Real Patterns Research Articles

Capturing Unobserved Tourists: Challenges and Opportunities of Processing Mobile Positioning Data in Tourism Research

Even though tourism (both domestic and international) is one of the main triggering factors of human mobility worldwide, some of its forms are unexplored. This can be partly linked with the lack of reliable data and obstacles related to data processing and interpretation. Mobile Positioning Data (MPD) allows us to identify various forms of tourism that are undetectable through traditional data sources such as accommodation statistics. Using MPD, not only same-day tourists but also the real time mobility patterns of tourists among various destinations can be revealed, and even hidden (i.e., unobserved) forms of tourism can be detected. However, despite the obvious benefits of such data, very few comprehensive studies exist to date on the processing, and interpretation of MPD in tourism research. In this paper, a case study is presented on the challenges and opportunities of processing MPD from raw to good quality researchable data offering a baseline tool for MPD-based research in the field of tourism. With the methodology introduced in this paper, it is possible to provide a more accurate picture of tourist flows regarding unobserved tourists, including same-day visitors.

Using real mobility patterns to assess the impact of 100% electrified mobility in a German city

Until now, individual motorized mobility has been almost exclusively powered by fossil energy sources. The battle against climate change, however, requires a transformation of the mobility system with the ultimate objective of a full electrification of transport. Ultimately, this will increase considerably the load on the distribution grid both in overall size and through temporarily and locally distributed charging. This paper explores the effects of an assumed full electrification of individual motorized transport on the power grid in a major German city using real traffic data collected from a German traffic panel and employing a microscopic and dynamic travel simulation with the Simulation of Urban MObility tool. The main finding of the traffic simulation is that the local and temporary impact on the distribution grid is very sensitive to the distribution of charging stations and their geographical aggregation in transformers. However, behavior change in the form of charging at different points in time and locations or using less power can alleviate local peaks, up to 50% in the simulated scenario. Therefore, this paper addresses the previously uncovered need for a more profound and realistic computation that includes all important aspects of a proper traffic simulation. It further extends the barely covered field of dynamic simulations that operate on real mobility data. With that, limitations arise from the process of transposing driving data into geographical data as it is very sensitive to underlying assumptions. However, we are convinced that the spatialization of charging stations and the randomization of trip assignments represent the most transparent and meaningful way of overcoming these limiting factors.

Development of a probabilistic behavioural model creating diverse A/C operation patterns of households

Studies that attempt to model real patterns of residential occupant behaviour have become increasingly popular over the last decade. Most research of this kind tend to produce behaviour profiles based on an average occupant, yet the prediction of residential energy consumption varies significantly according to behavioural profiles of individual households. Oftentimes, averaged behavioural profiles are inadequate for predicting energy demands accurately, therefore requiring models to incorporate the diversity of occupants’ behaviours. The main objective was to develop an air-conditioning (A/C) usage model that produces diverse A/C cooling and heating operation patterns in residential settings. Longitudinal field observations were conducted in 41 Brisbane region households of Australia over a one-year period. Regression coefficients in the model were set to be drawn from a normal distribution to reproduce observed variability in A/C operation patterns amongst the households. Simulations were performed on a validation dataset to test the predictive skill of the model. The result indicated that the frequency and duration of A/C usage observed in the validation dataset well matched with the prediction made by the model. The proposed model could be incorporated in building energy simulation tools to predict realistic energy use for A/C in a given residential building design.

Individual variable speed limit trajectory planning considering stochastic arriving patterns

AbstractConnected vehicles enabled by communication technologies have the potential to improve traffic mobility and enhance roadway safety such that traffic information can be shared among vehicles and infrastructure. Fruitful speed advisory strategies have been proposed to smooth connected vehicle trajectories for better system performance with the help of different car-following models. Yet, there has been no such comparison about the impacts of various car-following models on the advisory strategies. Further, most of the existing studies consider a deterministic vehicle arriving pattern. The resulting model is easy to approach yet not realistic in representing realistic traffic patterns. This study proposes an Individual Variable Speed Limit (IVSL) trajectory planning problem at a signalized intersection and investigates the impacts of three popular car-following models on the IVSL. Both deterministic and stochastic IVSL models are formulated, and their performance is tested with numerical experiments. The results show that, compared to the benchmark (i.e., without speed control), the proposed IVSL strategy with a deterministic arriving pattern achieves significant improvements in both mobility and fuel efficiency across different traffic levels with all three car-following models. The improvement of the IVSL with the Gipps’ model is the most remarkable. When the vehicle arriving patterns are stochastic, the IVSL improves travel time, fuel consumption, and system cost by 8.95%, 19.11%, and 11.37%, respectively, compared to the benchmark without speed control.

Enhanced graph neural network for session-based recommendation

A key problem of session-based recommendation is to predict items of interest based on anonymous user’s current interaction session. To improve the prediction accuracy, existing methods mainly adopted two paradigms, i.e., sequence pattern and co-occurrence pattern. Though decent results were achieved, these methods still could not better simulate the real intention of users, or consider the complex pattern transformation between interacted items. To address these limitations, we propose a novel framework called Enhanced Graph Neural Network (E-GNN) for session-based recommendation, where the interaction sequence of all anonymous users is modeled as a Weighted Global Item Graph (WGIG) and the current interaction session of a target user is modeled as a Local Session Graph (LSG). Specifically, the WGIG is first constructed to produce a weight matrix effectively revealing the connection patterns of all intereacted items. The LSG is then constructed to explicitly model the initial interaction patterns of items in the target user’s current session. Finally, the weighting matrix and LSG are input into our proposed fusion algorithm, and the E-GNN representing the target user’s real interaction pattern is the output. The main contributions of this paper are: (1) Multiple interaction patterns are explicitly modelled in one interaction session graph; and (2) A new fusion algorithm is proposed to reconstruct current session graph by integrating the extracted weights representing the directivity of interacted items from the WGIG. A large number of comparative experiments are conducted on two public datasets, and the results show that E-GNN has distinct advantages over other baseline models.

Geographic and socioeconomic access disparities to Phase 3 clinical trials in ophthalmology in the United States.

To identify geographic and socioeconomic variables associated with residential proximity to Phase 3 ophthalmology clinical trial sites. The geographic location of clinical trial sites for Phase 3 clinical trials in ophthalmology was identified using ClinicalTrials.gov. Driving time from each United States (US) census tract centroid to nearest clinical trial site was calculated using real traffic patterns. Travel data were crosslinked to census-tract level public datasets from United States Census Bureau American Community Survey (ACS). Cross-sectional multivariable regression was used to identify associations between census-tract sociodemographic factors and driving time (>60 min) from each census tract centroid to the nearest clinical trial site. There were 2330 unique clinical trial sites and 71,897 census tracts. Shortest median time was to retina sites [33.7 min (18.7, 70.1 min)]. Longest median time was to neuro-ophthalmology sites [119.8 min (48.7, 240.4 min)]. Driving >60 min was associated with rural tracts [adjusted odds ratio (aOR) 7.60; 95% CI (5.66-10.20), p < 0.0001]; Midwest [aOR 1.84(1.15-2.96), p = 0.01], South [aOR 2.57 (1.38-4.79), p < 0.01], and West [aOR 2.52 (1.52-4.17), p < 0.001] v. Northeast; and tracts with higher visual impairment [aOR 1.07 (1.03-1.10), p < 0.001)]; higher poverty levels [4th v.1st Quartile of population below poverty, aOR 2.26 (1.72-2.98), p < 0.0001]; and lower education levels [high school v. Bachelor's degree or higher aOR 1.02 (1.00-1.03), p = 0.0072]. There are significant geographic and socioeconomic disparities in access to ophthalmology clinical trial sites for rural, non-Northeastern, poorer, and lower education level census tracts, and for census tracts with higher levels of self-reported visual impairment.

Enhancing Mechanical Metamodels With a Generative Model-Based Augmented Training Dataset

Modeling biological soft tissue is complex in part due to material heterogeneity. Microstructural patterns, which play a major role in defining the mechanical behavior of these tissues, are both challenging to characterize and difficult to simulate. Recently, machine learning (ML)-based methods to predict the mechanical behavior of heterogeneous materials have made it possible to more thoroughly explore the massive input parameter space associated with heterogeneous blocks of material. Specifically, we can train ML models to closely approximate computationally expensive heterogeneous material simulations where the ML model is trained on datasets of simulations with relevant spatial heterogeneity. However, when it comes to applying these techniques to tissue, there is a major limitation: the number of useful examples available to characterize the input domain under study is often limited. In this work, we investigate the efficacy of both ML-based generative models and procedural methods as tools for augmenting limited input pattern datasets. We find that a style-based generative adversarial network with an adaptive discriminator augmentation mechanism is able to successfully leverage just 1000 example patterns to create authentic generated patterns. In addition, we find that diverse generated patterns with adequate resemblance to real patterns can be used as inputs to finite element simulations to meaningfully augment the training dataset. To enable this methodological contribution, we have created an open access finite element analysis simulation dataset based on Cahn-Hilliard patterns. We anticipate that future researchers will be able to leverage this dataset and build on the work presented here.

Charging infrastructure facilitate a large-scale Introduction of electric vehicle in urban areas using hybrid technique: A RBFNN-SPOA approach

A hybrid technique is proposed for the effects of different roll-out techniques for charging the infrastructure that enables large-scale introduction of electric vehicles (EVs). The proposed hybrid technique is the combined execution of both Radial Basis Function Neural Network (RBFNN) and Student psychology optimization algorithm (SPOA), together called as RBFNN-SPOA strategy. The proposed method contains 3 kinds of agents operate in the environment from which charging stations is located. The communication among these agents are simulated in 3 ways. They are (1) charging process from which the EV driver cooperates with available charging stations and other electric vehicle drivers, (2) the process of purchasing a vehicle of non-electric vehicle car owners from which they take current charging infrastructure utilization into account, (3) the installment process of new charging stations through charging point operator (CPO) is based on placement tactic depends on charging station (CS) utilization. Firstly, a larger dataset on real charging patterns are utilized to design the charging behaviour of agents by RBFNN. Secondly, the proposed approach contains significantly more communication among electric vehicle drivers compared to existing approaches and also more precisely specifies the difficult system of on-street electric vehicle charging at an urban context. Thirdly, the relation among charging infrastructure and electric vehicle adoption based on experimental choice, while the existing methods provides assumptions by using SPOA. Finally, the proposed approach is implemented in MATLAB or Simulink platform and its performance is compared with existing approaches.

Revisiting the chondrichthyan egg capsules inventory from the Pennsylvanian (Carboniferous) of Belgium: new data and perspectives.

Records of chondrichthyan egg capsule morphotypes from the paralic deposits of the Belgian Coal Measures Group (Pennsylvanian; Bashkirian-Moscovian; Namurian B-Westphalian B according to the traditional subdivision) are presented and discussed. These include several species of the hybodontiform type Palaeoxyris as well as the putative holocephalian types Vetacapsula and Crookallia. Furthermore, the type specimens of Scapellites cottoni and S. minor, two additional putative and enigmatic egg capsules from the same lithostratigraphic unit, are figured and discussed. Altogether, a highly diverse egg capsule assemblage documented from the Belgian deposits implies the presence of at least eleven different Carboniferous chondrichthyan species using the ancient aquatic environments for spawning and as nurseries. The absence of the xenacanthiform morphotype Fayolia, known from surrounding coeval Coal Measures areas of northern France, the Netherlands, and Germany, is conspicuous. This lack may be a result of collecting bias and does not reflect a real pattern.

Human Motion Pattern Recognition and Feature Extraction: An Approach Using Multi-Information Fusion.

An exoskeleton is a kind of intelligent wearable device with bioelectronics and biomechanics. To realize its effective assistance to the human body, an exoskeleton needs to recognize the real time movement pattern of the human body in order to make corresponding movements at the right time. However, it is of great difficulty for an exoskeleton to fully identify human motion patterns, which are mainly manifested as incomplete acquisition of lower limb motion information, poor feature extraction ability, and complicated steps. Aiming at the above consideration, the motion mechanisms of human lower limbs have been analyzed in this paper, and a set of wearable bioelectronics devices are introduced based on an electromyography (EMG) sensor and inertial measurement unit (IMU), which help to obtain biological and kinematic information of the lower limb. Then, the Dual Stream convolutional neural network (CNN)-ReliefF was presented to extract features from the fusion sensors’ data, which were input into four different classifiers to obtain the recognition accuracy of human motion patterns. Compared with a single sensor (EMG or IMU) and single stream CNN or manual designed feature extraction methods, the feature extraction based on Dual Stream CNN-ReliefF shows better performance in terms of visualization performance and recognition accuracy. This method was used to extract features from EMG and IMU data of six subjects and input these features into four different classifiers. The motion pattern recognition accuracy of each subject under the four classifiers is above 97%, with the highest average recognition accuracy reaching 99.12%. It can be concluded that the wearable bioelectronics device and Dual Stream CNN-ReliefF feature extraction method proposed in this paper enhanced an exoskeleton’s ability to capture human movement patterns, thus providing optimal assistance to the human body at the appropriate time. Therefore, it can provide a novel approach for improving the human-machine interaction of exoskeletons.

Enhancing Model-Based Anticipatory Traffic Signal Control with Metamodeling and Adaptive Optimization

Traffic signal control is one effective way to alleviate traffic congestion. Anticipatory traffic signal control determines signal settings from a network planning perspective, which takes into account the influence of travelers’ route choice response and triggers better equilibrium flow patterns for better network performance. For the route choice response, it is usually predicted by a response function known as traffic assignment model. However, the response behavior can never be precisely modeled, leading to a mismatch between the modeled and real traffic flow patterns. This model-reality mismatch generally contributes to suboptimal control performance and hence brings unexpected congestion in real-life traffic operations. This study aims to address the model-reality mismatch and proposes an effective anticipatory traffic control for real operations. A metamodel is introduced that serves as a surrogate of the unknown structural model bias. Then an iterative optimizing control scheme is applied to correct the model bias by learning from observations. By integrating the model-based control design with data-driven learning techniques, the metamodeling framework is able to enhance the control performance. Moreover, the analytical model bias formulation allows theoretical investigation of the model approximation error. To further improve the control performance, a joint traffic model parameter estimation is developed, hence achieving a better model calibration jointly with the model bias correction. The proposed control method is examined on a test network. Numerical examples confirm the effectiveness of the proposed method in improving control performance despite the model-reality mismatch. Comparison results show that the proposed method outperforms the traditional model-based control method and an improvement of 14.8% in total travel time is achieved in the example network.

A Combined Approach for RT-Systems Development and Analysis

In this paper, we propose an approach for the development of real time systems and applications with a formal support based on rewrite logic. The design pattern based development process permits to represent the real time design patterns according to the new profile “DPP-RTSA” and provide, so, a manner to correctly execution of the instantiation and the composition of patterns. The obtained design from the application of the proposed process is translated into formal specification in rewriting logic (RT-Maude). The execution of this RT-Maude specification serve to perform the verification of the modelled system proprieties.

Wrap reduction algorithm for fringe projection profilometry

Carrier removal has been typically associated with wrap reduction, and its application has strongly been recommended as a previous step to phase unwrapping. In general, the carrier signal contributes to an increase of the number of wraps, and its removal leads to a reduction of the number of phase inconsistencies, having a positive impact on the subsequent unwrapping operation. In this paper, we show that phase wrap reduction can be better tackled by other alternative and more effective methods that exploit local information and attempt to also remove the signal component associated with the object’s surface. The results are analyzed on a series of real fringe patterns and confirm the better performance of the proposed method, which leads to signals with a lesser number of wraps than by just removing the carrier.

REKONSTRUKSI PERKARA SEBAGAI UPAYA DALAM MENGUNGKAP KEJAHATAN

In essence, law enforcement is a process of adjustment between values, rules and real behavior patterns that aim to achieve peace, so that the main task of law enforcement itself is actually an effort, activity or work so that the law is upright and peace is maintained. stand up. In order to achieve this goal, there must be an adjustment between values ​​or rules and the real behavior faced by law enforcers. Law enforcement, which is a process of concretizing an abstract form of law into a concrete one, means that laws and regulations are meaningless if they are not applied concretely by law enforcement officials. In law enforcement itself, it is necessary to have evidence activities that can be used by law enforcers (investigators) in this case to take other actions, including reconstructing. In this case, the police in carrying out law enforcement use their coercive power to obtain clear evidence that is in accordance with law enforcement when viewed from the point of view of the subject, law enforcement can be carried out by a broad subject and can also be interpreted as law enforcement efforts that involve all subjects. law in every legal relationship. In a narrow sense, in terms of the subject matter, law enforcement is only defined as the efforts of certain law enforcement officials to guarantee and ensure that the law is enforced, if necessary, law enforcement officials are allowed to use force. Law enforcement efforts are divided into two, namely by penal (criminal law) and non-penal (outside criminal law). In this case, reconstruction is included in the penal method, namely to enforce the law for the victim so that the incident that caused harm to the victim can be revealed so that the real incident is clear. This also applies to law enforcement against suspects, meaning that in the reconstruction the suspect needs to be proven whether it is true that the suspect committed the crime or not. The implementation of reconstruction or other activities carried out by police investigators must have the basis of the laws and regulations governing it. Without the rules that regulate it, it can be said that law enforcers themselves do not enforce the law, even against the law.

Effects of an Online Solution-Focused Psychoeducation Programme on Children's Emotional Resilience and Problem-Solving Skills.

This study investigates the effects of a solution-oriented and approach-based psychoeducation programme, initiated in December 2019 and conducted online during the worldwide COVID-19 pandemic, on children’s emotional resilience and problem-solving skills. In the world that was closed with the pandemic, children were kept away from the social support system of teachers and friends. Pandemic isolated the children. The negative experiences associated with COVID-19 have placed both traditional approaches and important online learning and support applications on the agenda, since both can help to solve the problems we face. Educators and experts have provided psychological support services, questioning the methods used to reach people during the pandemic and rediscovering alternative ways of connecting with individuals through online media. The online framework of this study emerged in response to the needs caused by the pandemic. The study is designed using a real experimental pattern and is based on a pretest-posttest, experimental/control-group model. The Social Support Evaluation Scale for Children and Teenagers was used to select the participants. In total, 18 participants were equally assigned to research groups (experiment n = 9, control n = 9). The psychoeducation programme consisted of six sessions, each lasting 50–60 min on average. Research carried out online during the pandemic shows that this psychoeducation programme is effective in strengthening students’ problem-solving and emotional resilience skills. These findings are discussed within the framework of the literature, followed by recommendations.

Statistical traffic pattern for mixed torus topology and pathfinder based traffic and thermal aware routing protocol on NoC

The network-on-chip (NoC) is an intra-communication on the chip extended from system-on-chip (SoC). The NoC design suffers from high failure rates due to the problem of routing in the traffic conditioning. In this paper, a realistic traffic pattern is used to verify the proposed routing scheme of the NoC with the novel design topology. Here a topology named mixed-torus topology is designed with the combination of processing block and the non-processing block that has the information about the processing block, which are associated with its torus block. The designed topology is presented with the encoder application model, and the set of tasks are mapped and scheduled to the processing block by the spider monkey optimization algorithm. Then the routing of data through the non-processing block is done by the pathfinder based traffic and thermal aware adaptive routing protocol (PFTTAR), which will perform the path diversity phase and the path selection phase for better routing. The designed NoC is then treated with the statistical traffic pattern generation for the analytical study of the proposed work and is implemented in Xilinx.

Android malware detection as a Bi-level problem

Malware detection is still a very challenging topic in the cybersecurity field. This is mainly due to the use of obfuscation techniques. To solve this issue, researchers proposed to extract frequent API (Application Programming Interface) call sequences and then use them as behavior indicators. Several methods aiming at generating malware detection rules have been proposed with the goal to come up with a set of rules that is able to accurately detect malicious code patterns. However, the rules generation process heavily depends on the training database content which will affect the detection rate of the model when confronted to new variants of malicious patterns. In order to assess a rule’s detection accuracy, we need to execute the rule on the whole malware database which makes the detection rule quality evaluation very sensitive to the database content. To solve this issue, we suggest in this paper to consider the detection rules generation process as a BLOP (Bi-Level Optimization Problem), where a lower-level optimization task is embedded within the upper-level one. The goal of the upper-level is to generate a set of detection rules in the form of: trees of combined patterns. Those rules are able to detect not only the real patterns from the base of examples but also the artificial patterns generated by the lower-level. The lower-level aims to generate a set of artificial malicious patterns that escape the rules of the upper-level. An efficient co-evolutionary algorithm is adopted as a search engine to ensure optimization at both levels. Such an automated competition between the two levels makes our new method BMD (Bi-level Malware Detection) able to produce effective detection rules that are capable of detecting new predictable malicious behaviors in addition to existing ones. Based on the statistical analysis of the experimental results, our BMD method has shown its merits when compared to several relevant state-of-the-art malware detection techniques on different Android malware datasets.

Supervised Nonparametric Classification in the Context of Replicated Point Patterns

A spatial point pattern is a collection of points in space, representing, e.g. observed locations of trees, bird nests, centers of cells in a histological sample, etc. When several independent realizations of the underlying stochastic process are observed, these realizations are referred to as replicated point patterns. The main objective of this paper is to classify a newly observed pattern into one of the existing classes using a supervised nonparametric classification method, namely the Bayes classifier in combination with the k-nearest neighbors algorithms and the kernel regression method. The dissimilarity between a pair of patterns is defined using the functional summaries extracted from the point patterns via the Cramér-von Mises or Kolmogorov-Smirnov type formula. A set of simulation experiments is presented to investigate the performance of the proposed classifier with a dissimilarity measure based on functional summaries, such as the pair correlation function. The application of such a classifier to a real point pattern dataset is also illustrated.

Determining crack kinematics from imaged crack patterns

Determining the relationship between the cause of damage and the subsequent structural behavior of infrastructure systems requires an accurate characterization of the propagation of cracks, which represents the evolution of the damage state. When no information about the cause of damage is available, kinematic approaches can be used to describe the motion of crack contours. Current image-based approaches to derive crack kinematics use digital image correlation (DIC) on a set of sequential images as the crack propagates. However, DIC is invasive in that the structure surfaces must be painted with random speckle patterns, limiting its use primarily to controlled experiments. In this paper, we propose a novel image-based methodology for computing crack opening in Mode I or Mode II. As an input, this method takes a binary image from a semantic segmentation of an image of a crack pattern. This binary image is used to detect the opposite edges along the crack, which are then registered using an optimization algorithm based on the Euclidean transformation model and non-linear least squares. As a final output, this method produces displacement maps in the tangential and normal directions to the crack skeleton. To demonstrate its performance, we validate our methodology first with synthetic crack patterns and then with real crack patterns. Because this methodology for determining crack openings requires only simple data (just a binary crack pattern image), it is straightforward, robust, and adaptable, thus contributing to the development of structural image-based damage assessments. The computational codes and datasets are available to the public for future research and benchmarking on https://github.com/eesd-epfl/crack_kinematics and https://doi.org/10.5281/zenodo.6632071.

An effective dimensionality reduction approach for short-term load forecasting

Accurate power load forecasting has a significant effect on a smart grid by ensuring effective supply and dispatching of power. However, electric load data generally possesses the characteristics of nonlinearity, periodicity, and seasonality. For complex electric load systems, the presence of redundant information potentially reduces the real pattern extraction for load forecasting. Bearing in mind these issues, we propose an effective forecasting model in which a feature extraction module is introduced that is combined with the variational mode decomposition (VMD) with the variational autoencoder (VAE). In this combination, VMD is utilized for decomposing complex load series and VAE is used to filter the redundant information (noises) from each decomposed series. With two real data sets from China, we demonstrate that the proposed model can achieve highly accurate predictions, as we find the mean absolute percentage error (MAPE) values for one-step-ahead prediction to be 1% (Nanjing) and 0.8% (Taixing), respectively.