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Terrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.

In practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface S_ref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.

The study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.

Due to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.

This study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.

The results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.

Although a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

The paper targets on providing new experimental data for validation of the well-established mathematical models within the framework of the lattice Boltzmann method (LBM), which are applied to problems of casting processes in complex mould cavities.

An experimental campaign aiming at the free-surface flow within a system of narrow channels is designed and executed under well-controlled laboratory conditions. An in-house lattice Boltzmann solver is implemented. Its algorithm is described in detail and its performance is tested thoroughly using both the newly recorded experimental data and well-known analytical benchmark tests.

The benchmark tests prove the ability of the implemented algorithm to provide a reliable solution when the surface tension effects become dominant. The convergence of the implemented method is assessed. The two new experimentally studied problems are resolved well by simulations using a coarse computational grid.

A detailed set of original experimental data for validation of computational schemes for simulations of free-surface gravity-driven flow within a system of narrow channels is presented.

Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.

In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario.

It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.

In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

Monocytes are a leukocytes’ subset that plays an important role in immunity. Protein kinase B (AKT) is involved in monocytes' survival, proliferation and differentiation. Using phorbol 12-myristate 13-acetate (PMA) as an inducer for cell line U937 differentiation into macrophage-like cells may be used as a model for cancer cell therapy or other biomedical research studies. The authors investigated the Akt1 signaling pathway's involvement with PMA as a differentiating agent and survival in the U937 cell line.

PMA was utilized to stimulate the differentiation of the U937 cell line into macrophage-like cells at a concentration of 10 nM. Akt1-phosphorylated Serine 473, Bad-phosphorylated Serine 136 and Caspase9-phosphorylated Serine 196 were tested by flow cytometry for the involvement of the Akt1 signaling pathway during differentiation in addition to the expression of CD14, CD206 and CD83. DNA cell cycle variation analysis was done using PI staining and cell viability and apoptosis detection using Annexin V and PI flow cytometry.

There was a decrease in phosphorylated Akt1 and Bad activation and an increase in Caspase9 activation, with an increase in surface markers CD14, CD206 and CD83 acquired by PMA-differentiated cells. DNA cell cycle analysis revealed cell accumulation in the G2/M phase and fewer cells in the S phase of PMA-induced U937. Apoptosis induction for Ly294002 or Wortmannin-inhibited cells and part of PMA-induced cells were detected.

These results may be used to create a model for biomedical research studies and advance the understanding of the mechanism involving differentiation of the U937 cell line.

This paper aims to introduce a method to couple truss finite elements to the material point method (MPM). It presents modeling reinforced material using MPM and describes how to consider the bond behavior between the reinforcement and the continuum.

The embedded approach is used for coupling reinforcement bars with continuum elements. This description is achieved by coupling continuum elements in the background mesh to the reinforcement bars, which are described using truss- finite elements. The coupling is implemented between the truss elements and the continuum elements in the background mesh through bond elements that allow for freely distributed truss elements independent of the continuum element discretization. The bond elements allow for modeling the bond behavior between the reinforcement and the continuum.

The paper introduces a novel method to include the reinforcement bars in the MPM applications. The reinforcement bars can be modeled without any constraints with a bond-slip constitutive model being considered.

As modeling of reinforced materials is required in a wide range of applications, a method to include the reinforcement into the MPM framework is required. The proposed approach allows for modeling reinforced material within MPM applications.

Mucorales has been described to be widely distributed during the most recent COVID-19 pandemic, with a greater frequency of disease in India, particularly among those with immune deficiencies. This study aims to use computational tools to develop a vaccine.

The authors investigated at Mucorales proteins that had previously been associated to virulence factors. Recent research suggests that a vaccine based on high-level cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) and B-cell lymphocyte (BCL) epitopes from diverse proteins might be developed. Furthermore, the vaccine assembly contains the targeted epitopes as well as PADRE peptides to induce an immune response. Computational approaches were used to analyze the immunological parameters used to build the suggested vaccine and validate its TLR-3 binding.

These studies show that the vaccination is capable of triggering a particular immune response. The authors offer a technique for developing and evaluating candidate vaccines using computational tools. To the best of their knowledge, this is the first immunoinformatic research of a prospective mucormycosis vaccine.

During this audit, a successful attempt was made to create a subunit MEV against black fungus. In the current study, MEV has been proposed as a suitable neutralizer candidate since it is immunogenic, secure, stable and interacts with human receptors. A stream study, on the other hand, is produced via a mixed vaccinosis approach. Following that, vaccinologists may perform more exploratory testing to evaluate whether the vaccine is effective.

Purpose – This study aims to obtain the best lipid content in locally isolated microalga that grows in palm oil mill effluent (POME).

Design/Methodology/Approach – Microalgae were cultured in POME with 25% dilution (LP₂₅), 50% (LP₅₀), and no dilution (LP) in 1,500 ml glass vessel at room temperature using a lighting intensity of 13,000 lux and continuous aeration for 24 hours and 12 hours, respectively. The biomass (in dry weight) of microalgae was analyzed daily by means of spectrophotometry using 624 nm wavelength to determine their growth.

Findings – The results showed that the acclimatized growth of microalgae in POME media adapted faster to the POME concentration. Acclimatized biomass content tends to increase to 1.014 g/L, while the content of non-acclimatized biomass reached only 0.752 g/L. Lipid content resulting from the direct extraction process using the modified Bligh and Dyer method provided the best yield of 47% in the microalgae grown in the LP₅₀ POME medium.

Research Limitations/Implications – Lipid was produced from locally isolated microalgae cultured in POME medium with 25% dilution (LP₂₅), 50% (LP₅₀), and no dilution (LP).

Practical Implications – The lipid produced had the potential for biodiesel energy.

Originality/Value – In this study, microalga was used not only to treat POME liquid wastes but also to produce lipids as biodiesel energy potentials.

This paper aims to identify and determine regional tourism axes in Hormozgan Province, Iran, as a region with significant potential

The research method is quantitative and uses the fuzzy accreditation tool and TOPSIS model; the identification, determination and ranking of regional tourism axes have been performed by analyzing the spatial distribution of tourism attractions in the GIS environment.

The results show that given the capacities of Hormozgan Province, at least 15 axes are recognizable. This paper highlights regional tourism planning as a tool for urban and rural socio-economic development in potential provinces such Hormozgan.

This study provides a number of practical implications for regional tourism development as follows: it identifies some of the most important potential axes in Hormozgan Province, which can be considered as investment areas in the national and regional tourism development strategy. The spatial results of this study could be embedded in all urban and rural developmental plans in the province. Tourism investment should shift its spatial concentration from the spot approach, especially islands and cities, to the axis approach while equipping those axes as comprehensive spatial strategic regional tourism plans. Sectoral tourism in each sector including sports, economy and nature could be planned as if sectoral institutions and organizations are going to develop their own tourism goals.