Search Results (5)

In reinforced concrete structures, the quality of rebar sleeve connections directly impacts the structure’s safety reserve and durability. However, quality inspection is complicated by the periodic distribution of stirrups, concrete obstruction, and noise interference, presenting challenges for assessing sleeve connection integrity. This paper proposes a training-free, interpretable framework for automated rebar sleeve connection quality inspection, leveraging point cloud semantic filtering and geometric a priori segmentation. The method constructs a polar-cylindrical framework, employing hierarchical semantic filtering to eliminate stirrup layers. Geometric a priori instance segmentation techniques are then applied, integrating θ histograms, Kasa circle fitting, and axial bridging domain constraints to reconstruct each longitudinal rebar. Sleeve detection occurs within the rebar coordinate system via radial profile analysis of length, angular coverage, and stability tests, subsequently stratified into two layers and parameterised. Sleeve projections onto column axes calculate spacing and overlap area percentages. Experiments using 18 BIM-TLS paired datasets demonstrate that this method achieves zero residual error in stirrup detection, with sleeve parameter accuracy reaching 98.9% in TLS data and recall at 57.5%, alongside stable runtime transferability. All TLS datasets meet the quality requirements of rebar sleeve connection spacing ≥35d and percentage of overlap area ≤50%. This framework enhances on-site quality inspection efficiency and consistency, providing a viable pathway for digital verification of rebar sleeve connection quality. Full article

This paper compares different point measurement strategies for spherical surfaces using a coordinate measuring machine. A total of 77 points were measured using the Sphere, Touch Point, Space Point, and Mid Point strategies, and approximately 2200 points were measured by scanning the surface with eight circles. Three probe configurations with different probe tip diameters were used for the measurements. The acquired data were processed with Calypso software, and the results for sphere diameters and centers were verified using the LSQ algorithms with the Kåsa and Newton methods, as well as Chebyshev fitting. Spherical shell half-widths and Root Mean Square Errors were used to evaluate accuracy. The results show that the sensor with $r_{\mathrm{probe}}=2.500557$ mm provides the smallest deviation from the certified value for both diameter and center position. The deviations remain smaller than the maximum permissible error of length measurement ${\mathrm{E}}_{0,\mathrm{MPE}}$. The scanning strategy does not consistently provide better results for either the diameter or the sphere center compared to point measurement. The results of the LSQ method in Calypso correspond to those obtained by the Kåsa/Newton methods (with results identical at the $\mathsf{\mu }$m scales), while the Calypso Minimum Feature evaluation method corresponds to the Chebyshev fit. Full article

In the present investigation, the parent compound 4-amino-5-(4-fluoro-3-phenoxyphenyl)-4H-1,2,4-triazole-3-thiol (1) and its Schiff bases 2, 3, and 4 were subjected to whole-cell anti-TB against H37Rv and multi-drug-resistant (MDR) strains of Mycobacterium tuberculosis (MTB) by resazurin microtiter assay (REMA) plate method. Test compound 1 exhibited promising anti-TB activity against H37Rv and MDR strains of MTB at 5.5 µg/mL and 11 µg/mL, respectively. An attempt to identify the suitable molecular target for compound 1 was performed using a set of triazole thiol cellular targets, including β-ketoacyl carrier protein synthase III (FABH), β-ketoacyl ACP synthase I (KasA), CYP121, dihydrofolate reductase, enoyl-acyl carrier protein reductase, and N-acetylglucosamine-1-phosphate uridyltransferase. MTB β-ketoacyl ACP synthase I (KasA) was identified as the cellular target for the promising anti-TB parent compound 1 via docking and molecular dynamics simulation. MM(GB/PB)SA binding free energy calculation revealed stronger binding of compound 1 compared with KasA standard inhibitor thiolactomycin (TLM). The inhibitory mechanism of test compound 1 involves the formation of hydrogen bonding with the catalytic histidine residues, and it also impedes access of fatty-acid substrates to the active site through interference with α5–α6 helix movement. Test compound 1-specific structural changes at the ALA274–ALA281 loop might be the contributing factor underlying the stronger anti-TB effect of compound 1 when compared with TLM, as it tends to adopt a closed conformation for the access of malonyl substrate to its binding site. Full article

A smart home is a residence that provides a variety of automation services based on Internet of Things (IoT) devices equipped with sensors, cameras, and lights. These devices can be remotely controlled through controllers such as smartphones and smart speakers. In a smart home, IoT devices collect and process data related to motion, temperature, lighting control, and other factors and store more diverse and complex user data. This data can be useful in forensic investigations but it is a challenge to extract meaningful data from various smart home devices because they have different data storage methods. Therefore, data collection from different smart home devices and identification and analysis of data that can be used in digital forensics is crucial. This study focuses on how to acquire, classify, and analyze smart home data from Google Nest Hub, Samsung SmartThings, and Kasa cam for forensic purposes. We thus analyzed the smart home data collected using companion apps, Web interfaces, and APIs to identify meaningful data available for the investigation. Moreover, the paper discusses various types of smart home data and their usage as core evidence in some forensic scenarios. Full article

This review discusses the use of molecular modeling tools, together with existing experimental findings, to provide a complete atomic-level description of enzyme dynamics and function. We focus on functionally relevant conformational dynamics of enzymes and the protonation states of substrates. The conformational fluctuations of enzymes usually play a crucial role in substrate recognition and catalysis. Protein dynamics can be altered by a tiny change in a molecular system such as different protonation states of various intermediates or by a significant perturbation such as a ligand association. Here we review recent advances in applying atomistic molecular dynamics (MD) simulations to investigate allosteric and network regulation of tryptophan synthase (TRPS) and protonation states of its intermediates and catalysis. In addition, we review studies using quantum mechanics/molecular mechanics (QM/MM) methods to investigate the protonation states of catalytic residues of β-Ketoacyl ACP synthase I (KasA). We also discuss modeling of large-scale protein motions for HIV-1 protease with coarse-grained Brownian dynamics (BD) simulations. Full article