Local Point Research Articles

Structure and properties of surface layer of TiNi alloy subjected to ion-plasma- and ultrasonic treatment

The paper presents the results of research on morphology, elemental composition, microhardness, corrosion properties of the surface layer of TiNi alloy subjected to ion-plasma (vacuum-arc method) deposition of TiN coating and ultrasonic treatment (UT) with different number of passes (n). The SEM method showed that ultrasonic treatment provides a significant reduction in the amount of the droplet phase on the TiN coating surface. The surface discontinuity of TiN coating at local points was observed with an increase in the number of passes during ultrasonic treatment. The effect of combined processing methods on the microhardness of TiNi sample was studied. It was shown that the synergistic effect can be observed for two hardening methods. The combined strengthening method increased the microhardness of TiNi alloy (1.6 GPa in the as-received state): due to the deposition of a TiN coating – up to 10.9 GPa, due to subsequently ultrasonic treatment – up to 14.5–18.4 GPa depending on the number of passes. For UT + TiN scheme, it was shown that the open circuit potential Ecorr was about –250 mV which is practically independent of the number of passes and determined by the potential value of TiN coating. For TiN + UT scheme, it was found that with an increase in the number of passes, the value of Ecorr shifts towards more negative values approaching the open circuit potential value of the TiNi sample in the as-received state (–350 mV). The analysis of Scanning Vibrating Electrode Technique (SVET) results showed high electrochemical compatibility of substrate (TiNi) and coating (TiN) materials in a chloride environment with minimal current density fluctuations for the samples subjected to UT + TiN and TiN + UT (n = 1). The proposed method for TiNi alloy treatment according to TiN + UT scheme (n = 1) promotes an improvement of surface morphology and corrosion resistance.

KHz Rate Fs/Ps-CARS Thermometry For Supersonic H_2/Air Combustion Studies

Accurate flow field measurements are essential to probe the phenomena involved in aeronautical engines undergoing high temperature and high turbulences. Indeed, reliable experimental datasets are needed to compare and validate numerical models. Among other laser diagnostics, Coherent Anti-stokes Raman Scattering (CARS) is a well-established non-linear spectroscopic technique used to probe harsh environments, since it provides non-intrusive local point measurements of chemical composition and temperature. We report here hybrid fs/ps Coherent Anti-Stokes Raman Scattering (fs/ps-CARS) thermometry campaign performed on N 2 in a supersonic H2 /air combustion. We used a mobile and compact laser architecture that shapes, synchronize and generates the laser beams necessary for CARS spectroscopy. The instrument was moved to a large-scale facility representative of a scramjet combustor (ONERA LAERTE/LAPCAT-II bench). H2 /air was injected during several burst at 0,12 and 0,15 equivalent ratios. Using kHz single-shot measurements combined with motorized translation stages, spatial and temporal resolution were obtained to probe the flow and the combustion at various positions upstream and downstream the hydrogen injectors. At these positions, horizontal and vertical temperature profiles were retrieved and compared with a numerical unsteady fluid dynamic simulation using delayed detached eddy simulations (DDES) developed at ONERA (CEDRE). The position of the combustion inside the combustion chamber could be identified through several horizontal and vertical profile temperature measurements. The signal-to-noise ratio of the detection was optimized by empirically adjusting the number of averaged laser shots based on the level of flow turbulence. For instance, far downstream the H2 injection, averaging over 10 to 100 pulses was possible as the heated air flow was steady. This was confirmed by the good agreement with numerical simulations although some distortion could still be observed and will be used to adjust the numerical simulation algorithm. However, averaging was no longer possible close to the injectors, where a highly turbulent H2 /air combustion was observed. Nevertheless, taking advantages of the high rate single-shot capacity of the spectroscopic technique, temperature could still be retrieved inside this area. These measurements provided a valuable experimental database for comparing and refining computational fluid dynamics modeling.

Sparse-to-Dense Point Cloud Registration Based on Rotation-Invariant Features

Point cloud registration is a critical problem because it is the basis of many 3D vision tasks. With the popularity of deep learning, many scholars have focused on leveraging deep neural networks to address the point cloud registration problem. However, many of these methods are still sensitive to partial overlap and differences in density distribution. For this reason, herein, we propose a method based on rotation-invariant features and using a sparse-to-dense matching strategy for robust point cloud registration. Firstly, we encode raw points as superpoints with a network combining KPConv and FPN, and their associated features are extracted. Then point pair features of these superpoints are computed and embedded into the transformer to learn the hybrid features, which makes the approach invariant to rigid transformation. Subsequently, a sparse-to-dense matching strategy is designed to address the registration problem. The correspondences of superpoints are obtained via sparse matching and then propagated to local dense points and, further, to global dense points, the byproduct of which is a series of transformation parameters. Finally, the enhanced features based on spatial consistency are repeatedly fed into the sparse-to-dense matching module to rebuild reliable correspondence, and the optimal transformation parameter is re-estimated for final alignment. Our experiments show that, with the proposed method, the inlier ratio and registration recall are effectively improved, and the performance is better than that of other point cloud registration methods on 3DMatch and ModelNet40.

Zoning’s treatment of housing: Analysis from a nationwide sample of places

ABSTRACT Advocates of increased housing supply and affordability in the United States have targeted zoning regulations as drivers of housing cost growth, but they lack an understanding of the prevalence and geographical distribution of different types of zoning regulations because few efforts exist to compile zoning data across localities. This study measures the amount of land allocated to different housing types in a random, stratified sample of 90 places of over 10,000 residents across the U.S. Despite our exclusion of smaller communities, we find that variations of single-family zoning are more than three times as common as zoning for three or more units per lot. We also analyze socio-demographic, built environment, and political characteristics of the sampled places in relation to their zoning practices, finding few relationships between place-level characteristics and the shares of land dedicated to different housing types. Our results from a national sample suggest significant variation in zoning practices relative to local demographic characteristics and point to the importance of understanding zoning practices and paths for reform in their local and regional contexts.

A Hybrid Improved SAC-IA with a KD-ICP Algorithm for Local Point Cloud Alignment Optimization

To overcome incomplete point cloud data obtained from laser scanners scanning complex surfaces, multi-viewpoint cloud data needs to be aligned for use. A hybrid improved SAC-IA with a KD-ICP algorithm is proposed for local point cloud alignment optimization. The scanned point cloud data is preprocessed with statistical filtering, as well as uniform down-sampling. The sampling consistency initial alignment (SAC-IA) algorithm is improved by introducing a dissimilarity vector for point cloud initial alignment. In addition, the iterative closest point (ICP) algorithm is improved by incorporating bidirectional KD-tree to form the KD-ICP algorithm for fine point cloud alignment. Finally, the algorithms are compared in terms of runtime and alignment accuracy. The implementation of the algorithms is based on the Visual Studio 2013 software configurating point cloud library environment for testing experiments and practical experiments. The overall alignment method can be 40%~50% faster in terms of running speed. The improved SAC-IA algorithm provides better transformed poses, combined with the KD-ICP algorithm to select the corresponding nearest neighbor pairs, which improves the accuracy, as well as the applicability of the alignment.

Simulation of Dynamic Path Planning of Symmetrical Trajectory of Mobile Robots Based on Improved A* and Artificial Potential Field Fusion for Natural Resource Exploration

With the rapid development of new-generation artificial intelligence and Internet of Things technology, mobile robot technology has been widely used in various fields. Among them, the autonomous path-planning technology of mobile robots is one of the cores for realizing their autonomous driving and obstacle avoidance. This study conducts an in-depth discussion on the real-time and dynamic obstacle avoidance capabilities of mobile robot path planning. First, we proposed a preprocessing method for obstacles in the grid map, focusing on the closed processing of the internal space of concave obstacles to ensure the feasibility of the path while effectively reducing the number of grid nodes searched by the A* algorithm, thereby improving path search efficiency. Secondly, in order to achieve static global path planning, this study adopts the A algorithm. However, in practice, algorithm A has problems such as a large number of node traversals, low search efficiency, redundant path nodes, and uneven turning angles. To solve these problems, we optimized the A* algorithm, focusing on optimizing the heuristic function and weight coefficient to reduce the number of node traversals and improve search efficiency. In addition, we use the Bezier curve method to smooth the path and remove redundant nodes, thereby reducing the turning angle. Then, in order to achieve dynamic local path planning, this study adopts the artificial potential field method. However, the artificial potential field method has the problems of unreachable target points and local minima. In order to solve these problems, we optimized the repulsion field so that the target point is at the lowest point of the global energy of the gravitational field and the repulsive field and eliminated the local optimal point. Finally, for the path-planning problem of mobile robots in dynamic environments, this study proposes a hybrid path-planning method based on a combination of the improved A* algorithm and the artificial potential field method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. By introducing symmetry, we can more intuitively judge whether the path is close to the optimal state. Symmetry is an important criterion for us to evaluate the performance of the final path.

Multi-strategy improved sand cat optimization algorithm-based workflow scheduling mechanism for heterogeneous edge computing environment

Edge computing is one of the predominant technologies which facilitates the option of bringing out the computing resources closer to the location of the end users when they are utilized by them. This facility offered by edge computing technology need to reduce the utilization of network bandwidth and response time with respect to the user’s workflow. In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation Algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users’ satisfaction of resources. This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. This improvement is introduced over Sand Cat Optimization Algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. It specifically improved SCOA using random variation for escaping from the local point of optimality. It also used well distributed pareto fronts and population evolution multi-strategy that aids in searching solutions with maximized diversity. The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 %, better than the baseline Ant Colony Optimization Algorithm-Based Workflow Scheduling (IACOAWS), Quadratic Penalty Function-based Particle Swarm Optimization Algorithm (QPF-PSOA), Biogeography Optimization (BBO) Algorithm based Multi-Objective Task Scheduling (BBOAMOTS) and Different Evolution-based Task Clustering and Scheduling (DETCS) approaches used for comparative investigation.

Distributed chaotic bat algorithm for sensor fault diagnosis in AHUs based on a decentralized structure

The effective operation of sensors in heating, ventilation and air conditioning (HVAC) systems to promote high-efficiency, energy-saving and low-risk intelligent buildings. However, most existing methods are either based on a centralized architecture or employ only pure digital simulation platforms for HVAC sensor fault diagnosis and verification, which are difficult to meet the complex and dynamic needs of HVCA systems. To overcome the difficulty in employment of centralized architecture involved multi sensor fault diagnosis and fault diagnosis methods limited by pure digital simulation platform, a fully distributed chaotic bat algorithm is proposed to diagnosis multiple-sensor faults and is verified by the hardware-in-the-loop simulation platform in HVAC systems. First, inspired by the bat swarm intelligence and multiple agents, we characterize the sensor fault diagnosis problem as an optimization problem of the bat predation behavior. Specifically, each sensor node is abstracted as a bat colony, and each bat colony communicates with directly connected neighboring bat colony in physical space. Second, to avoid the tendency of bat algorithm to fall into locally optimal solutions, the tent chaotic map is employed to enhance the bat colony search ability for improving the randomness of bat algorithm, involves escaping from local extreme points and increasing the diversity of the bat colony. Finally, we embed the temperature and humidity sensors into the hardware-in-the-loop simulation platform to construct the near-actual physical environment. The performance and convergence of the proposed method are verified using both a pure digital simulation platform and a built-in hardware-in-the-loop simulation platform. Compared with basic bat algorithm (BA), particle swarm optimization algorithm (PSO) and chaotic bat algorithm (CBA), the proposed fully distributed chaotic bat algorithm (DCBA) have higher accuracy and can achieve the 98.41 % accuracy in fixed bias (FB) sensor faults, 96.96 % accuracy in complete failure (CF), 95.84 % in drift bias (DB) and 96.08 % accuracy in accuracy drops (AD).

A knee-oriented many-objective differential evolution with bi-strategy and Manhattan distance-domination range

Differential evolution multi-objective algorithms effectively address problems with two or three objectives. However, many-objective problems with more than three objectives are challenging because of inadequate selection pressure in environment selection, excessive variation in mutation, and difficulties in representing the Pareto front. This paper introduces a novel approach that combines differential evolution with a knee-oriented strategy to address the mentioned challenges. The approach allows the algorithm to focus on trade-off solutions for representing the surface instead of searching the entire Pareto front. Specifically, the algorithm consists of two stages: knee exploration stage and knee exploitation stage. Each stage utilizes different dominance relationships and mutation operators to enhance selection pressure in high-dimensional spaces. Moreover, a Manhattan distance-domination range is designed to update the reference vector by identifying multiple localized knee points in high-dimensional space. The experimental results demonstrate superior performance to other state-of-the-art algorithms on 50 knee-oriented benchmark problem test sets, including CKP, DO2DK, DEB2DK, DEB3DK, and PMOPs.

Automatic Classification of Submerged Macrophytes at Lake Constance Using Laser Bathymetry Point Clouds

Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the classification of submerged vegetation captured with bathymetric LiDAR (Light Detection And Ranging) as a basis for monitoring their state and change, and we validated the results against established monitoring techniques. Employing full-waveform airborne laser scanning, which is routinely used for topographic mapping and forestry applications on dry land, we extended its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of bathymetric 3D LiDAR point clouds using a decision-based approach, distinguishing the three vegetation classes, (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy, based on their height and other properties like local point density. The results reveal detailed 3D representations of submerged vegetation, enabling the identification of vegetation structures and the inference of vegetation types with reference to pre-existing knowledge. While the results within the training areas demonstrate high precision and alignment with the comparison data, the findings in independent test areas exhibit certain deficiencies that are likely addressable through corrective measures in the future.

Vibration analysis and experimental study of three-dimensional orthogonally coupled plates with through edge cracks

A modeling method for transverse vibration of three-dimensional (3D) orthogonally coupled plates is proposed and the through cracks located at coupling edges are considered. Corresponding experimental tests are performed to validate the effectiveness of this method. The main conflict for modeling 3D orthogonally coupled plates lies in the conflict of local coordinates of neighboring plates at the orthogonally coupled edge. The resolution of this conflict is effectively achieved by employing local collocation points method, this method is used to derive a universal expression for the coupling of plates under any coordinate system. Several cases were analyzed to compare natural frequencies, modal shapes, and dynamic responses against reference and experimental findings. Consequently, the present results are found to closely align with the measured outcomes, confirming the convergence and correctness of the proposed model and its solutions. The effects of crack parameters, such as length and number, on the vibration characteristics of orthogonally coupled plate structures are investigated. It is found that the vibrational characteristics of the coupled plates are more sensitive to the presence and variation of cracks at high frequencies. In the case of this study, as the number of cracks increases, response peaks appear at lower frequencies, with the maximum peak differing by up to twice. These findings are shown to have significant practical implications for understanding and predicting the effects of cracks in real orthogonally coupled plate structures. The effectiveness and convenience of the modeling approach for three-dimensional orthogonally coupled plates with through cracks are demonstrated in this study.

Bone Connectivity and the Evolution of Ichthyosaur Fins

After the end-Triassic extinction, parvipelvian ichthyosaurs diversified and became dominant elements of marine ecosystems worldwide. By the Early Jurassic, they achieved a thunniform body plan that persisted for the last 100 m.y.a of their evolution. Diversification and extinctions of thunniform ichthyosaurs, and their swimming performance, have been studied from different perspectives. The transformation of limbs into hydrofoil-like structures for better control and stability during swimming predates thunniform locomotion. Despite their importance as control surfaces, fin evolution among thunnosaurs remains poorly understood. We explore ichthyosaur fin diversity using anatomical networks. Our results indicate that, under a common hydrofoil controller fin, the bone arrangement diversity of the ichthyosaur fin was greater than traditionally assumed. Changes in the connectivity pattern occurred stepwise throughout the Mesozoic. Coupled with other lines of evidence, such as the presence of a ball-and-socket joint at the leading edge of some derived Platypterygiinae, we hypothesize that fin network disparity also mirrored functional disparity likely associated with different capabilities of refined maneuvering. The ball-and-socket articulation indicates that this local point could be acting like a multiaxial intrafin joint changing the angle of attack and thus affecting the maneuverability, similar to the alula of flying birds. Further studies on large samples and quantitative experimental approaches would be worthy to test this hypothesis.

Autonomous Navigation by Mobile Robot with Sensor Fusion Based on Deep Reinforcement Learning.

In the domain of mobile robot navigation, conventional path-planning algorithms typically rely on predefined rules and prior map information, which exhibit significant limitations when confronting unknown, intricate environments. With the rapid evolution of artificial intelligence technology, deep reinforcement learning (DRL) algorithms have demonstrated considerable effectiveness across various application scenarios. In this investigation, we introduce a self-exploration and navigation approach based on a deep reinforcement learning framework, aimed at resolving the navigation challenges of mobile robots in unfamiliar environments. Firstly, we fuse data from the robot's onboard lidar sensors and camera and integrate odometer readings with target coordinates to establish the instantaneous state of the decision environment. Subsequently, a deep neural network processes these composite inputs to generate motion control strategies, which are then integrated into the local planning component of the robot's navigation stack. Finally, we employ an innovative heuristic function capable of synthesizing map information and global objectives to select the optimal local navigation points, thereby guiding the robot progressively toward its global target point. In practical experiments, our methodology demonstrates superior performance compared to similar navigation methods in complex, unknown environments devoid of predefined map information.

Chess Game Rook‐Icon Movement‐Based Photovoltaic Array Reconfiguration for Higher Shade Dispersion and Improved Global Power Peaks Under Partial Shading Conditions: An Experimental Study

Solar photovoltaic (PV) system generates low power because of nonuniform sun irradiation compared to standard test conditions. Partial shading conditions (PSCs) can arise from various factors, with passing clouds, trees, telecommunication towers, bird droppings, and similar elements being among the most prevalent causes. Under the shading scenario, multiple global and local maxima power points exist on the P–V curves. Herein, the chess game rules for the “Rook” icon are followed to attain the optimal placement of integer numbers (1–9) in the 9 × 9 size game puzzle matrix. The integer number‐based game puzzles such as Su‐Do‐Ku, symmetric matrix, and proposed Rook's movement‐game theory (RMGT) to rearrange the PV modules in arrays are investigated and compared with the standard arrangements such as series‐parallel, total‐cross‐tied during PSCs. The suggested RMGT puzzle‐based arrangement is able to achieve higher power at global maximum power point, performance enhancement, fill factor, and low power loss. As a result, the experimental study proves the superiority of RMGT configuration compared to conventional methodologies under the shading scenarios. The proposed RMGT‐based PV array is tested and obtained the performance higher side as, , , , and compared to existing reconfiguration methodologies.

Indications of preferential groundwater seepage feeding northern peatland pools

Groundwater seepage from underlying permeable glacial sedimentary structures, such as eskers, has been hypothesized to directly feed pools in northern peat bogs. These hypotheses directly contradict classical peat bog models for ombrogenous systems, wherein meteoric water is the sole water input to these systems. Variations in the underlying mineral sediment in contact with the peat imply that unrecognized hydrogeologic connectivity may exist with pools in northern peat bogs, particularly where high permeability materials are in contact with the peat. Seepage dynamics originating from these structural variations were investigated using a suite of thermal and hydrogeophysical methods deployed around pools in a peat bog of northeastern Maine, USA. Thermal characterization methods mapped anomalies that were confirmed as matrix seepage or preferential flow pathways (PFPs). Geochemical methods were employed at identified thermal anomalies to confirm upwelling of solute-rich groundwater. Conduits around pools were associated with surficial terminations of suspected peat pipes, based on the inference of pathways extending down into the peat, that focus flow through PFPs in the peat matrix. Discharge also occurred through the peat matrix adjacent to suspected pipe structures and matrix seepage rates were quantified using analysis of diurnal temperature signals recorded at multiple depths. Seepage rates, with a maximum of nearly 0.4 m/d, were measured at localized points around pools. Periods of synchronized temperatures paired with highly muted diurnal temperature signals, recorded in diurnal temperature with depth data, were interpreted qualitatively as activation of strong upward discharge rates through suspected peat pipes. These time periods correlated strongly with local precipitation events around the peatland. Ground-penetrating radar surveys revealed discontinuities in the low permeability glacio-marine clay at the mineral sediment-peat interface, interpreted to be regional glacial esker deposits, which were located beneath and around pools. Heat tracing, specific conductance contrasts, seepage rates, and trace metal concentrations all imply groundwater seepage originating from underlying permeable glacial esker deposits and directly sourcing pools. Preferential groundwater inputs into northern peat bogs may play a key role in developing and maintaining pool systems, with enhanced solute transport impacting peatland ecology, water resources, and carbon cycling.

Graph Attentive Dual Ensemble learning for Unsupervised Domain Adaptation on point clouds

Due to the annotation difficulty of point clouds, Unsupervised Domain Adaptation (UDA) is a promising direction to address unlabeled point cloud classification and segmentation. Recent works show that adding a self-supervised learning branch for target domain training consistently boosts UDA point cloud tasks. However, most of these works simply resort to geometric deformation, which ignores semantic information and is hard to bridge the domain gap. In this paper, we propose a novel self-learning strategy for UDA on point clouds, termed as Graph Attentive Dual Ensemble learning (GRADE), which delivers semantic information directly. Specifically, with a pre-training process on the source domain, GRADE further builds dual collaborative training branches on the target domain, where each of them constructs a temporal average teacher model and distills its pseudo labels to the other branch. To achieve faithful labels from each teacher model, we improve the popular DGCNN architecture by introducing a dynamic graph attentive module to mine the relation between local neighborhood points. We conduct extensive experiments on several UDA point cloud benchmarks, and the results demonstrate that our GRADE method outperforms the state-of-the-art methods on both classification and segmentation tasks with clear margins.

Impacts of substitution of alkali by zinc on thermo-mechanical properties in borosilicate glasses

To decipher the effects of replacing alkali with zinc on thermo-mechanical properties, a series of borosilicate glasses with the molar compositions of 70.65SiO2·21.09B2O3·1.88Al2O3·1Li2O·(5.38-x)R2O·xZnO (x = 0, 0.34, 0.67, 1, 1.34, 2.69, 4.04, and 5.38) were prepared. Upon adding a slight quantity of ZnO, a local extreme point for glass transition temperature (Tg) appears. This behavior is deciphered to be originated from two opposite factors. On one hand, Tg reduces as a characteristic of the colligative properties for a dilute solution; in addition, Tg rises due to the higher field strength, bond energy and covalent characteristic of zinc cations compared to that of alkali ions. Otherwise, based on the evolution of intermediate-range clusters revealed by infrared, Raman and NMR spectra, we elucidate the atomic-scale structural origin, which leads to the nonlinear evolution of elastic moduli and coefficient of thermal expansion with the molar ratio of [ZnO]/([R2O]+[ZnO]).

Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time Series

Recently, order-preserving pattern (OPP) mining has been proposed to discover some patterns, which can be seen as trend changes in time series. Although existing OPP mining algorithms have achieved satisfactory performance, they discover all frequent patterns. However, in some cases, users focus on a particular trend and its associated trends. To efficiently discover trend information related to a specific prefix pattern, this article addresses the issue of co-occurrence OPP mining (COP) and proposes an algorithm named COP-Miner to discover COPs from historical time series. COP-Miner consists of three parts: extracting keypoints, preparation stage, and iteratively calculating supports and mining frequent COPs. Extracting keypoints is used to obtain local extreme points of patterns and time series. The preparation stage is designed to prepare for the first round of mining, which contains four steps: obtaining the suffix OPP of the keypoint sub-time series, calculating the occurrences of the suffix OPP, verifying the occurrences of the keypoint sub-time series, and calculating the occurrences of all fusion patterns of the keypoint sub-time series. To further improve the efficiency of support calculation, we propose a support calculation method with an ending strategy that uses the occurrences of prefix and suffix patterns to calculate the occurrences of superpatterns. Experimental results indicate that COP-Miner outperforms the other competing algorithms in running time and scalability. Moreover, COPs with keypoint alignment yield better prediction performance.

Comparison of Source Apportionment and Background PM2.5 Contribution in Six U.S. Metropolitan Areas

Total ambient PM2.5 concentrations within six major metropolitan core-based statistical areas (CBSAs) were evaluated using a previously published approach that combines interpolation and fixed effect regression models to disentangle background from local PM2.5 contributions. The results demonstrate differences and similarities across the cities, including seasonal patterns, the magnitude of total PM2.5 concentrations, and local vs. background apportionment. Los Angeles generally had the greatest total and local PM2.5 concentrations, and monitoring locations in Seattle generally had the lowest total and local PM2.5 concentrations. In terms of seasonal change, mean total PM2.5 concentrations varied the most in Dallas and the least in Tampa, while mean total PM2.5 concentrations varied the most spatially in Los Angeles and the least spatially in Dallas. Additionally, mean local PM2.5 concentrations varied the most seasonally in Los Angeles and the least seasonally in Tampa, while mean local PM2.5 concentrations varied the most spatially in Seattle and the least spatially in Charlotte and Dallas. Local attribution in Charlotte, Dallas, Pittsburgh, and Tampa were most similar. Local attribution varied more widely in Los Angeles and Seattle. The analyses found distance to major U.S. roadways as an important indicator for total and local ambient PM2.5 concentrations. Accurate distinctions of local vs. background contributions and point vs. non-point source contributions allow for regulatory, mitigatory, and community efforts to be optimized at the appropriate spatial levels (i.e., local, regional, state, or national) and target toward the most impactful source sectors when seeking to take steps that reduce total community PM2.5 exposures (e.g., reducing vehicular emissions).

Topological defects as nucleation points of the nematic-isotropic phase transition in liquid crystal shells.

The transition from a nematic to an isotropic state in a self-closing spherical liquid crystal shell with tangential alignment is a stimulating phenomenon to investigate, as the topology dictates that the shell exhibits local isotropic points at all temperatures in the nematic phase range, in the form of topological defects. The defects may thus be expected to act as nucleation points for the phase transition upon heating beyond the bulk nematic stability range. Here we study this peculiar transition, theoretically and experimentally, for shells with two different configurations of four +1/2 defects, finding that the defects act as the primary nucleation points if they are co-localized in each other's vicinity. If the defects are instead spread out across the shell, they again act as nucleation points, albeit not necessarily the primary ones. Beyond adding to our understanding of how the orientational order-disorder transition can take place in the shell geometry, our results have practical relevance for, e.g., the use of curved liquid crystals in sensing applications or for liquid crystal elastomer actuators in shell shape, undergoing a shape change as a result of the nematic-isotropic transition.