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The common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common techniques used in data mining to increase the accuracy of clustering. In this study, based on segmentation, selecting the best segments, and using ensemble clustering for selected segments, a multistep approach has been developed for the whole clustering of time series data.

First, this approach divides the time series dataset into equal segments. In the next step, using one or more internal clustering criteria, the best segments are selected, and then the selected segments are combined for final clustering. By using a loop and how to select the best segments for the final clustering (using one criterion or several criteria simultaneously), two algorithms have been developed in different settings. A logarithmic relationship limits the number of segments created in the loop.

According to Rand's external criteria and statistical tests, at first, the best setting of the two developed algorithms has been selected. Then this setting has been compared to different algorithms in the literature on clustering accuracy and execution time. The obtained results indicate more accuracy and less execution time for the proposed approach.

This paper proposed a fast and accurate approach for time series clustering in three main steps. This is the first work that uses a combination of segmentation and ensemble clustering. More accuracy and less execution time are the remarkable achievements of this study.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

The purpose of this article is to identify how the organisations are able to improve their business value through acquisition of business analytics capabilities and by improving their performance.

With the help of literature survey, along with standard resource-based view framework, a conceptual model has been developed. These have been statistically tested by collecting the data using the survey questionnaire from 306 selected respondents from various service sector- and product-based organisations in India. To analyse the data we have used partial least square–based structural equation modelling.

The study highlights that by the help of data acquisition and tool acquisition as two vital components the acquisition of business analytics capabilities could improve the business value of the organisation by strengthening its organisational performance. The findings of this research also indicated that acquisition of business analytics capabilities has a significant influence on an organisation's business process performance and business decision, which in turn significantly influences organisational performance. And organisational performance eventually positively influences its business value. The model was found to provide an explanative power of 71%.

The proposed research model can provide effective recommendations to the management of the organisations to realise the importance of acquisition of effective business analytics capabilities to eventually improve the business value of the organisation.

No specific studies, as yet, have analysed the effects of acquisition of business analytics capabilities for improving organisational performance mediated through business process performance and business decision. Therefore, this research has explored the distinctive effort to empirically validate this understanding.

This research aims to investigate the process of value creation, value delivery, and value capture in project-based companies. Most previous research focused on companies that operate regularly and offer manufactured products or services. This research used companies in the field of information technology that developed software to explain how value creation, value delivery, and value capture occurred. A case study with qualitative research was applied to analyze between cases. Empirical findings showed that companies carry out six activities to ensure that value creation, value delivery, and value capture can be realized in the software development process. The six activities were iterative and not a rigid sequence. This research was limited to the software industry, and further research can test the results of this study by using a survey to increase the generalizability theory developed in this study.

This article analyzes the relationships between different conceptions of time, socioeconomic development and cultural values.

We focus on three major aspects of time, namely, 1) duration, 2) orientation and 3) tempo. Furthermore, we draw on modernization theory to distinguish between agrarian/traditional and industrial/modern societies and their respective cultural values.

Analyses indicate that agrarian/traditional societies with cultural values such as collectivism, survival, religiosity and hierarchical structures are marked by subjective/cyclical/inaccurate, past-oriented and slow-paced conceptions of time. In contrast, industrial/modern societies with cultural values such as individualism, self-expression, secularism and egalitarianism are marked by objective/linear/accurate, future-oriented and accelerated conceptions of time.

This paper introduces an original conceptualization of the three dimensions of time – duration, orientation and tempo – previously overlooked in the literature. Additionally, it provides an in-depth and comprehensive analysis of the relationships between time, culture and socioeconomic development.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

Digital data acquisition is crucial for operations in the digital transformation era. Reality capture (RC) has made an immeasurable contribution to various fields, especially in the built environment. This paper aims to review RC applications, potentials, limitations and the extent to which RC can be adopted for cost monitoring of construction projects.

A mixed-method approach, using Bibliometric analysis and the PRISMA framework, was used to review and analyse 112 peer-reviewed journal articles from the Scopus and Web of Science databases.

The study reveals RC has been applied in various areas in the built environment, but health and safety, cost and labour productivity monitoring have received little or no attention. It is proposed that RC can significantly support cost monitoring owing to its ability to acquire accurate and quick digital as-built 3D point cloud data, which contains rich measurement points for the valuation of work done.

The study’s conclusions are based only on the Scopus and Web of Science data sets. Only English language documents were approved, whereas others may be in other languages. The research is a non-validation of findings using empirical data to confirm the data obtained from RC literature.

This paper highlights the importance of RC for cost monitoring in construction projects, filling knowledge gaps and enhancing project outcomes.

The implementation of RC in the era of the digital revolution has the potential to improve project delivery around the world today. Every project’s success is largely determined by the availability of precise and detailed digital data. RC applications have pushed for more sustainable design, construction and operations in the built environment.

The study has given research trends on the extent of RC applications, potentials, limitations and future directions.

The purpose of this paper is to investigate the two-phase flow problems involving gas–liquid mixture.

The governed equations consist of a range of conservation laws modeling a classification of two-phase flow phenomena subjected to a velocity nonequilibrium for the gas–liquid mixture. Effects of the relative velocity are accounted for in the present model by a kinetic constitutive relation coupled to a collection of specific equations governing mass and volume fractions for the gas phase. Unlike many two-phase models, the considered system is fully hyperbolic and fully conservative. The suggested relaxation approach switches a nonlinear hyperbolic system into a semilinear model that includes a source relaxation term and characteristic linear properties. Notably, this model can be solved numerically without the use of Riemann solvers or linear iterations. For accurate time integration, a high-resolution spatial reconstruction and a Runge–Kutta scheme with decreasing total variation are used to discretize the relaxation system.

The method is used in addressing various nonequilibrium two-phase flow problems, accompanied by a comparative study of different reconstructions. The numerical results demonstrate the suggested relaxation method’s high-resolution capabilities, affirming its proficiency in delivering accurate simulations for flow regimes characterized by strong shocks.

While relaxation methods exhibit notable performance and competitive features, as far as we are aware, there has been no endeavor to address nonequilibrium two-phase flow problems using these methods.

The purpose of this paper is to analyze numerically the blowup in finite time of the solutions to a one-dimensional, bidirectional, nonlinear wave model equation for the propagation of small-amplitude waves in shallow water, as a function of the relaxation time, linear and nonlinear drift, power of the nonlinear advection flux, viscosity coefficient, viscous attenuation, and amplitude, smoothness and width of three types of initial conditions.

An implicit, first-order accurate in time, finite difference method valid for semipositive relaxation times has been used to solve the equation in a truncated domain for three different initial conditions, a first-order time derivative initially equal to zero and several constant wave speeds.

The numerical experiments show a very rapid transient from the initial conditions to the formation of a leading propagating wave, whose duration depends strongly on the shape, amplitude and width of the initial data as well as on the coefficients of the bidirectional equation. The blowup times for the triangular conditions have been found to be larger than those for the Gaussian ones, and the latter are larger than those for rectangular conditions, thus indicating that the blowup time decreases as the smoothness of the initial conditions decreases. The blowup time has also been found to decrease as the relaxation time, degree of nonlinearity, linear drift coefficient and amplitude of the initial conditions are increased, and as the width of the initial condition is decreased, but it increases as the viscosity coefficient is increased. No blowup has been observed for relaxation times smaller than one-hundredth, viscosity coefficients larger than ten-thousandths, quadratic and cubic nonlinearities, and initial Gaussian, triangular and rectangular conditions of unity amplitude.

The blowup of a one-dimensional, bidirectional equation that is a model for the propagation of waves in shallow water, longitudinal displacement in homogeneous viscoelastic bars, nerve conduction, nonlinear acoustics and heat transfer in very small devices and/or at very high transfer rates has been determined numerically as a function of the linear and nonlinear drift coefficients, power of the nonlinear drift, viscosity coefficient, viscous attenuation, and amplitude, smoothness and width of the initial conditions for nonzero relaxation times.