Object Surface Research Articles

Students’ Habits and Identification of Bacteria on Inanimate Surface of Educational Setting

Introduction: Diponegoro Clinic reported 29.1% of students seek infectious diseases treatment. Student habits in the classroom were thought to play roles in the presence of bacteria. Surfaces of inanimate objects in the classroom were potential source for bacteria. The research objectives were analyzing student’s habits and identifying bacteria on the surface of inanimate objects in the classroom. Methods: Four types of samples, including the surfaces of tables, chairs, flips of air conditioners, and floors were taken from 13 faculties at Universitas Diponegoro. Plate count agar media were used to isolate bacterial colonies, and PCR analysis was performed for DNA extraction and amplification. DNA Sanger sequencing techniques were used for genetic bacterial identification. The online questionnaire was used to assess student habits in the classroom. Two hundred students responded. Results and Discussion: Acinetobacter baumannii and Staphylococcus aureus were found in classrooms. These bacteria were associated with respiratory tract infections. This study revealed that 86.5% were between the ages of 17-21, 60.81% were from outside Semarang City, and 88.33% lived in Semarang City. About 60.81% of respondents studied in health sciences. Furthermore, it was reported that 66.67% of respondents were sick in the last few weeks, attended class despite being sick (72.52%), and coughed and sneezed in class (40.99%). Conclusion: Bacteria associated with respiratory tract infection were found. Students' habits in the classroom were potentially caused by the presence of these bacteria. The use of antibacterial agents could reduce the presence of bacteria on inanimate object surfaces.

Voxel-wise segmentation for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks

AbstractAdditive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all samples of a batch, X-ray computed tomography (X-CT) is often used in combination with automated anomaly detection. For the latter, deep learning (DL) anomaly detection techniques are increasingly used, as they can be trained to be robust to the material being analysed and resilient to poor image quality. Unfortunately, most recent and popular DL models have been developed for 2D image processing, thereby disregarding valuable volumetric information. Additionally, there is a notable absence of comparisons between supervised and unsupervised models for voxel-wise pore segmentation tasks. This study revisits recent supervised (UNet, UNet++, UNet 3+, MSS-UNet, ACC-UNet) and unsupervised (VAE, ceVAE, gmVAE, vqVAE, RV-VAE) DL models for porosity analysis of AM samples from X-CT images and extends them to accept 3D input data with a 3D-patch approach for lower computational requirements, improved efficiency and generalisability. The supervised models were trained using the Focal Tversky loss to address class imbalance that arises from the low porosity in the training datasets. The output of the unsupervised models was post-processed to reduce misclassifications caused by their inability to adequately represent the object surface. The findings were cross-validated in a 5-fold fashion and include: a performance benchmark of the DL models, an evaluation of the post-processing algorithm, an evaluation of the effect of training supervised models with the output of unsupervised models. In a final performance benchmark on a test set with poor image quality, the best performing supervised model was UNet++ with an average precision of 0.751 ± 0.030, while the best unsupervised model was the post-processed ceVAE with 0.830 ± 0.003. Notably, the ceVAE model, with its post-processing technique, exhibited superior capabilities, endorsing unsupervised learning as the preferred approach for the voxel-wise pore segmentation task.

3D shape measurement method for high-reflective surface based on a color camera

Fringe projection profilometry (FPP) has been widely utilized in many fields due to its non-contact, high accuracy, high resolution and full-field measurement capabilities. However, the limited dynamic range of the camera sensor can result in overexposure of high-reflective parts in industrial production measurement. To effectively solve the above issue, this paper proposes a 3D shape measurement method for the high-reflective surface based on a color camera. Firstly, the optimal exposure time for the low-reflective region is estimated using the relationship between the standard variance of the phase error and the modulation. Twelve blue phase-shifted fringe patterns and a uniform blue image are utilized to obtain 3D shape of high-reflective surface. Secondly, captured images are separated into red, green and blue components. The fused Gaussian low-pass filtering method with different cut-off frequencies is used to filter and denoise the green or red components and the true intensity of the blue component in the saturated regions is calculated by the unsaturated intensity of the green or red components based on the calibrated crosstalk matrix. Then the image in saturated regions is fused and normalized with the unsaturated region. The absolute phase obtained from the fused normalized fringe patterns is converted into 3D data. Finally, experiments have been carried out on measuring. The experimental results show that the proposed method is capable of obtaining the 3D shape of the surface of a high-reflective object with fewer patterns and high measurement accuracy.

Artificial Tactile Sensory Finger for Contact Pattern Identification Based on High Spatiotemporal Piezoresistive Sensor Array.

Human fingertip tactile perception relies on the activation of densely distributed tactile receptors to identify contact patterns in the brain. Despite significant efforts to integrate tactile sensors with machine learning algorithms for recognizing physical patterns on object surfaces, developing a tactile sensing system that emulates human fingertip capabilities for identifying contact patterns with a high spatiotemporal resolution remains a formidable challenge. In this study, we present the development of an artificial tactile finger for accurate contact pattern identification, achieved through the integration of a high spatiotemporal piezoresistive sensor array (PRSA) and a convolutional neural network (CNN) model. Spatiotemporal characterization tests reveal that the artificial finger exhibits a fast temporal resolution of approximately 7 ms and achieves a two-point threshold of 1.5 mm, surpassing that of the human fingertip. To compare the performance of the artificial finger with the human finger in recognizing different patterns, we acquired pressure images by pressing the artificial finger, coated with a flexible PRSA film, onto both simple embossed and complex curved patterns while also recording human recognition results of perceiving these patterns. Experimental findings demonstrate that the artificial finger achieves higher classification accuracy in recognizing both simple and complex patterns (99.0 and 96.1%, respectively) compared to the human fingertip (69.1 and 22.7%). This artificial finger serves as a promising platform with great potential for various robotic tactile sensing applications including prosthetics, skin electronics, and robotic surgery.

GS‐Octree: Octree‐based 3D Gaussian Splatting for Robust Object‐level 3D Reconstruction Under Strong Lighting

AbstractThe 3D Gaussian Splatting technique has significantly advanced the construction of radiance fields from multi‐view images, enabling real‐time rendering. While point‐based rasterization effectively reduces computational demands for rendering, it often struggles to accurately reconstruct the geometry of the target object, especially under strong lighting conditions. Strong lighting can cause significant color variations on the object's surface when viewed from different directions, complicating the reconstruction process. To address this challenge, we introduce an approach that combines octree‐based implicit surface representations with Gaussian Splatting. Initially, it reconstructs a signed distance field (SDF) and a radiance field through volume rendering, encoding them in a low‐resolution octree. This initial SDF represents the coarse geometry of the target object. Subsequently, it introduces 3D Gaussians as additional degrees of freedom, which are guided by the initial SDF. In the third stage, the optimized Gaussians enhance the accuracy of the SDF, enabling the recovery of finer geometric details compared to the initial SDF. Finally, the refined SDF is used to further optimize the 3D Gaussians via splatting, eliminating those that contribute little to the visual appearance. Experimental results show that our method, which leverages the distribution of 3D Gaussians with SDFs, reconstructs more accurate geometry, particularly in images with specular highlights caused by strong lighting. The source code can be downloaded from https://github.com/LaoChui999/GS-Octree.

TaNSR:Efficient 3D Reconstruction with Tetrahedral Difference and Feature Aggregation

AbstractNeural surface reconstruction methods have demonstrated their ability to recover 3D surfaces from multiple images. However, current approaches struggle to rapidly achieve high‐fidelity surface reconstructions. In this work, we propose TaNSR, which inherits the speed advantages of multi‐resolution hash encodings and extends its representation capabilities. To reduce training time, we propose an efficient numerical gradient computation method that significantly reduces additional memory access overhead. To further improve reconstruction quality and expedite training, we propose a feature aggregation strategy in volume rendering. Building on this, we introduce an adaptively weighted aggregation function to ensure the network can accurately reconstruct the surface of objects and recover more geometric details. Experiments on multiple datasets indicate that TaNSR significantly reduces training time while achieving better reconstruction accuracy compared to state‐of‐the‐art nerual implicit methods.

Generation of low-temperature plasma by pulse-width modulated signals and monitoring of the interaction thereof with the surface of objects

Abstract The article discusses the use of pulse-width modulation signals to generate low-temperature at-mospheric plasma in an inert gas environment. The results of studies of the energy consumption of a low-temperature plasma generation system depending on the duty rate, as well as the pulse repetition rate, are presented. The operating modes of the system have been established, in which a minimum of energy consumption is achieved. The issues of evaluating the interaction of plasma with objects based on the analysis of changes in signal parameters in the high-voltage circuit of the generator are also considered.

Three-dimensional Change Detection and Description in Complex Construction Scenarios

Abstract. In large-scale engineering construction scenarios, construction objects and equipment are numerous; their spatial positions and postures change frequently and are complex and varied. Describing the changes between construction entities and the entities themselves in a three-dimensional (3D) geometric space and functional semantic space is challenging. In response to this challenging, we propose a novel workflow to detect and describe the changes in a construction site. First, we add the constraint of object surface continuity to the octree change detection (OCD), exclude the independently changing cells, and improve the change detection robustness. Second, based on the changes in entity elements that occur in the 3D geometric space, we introduce six-type semantics to characterize the changes that occur. Differing from existing change detection semantic description methods that focus on the attribute semantics of changed entity elements, our method focuses on the semantic description of the change process. Finally, for the variations in different semantic types, we propose specific quantization methods that can better quantify the variations in entity elements in 3D geometric space. For the proposed solution, we tested it using two sets of multi-period time-series point cloud data, which can accurately identify the changing construction entities in space and complete the calculation of bridge construction progress, including the building demolition and construction, and the change in the construction apparatus position.

Filling the Holes on 3D Heritage Object Surface Based on Automatic Segmentation Algorithm

ABSTRACTReconstructing and processing 3D objects are activities in the research field of computer graphics, image processing and computer vision. The 3D objects are processed based on geometric modelling (a branch of applied mathematics and computational geometry) or machine learning algorithms based on image processing. The computation of geometrical objects includes processing the curves and surfaces, subdivision, simplification, meshing, holes filling or reconstructing the 3D surface's objects on both point cloud data and triangular mesh. While the machine learning methods are developed using deep learning models. With the support of 3D laser scan devices and LiDAR techniques, the obtained dataset is close to the original shape of the real objects. Besides, photography and its application based on modern techniques in recent years help us collect data and process the 3D models more precisely. This article proposes a new method for filling holes on the 3D object's surface based on automatic segmentation. Instead of filling the hole directly as the existing methods, we now subdivide the hole before filling it. The hole is first determined and segmented automatically based on the computation of its local curvature. It is then filled on each part of the hole to match its local curvature shape. The method can work on both 3D point cloud surfaces and triangular mesh surfaces. Compared to the state‐of‐the‐art (SOTA) methods, our proposed method obtained higher accuracy of the reconstructed 3D objects.

The Mycobiota of Library Books in Russia.

Abstract-Numerous studies of microorganisms isolated from the surface of cultural heritage objects, including library documents, are regularly carried out in different countries. Although the micromycete composition in each case varies, some species are constantly isolated. The structure of micromycete communities that inhabit library documents was studied in 57 cities of Russia located in seven federal districts (Northwestern, Central, Southern, Volga, Ural, Siberian, and Far Eastern). Micromycetes of 95 species from 32 genera were isolated and identified. The mycobiota of the library documents represented by Ascomycota has more than 90% of the species richness; Mucoromycota make up 3-9% and Basidiomycota, 3-4%. The Aspergillaceae family was the leading family: it accounted for 48.5-67.3% of the total species richness. In all regions, species diversity is moderate: the Shannon index ranged from 2.7 to 3.3. The Mcintosh species richness index is high everywhere (48-126), except in the Ural district (15.3). The McIntosh (0.76-0.84) and Pielow (0.80-0.91) dominance indices indicate a high level of species evenness in the mycobiota. The obtained values demonstrate the stability the document mycobiota in libraries from different regions. Significant species similarity between the districts was revealed by calculation of binary coefficients: the Jaccard coefficient was from 0.44 to 0.60; the Sørensen's qualitative measure of similarity was from 0.63 to 0.75; the quantitative similarity measure of Sørensen was from 0.44 to 0.71, and Morisita-Horn was from 0.66 to 1.0. The groups of dominant species in different regions are quite similar. The study of the ecological diversity of mycobiota on library books demonstrated the moderate diversity and the stability of the community. A high degree of similarity of taxonomic structures was established regardless of the climate conditions of the regions. Cosmopolitans characterized by high frequency of occurrence formed the major core of the library book mycobiota: Alternaria alternata, Aspergillus niger, Cladosporium cladosporioides, Mucor plumbeus, and Penicillium aurantiogriseum.

Gain improvement of conformal circularly polarized Fabry-Perot resonator antenna based on metasurface

Conformal antenna can keep consistent with the shape of the carrier perfectly, which greatly saves the space of the carrier, so it is widely used in military, aerospace and other fields. In this paper, a cylindrical conformal circular polarization Fabry-Perot resonator antenna is designed, and the adverse effect of conformal on antenna gain is overcome by transmission phase compensation. Firstly, a linear to circular polarization conversion metasurface with adjustable transmission phase is designed. The main reason for choosing the metasurface here is to take advantage of its excellent electromagnetic control ability. The metal structures of the metasurface are printed on the ultra-thin dielectric plates, and then attached to the support structure machined by 3D printing, so as to achieve the conformal design of the metasurface. Secondly, the proposed metasurface is used as the superstructure and is combined with a conformal feed to form a cylindrical conformal circularly polarized Fabry Perot resonator antenna. The conformal feed is a patch antenna which is conformal designed in the same way as the metasurface. The conformal feeder is placed under the conformal metasurface to ensure that the electromagnetic wave radiated by the feeder can enter the resonant cavity composed of the feeder and the metasurface. The curvature of the cylindrical conformal of feed and metasurface is the same, which can ensure the high unity of the different positions in the resonator. Next, by adjusting the transmission phase of the unit on the metaurface, the phase compensation of the transmitted electromagnetic wave is realized, so that the electromagnetic wave radiated by the conformal antenna still presents the characteristic of plane wave. Finally, the conformal Fabry-Perot resonator antenna obtains the same high-gain radiation characteristics as the planar resonator antenna. The designed antenna achieves a maximum gain of 13.1dBic at 10 GHz. The size of the antenna is 2.9λ × 2.9λ, so the aperture efficiency is about 19.9 %. The antenna proposed in this paper overcomes the influence of conformal design on the gain of the antenna, so that it can be conformal on the surface of a cylindrical object and obtain better gain characteristics.

Luminescence, temperature sensing and Judd-Ofelt analysis of Bi2Mo3O12:Eu3+ phosphors and the application in latent fingerprint visualization

Eu3+-activated materials have garnered significant attention due to their outstanding optical characteristics. In this work, the sol–gel method was successfully used to prepare Bi2Mo3O12 phosphors doped with different amounts of Eu3+. The generated samples were identified as orthorhombic Bi2Mo3O12 with a scheelite-like structure by X-ray diffraction analysis of the crystal structure. The sample’s morphology and size were examined using scanning electron microscopy and transmmission electron microscopy, which revealed irregular block morphology and tens to hundreds nanometers scale dimension. From the analysis of the concentration-dependent luminescence intensity of Eu3+, it was confirmed that the exchange interaction was responsible for the quenching of 5D0 fluorescence of Eu3+. When excited at 374 nm, the phosphor emitted brilliant red light, with the highest emission occurring at 616 nm (5D0→7F2 transition), and the calculated color coordinates of the sample were (0.663, 0.336). By examining the temperature dependence of the emission spectra, the temperature sensing performance of the sample and the thermal quenching behavior of Eu3+ luminescence was explored. Furthermore, the optical transition property of Eu3+ was investigated by using the emission spectra and fluorescence lifetime within the context of Judd-Ofelt theory. Ultimately, latent fingerprint visualization of the sample on various object surfaces was studied, thanks to the intense luminescence and small particle size of the Bi2Mo3O12:Eu3+ phosphor. The results indicated that the sample can clearly display the different hierarchical features of fingerprint on different object surfaces.

Fast spline collision detection (FSCD) algorithm for solving multiple contacts in real-time

Collision detection has been widely studied in the last decades. While plenty of solutions exist, certain simulation scenarios are still challenging when permanent contact and deformable bodies are involved. In this paper, we introduce a novel approach based on volumetric splines that is applicable to complex deformable tubes, such as in the simulation of colonoscopy and other endoscopies. The method relies on modeling radial control points, extracting surface information from a triangle mesh, and storing the volume information around a spline path. Such information is later used to compute the intersection between the object surfaces under the assumption of spatial coherence between neighboring splines. We analyze the method’s performance in terms of both speed and accuracy, comparing it with previous works. Results show that our method solves collisions between complex meshes with over 300k triangles, generating over 1,000 collisions per frame between objects while maintaining an average time of under 1ms without compromising accuracy.

Single-layer iron network microstructure magnetorheological elastomer for transparent soft actuator

The motion of a soft magnetic magnetorheological elastomer, a composite material consisting of soft magnetic fillers and an elastomeric matrix, is limited by the gradient of the applied magnetic field, making its application as an out-of-plane soft actuator difficult. In this study, an anisotropic soft magnetic magnetorheological elastomer-based transparent soft actuator with large out-of-plane deformation was fabricated using a very low concentration (1 vol%) of carbonyl iron particles as the soft magnetic fillers. An applied AC electric field treatment method was used to manipulate iron particles in silicone oil, assembling and depositing them onto a glass substrate to form a single-layer iron-network microstructure. A single-layer iron-network microstructure-based magnetorheological elastomer (SL-sMRE) film with initial self-buckling was fabricated by exploiting the swelling properties of the residual silicone oil and polydimethylsiloxane. The SL-sMRE film exhibited out-of-plane deformation with a very short reaction time (228 ms) under a uniform magnetic field. When stimulated by a non-uniform magnetic field, the SL-sMRE film deformed away from the permanent magnet, which was not possible with an isotropic film. An optically transparent SL-sMRE film was applied to a transparent flexible gripping device that could detect the surface of a gripped object in real time.

Molecular H2 in silicate melts

A series of hydrothermal diamond anvil cell experiments was conducted to constrain the equilibrium distribution of molecular H2 between H2O-saturated sodium aluminosilicate melts and H2O at elevated temperatures (600–800 °C) and pressures (317–1265 MPa). The distribution of H2 between the silicate liquid and the aqueous fluid was achieved through real-time monitoring of the H-H stretching vibration under in situ conditions using Raman vibrational spectroscopy. Results show that the solubility of H2 in silicate melts saturated with H2O decreases as the temperature increases, with control exerted by the mole fraction of H2O in the melt. The dissolution of H2 in the hydrous silicate melts appears to follow Henrian behavior, resembling that of an inert, neutral non-polar species. To express species solubility as a function of temperature (T in K) an empirical equation was developed:ln Km/f = 11.4 (±1.3) *1000 / T (K) − 18.7 (±1.1)where Km/f is the equilibrium constant for the reaction H2(g) = H2(melt). This equation was derived by integrating data from the current and prior experimental studies that include silicate melts with varying H2O saturation levels. It should be deemed applicable within the temperature range of 600–1450 °C and pressures ranging from 0.3 to 3 GPa. The implications are extended into developing an understanding of the H partitioning between H2-rich atmospheres blanketing magma oceans in the early history of planetary bodies. For example, transferring H from primordial atmospheric envelopes to the interior of rocky exoplanets may be less efficient than previously believed, which should be considered in models of volatile retention. Experimental data also suggest that minimal amounts of solar nebula H2 are likely to dissolve in the molten surface of primitive objects in the protoplanetary disk (∼10−5 to 10−9 mol faction H2 in melt), contradicting the highly reducing conditions observed in chondrule mineral compositions.

Deep-learning based single-shot 3D reconstruction with simulated color-crosstalk and randomized extrinsics

In recent years, many single-frame 3D reconstruction schemes based on fringe projection profilometry (FPP) have been proposed. However, most single-frame reconstruction schemes still face the following three issues: (1) obtaining large datasets is very time-consuming, (2) focusing only on achieving single-frame reconstruction for white objects, and (3) requiring fixing the camera-projector positional relationship, increasing operational difficulty. By building a virtual FPP simulation system, our method can quickly render the required datasets, avoiding cumbersome manual operations. When rendering the datasets, we simulate adverse factors such as color channel crosstalk, system extrinsic parameter variations, and object surface colors to guide the training of the neural network. Ultimately, from a single three-frequency color image, the corresponding three-frequency three-step phase-shift images are predicted, achieving single-frame 3D reconstruction of colored objects and allowing some variation in system extrinsic parameters. Real-world experiments demonstrate that the network trained with the diverse data generated by our virtual system has good accuracy, providing valuable guidance for the practical application of deep learning methods.

Analisis Karakteristik Material Baja dengan Metode Digital Image Correlation (DIC)

The use of steel materials in building construction opens new opportunities for sustainable development, as steel exhibits corrosion resistance, durability, and reliability in terms of strength and ductility. Digital Image Correlation (DIC) is non-contact technique in which digital images of the surface of a test object are captured using high-resolution cameras. This study conducted measurements of strain distribution on the specimen's surface using the DIC method throughout the entire tensile testing process. The study particularly focuses on examining changes in strain distribution during the melting phase and the local deformation phase leading to fracture. In this research, a comparison will be made between the load-displacement curves obtained from experimental laboratory testing and the results analyzed using the DIC method for SS400-grade steel material. Based on the results of the tensile test and DIC analysis that have been conducted, conclusions have been drawn in the research. The tensile test results of SS400 steel material with a thickness of 6 mm, 8 mm, 10 mm, and 12 mm meet the quality requirements in the tested specification standards, and the results of the force-displacement curve between the experimental test results and the DIC method obtained a minimum deviation with a value below 10%,. Therefore, it can be concluded that the DIC method exhibits a reasonably good level of accuracy, making it suitable for validating the results of experimental tests.

Lipschitz stability of an inverse conductivity problem with two Cauchy data pairs

Abstract In 1996 Seo proved that two appropriate pairs of current and voltage data measured on the surface of a planar homogeneous object are sufficient to determine a conductive polygonal inclusion with known deviating conductivity. Here we show that the corresponding linearized forward map is injective, and from this we deduce Lipschitz stability of the solution of the original nonlinear inverse problem. We also treat the case of an insulating polygonal inclusion, in which case a single pair of Cauchy data is already sufficient for the same purpose.

An Underwater Stereo Matching Method: Exploiting Segment-Based Method Traits without Specific Segment Operations

Stereo matching technology, enabling the acquisition of three-dimensional data, holds profound implications for marine engineering. In underwater images, irregular object surfaces and the absence of texture information make it difficult for stereo matching algorithms that rely on discrete disparity values to accurately capture the 3D details of underwater targets. This paper proposes a stereo method based on an energy function of Markov random field (MRF) with 3D labels to fit the inclined plane of underwater objects. Through the integration of a cross-based patch alignment approach with two label optimization stages, the proposed method demonstrates features akin to segment-based stereo matching methods, enabling it to handle images with sparse textures effectively. Through experiments conducted on both simulated UW-Middlebury datasets and real deteriorated underwater images, our method demonstrates superiority compared to classical or state-of-the-art methods by analyzing the acquired disparity maps and observing the three-dimensional reconstruction of the underwater target.

Fraction Activities Rosella Flower (Hibiscus Sabdariffa L) as an Antibacterial and Antibiofilm Escherichia Coli ATCC 25922

Rosella flowers (Hibiscus sabdariffa L) contain alkaloid, flavonoid, saponin and tannin compounds. The compounds contained in rosella flowers have activity as antibacterial and antibiofilm. Biofilm is the ability of microorganisms that stick to the surface of objects and form a matrix. This study aims to antibacterial and biofilm activity in the form of inhibition of biofilm formation and biofilm degradation of E.coli bacteria. Rosella flowers were extracted using maceration method with 96% ethanol solvent. The thick extract was then fractionated with n-hexane, ethyl acetate and water solvents. Antibacterial activity test using diffusion and dilution methods followed by observation of cell wall and membrane damage using SEM and ion leakage with AAS. Biofilm activity test was conducted using a microplate device and readings were taken at a wavelength of 595 nm. The absorbance results of the inhibition percentage to determine the IC50 and EC50 values. The average IC50 values of biofilm formation inhibition test on extract, n-hexane fraction, ethyl acetate fraction and water fraction against E. coli bacteria are 1.059; 1.070; 0.921; 0.863. The EC50 values of biofilm degradation test on extract, n-hexane fraction, ethyl The minimum killing concentration value of rosella flower water fraction against E. coli is 25 mg/ml. the effect of the active fraction of rosella flower on E. coli bacteria is on the cell wall and cell membrane