Rectangular Box Research Articles

Laboratory Study of Interface Friction between Sand and Steel Surfaces

Abstract In this study, results have been reported from a series of laboratory tests to determine the interface friction angle of steel surfaces with sand through modified direct shear testing. For completion, steel surfaces with different surface geometry to replicate the roughness have been slid against various sands compacted at different relative densities under both dry and wet conditions. The standard square shape direct shear box has been modified into circular and rectangular boxes to sufficiently enhance the contact area and to subsequently quantify its effect on interface friction angle. A mild steel interface of smooth and rough texture with a groove depth of 0.2 mm and tipping at an angle of 90° has been used in this study. The analysis of results revealed that the interface friction angles obtained from the standard direct shear box agree closely with those obtained from both modified rectangular and circular shear boxes. However, the ratio of the interface friction angle from the modified apparatus and soil’s angle of internal friction significantly depends upon the roughness of the surface in contact with the sand, its median particle size, and is independent of the effects of box size, soil’s relative density, and dry or wet condition. A comparison between existing well-accepted guidelines and current observations has been presented for use by the practicing engineers dealing with soil-structure interaction.

Inhibition of return in a 3D scene depends on the direction of depth switch between cue and target.

Inhibition of return (IOR) is a phenomenon that reflects slower target detection when the target appears at a previously cued rather than uncued location. In the present study, we investigated the extent to which IOR occurs in three-dimensional (3D) scenes comprising pictorial depth information. Peripheral cues and targets appeared on top of 3D rectangular boxes placed on the surface of a textured ground plane in virtual space. When the target appeared at a farther location than the cue, the magnitude of the IOR effect in the 3D condition remained similar to the values found in the two-dimensional (2D) control condition (IOR was depth-blind). When the target appeared at a nearer location than the cue, the magnitude of the IOR effect was significantly attenuated (IOR was depth-specific). The present findings address inconsistencies in the literature on the effect of depth on IOR and support the notion that visuospatial attention exhibits a near-space advantage even in 3D scenes consisting entirely of pictorial depth information.

Research on Improved Image Segmentation Algorithm Based on GrabCut

The classic interactive image segmentation algorithm GrabCut achieves segmentation through iterative optimization. However, GrabCut requires multiple iterations, resulting in slower performance. Moreover, relying solely on a rectangular bounding box can sometimes lead to inaccuracies, especially when dealing with complex shapes or intricate object boundaries. To address these issues in GrabCut, an improvement is introduced by incorporating appearance overlap terms to optimize segmentation energy function, thereby achieving optimal segmentation results in a single iteration. This enhancement significantly reduces computational costs while improving the overall segmentation speed without compromising accuracy. Additionally, users can directly provide seed points on the image to more accurately indicate foreground and background regions, rather than relying solely on a bounding box. This interactive approach not only enhances the algorithm’s ability to accurately segment complex objects but also simplifies the user experience. We evaluate the experimental results through qualitative and quantitative analysis. In qualitative analysis, improvements in segmentation accuracy are visibly demonstrated through segmented images and residual segmentation results. In quantitative analysis, the improved algorithm outperforms GrabCut and min_cut algorithms in processing speed. When dealing with scenes where complex objects or foreground objects are very similar to the background, the improved algorithm will display more stable segmentation results.

Comparative evaluation of accuracy of three different electronic apex locators in the presence of various endodonticirrigants: An in vitro study

AIM AND OBJECTIVES. To evaluate and compare the accuracy of Root ZX, Raypex6, and I Root and electronic apex locators in the presence of NaOCl (3%), Citric acid (10%), MTAD and Green tea extract as endodontic irrigants.MATERIAL AND METHOD. Sixty single rooted human permanent teeth were used for the study. Access cavities were prepared and the teeth were decoronated. Teeth were divided as follows: Group I-Root ZX apex locator (n=20), IA – NaOCl 3% endodontic irrigant (n=5), IB – Citric Acid 10% endodontic irrigant (n=5), IC – MTAD endodontic irrigant (n=5), ID- Green Tea extract endodontic irrigant (n=5). Group II: Raypex 6 apex locator (n=20). IIA – NaOCl 3% endodontic irrigant (n=5), IIB – Citric acid 10% endodontic irrigant (n=5). IIC – MTAD endodontic irrigant (n = 5), IID – Green tea extract endodontic irrigant (n=5). Group III-i-ROOT apex locator (n=20). IIIA – NaOCl 3% endodontic irrigant (n=5), IIIB – Citric Acid 10% endodontic irrigant (n=5), IIIC – MTAD endodonticirrigant (n=5), IIID – Green tea extract endodontic irrigant (n=5). The actual working length was determined using a 15 K file and the working model was prepared using Alginate in a rectangular box. The teeth were embedded into the working model and the electronic measurements were made with each electronic apex locator. The data was collected, analyzed and subjected to statistical analysis.RESULTS. No significant difference was found when Raypex 6 apex locator was used along with all the endodontic irrigants except green tea extract. Root ZXshowed statistically significant difference with green tea extract and MTAD. I-root showed statistically significant difference with all the irrigants except green tea extract.CONCLUSION. Citric acid 10% was consistently accurate when assessed with all the three apex locators, whereas Green tea extract demonstrated the least accurate readings with all apex locators.

An Experimental and Numerical Study of Motion Responses of Multi-Body Arrays with Hinge Connections

Hinged multi-body systems are gaining popularity in the field of ocean engineering. Their performance is commonly evaluated using numerical simulations, but comparisons with experimental data are required to ensure the accuracy of the computational tools. However, there is a dearth of experimental studies on the motion performance of hinged multi-body systems, particularly those involving more than two hinged floating bodies. This study aims to fill this gap in experimental data for hinged multi-body systems beyond two bodies. The rectangular box was chosen as the test model due to its stable hydrodynamic properties and ease of numerical modelling. Five identical boxes were prefabricated and subsequently tested in the pool in a sequence ranging from one to five boxes to capture the motion performance. Additionally, a numerical programme based on potential flow theory was developed for mutual validation with the experimental data. Firstly, the physical properties of each box were determined through equations calculation and a free decay test, enabling the acquisition of all parameters for conducting numerical simulations. Then, the response amplitude operator (RAO) curves for the heave and pitch motion of a single box were depicted, and the results indicated that the resonant frequency in pitch direction obtained from the regular wave test was consistent with that obtained from the free decay test. Finally, the motion RAO curves of hinged multi-body systems were presented and analysed. The agreement between the measured and computed results confirms the suitability of the experimental data presented in this study as benchmark data for validating numerical simulations.

Incident investigation of hydrogen explosion and fire in a residue desulfurization process

A large hydrogen explosion and fire occurred on October 27, 2022 in a residue desulfurization (RDS) process in a refinery in Kaohsiung, Taiwan. RDS is an important process that use hydrogen to convert the sulfur in the fuel into hydrogen sulfide for producing low sulfur fuel. The incident occurred during pressurization of the RDS reactors and downstream coolers by hydrogen. The incident led to significant damages to the downstream coolers and some part of the RDS reactors. Detailed incident investigation was carried out. A reactor effluent air cooler (REAC) in the downstream of RDS reactors was found to be ruptured in the rectangular header box. With security videos, process DCS data, leak and dispersion modelling, and actual damage on the process, it is possible to reproduce the leak rate, size of the initial fireball and damages done to the process. It is also found that the hydrogen leak was accompanied by prompt ignition which is commonly encountered in high-pressure hydrogen services. Recommendations are made to prevent and mitigate any future incident from the RDS process.

Wheat leaf localization and segmentation for yellow rust disease detection in complex natural backgrounds

Wheat yellow rust disease poses a significant threat to global wheat yield and grain quality. Early detection of this disease will help to minimize the loss caused by its effects. Existing models work well on images taken in a controlled environment, whereas a uniform background is placed behind the leaf, but these models fail to produce good results in natural settings. Previous research also involves manual interventions in the pipeline to achieve good classification results such as cropping the images, using uniform backgrounds, etc. These systems are not practical to use in natural environments where there will be a lot of background noise to the image and manual cropping becomes an extra step for the farmer. Moreover, the unavailability of the dataset in which images of leaves are taken in a natural setting became another challenge. In this research, a dataset is curated and leaves are annotated for object detection, object segmentation further the leaves are classified into 3 classes ie healthy, resistant, and susceptible. A novel unsupervised image rotation algorithm is proposed that takes input from YOLOv8 to align the leave in such a way that maximum background can be removed by a rectangular bounding box . Then the comparison between multiple state-of-the-art segmentation models ie. UNET, Segment-Anything (SAM), Segnet, LinkNet, PSPNet, FPN, Deep-Labv3+ (Xception), and DeepLabv3+ (Mo-bileNet) has shown that UNET has outperformed all the other segmentation models with an IOU score of 0.9563. Lastly for classification, the performance of multiple convolution neural networks ie. VGG16, Resnet 101(v2), Xception, Mo-bileNetV2, and Transformer-based models ie. Swin trans-former and MobileVit have been compared. Swin transformer has outperformed the state-of-the-art CNN models with an accuracy of 95.8%. This paper proposes a complete robust pipeline that can be deployed in natural environment and does not need any manual intervention to produce good results. This research shows that good localization of leaves and removal of unwanted background noise at the earliest stage of the pipeline will assist the segmentation model to effectively segment the leaf from the background which will enable classification models to achieve high classification accuracy, even when dealing with very small datasets.

Convection Heat Transfer and Performance Analysis of a Triply Periodic Minimal Surface (TPMS) for a Novel Heat Exchanger

Heat exchangers are necessary in most engineering systems that move thermal energy from a hot source to a colder location. The development of additive manufacturing technology facilitates the design and optimization of heat exchangers by introducing triply periodic minimal surface (TPMS) structures. TPMSs have shown excellent mechanical and thermal performance, which can improve heat energy transfer efficiency in heat exchangers. This current study intends to design and develop efficient, lightweight heat exchangers for aerospace and space applications. Using the TPMS structure, a porous construction encloses a horizontal tube that circulates heated fluid. Low-temperature water circulates inside a rectangular box that houses the complete system to remove heat from the horizontal pipe. Three porous structures, the gyroid, diamond, and FKS structures, were employed and examined. Porous models with various porosities and surface areas (15 cm2 and 24 cm2) were investigated. The results revealed that the gyroid structure exhibits the highest Nusselt number for heat removal (Nu max = 2250), confirming the highest heat transfer and lowest pressure drop among the three structures under investigation. The maximum Nusselt number obtained for the FKS structure is less than 1000, whereas, for the diamond structure, it is near 1250. A linear variation in the average Nusselt number as a function of the structure surface area was found for the FKS and diamond structures. In contrast, nonlinearity was observed in the gyroid structures.

A deep top-down framework towards generalisable multi-view pedestrian detection

Multiple cameras have been frequently used to detect heavily occluded pedestrians. The state-of-the-art methods, for deep multi-view pedestrian detection, usually project the feature maps, extracted from multiple views, to the ground plane through homographies for information fusion. However, this bottom-up approach can easily overfit the camera locations and orientations in a training dataset, which leads to a weak generalisation performance and compromises its real-world applications. To address this problem, a deep top-down framework TMVD is proposed, in which the feature maps within the rectangular boxes, sitting at each cell of the discretized ground plane and of the average pedestrians’ size, in the multiple views are weighted and embedded in a top view. They are used to infer the locations of pedestrians by using a convolutional neural network. The proposed method significantly improves the generalisation performance when compared with the benchmark methods for deep multi-view pedestrian detection. Meanwhile, it also significantly outperforms the other top-down methods.

A systematic evaluation of DEM input parameters measurement and calibration

Rotary drums are widely used in industry, however, determining granular dynamics within them is still a complex task, thus remaining a focal point of research over the years. In this context, experimental techniques require numerical support to obtain data that is not easily acquired experimentally. One well-established numerical technique for studying particulate systems is the Lagrangian approach embed with Discrete Element Method (DEM). Although, DEM has limitations related to the definition of input parameters. It is crucial that DEM input parameters be measured and/or calibrated to reliably represent the granular behavior. These parameters are related to particle properties such as density, Young’s modulus, Poisson’s ratio, among others, and particle interactions, such as coefficients of restitution, static friction, and rolling friction. This study explores the experimental determination of interaction parameters preceding the acquisition of DEM input parameters in a rotary drum. Statistical analysis highlights the importance of each parameter and their combined interactions with the static angle of repose. The granular materials used were: soybeans, glass beads, polyacetal, 6 mm steel, and 4 mm steel. The coefficient of restitution was determined via free-fall methodology using equipment that verifies particle trajectory, static friction coefficient through equilibrium between weight and resistance forces, rolling friction coefficient using a launching device on surfaces of different roughness, and static angle of repose in a rectangular box with sections where particles rested. After removing the partition, angles were measured at both top and bottom sections. Regression techniques using a neural network created in Python were employed for both measurements. Experimental investigations and numerical simulations using DEM indicated that the coefficient of restitution depends on the thickness and type of material of the flat surfaces, showing an asymptotic behavior with increasing surface thickness. The static friction coefficient increased on rougher surfaces and in particles with greater imperfections. Rolling friction coefficient also increased on rough surfaces. Analysis of the angle of repose revealed that granular properties influence the response, such as static and rolling friction coefficients, whereas the coefficient of restitution had no significant influence. Neural network correlation coefficients exceeded 0.95, indicating a strong association between the variables. Our findings revealed that mathematical models can be used in conjunction with static repose angle experiments to calibrate DEM parameters for more robust and complex simulations. This research also quantifies and provides insights into the interactions between DEM parameters in direct measurement experiments, promoting more efficient calibration for future research.

Path planning in additive manufacturing with multi-robot collaboration based on structural primitive partitioning

Directed Energy Deposition (DED) technology is increasingly favored for swiftly fabricating large structural components due to its high printing efficiency. Despite its advantages, challenges persist in achieving satisfactory surface finish and forming precision, hindering its widespread adoption across industries. To address these issues, this paper presents a novel multi-robot collaborative path planning method based on structural primitive partitioning. This method simplifies path planning complexities and seamlessly integrates into process planning software, thereby enhancing overall functionality. This method decomposes complex polygons into tiny primitives (TP), organizing them into TP sets based on bridge and adjacency relations. These sets are then structured into first-level structural TP (F-TP) and second-level structural TP (S-TP), followed by the establishment of monotonic structural TP (M-TP). A minimum rectangular box recalculates the filling path for each M-TP, while the external contour path and internal zigzag path form a complete printing path. Additionally, an optimal printing sequence planning algorithm for multi-robot using a KD-tree-based search algorithm is presented, ensuring the shortest non-productive path and collision avoidance during printing. Experimental verification with four structures of varying geometric features demonstrates a partitioning accuracy of 99.5 % and absence of surface defects in the printed parts. The proposed method presents a viable and effective solution for enhancing the quality of parts produced via DED.

Wideband, polarization-insensitive rectangular-shaped with thin wire MXene-based resonator solar absorber for solar energy harvesting application

Solar absorbers are used in many different applications to collect solar energy. The Rectangular-Shaped with Thin Wire MXene-based resonator (RSWTWMR) is a unique solar absorber design that is proposed in this study. Aluminium and MXene are the materials used in the substrate and resonator layers of the RSWTWMR, respectively. MXene allows for the absorption of a large amount of solar light because of its remarkable absorption qualities and unique optical properties. The MXene-based resonator has a unique design with a thin wire and a rectangular box that makes it insensitive to polarization. RSWTWMR shows an astounding 95.67 % average absorption over three different wide bands. It is noteworthy that the absorption values in the 920 nm and 720 nm bandwidths exceed 90 % and 94 %, respectively. In addition, the absorption is more than 96 % at wavelengths of 550 nm. Additional research examined how well the system performs in air mass (AM1.5) conditions and examined how different factors, such as substrate and resonator thickness, affect absorption properties. Furthermore, the research looks at the impact of an angle of incidence, which is unaffected between 0 and 70°. This study suggests the RSWTWMR solar absorber has great promise for applications in solar energy harvesting.

Artificial High- and Low-density Materials in Bone Mineral Densitometry Using Dual-energy X-ray Absorptiometry: A GATE Monte Carlo Simulation of “Black-hole” Artifact

Objective: The objective of the study was to evaluate the effect of artificial high- and low-density materials on Bone mineral density (BMD)scans in dual-energy X-ray absorptiometry (DXA) method and emergence of black-hole artifact through GATE Monte Carlo simulation. Materials and Methods: GATE Monte Carlo code was utilized to simulate the artifact encountered in clinical scans acquired by HOLOGIC® bone densitometer. Two simplified phantoms were designed. The first one was a rectangular box with six smaller cubes inside and the second one was a body torso. Materials of cubes were spine bone, polymethyl methacrylate (PMMA), barium sulfate suspension in water, stainless steel, titanium alloy, and gold. The torso phantom contained objects of 5 vertebrae, bowel and 3 small spherical objects near the surface of the torso as piercing objects on the abdominal wall, each overlying the vertebrae. Using 100 and 140 kVp, spectral X-rays were generated to simulate DXA. For both phantoms, two simulations were carried out. The pair of projections acquired for each phantom were then subtracted and analyzed by curve fitting techniques. Results: Except for spine bone, in which radio-opacity decreases with increasing spectral X-ray energy (from 100 to 140 kVp), other squares exhibit little changes over different energies. PMMA shows consistently very low radio-opacity. Four other materials (barium sulfate in water, stainless steel alloy, titanium alloy, and gold), all attenuate the X-ray photons substantially. Except for spine bone, other materials are barely noticeable in pairwise subtracted images. In torso phantom, piercing objects are visualized as “holes” in vertebrae. Conclusion: Both artificial high- and low-density materials, compared to bone, are eliminated during the subtraction of dual-energy X-ray profiles and therefore, can create black-hole artifact.

Two-dimensional diffraction and radiation problems of a floating body in varying bathymetry

In this study, a coupling method of the higher order boundary element method and the eigenfunction expansion method is applied to investigate the diffraction and radiation problems of a floating body over an uneven seabed under the assumption of linear small-amplitude wave theory. The wave exciting forces and hydrodynamic coefficients are calculated for a floating rectangular box. It is found that the curves of the wave exciting forces and hydrodynamic coefficients versus frequency become fluctuating as the horizontal length L and vertical height D of the sloping seabed gradually increase. The period of the fluctuations decreases with the increase of L, and the magnitude of the fluctuations increases with the increase of D. The mechanism of the fluctuation phenomenon is then examined, and found that it is associated with the wave reflection from the slope seabed. With increasing horizontal scale L and vertical scale D of the sloping seabed, the amplitude and phase of the reflection wave change, which leads to the combined action of the incident, scattering and reflection waves fluctuating with the wave frequency.

Level set simulation of focused ion beam sputtering of a multilayer substrate.

The evolution of a multilayer sample surface during focused ion beam processing was simulated using the level set method and experimentally studied by milling a silicon dioxide layer covering a crystalline silicon substrate. The simulation took into account the redeposition of atoms simultaneously sputtered from both layers of the sample as well as the influence of backscattered ions on the milling process. Monte Carlo simulations were applied to produce tabulated data on the angular distributions of sputtered atoms and backscattered ions. Two sets of test structures including narrow trenches and rectangular boxes with different aspect ratios were experimentally prepared, and their cross sections were visualized in scanning transmission electron microscopy images. The superimposition of the calculated structure profiles onto the images showed a satisfactory agreement between simulation and experimental results. In the case of boxes that were prepared with an asymmetric cross section, the simulation can accurately predict the depth and shape of the structures, but there is some inaccuracy in reproducing the form of the left sidewall of the structure with a large amount of the redeposited material. To further validate the developed simulation approach and gain a better understanding of the sputtering process, the distribution of oxygen atoms in the redeposited layer derived from the numerical data was compared with the corresponding elemental map acquired by energy-dispersive X-ray microanalysis.

Investigation into the relationship between wave forces and characteristic velocity during wave impact on the deck structures

Deck structures, whose safety is threatened by catastrophic waves, are widely used in offshore and coastal engineering. This study aims to enhance the understanding of the impact of waves on deck structures through a combined approach of hydrodynamic experimentation and numerical simulations. In this study, the wave impact forces and pressure on a deck structure, which was simplified as a rectangular box model, were experimentally measured. The numerical model based on smoothed particle hydrodynamics was validated using experimental data due to its capacity to compute the wave force exerted on the box structure accurately. A characteristic velocity parameter was defined to explore the relationship between the wave-slamming forces and the wave velocity field. It was found that the characteristic velocity was highly correlated with the wave impact forces, which could be fitted using a negative exponential curve. In addition, the characteristic velocity can be roughly calculated using Stokes’ second wave theory, which offers a preliminary method for estimating wave impact loads on deck structures.

Designing a Novel Hybrid Technique Based on Enhanced Performance Wideband Millimeter-Wave Antenna for Short-Range Communication.

This paper presents the design of a performance-improved 4-port multiple-input-multiple-output (MIMO) antenna proposed for millimeter-wave applications, especially for short-range communication systems. The antenna exhibits compact size, simplified geometry, and low profile along with wide bandwidth, high gain, low coupling, and a low Envelope Correlation Coefficient (ECC). Initially, a single-element antenna was designed by the integration of rectangular and circular patch antennas with slots. The antenna is superimposed on a Roger RT/Duroid 6002 with total dimensions of 17 × 12 × 1.52 mm3. Afterward, a MIMO configuration is formed along with a novel decoupling structure comprising a parasitic patch and a Defected Ground Structure (DGS). The parasitic patch is made up of strip lines with a rectangular box in the center, which is filled with circular rings. On the other side, the DGS is made by a combination of etched slots, resulting in separate ground areas behind each MIMO element. The proposed structure not only reduces coupling from -17.25 to -44 dB but also improves gain from 9.25 to 11.9 dBi while improving the bandwidth from 26.5-30.5 GHz to 25.5-30.5 GHz. Moreover, the MIMO antenna offers good performance while offering strong MIMO performance parameters, including ECC, diversity gain (DG), channel capacity loss (CCL), and mean effective gain (MEG). Furthermore, a state-of-the-art comparison is provided that results in the overperforming results of the proposed antenna system as compared to already published work. The antenna prototype is also fabricated and tested to verify software-generated results obtained from the electromagnetic (EM) tool HFSS.

A Foam Line Position Detection Algorithm for A/O Pool Based on YOLOv5

During the biochemical pretreatment process of leachate in urban landfill sites, if the foam in the A/O pool is not promptly addressed, it can lead to overflow, posing hazards to the surrounding environment and personnel. Therefore, a real-time foam line detection algorithm based on YOLOv5x was proposed, which enhances feature information and improves anchor box regression prediction to accurately detect the position of foam lines. Firstly, in the preprocessing stage, employing a rectangular box to simultaneously label the foam line and the edge of the A/O pool within the same region, enhances the feature information of the foam line. Then, the C3NAM module was proposed, which applies weight sparse penalties to attention modules in the feature extraction section, to enhance the capability of extracting foam line features. Subsequently, a B-SPPCSPC module was proposed to enhance the fusion of shallow and deep feature information, addressing the issue of susceptibility to background interference during foam line detection. Next, the Focal_EIOU was introduced to ameliorate the issue of class imbalance in detection, providing more accurate bounding box predictions. Lastly, optimizing the detection layer scale improves the detection performance for smaller targets. The experimental results demonstrate that the accuracy of this algorithm reaches 98.9%, and the recall reaches 88.1%, with a detection frame rate of 26.2 frames per second, which can meet the actual detection requirements of real-world application scenarios.

Build Deep Neural Network Models to Detect Common Edible Nuts from Photos and Estimate Nutrient Portfolio.

Nuts are nutrient-dense foods and can be incorporated into a healthy diet. Artificial intelligence-powered diet-tracking apps may promote nut consumption by providing real-time, accurate nutrition information but depend on data and model availability. Our team developed a dataset comprising 1380 photographs, each in RGB color format and with a resolution of 4032 × 3024 pixels. These images feature 11 types of nuts that are commonly consumed. Each photo includes three nut types; each type consists of 2-4 nuts, so 6-9 nuts are in each image. Rectangular bounding boxes were drawn using a visual geometry group (VGG) image annotator to facilitate the identification of each nut, delineating their locations within the images. This approach renders the dataset an excellent resource for training models capable of multi-label classification and object detection, as it was meticulously divided into training, validation, and test subsets. Utilizing transfer learning in Python with the IceVision framework, deep neural network models were adeptly trained to recognize and pinpoint the nuts depicted in the photographs. The ultimate model exhibited a mean average precision of 0.7596 in identifying various nut types within the validation subset and demonstrated a 97.9% accuracy rate in determining the number and kinds of nuts present in the test subset. By integrating specific nutritional data for each type of nut, the model can precisely (with error margins ranging from 0.8 to 2.6%) calculate the combined nutritional content-encompassing total energy, proteins, carbohydrates, fats (total and saturated), fiber, vitamin E, and essential minerals like magnesium, phosphorus, copper, manganese, and selenium-of the nuts shown in a photograph. Both the dataset and the model have been made publicly available to foster data exchange and the spread of knowledge. Our research underscores the potential of leveraging photographs for automated nut calorie and nutritional content estimation, paving the way for the creation of dietary tracking applications that offer real-time, precise nutritional insights to encourage nut consumption.

Generalized geometric pore size distribution code GPSD-3D for periodic systems composed of monodisperse spheres

The generalized geometric pore size distribution P(r;rp|rc) as function of pore radius r, probe sphere radius rp, and coating thickness rc for a periodic two-dimensional system composed of circles (GPSD-2D) had been defined recently. For rp=rc=0 it reduces to the widely accepted pore radius distribution P(r) introduced by Gelb and Gubbins. The three-dimensional counterpart GPSD-3D for periodic systems composed of spheres is implemented here using an efficient Voronoi-based semi-analytic strategy that offers significant advantages compared with both a grid-based implementation and constrained nonlinear optimization with respect to speed, precision and memory requirements. Moreover, GPSD-3D is fully parallelized using OpenMP. Program summaryProgram title:GPSD-3DCPC library link to program files:https://doi.org/10.17632/ntcr87fgr5.1Developers repository link:https://github.com/mkmat/CODE-GPSD-3DLicensing provisions: MITProgramming languages:fortran, c++, perlNature of problem: Calculation of a set of pore radii for a user-provided configuration of (material) spheres contained in a periodic three-dimensional, rectangular box. This set depends on the user-specified radius rp of a probe sphere, and a user-specified hypothetical coating thickness rc of the material spheres. The variable size of the set, and thus the resolution of the resulting generalized geometric pore size distribution increases linearly with computing time.Solution method: Analytic calculation of pore radii from the Voronoi faces for the case of monodisperse systems, constrained nonlinear optimization or grid-based approach for polydisperse systems.Additional comments: This code requires a c++ compiler such as g++, a OpenMP-supporting fortran compiler such as gfortran, perl, and the voro++ library. Download links will be automatically provided during installation of GPSD-3D.