–wood connection configurations surpassed the 45-min mark, which is the minimum prescribed fire resistance rating for timber connections in applicable codes, with the two configurations that employed six bolts sustained the applied load for more than 60 min in standard fire conditions. Increasing the number of bolts from four to six enhanced the failure time of the proposed connection configurations with time increments more than those due to changing the bolt pattern. Most importantly, test results confirm that reinforcing the connections with STS fully curtailed wood splitting and averted row shear failure that is frequently encountered in similar but unreinforced connection configurations. [ABSTRACT FROM AUTHOR]

Number sense is fundamental to the development of numerical problem‐solving skills. In early childhood, children establish associations between non‐symbolic (e.g., a set of dots) and symbolic (e.g., Arabic numerals) representations of quantity. The developmental estrangement theory proposes that the relationship between non‐symbolic and symbolic representations of quantity evolves with age, with increased dissociation across development. Consistent with this theory, recent research suggests that cross‐format neural representational similarity (NRS) between non‐symbolic and symbolic quantities is correlated with arithmetic fluency in children but not in adolescents. However, it is not known if short‐term training (STT) can induce similar changes as long‐term development. In this study, children aged 7–10 years underwent a theoretically motivated 4‐week number sense training. Using multivariate neural pattern analysis, we investigated whether short‐term learning could modify the relation between cross‐format NRS and arithmetic skills. Our results revealed a significant correlation between cross‐format NRS and arithmetic fluency in distributed brain regions, including the parietal and prefrontal cortices, prior to training. However, this association was no longer observed after training, and multivariate predictive models confirmed these findings. Our findings provide evidence that intensive STT during early childhood can promote behavioral improvements and neural plasticity that resemble and recapitulate long‐term neurodevelopmental changes that occur from childhood to adolescence. More generally, our study contributes to our understanding of the malleability of number sense and highlights the potential for targeted interventions to shape neurodevelopmental trajectories in early childhood. Research Highlights: We tested the hypothesis that short‐term number sense training induces the dissociation of symbolic numbers from non‐symbolic representations of quantity in children.We leveraged a theoretically motivated intervention and multivariate pattern analysis to determine training‐induced neurocognitive changes in the relation between number sense and arithmetic problem‐solving skills.Neural representational similarity between non‐symbolic and symbolic quantity representations was correlated with arithmetic skills before training but not after training.Short‐term training recapitulates long‐term neurodevelopmental changes associated with numerical problem‐solving from childhood to adolescence. [ABSTRACT FROM AUTHOR]

In this exploratory descriptive study, changes in one cohort's responses to an authentic statistical investigation at the commencement of years 3 and 4 were analysed. Forty-four students made predictions by interpreting a data table of historical monthly temperatures, represented these data and explained their reasoning. An Awareness of Mathematical Pattern and Structure (AMPS) framework was extended to analyse students' responses at five increasing levels of predictive reasoning. More developed predictive reasoning was observed in year 4 than for year 3, as well as large individual differences in both years. Most year 4 students (87%) made predictions within the historical range, relative to half the same cohort in year 3 (54%). More year 4 students (79%) made predictions based on extraction, clustering and aggregation of these data than those in year 3 (51%). Year 4 students noticed patterns such as seasonal trends and variability in these data and observed measures of central tendency. By year 4, 57% of students' representations demonstrated transnumeration using extracted data from the table, including pictorial, column and line graphs. However, most students' representations and explanations of these data lagged behind their predictions at both year levels. [ABSTRACT FROM AUTHOR]

The task of accurately predicting scientific impact and ranking the researcher based on impact has emerged as a crucial research challenge, captivating the interest of scholars across diverse domains. This task holds immense importance in enhancing research efficiency, aiding decision-making processes, and facilitating scientific evaluations. For this, the scientific community has put forth a wide array of parameters to identify the most influential researchers. These include citation count, total publication count, hybrid methodologies, the h-index, and also its extended or modified versions. But still, there is a lack of consensus on a single optimal parameter for identifying the most influential author. In this study, we introduce a novel index derived from learning hidden patterns within the mathematics field dataset, comprising data from 1050 researchers evenly split between awardees and non-awardees. Initially, we ranked selected parameters by assessing their values for individual researchers, identifying the top five parameters that most frequently placed awardees within the top 100 records. Additionally, we employed deep learning techniques to identify the top five influential parameters from the initially selected set. Subsequently, we evaluated the disjointness between the results produced by these parameters. To further refine our analysis, we assessed seven different statistical models for combining the top disjoint pair to retain the maximum properties of both parameters. The study's findings revealed that the gf and k indices exhibited a 0.96 percent disjointness ratio, establishing them as the highest disjoint pair. Moreover, the geometric mean demonstrated a 0.87 percent average impact in retaining the properties of the top disjoint pair, surpassing the other seven models. As a result of this study, we propose a new index obtained by taking the geometric mean of the top disjoint pair which increase the result by 12% as compared to existing best performing individual index performance. [ABSTRACT FROM AUTHOR]

The exponential growth in internet data usage has created a pressing demand for highly efficient Network Intrusion Detection Systems (NIDS) capable of scaling with ever-increasing bandwidths to safeguard sensitive information. A cornerstone of NIDS, packet inspection, hinges on the ability to rapidly identify and analyze patterns within incoming data streams. The more diverse and extensive the pattern database, the more robust and effective the NIDS becomes. While the parallel failure-less version of the Aho-Corasick (AC) algorithm provides maximum parallelism, it faces significant memory constraints due to the large transition tables generated when dealing with a vast number of patterns. To mitigate this limitation and enhance the scalability of NIDS, we introduce a novel parallel failure-less compressed hashed variation of the Aho-Corasick algorithm. Our proposed approach leverages the power of compression and hashing techniques to significantly reduce memory consumption without compromising performance. Empirical evaluations demonstrate that our algorithm requires only a fraction (approximately the square root) of the memory footprint compared to the original parallel failure-less Aho-Corasick algorithm, making it a more practical and scalable solution for modern NIDS architectures. [ABSTRACT FROM AUTHOR]

Fuzzy sets possess remarkable abilities in expressing and handling information uncertainty, which has resulted in their widespread application in various fields. Nevertheless, distance measurement between IFSs for quantitating their differences and levels of differentiation has remained an open problem that deserves attention. Despite the development of various metrics, they either lack intuitive insight or do not satisfy the axioms of distance measurement, leading to counterintuitive results. To address these issues, this paper proposed a distance measurement method based on Clark divergence, which satisfies the distance measurement axioms and exhibits nonlinearity. Numerical examples demonstrate that our method effectively distinguishes different indicators, yielding more reasonable results. Moreover, when comparing relative differences of the results, our method demonstrated superior adaptability to complex environmental decision-making, providing decision-makers with more accurate and confidential judgments. In our numerical and pattern classification application tests, we achieve an accuracy of 98%, a 40% increase in computing time efficiency and a relative diversity improvement of 35%. The pattern classification algorithm designed in this paper will offer a promising solution to inference problems. [ABSTRACT FROM AUTHOR]

Homomorphic Encryption (HE) allows for arbitrary computation of encrypted data, offering a method for preserving privacy in cloud computations. However, efficiency remains a significant obstacle, particularly with the polynomial multiplication of large parameter sets, which occupies substantial computing and memory overhead. Prior studies proposed the use of Number Theoretic Transform (NTT) to accelerate polynomial multiplication, which proved efficient, owing to its low computational complexity. However, these efforts primarily focused on NTT designs for small parameter sets, and configurability and memory efficiency were not considered carefully. This paper focuses on designing a unified NTT/Inverse NTT (INTT) architecture with high area efficiency and configurability, which is more suitable for HE schemes. We adopt the Constant-Geometry (CG) NTT algorithm and propose a conflict-free access pattern, demonstrating a 16.7% reduction in coefficients of storage capacity compared to the state-of-the-art CG NTT design. Additionally, we propose a twiddle factor generation strategy to minimize storage for Twiddle Factors (TFs). The proposed architecture offers configurability of both compile time and runtime, allowing for the deployment of varying parallelism and parameter sets during compilation while accommodating a wide range of polynomial degrees and moduli after compilation. Experimental results on the Xilinx FPGA show that our design can achieve higher area efficiency and configurability compared with previous works. Furthermore, we explore the performance difference between precomputed TFs and online-generated TFs for the NTT architecture, aiming to show the importance of online generation-based NTT architecture in HE applications. [ABSTRACT FROM AUTHOR]

Investigating different pier shapes and debris Finteractions in scour patterns is vital for understanding the risks to bridge stability. This study investigates the impact of different shapes of pier and debris interactions on scour patterns using numerical simulations with flow-3D and controlled laboratory experiments. The model setup is rigorously calibrated against a physical flume experiment, incorporating a steady-state flow as the initial condition for sediment transport simulations. The Fractional Area/Volume Obstacle Representation (FAVOR) technique and the renormalized group (RNG) turbulence model enhance the simulation's precision. The numerical results indicate that pier geometry is a critical factor influencing the scour depth. Among the tested shapes, square piers exhibit the most severe scour, with depths reaching 5.8 cm, while lenticular piers show the least scour, with a maximum depth of 2.5 cm. The study also highlights the role of horseshoe, wake, and shear layer vortices in determining scour locations, with varying impacts across different pier shapes. The Q-criterion study identified debris-induced vortex generation and intensification. The debris amount, thickness, and pier diameter (T/Y) significantly affect the scouring patterns. When dealing with high wedge (HW) debris, square piers have the largest scour depth at T/Y = 0.25, while lenticular piers exhibit a lower scour. When debris is present, the scour depth rises at T/Y = 0.5. Depending on the form of the debris, a significant fluctuation of up to 5 cm was reported. There are difficulties in precisely estimating the scour depth under complicated circumstances because of the disparity between numerical simulations and actual data, which varies from 6% for square piers with a debris relative thickness T/Y = 0.25 to 32% for cylindrical piers with T/Y = 0.5. The study demonstrates that while flow-3D simulations align reasonably well with the experimental data under a low debris impact, discrepancies increase with more complex debris interactions and higher submersion depths, particularly for cylindrical piers. The novelty of this work lies in its comprehensive approach to evaluating the effects of different pier shapes and debris interactions on scour patterns, offering new insights into the effectiveness of flow-3D simulations in predicting the scour patterns under varying conditions. [ABSTRACT FROM AUTHOR]

In Older to explore the shape characteristics and practical value of the petal pattern of painted pottery from the Dawenkou culture, and to solve the application problems of traditional patterns in modern design, the shape characteristics, organizational form and aesthetic characteristics of petal patterns were analyzed and summarized through literature review and field investigation. Shape grammar was introduced to extract design factors from the petal pattern of painted pottery, and objective constraints were formulated based on generative derivative rules, a large number of pattern design schemes with the characteristics of Dawenkou culture were generated, and applied to modan sock design. Through target audience research and evaluation, the feasibility of innovative patterns in modern design was verified, and a new idea and solution for the current innovative design and application of traditional Chinese patterns was provided. [ABSTRACT FROM AUTHOR]

Recognizing patterns is an essential skill in early mathematics education. However, first graders often have difficulties with tasks such as extending patterns of the form ABCABC. Studies show that this pattern-recognition ability is a good predictor of later pre-algebraic skills and mathematical achievement in general, or the development of mathematical difficulties on the other hand. To be able to foster children's pattern-recognition ability, it is crucial to investigate and understand their pattern-recognition processes early on. However, only a few studies have investigated the processes used to recognize patterns and how these processes are adapted to different patterns. These studies used external observations or relied on children's self-reports, yet young students often lack the ability to properly report their strategies. This paper presents the results of an empirical study using eye-tracking technology to investigate the pattern-recognition processes of 22 first-grade students. In particular, we investigated students with and without the risk of developing mathematical difficulties. The analyses of the students' eye movements reveal that the students used four different processes to recognize patterns—a finding that refines knowledge about pattern-recognition processes from previous research. In addition, we found that for patterns with different units of repeat (i.e. ABABAB versus ABCABCABC), the pattern-recognition processes used differed significantly for students at risk of developing mathematical difficulties but not for students without such risk. Our study contributes to a better understanding of the pattern-recognition processes of first-grade students, laying the foundation for enhanced, targeted support, especially for students at risk of developing mathematical difficulties. [ABSTRACT FROM AUTHOR]

The paper expounds a simple non-cooperative game, which can model discrimination in market and non-market transactions. The model has two equilibriums where individuals do not discriminate and several where they do. One non-discriminatory equilibrium dominates in the sense of Pareto all discriminatory equilibriums. The second non-discriminatory equilibrium is dominated by all other equilibriums. While there is no discrimination in the first-best outcome, the introduction of discrimination can thus be Pareto-improving in some circumstances. For instance, in the model, a move from an equilibrium where they face discrimination to another equilibrium where they discriminate too can sometimes make the members of a group better off, without making anyone else worse off. Another implication of the model is that interactions between officials of the state and ordinary citizens generate a greater number of potential patterns of discrimination than interactions between ordinary citizens. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rapid environmental change poses unprecedented challenges to species persistence. To understand the extent that continued change could have, genomic offset methods have been used to forecast maladaptation of natural populations to future environmental change. However, while their use has become increasingly common, little is known regarding their predictive performance across a wide array of realistic and challenging scenarios. Here, we evaluate the performance of currently available offset methods (gradientForest, the Risk‐Of‐Non‐Adaptedness, redundancy analysis with and without structure correction and LFMM2) using an extensive set of simulated data sets that vary demography, adaptive architecture and the number and spatial patterns of adaptive environments. For each data set, we train models using either all, adaptive or neutral marker sets and evaluate performance using in silico common gardens by correlating known fitness with projected offset. Using over 4,849,600 of such evaluations, we find that (1) method performance is largely due to the degree of local adaptation across the metapopulation (LA), (2) adaptive marker sets provide minimal performance advantages, (3) performance within the species range is variable across gardens and declines when offset models are trained using additional non‐adaptive environments and (4) despite (1) performance declines more rapidly in globally novel climates (i.e. a climate without an analogue within the species range) for metapopulations with greater LA than lesser LA. We discuss the implications of these results for management, assisted gene flow and assisted migration. [ABSTRACT FROM AUTHOR]

Copyright of Education Policy Analysis Archives / Archivos Analíticos de Políticas Educativas / Arquivos Analíticos de Políticas Educativas is the property of Educational Policy Analysis Archives & Education Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to develop a detection method based on morphological features of spike–wave (SW) patterns in the EEG of epilepsy patients and evaluate the effect of cathodal transcranial direct current stimulation (ctDCS) treatment. The proposed method is based on several simple features describing the shape of SW patterns and their synchronous occurrence on at least two EEG channels. High sensitivity, specificity and selectivity values were achieved for each patient and condition. ctDCS resulted in a significant reduction in the number of detected patterns, a decrease in spike duration and amplitude, and an increased spike mobility. The proposed method allows efficient identification of SW patterns regardless of brain condition, although the recruitment of patterns may be modified by ctDCS. This method can be useful in the clinical evaluation of ctDCS effects. [ABSTRACT FROM AUTHOR]

Serial crystallography (SX) involves combining observations from a very large number of diffraction patterns coming from crystals in random orientations. To compile a complete data set, these patterns must be indexed (i.e. their orientation determined), integrated and merged. Introduced here is TORO (Torch‐powered robust optimization) Indexer, a robust and adaptable indexing algorithm developed using the PyTorch framework. TORO is capable of operating on graphics processing units (GPUs), central processing units (CPUs) and other hardware accelerators supported by PyTorch, ensuring compatibility with a wide variety of computational setups. In tests, TORO outpaces existing solutions, indexing thousands of frames per second when running on GPUs, which positions it as an attractive candidate to produce real‐time indexing and user feedback. The algorithm streamlines some of the ideas introduced by previous indexers like DIALS real‐space grid search [Gildea, Waterman, Parkhurst, Axford, Sutton, Stuart, Sauter, Evans & Winter (2014). Acta Cryst. D70, 2652–2666] and XGandalf [Gevorkov, Yefanov, Barty, White, Mariani, Brehm, Tolstikova, Grigat & Chapman (2019). Acta Cryst. A75, 694–704] and refines them using faster and principled robust optimization techniques which result in a concise code base consisting of less than 500 lines. On the basis of evaluations across four proteins, TORO consistently matches, and in certain instances outperforms, established algorithms such as XGandalf and MOSFLM [Powell (1999). Acta Cryst. D55, 1690–1695], occasionally amplifying the quality of the consolidated data while achieving superior indexing speed. The inherent modularity of TORO and the versatility of PyTorch code bases facilitate its deployment into a wide array of architectures, software platforms and bespoke applications, highlighting its prospective significance in SX. [ABSTRACT FROM AUTHOR]

ALK detection was performed on 2813 EGFR-unmutated NSCLC cases by simultaneous use of immunohistochemistry (VENTANA® anti-ALK D5F3, Roche Molecular Systems, Inc., Rotkreuz, Switzerland) and fluorescence in situ hybridization with the ALK break apart and the ALK/EML4 fusion probe (ZytoVision, Bremerhaven, Germany). A total of 33 cases were positive discordant (FISH-positive, IHC-negative) and 17 cases were negative discordant (FISH-negative, IHC-positive). This study's aim was to reevaluate the methods used and compare discordant samples to positive concordant samples in order to ellucidate the differences. FISH signal variants were examined and compared. Positive discordant cases featured one pattern of ALK rearrangement in 41.4%, two patterns in 48.3%, and three patterns in 10.3% of analysed samples, with a higher variability of detected patterns and a higher number of ALK copy gains. Positive concordant cases displayed one pattern of rearrangement in 82%, two patterns in 17.8%, and three patterns in 0.6% of analysed samples. The association between number of patterns and concordance/discordance was statistically significant (p < 0.05). Eleven positive discordant and two negative concordant cases underwent NGS analysis, which resulted in identification of ALK fusion in one positive discordant and two negative discordant cases. Positive protein expression regardless of FISH result correlated more with a positive NGS result compared to samples with a positive FISH result with negative protein expression. FISH analysis was able to detect atypical or heterogenous patterns of rearrangement in a proportion of cases with negative protein expression, which may be associated with more extensive genetic alterations rather than true ALK rearrangement. [ABSTRACT FROM AUTHOR]

Due to mutual interference between various components, the transitional motion of fluid particles in the flow field of pump jet propulsion becomes more complex and variable. Additionally, the vortex structure at the wake can undergo severe contraction and deformation. To further improve the accuracy of predicting the hydrodynamic performance of pump jet propulsion, this paper proposes a time-varying, space-varying, and induced velocity-varying free wake numerical calculation model based on potential flow theory. A set of hydrodynamic performance prediction methods that consider the interference of the free wake model is also established. Furthermore, a velocity smoothing model based on time reversal is constructed based on the distribution pattern of numerical singularity points during the induced velocity calculation process, which enhances the robustness of the calculation program. By comparing and analyzing the numerical calculation results with experimental results, including pressure, circulation distribution, and hydrodynamic performance, the accuracy of the free wake model proposed in this paper is verified. The results also demonstrate that the free wake model proposed in this paper can effectively improve the prediction accuracy of the hydrodynamic performance of the pump jet propulsion. [ABSTRACT FROM AUTHOR]

Researchers agree that both domain‐general skills and domain‐specific skills contribute to mathematics knowledge, but questions arise as to which skills can and should be trained to improve children's learning outcomes. In this article, we synthesize research on training three domain‐general constructs in early childhood (patterning skills, working memory, and spatial reasoning) and their causal links to mathematics knowledge. The results are clear: Practice with these domain‐general tasks is unlikely to transfer to higher scores on measures of mathematics knowledge, especially for preschool‐aged children. Based on this evidence, we argue against using isolated domain‐general training to enhance math knowledge in early childhood. We offer recommendations for researchers and practitioners to optimize best practices in this area. [ABSTRACT FROM AUTHOR]

The theory of limits of permutations leads to limit objects called permutons, which are certain Borel measures on the unit square. We prove that permutons avoiding a given permutation of order k$$ k $$ have a particularly simple structure. Namely, almost every fiber of the disintegration of the permuton (say, along the x‐axis) consists only of atoms, at most (k−1)$$ \left(k-1\right) $$ many, and this bound is sharp. We use this to give a simple proof of the "permutation removal lemma." [ABSTRACT FROM AUTHOR]

Historical architecture has different special styles attributed to each era, dynasty, or region. These styles are common features such as geometric properties, ratios, scales, colors, and artistic techniques. Historical geometric ornaments have an enormous capability for classification based on their geometric characteristics. Smart pattern recognition allows researchers to classify huge databases of heritage for useful internet searches. So, our main goal in this paper is to implement the detection of categories in geometric patterns for classification and documentation in which by the photography of ornaments in every monument, the type of patterns and the number of every type of pattern would be estimated quickly. Furthermore, due to occurring deterioration in these patterns, our method also contributes to recognizing the deteriorated patterns. When we encounter numerous pieces of deteriorated patterns, manual recognition in order to reassemble and reconstruct is usually impossible or time-consuming. With the aid of artificial intelligence, in this paper, our aim is to seek to solve the automatic recognition of historical geometric patterns, even patterns having deterioration as an occlusion via image processing and machine learning methods. A challenging issue that researchers would tackle in detecting historical geometric pattern's types is the variety in geometric textures, especially when they have occlusion such as deterioration. This issue leads to limited success in classifying via extracting only one feature. The other issue is that the extracted feature must be invariant to the transformation, such as scale, rotation, and noise variation. To cope with the challenges mentioned above and accurately classify, we plan to use the fusion method based on extracting global and local features. So, the features extracted from images in this research are based on local and global. In other words, the proposed fusion strategy lies both in feature and decision level, but the core is the proposed three combination methods in fusion decision methods. In this method, the dataset is composed of four main Persian geometric pattern types: Tond dah, Kond tablghenas, Hashtva 4 lengeh, and HashtvatablKond. So, the model will be trained by extracting global and local features of the images separately. Random forest, as the prevalent machine learning algorithm, is proposed for training data and predicting the class of input images. Finally, the probability of prediction for random forest classifiers is fused by the Decision Templates (DT) combiner, Naïve Bayes (NB) combiner, and Dempster–Shafer combination methods. In comparison with the individual classifier accuracy results of 80% and 85% for global and local features respectively, our proposed approach achieves an improved accuracy of 90%, 88%, and 90% in three fusion decision methods including DT combiner, NB combiner, and Dempster–Shafer combination methods. [ABSTRACT FROM AUTHOR]

Background: To master the secondary–tertiary transition into fields of science, technology, engineering, and mathematics (STEM), academic self-beliefs play a pivotal role, especially those related to learning mathematics. The framework of expectancy-value theory has been used widely in primary and secondary education and partly in tertiary education to assess the self-beliefs of students in terms of expectancy of success and perceived value of mathematics. Based on this framework, we measured how the intrinsic value, the attainment value, the utility value, and the cost of learning mathematics as well as the expectancy of success when learning mathematics developed during the secondary–tertiary transition of students into STEM fields. Data were collected in a quantitative repeated-measures questionnaire study with two measurement points (measurement point 1: n = 710, measurement point 2: n = 487, listwise: n = 409). We conducted a latent profile analysis to identify the prevalent patterns of mathematics self-beliefs, called profiles, at each of the two measurement points. We studied the relation of these profiles to prior education, achievement at school, and achievement at university. By performing a latent transition analysis, we determined the probabilities of transitioning from the initial profiles to the posterior profiles. Results: Our analysis revealed four distinct prevalent profiles at each measurement point, ranging from highly favorable (i.e., high expectancy, high value, low cost) to highly unfavorable with respect to learning mathematics. The profiles with favorable manifestations remained stable over time, while those with undesirable manifestations deteriorated further. We observed a sharp increase in cost across all profiles. Prior achievement correlated strongly with profile membership. Conclusions: The expenditure of time and energy increased sharply during the secondary–tertiary transition, independently of the students' initial motivational patterns. The perceived utility of mathematics for potential future careers was shown to be a significant source of motivation. The role of mathematics in future careers should thus be made visible in university teaching. Keeping the detrimental development of initially undesirable motivational profiles in mind, university teachers should create ample opportunities for students to gain a sense of accomplishment. [ABSTRACT FROM AUTHOR]

To examine the level of number line estimation (NLE) in Chinese children with respect to representations of both numerical (Arabic numerals) and nonnumerical symbols (dots), a total of 192 Chinese preschoolers aged between 4 and 5 years participated in four different NLE tasks. These tasks were paired to evaluate the accuracy and patterns of children's estimations in both numerical and non-numerical symbol contexts. Our findings indicate that, for Chinese preschoolers, relatively precise numerical symbol representations begin to emerge as early as 4 years of age. The accuracy of number line estimates for both 4- and 5-year-old children gradually increases in tasks involving both numerical and non-numerical symbols. Additionally, the development and patterns observed in the number line estimates of 4- and 5-year-old Chinese preschoolers are similar in both numerical symbol and non-numerical symbol tasks. These results indicate that the initiation of relatively precise numerical symbol representation and the turning point in the developmental trajectory, where the relatively precise representation for numerical symbols surpasses that of non-numerical ones, occur earlier in Chinese children than in their Western counterparts. [ABSTRACT FROM AUTHOR]

This study provides an in-depth analysis of the frequency of extreme streamflow in Italy, adopting the innovative perspective of the theory of records, and focusing on record-breaking floods. (i.e., annual maximum series, AMS) observed in Italy between 1911 and 2020. Our research employs an extensive dataset of 522 annual maximum series (AMS) of streamflow observed across Italy between 1911 and 2020. We consider three time intervals (1911–2020, 1911–1970, and 1971–2020), and we define pooling-groups of AMSs based on (a) hydrological (e.g., catchment size, mean annual precipitation, etc.) and (b) spatial proximities of the gauged sites. First, within each group and for each time period, we compute the regional average number of record-breaking events (NRbins). Second, with a series of resampling experiments that preserve the spatial correlation among the AMSs, we test the hypothesis that NRbins result from a group of stationary sequences. Our results show spatially coherent patterns of an increasing number of record-breaking floods in central and in northeastern Italy over the last 50 years. In the same time interval, significant deviations in the regional number of record-breaking events from what would be expected for stationary flood sequences seem to be more common in drier climates or at higher altitudes, while the catchment size does not seem to be a meaningful descriptor. [ABSTRACT FROM AUTHOR]

This paper develops a new method for analyzing the relationship between a set of points and another single point, the latter of which we call a reference point. This relationship has been discussed in various academic fields, such as geography, criminology, and epidemiology. Analytical methods, however, have not yet been fully developed, which has motivated this paper. Our method reveals how the number of points varies by the distance from a reference point and by direction. It visualizes the spatial pattern of points in relation to a reference point, describes the point pattern using mathematical models, and statistically evaluates the difference between two sets of points. We applied the proposed method to analyze the spatial pattern of the climbers of Mt. Azuma, Japan. The result gave us useful and interesting findings, indicating the method's soundness. [ABSTRACT FROM AUTHOR]