Dimensional Patterns Research Articles

The Comparison of Urban Migration Patterns Between Eastern and Western During Holiday Seasons

This research paper examines and compares urban migration patterns during Chinese New Year and Christmas in Asian countries and Western countries. It explores the economic, cultural, and social factors that drive migration patterns and their implications for urbanization and globalization. The study draws on existing literature and data from secondary sources, including travel agencies and government agencies. Preliminary findings indicate that holiday travel during these seasons is influenced by various factors, such as economic opportunities, cultural practices, and social dynamics. Economic impacts are significant, contributing to local economies through increased consumer spending and tourism. However, challenges arise from overcrowding and resource strain, as well as potential inequalities resulting from consumerism and commercialization. Cultural and social implications shape migration patterns and impact urban landscapes, maintaining identity and belonging for diasporic communities and transforming public spaces. Understanding these dynamics can inform strategies to manage the impact of holiday travel and address challenges and opportunities associated with globalization, urbanization, and cultural exchange. This research contributes to a comprehensive understanding of the economic, cultural, and social dimensions of holiday migration patterns and their implications for urbanization and globalization.

Temperature and predators as interactive drivers of community properties.

The effects of warming on ecological communities emerge from a range of potentially asymmetric impacts on individual physiology and development. Understanding these responses, however, is limited by our ability to connect mechanisms or emergent patterns across the many processes that drive variation in demography. Further complicating this understanding is the gain or loss of predators to many communities, which may interact with changes in temperature to drive community change. Here we conducted a factorial warming and predation experiment to test generalized predictions about responses to warming. We used microcosms with a range of protists, rotifers, and a gastrotrich, with and without the predator Actinosphaerium, to assess changes in diversity, body size, function, and composition in response to warming. We find that community respiration and predator:prey biovolume ratios peak at intermediate temperatures, while species richness declined with temperature. We also found that overall biomass increased with species richness, driven by the effect of temperature on richness. There was little evidence of an interaction between predation and temperature change, likely because the predator was mostly limited to the intermediate temperatures. Overall, our results suggest that general predictions about community change are still challenging to make but may benefit by considering multiple dimensions of community patterns in an integrated way.

Magnetic tunnel junction platforms for linear positioning and nanoscale displacement sensing

An array of magnetic tunnel junctions (MTJs), connected in a Wheatstone bridge, is characterized as a positioning sensor to detect spatial displacement of a magnetized object. To perform the experiment, the sensor was mounted on a fixed frame, and the magnet was installed on top of nano-positioning piezo-stage, operating in a closed-loop feedback circuit. The sensor signal was recorded in time domain, to detect displacement of the magnet, defined by commands to the stage. The positioning transfer curves were obtained for different orientations of magnetization with respect to the direction of the magnet displacement and the field sensing direction of the MTJs in the arrays. For configurations with linear transfer curve between positioning displacements and the sensor signal, the effects of magnet size, sensor pattern dimensions and interspace distance between the sensor and magnet positioning trajectories, are characterized. In the optimized experimental configuration, the displacement steps down to 10 nm were detected.

MSFANet: Multi-Scale Strip Feature Attention Network for Cloud and Cloud Shadow Segmentation

Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when directly used to cloud and cloud shade detection and division, have a tendency to lose fine features and spatial data, leading to coarse segmentation of cloud and cloud shadow borders, false detections, and omissions of targets. To address the aforementioned issues, a multi-scale strip feature attention network (MSFANet) is proposed. This approach uses Resnet18 as the backbone for obtaining semantic data at multiple levels. It incorporates a particular attention module that we name the deep-layer multi-scale pooling attention module (DMPA), aimed at extracting multi-scale contextual semantic data, deep channel feature information, and deep spatial feature information. Furthermore, a skip connection module named the boundary detail feature perception module (BDFP) is introduced to promote information interaction and fusion between adjacent layers of the backbone network. This module performs feature exploration on both the height and width dimensions of the characteristic pattern to enhance the recovery of boundary detail intelligence of the detection targets. Finally, during the decoding phase, a self-attention module named the cross-layer self-attention feature fusion module (CSFF) is employed to direct the aggregation of deeplayer semantic feature and shallow detail feature. This approach facilitates the extraction of feature information to the maximum extent while conducting image restoration. The experimental outcomes unequivocally prove the efficacy of our network in effectively addressing complex cloud-covered scenes, showcasing good performance across the cloud and cloud shadow datasets, the HRC_WHU dataset, and the SPARCS dataset. Our model outperforms existing methods in terms of segmentation accuracy, underscoring its paramount importance in the field of cloud recognition research.

Modification Effect of Stone Column Dimensions and Configuration on Bearing Capacity Efficiency and Settlement in Soft Soils

In airport construction, one crucial aspect to consider is the condition of the underlying soil on the runway. Following soil investigations at X project, it was determined that the soil condition is primarily characterized by soft soil layers, with Standard Penetration Test (SPT) values ranging from 4 to 10. In response to this condition, soil reinforcement was carried out using the stone column method. The objective of this study was to compare the bearing capacity and settlement of stone columns under various dimensional variations (spacing of 1.5 m, 2.0 m, 2.2 m; diameters of 0.4 m, 0.5 m, 0.6 m) and configuration patterns (square and triangular). Bearing capacity calculations were done manually, and settlement calculations were performed both manually and with the assistance program. The results were presented in tables and graphs. Using the FHWA method for bearing capacity calculations, it was found that bearing capacity increases as the diameter is increased and the spacing is reduced. Conversely, bearing capacity decreases as the diameter is reduced and the spacing is increased. The ultimate bearing capacity value for the triangular pattern installation exceeded that of the square pattern. Regarding settlement calculations after manual stone column installation, it was observed that settlement decreases as the diameter increases and the spacing between columns decreases. Settlement increases under opposite conditions. Settlement calculations using the Plaxis program focused on diameter and spacing between stone columns. During the service life, the highest settlement occurred with the triangular pattern, while the square pattern exhibited the lowest settlement.

Research on Land Use Classification Method of High Resolution Remote Sensing Image Based on SVM

Considering the high spatial resolution remote sensing image has huge amounts of data and complex spectral distribution and the characteristics of the space characteristics of the rich, in combination with support vector machine (SVM) in tackling small sample, nonlinear and high dimensional pattern recognition problems show unique advantages, in this chapter will experiment data by using support vector machine (SVM) for high spatial resolution remote sensing image classification.

Dimensions of child maltreatment and longitudinal diurnal cortisol patterns: The roles of resilience and child sex.

Informed by the dimensional approach to adversity, this study disaggregated child maltreatment effects to examine how abuse versus neglect influenced cortisol at the baseline assessment and longitudinal changes in diurnal cortisol among a sample of Chinese children and adolescents (N = 312; aged 9-13 years; M age = 10.80, SD = 0.84; 67% boys). The moderating roles of resilience and sex differences in these associations were also explored. Results revealed distinct effects of abuse versus neglect on diurnal cortisol in girls, but not boys, which varied by the time scale of assessment and type of cortisol measure. Specifically, abuse was associated with girls' longitudinal changes in awakening cortisol, cortisol awakening response, and diurnal cortisol slope over one year, whereas neglect was associated with girls' awakening cortisol and cortisol awakening response at the baseline assessment. Further, resilience moderated the effects of abuse on girls' baseline awakening cortisol and longitudinal changes in diurnal cortisol slope, suggesting both the potential benefits and costs of resilience. Findings support the application of the dimensional approach to research on stress physiology and deepen our understanding of individual differences in the associations between child maltreatment and diurnal cortisol.

Fabrication of Nanogroove Arrays on Acrylic Film Using Micro-Embossing Technique.

The fabrication of nanostructures is of great importance in producing biomedical devices. Significantly, the nanostructure of the polymeric film has a significant impact on the physical and biophysical behavior of the biomolecules. This study presents an efficient nanofabrication method of nanogroove structures on an acrylic film by the micro-embossing process. In this method, a master mold was made from a thermos oxide silicon substrate using photolithography and etching techniques. An isotropic optical polymethyl methacrylate (PMMA) film is used in the experiment. The acrylic film is known for its excellent optical properties in products such as optical lenses, medical devices, and various general purpose engineering plastics. Then, the micro-embossing process was realized to fabricate nanogroove patterns on an acrylic film by using a micro-embossing machine. However, the morphology of the nanopatterns on an acrylic film was characterized by using an atomic force microscope to measure the dimensions of the nanogroove patterns. The impact of embossing temperature on the morphology of nanogroove patterns on acrylic film is experimentally investigated. The results show that when the embossing temperature is too small, the pattern is not fully formed, and slipping occurs in nanopatterns on the acrylic film. On the other hand, the effect of increasing the embossing temperature on the morphology of nanogrooves agrees with the master mold, and the crests between the nanogrooves form straight edges. It should be noted that the micro-embossing temperature also strongly influences the transferability of nanopatterns on an acrylic film. The technique has great potential for rapidly fabricating nanostructure patterns on acrylic film.

The Evaluation of Maxillary Sinus Dimensions in Different Craniofacial Patterns: A Systematic Review and Meta-Analysis.

This systematic review was intended to evaluate the maxillary sinus dimensions in vertical and sagittal craniofacial patterns and to assess if there was a difference among the craniofacial patterns. A systematic search was performed in seven databases till February 2021. The risk of bias was performed with modified Newcastle Ottawa scale. Meta-analysis was performed using random effects model. Twelve studies were included in the review and 8 in the meta-analysis. Compared to Class I malocclusion, the maxillary sinus area is greater in Class II and lesser in Class III malocclusion. On comparing normo-divergent growth pattern, the maxillary sinus area is lesser in hypo-divergent and greater in hyper-divergent individuals. Most of the studies were graded as satisfactory. The measurements are greater in hyper-divergent Class II malocclusion and in males.

Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations

One way of solving 3sat instances on a quantum computer is to transform the 3sat instances into instances of Quadratic Unconstrained Binary Optimizations (QUBOs), which can be used as an input for the QAOA algorithm on quantum gate systems or as an input for quantum annealers. This mapping is performed by a 3sat-to-QUBO transformation. Recently, it has been shown that the choice of the 3sat-to-QUBO transformation can significantly impact the solution quality of quantum annealing. It has been shown that the solution quality can vary up to an order of magnitude difference in the number of correct solutions received, depending solely on the 3sat-to-QUBO transformation. An open question is: what causes these differences in the solution quality when solving 3sat-instances with different 3sat-to-QUBO transformations? To be able to conduct meaningful studies that assess the reasons for the differences in the performance, a larger number of different 3sat-to-QUBO transformations would be needed. However, currently, there are only a few known 3sat-to-QUBO transformations, and all of them were created manually by experts, who used time and clever reasoning to create these transformations. In this paper, we will solve this problem by proposing an algorithmic method that is able to create thousands of new and different 3sat-to-QUBO transformations, and thus enables researchers to systematically study the reasons for the significant difference in the performance of different 3sat-to-QUBO transformations. Our algorithmic method is an exhaustive search procedure that exploits properties of 4×4 dimensional pattern QUBOs, a concept which has been used implicitly in the creation of 3sat-to-QUBO transformations before, but was never described explicitly. We will thus also formally and explicitly introduce the concept of pattern QUBOs in this paper.

Investigating Machine Learning and Control Theory Approaches for Process Fault Detection: A Comparative Study of KPCA and the Observer-Based Method.

The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process fault detection: Kernel Principal Component Analysis (KPCA) and the observer method. Both techniques are applied to observe water volume variation within a hydraulic system comprising three tanks. PCA is an unsupervised learning technique used for dimensionality reduction and pattern recognition. It is an extension of Principal Component Analysis (PCA) that utilizes kernel functions to transform data into higher-dimensional spaces, where it becomes easier to separate classes or identify patterns. In this paper, KPCA is applied to detect faults in the hydraulic system by analyzing the variation in water volume. The observer method originates from control theory and is utilized to estimate the internal states of a system based on its output measurements. It is commonly used in control systems to estimate the unmeasurable or hidden states of a system, which is crucial for ensuring proper control and fault detection. In this study, the observer method is applied to the hydraulic system to estimate the water volume variations within the three tanks. The paper presents a comparative study of these two techniques applied to the hydraulic system. The results show that both KPCA and the observer method perform similarly in detecting faults within the system. This similarity in performance highlights the efficacy of these techniques and their potential adaptability in various fault diagnosis scenarios within modern manufacturing processes.

Randomness properties of sequence generated using logistic map with novel permutation and substitution techniques

In this paper, a design of a chaos-based keystream generator (KSG) using a novel permutation technique with various two-dimensional patterns and a substitution technique with <em>Z</em><sub>4</sub> mapping is proposed. Initially, a chaotic function such as a logistic map is used to generate a pseudo-random number. Then these numbers are converted into binary sequences using binary mapping. In order to achieve statistical properties of the resultant binary sequences, a novel method of KSG is developed by considering parameters such as initial value “x<sub>0</sub>”, system parameter “<em>r</em>”, novel permutation techniques defined by 2-dimensional patterns, and substitution technique defined over Z<sub>4</sub> transformation. The binary sequences so obtained are subjected to randomness tests by applying the National Institute of Standards and Technology (NIST) SP-800-22 (Revision 1a) test suite for investigation of its randomness properties to obtain suitable sequences which can be used as a key for cryptographic applications. From the results obtained, it is found that the binary sequences exhibit better randomness properties as per the cryptographic requirements.

New Dimensions of Staffing Patterns in Nursing Homes and Nursing Home Quality: Comparing Staffing Instability to Staffing Turnover.

This study examines how measures of staffing-turnover and instability-are associated with one another and how they independently contribute to quality of care in nursing homes. Cross-sectional analysis of 2021-2022 administrative data. Data included the Payroll Based Journal for daily staffing information, merged with Nursing Home Care Compare (NHCC) data for nursing home characteristics, total staffing turnover, and nursing home quality. A total of 11,840 nursing homes nationally reporting data on daily staffing and staffing turnover. We explored correlations between measures of staffing and estimated facility-level regression models with robust standard errors. The dependent variables were indicators of nursing home quality included in the NHCC 5-star ratings. The independent variables of interest were average total staffing hours per resident-day, total staffing turnover, and total staffing instability. For the 11,840 nursing homes in the study, there was a weak positive correlation between turnover and instability, with some overlap between nursing homes with high instability and high turnover. Regression analysis revealed that staffing instability and turnover contributed independently to nursing home quality, with instability having a stronger association with some measures of quality and turnover with others. Staffing instability was positively and more strongly associated with long-stay residents' decline in activities of daily living levels and receipt of antipsychotic drugs and short-stay residents' functioning at discharge. Turnover was positively and more strongly associated with long-stay residents' prevalence of pressure ulcers and worsening mobility, and short-stay residents' hospitalizations. Instability and turnover in total nursing home staffing independently contribute to nursing home quality. This suggests that adding measures of staffing instability to the existing measures of average staffing and staff turnover in NHCC may enhance the report card's value for providers engaged in quality improvement and consumers searching for high-quality nursing homes.

Site-specific optical encryption via nanoscale integration of carbon on monolayer WS2

The ability to selectively tune the optical properties of two-dimensional (2D) materials at specific sites is of great interest for the development of optoelectronic and photonic devices. We report site-specific electron-beam (e-beam) induced deposition for creating patterned carbon nanostructures on atomically thin 2D materials such as WS2 and MoS2. Various patterns ranging from microns to nanometres in size have been produced and controlled by adjusting e-beam parameters such as accelerating voltage, spot size, magnification, and irradiation time. The ultimate pattern dimensions of ∼52 nm in width and ∼2.58 nm in height is achieved. The patterned areas exhibit a quenching of fluorescence intensity of about three times, making it simple to identify the created structure from the 2D materials. Moreover, Kelvin probe force microscopy measurements indicated that the surface potential of the patterned sites is roughly 95.6 mV different from that of non-patterned WS2. Finally, we have demonstrated optical encryption by selectively integrating carbon structures to modulate the optical emission of WS2.

Characteristics of fluid–fluid displacement in model mixed-wet porous media: patterns, pressures and scalings

We study numerically the characteristics of fluid–fluid displacement in simple mixed-wet porous micromodels using a dynamic pore network model. The porous micromodel consists of distinct water-wet and oil-wet regions, whose fractions are varied systematically to yield a variety of displacement patterns over a wide range of capillary numbers. We find that the impact of mixed-wettability is most prominent at low capillary numbers, and it depends on the complex interplay between wettability fraction and the intrinsic contact angle of the water-wet regions. For example, the fractal dimension of the displacement pattern is a monotonically increasing function of wettability fraction in flow cells with strongly water-wet clusters, but it becomes non-monotonic with respect to wettability fraction in flow cells with weakly water-wet clusters. Additionally, mixed-wettability also manifests itself in the injection pressure signature, which exhibits fluctuations especially at low wettability fraction. Specifically, preferential filling of water-wet regions leads to reduced effective permeability and higher injection pressure, even at vanishingly small capillary numbers. Finally, we demonstrate that scaling analyses based on a weighted average description of the overall wetting state of the mixed-wet system can effectively capture the variations in observed displacement pattern morphology.

Fractal analysis on the uniformity between the shear bands and serrated flows of a Zr-based bulk metallic glass

It is challenging to unravel the relationship between the serrated flows and complex shear bands of bulk metallic glasses (BMGs) when they are evolving. In this work, fractal analyses of the one-dimensional load-drop signals in time domain and the two-dimensional shear-band patterns were conducted at different strain stages. With the increase of strains, the fractal dimensions of both shear-band patterns and serrated flows show similar continuous increasing trends. From the perspective of chaos degree, a positive correlation between the load-drop distributions and shear-band patterns was obtained. The Hurst exponents less than 0.5 show that the distribution of load drops has a strong negative correlation behavior and there is a negative feedback mechanism in the development of shear bands. Moreover, at different strain stages, the fractal dimensions and Weibull moduli have indicated that the load drops demonstrate a relatively-high uniformity initially and then stabilize at a low uniformity, which is related to the regular shear-band patterns with fractal structure. The findings provide further understandings between the formation of shear bands and serrated flows of BMGs, which also open a new window to uncover the plastic deformation mechanisms of BMGs using fractal analysis.

Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks

The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters.

A multi-strategy improved tree–seed algorithm for numerical optimization and engineering optimization problems

Tree–seed algorithm is a stochastic search algorithm with superior performance suitable for solving continuous optimization problems. However, it is also prone to fall into local optimum and slow in convergence. Therefore, this paper proposes an improved tree–seed algorithm based on pattern search, dimension permutation, and elimination update mechanism (PDSTSA). Firstly, a global optimization strategy based on pattern search is used to promote detection ability. Secondly, in order to maintain the diversity of the population, a random mutation strategy of individual dimension replacement is introduced. Finally, the elimination and update mechanism based on inferior trees is introduced in the middle and later stages of the iteration. Subsequently, PDSTSA is compared with seven representative algorithms on the IEEE CEC2015 test function for simulation experiments and convergence curve analysis. The experimental results indicate that PDSTSA has better optimization accuracy and convergence speed than other comparison algorithms. Then, the Wilcoxon rank sum test demonstrates that there is a significant difference between the optimization results of PDSTSA and each comparison algorithm. In addition, the results of eight algorithms for solving engineering constrained optimization problems further prove the feasibility, practicability, and superiority of PDSTSA.

A biological camera that captures and stores images directly into DNA

The increasing integration between biological and digital interfaces has led to heightened interest in utilizing biological materials to store digital data, with the most promising one involving the storage of data within defined sequences of DNA that are created by de novo DNA synthesis. However, there is a lack of methods that can obviate the need for de novo DNA synthesis, which tends to be costly and inefficient. Here, in this work, we detail a method of capturing 2-dimensional light patterns into DNA, by utilizing optogenetic circuits to record light exposure into DNA, encoding spatial locations with barcoding, and retrieving stored images via high-throughput next-generation sequencing. We demonstrate the encoding of multiple images into DNA, totaling 1152 bits, selective image retrieval, as well as robustness to drying, heat and UV. We also demonstrate successful multiplexing using multiple wavelengths of light, capturing 2 different images simultaneously using red and blue light. This work thus establishes a ‘living digital camera’, paving the way towards integrating biological systems with digital devices.

Eco-hydrological modeling of soil wetting pattern dimensions under drip irrigation systems

Reliable information on the horizontal and vertical dimensions of the wetted soil beneath a point source is critical for designing accurate, cost-effective, and efficient surface and subsurface drip irrigation systems. Several factors, including soil properties, initial soil conditions, dripper flow rate, number of drippers, spacing between drippers, irrigation management, plant root characteristics, and evapotranspiration, influence the dimensions and shape of wetting patterns. The objective of this study was to briefly review previous studies, collect the analytical, numerical, and empirical models developed, and evaluate the effectiveness of the most common empirical method for predicting the dimensions of soil wetted around drippers using measured data from field surveys. With this review study, we aim to promote a better understanding of soil water dynamics under point-source drip irrigation systems, help improve soil water dynamics under point-source drip irrigation systems, and identify issues that should be better addressed in future modeling efforts. A drip irrigation system was configured with three different emitters with different capacities (2, 4, and 8 l h-1) in the point source to determine the soil wetting front under the point source. The five most selected empirical equations (Al-Ogaidi, Malek and Peters, Amin and Ekhmaj, Li and Schwartzman and Zur) were statistically analyzed to test the efficiency in sandy loam soil. According to the results of the field investigation, statistical comparisons of the empirical models with the field investigation data were performed using the mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe model efficiency (CE), and coefficients of determination (R2). The advanced simulation of the wetting front was used based on the best accuracy of the selected empirical model. In general, the Li model (MAE, RMSE, EF, and R2 were 0.698 cm, 0.894 cm, 0.970 cm2 cm-2, and 0.970, respectively, for the wetted soil width and 1.800 cm, 1.974 cm, 0.927 cm2 cm-2, and 0.986, for the vertical advance) proved to be the best after statistical analysis with field data.