Skeletonization Process Research Articles

Mechanisms of Reservoir Space Preservation in Ultra-Deep Shales: Insights from the Ordovician–Silurian Wufeng–Longmaxi Formation, Eastern Sichuan Basin

This study aims to explore the reservoir characteristics and formation mechanisms of ultra-deep shale gas in the Ordovician–Silurian Wufeng–Longmaxi Formation in the Sichuan Basin in order to provide theoretical support and practical guidance for the exploration and development of ultra-deep shale gas. With recent breakthroughs in ultra-deep shale gas exploration, understanding its organic matter development, mineral composition, and reservoir space characteristics has become particularly important. The background of this research lies in the significant potential of ultra-deep shale gas, which remains inadequately understood, necessitating an in-depth analysis of its pore structure and reservoir quality. Through a systematic study of the ultra-deep shale in well FS1 of Sichuan Basin, that the following was found: (i) The ultra-deep shale in the Wufeng–Longmaxi Formation is mainly composed of quartz and clay minerals, exhibiting high total organic carbon (TOC) and high porosity characteristics, indicating it is in an overmature thermal evolution stage. (ii) Organic pores and microcracks in the ultra-deep shale are more developed compared to middle-shallow and deep shale, forming a complex pore structure that is conducive to gas storage. (iii) In the diagenesis process, the dissolution and recrystallization of the biogenic skeleton promote the cementation between autogenetic quartz particles, forming a rigid skeleton that effectively inhibits the impact of mechanical compaction. (iv) The overpressure environment created by the hydrocarbon generation process, along with gas production from hydrocarbon cracking, can effectively offset the mechanical compaction of overburden pressure on micropores, and this overpressure environment also promotes the further development of microfractures, which is beneficial for the development and preservation of ultra-deep shale pores. In summary, this study not only reveals the reservoir characteristics and formation mechanisms of ultra-deep shale but also provides essential references for the exploration and development of ultra-deep shale gas in the Sichuan Basin and similar regions, emphasizing the ongoing significance of research in this field.

Deep multimodal spatio-temporal Harris Hawk Optimized Pose Recognition framework for self-learning fitness exercises

Human pose recognition from videotapes has become an emerging research topic for tracking human movements. The objective of this work is to develop a deep multimodal Spatio-Temporal Harris Hawk Optimized Pose Recognition (STHHO-PR) framework for self-learning fitness exercises. The presented STHHO-PR framework uses audio modality and visual modality to classify the different poses. In audio modality, the VGG-16 network paradigm is used to extract the audio traits for fitness pose recognition. In visual modality, Harris Hawks Optimization (HHO) along with the Minimum Cross Entropy (MCE) method is employed to find out the optimum threshold values for body parts segmentation. These segmented body parts highlight the human joint points that are connected through the skeletonization process to extract the skeletal information. The extracted spatio-temporal features from audio modality and visual modality are optimally fused and used in the classification process. Weighted Majority Voting Ensemble (WMVE) classifier is adopted to build the classification model. This work is experimented with yoga videos acquired from publicly available datasets. The results show that the presented STHHO-PR framework outperforms other state-of-art procedures in terms of prediction accuracy.

Approximation of multistage stochastic programming problems by smoothed quantization

We present an approximation technique for solving multistage stochastic programming problems with an underlying Markov stochastic process. This process is approximated by a discrete skeleton process, which is consequently smoothed down by means of the original unconditional distribution. Approximated in this way, the problem is solvable by means of Markov Stochastic Dual Dynamic Programming. We state an upper bound for the nested distance between the exact process and its approximation and discuss its convergence in the one-dimensional case. We further propose an adjustment of the approximation, which guarantees that the approximate problem is bounded. Finally, we apply our technique to a real-life production-emission trading problem and demonstrate the performance of its approximation given the “true” distribution of the random parameters.

Coral-mimetic production of aragonite films from CO2 captured by biogenic polyamines

We designed CaCO3 films comprised of aragonite nanorods by mimicking the microstructure and the formation process of the calcareous skeleton of a stony coral in the sea.

Insight into age-related changes of the human facial skeleton based on medieval European osteological collection

Aging changes in the facial skeleton are concentrated mostly in orbits, maxilla and mandible. The aim of this study was to analyze metric traits of the adult viscerocranium in a medieval sample from Cedynia (Poland, Central Europe) and confront the results with literature data for modern populations. It was assumed that diet-related greater biomechanical forces generated during mastication in medieval versus modern times led to slower rate of bone resorption with age. 3D models of the facial skeleton are created for 230 individuals, categorized into young, middle and old adults, and a subgroup of edentulous middle adults is distinguished. Orbits, piriform aperture, maxilla and mandible are measured using Geomagic Studio 12 and analyzed among age categories as well as dentate and edentulous subgroups. The values of the orbital and piriform aperture measurements tend to increase with age and reached statistical significance in males (right orbit height, left orbit width, piriform aperture surface area). In females, maxillary height significantly decrease at right first premolar and first molar, together with height of the right mandibular ramus. In edentulous individuals of both sexes the orbits are wider, and maxillary and mandibular heights are lower than in dentate individuals. This study reveals similar character and direction of the aging process of the facial skeleton in medieval and modern adults, however slower rate of resorptive changes is found in the former sample, which suggests, that diet-related biomechanical forces can influence intensification of the aging processes in the facial skeleton.

Innovative Macula Capillaries Plexuses Visualization with OCTA B-Scan Graph Representation: Transforming OCTA B-Scan into OCTA Graph Representation.

The aim of this study is to transform optical coherence tomography angiography (OCTA) scans into innovative OCTA graphs, serving as novel biomarkers representing the macular vasculature. The study included 90 healthy subjects and 39 subjects with various abnormalities (29 with diabetic retinopathy, 5 with age-related macular degeneration, and 5 with choroid neovascularization). OCTA 5µm macular coronal views (MCVs) were generated for each subject, followed by blood vessel segmentation and skeleton processing. Subsequently, the blood vessel density index, blood vessel skeleton index, and blood vessel tortuosity index were computed. The graphs of each metric were plotted against the axial axes of the OCTA B-scan, representing the integrity of vasculature at successive 5µm macular depths. The results revealed two significant findings. First, the B-scans from OCTA can be transformed into OCTA graphs, yielding three specific OCTA graphs in this study. These graphs provide new biomarkers for assessing the integrity of deep vascular complex (DVC) and superficial vascular complex (SVC) within the macula. Second, a statistically significant difference was observed between normal (n=90) and abnormal (n=39) subjects, with a t-test p-value significantly lower than 0.001. The Mann-Whitney u-test also yielded significant difference but only between the 90 normal and 29 DR subjects. The novel OCTA graphs offer a unique representation of the macula's SVC and DVC, suggesting their potential in aiding physicians in the diagnosis of eye health within OCTA clinics. Further research is warranted to finalize the shape of these newly derived OCTA graphs and establish their clinical relevance and utility.

Adaptive real-time similar repetitive manual procedure prediction and robotic procedure generation for human-robot collaboration

Manual procedure recognition and prediction are essential for practical human-robot collaboration in industrial tasks, such as collaborative assembly. However, current research mostly focuses on diverse human motions, while the similar repetitive manual procedures that are prevalent in real production tasks are often overlooked. Furthermore, the dynamic uncertainty caused by human-robot interferences and the generalisation of individuals, scenarios, and multiple sensor deployments pose challenges for implementing manual procedure prediction and robotic procedure generation. To address these issues, this paper proposes a real-time, similar repetitive procedure-oriented human skeleton processing system that employs the human skeleton as a robust modality. It utilises an improved deep spatial-temporal graph convolutional network and a FIFO queue-based discriminator for real-time data processing, procedure prediction, and generation. The proposed method is validated on multiple datasets with tens of individuals engaged in a real dynamic and uncertain human-robot collaborative assembly cell and able to run on entry-level hardware. The results demonstrate competitive performance of handcraft feature-free, early prediction and generalisation on individual variance, environment background, camera position, lighting conditions, and stochastic interference in human-robot collaboration.

Bone resorption and formation in the pedicles of European roe deer (Capreolus capreolus) in relation to the antler cycle-A morphological and microanalytical study.

We analyzed pedicle bone from roe bucks that had died around antler casting or shortly before or during the rutting period. Pedicles obtained around antler casting were highly porous and showed signs of intense osteoclastic activity that had caused the formation of an abscission line. Following the detachment of the antler plus a portion of pedicle bone, osteoclastic activity in the pedicles continued for some time, and new bone was deposited onto the separation plane of the pedicle stump, leading to partial pedicle restoration. Pedicles obtained around the rutting period were compact structures. The newly formed, often very large secondary osteons, which had filled the resorption cavities, exhibited a lower mineral density than the persisting older bone. The middle zones of the lamellar infilling frequently showed hypomineralized lamellae and enlarged osteocyte lacunae. This indicates a deficiency in mineral elements during the formation of these zones that occurred along with peak antler mineralization. We suggest that growing antlers and compacting pedicles compete for mineral elements, with the rapidly growing antlers being the more effective sinks. The competition between the two simultaneously mineralizing structures is probably more severe in Capreolus capreolus than in other cervids. This is because roe bucks regrow their antlers during late autumn and winter, a period of limited food and associated mineral supply. The pedicle is a heavily remodeled bone structure with distinct seasonal variation in porosity. Pedicle remodeling differs in several aspects from the normal bone remodeling process in the mammalian skeleton.

Contrasting faulting styles of salt domes and volcanoes: Can unsupervised learning techniques differentiate fault styles?

An attractive feature seen on seismic data, also known as funny-looking thing (FLT), has a wide range of interpretations, from noise patterns to amplitude anomalies. An example of an FLT is the similar faulting patterns between a volcanic intrusion and a salt intrusion from the point of view of a machine learning (ML) algorithm. Oftentimes, seismic interpreters do not have a complete data set or geologic background to determine the genesis of the observed features. This can be particularly perplexing when trying to determine if an intrusion is volcanic or halokinetic in origin because they exhibit similar geomorphologies. Examining the differences in these features in the Gulf of Mexico, a well-documented salt basin, and the Taranaki Basin in New Zealand, which is igneous prone, can help to understand the differences. The analysis aims to discern geologic features based on the geometries and attributes shared by seismic data and remote sensing tools. Seismic attributes and ML techniques highlight differences and similarities between the intrusions, including the discussion of using ML techniques, such as self-organized maps (SOMs), an unsupervised ML technique, and cluster fault systems, without regard to the geologic context. The attributes used in the SOM are fault probability, fault dip azimuth, fault dip magnitude, and thin-bed detector. Fault probability is performed through a combination of convolutional neural network fault prediction and a skeletonization process. Once the faults are clustered using SOM, the visualization of fault architecture due to the existing mount (either volcano or salt dome) is done considering high fault probabilities (>75%). The methodology consists of selecting the neurons from the SOM grid corresponding to the presence of faults and combining them with fault probability and a fault dip azimuth using a crossplot. The crossplot product assists in the automatic extraction of the fault planes using: (1) a voxel representation of the fault planes and (2) fault patches representing the fault planes. Moreover, the visualization technique defined demonstrates that the crossplot product yields better-defined fault planes. With the fault system characterized, compared horizon slices using coherence, fault dip magnitude, and azimuth against remote sensing images with similar attributes. In conclusion, our methodology combines technologies to differentiate the genesis of intrusion — salt or igneous — using the fault presence and could be helpful in frontier exploration or planetary exploration. Geologic feature: Comparison of extensional faults due to salt and igneous intrusions Seismic appearance: In the symmetric salt dome, crown faulting style with a set of synthetic and antithetic faults around the salt dome; for the igneous systems, symmetric radial faults Formation: Upper and Lower Wilcox (Gulf of Mexico); Ariki Formation — Mo hakatino Formation (Taranaki) Age: Upper Miocene and Lower Pliocene (Gulf of Mexico); Mid Pliocene (Taranaki) Location: Offshore Gulf of Mexico and Offshore Northeast New Zealand Seismic data: From the Gulf of Mexico: B-78-89-LA-3D, B-69-94-LA-3D, B-54d-94-LA-3D, and B-69a-94-LA-3D. From New Zealand: Nimitz and Kora 3D Analysis tools: Structural-oriented filter, Sobel similarity, fault dip magnitude and azimuth, CNN automatic fault prediction, and SOM classification

Two-Stage Classifier for Resource Constrained On-Board Cardiac Arrhythmia Detection

Early automatic detection of cardiac arrhythmia in an ambulatory health monitoring setup can reduce the mortality rates. In the existing approaches on multiclass arrhythmia detection (MCAD) techniques, adopting either time-domain morphological features (TDMFs), fails to extract hidden pattern from the single-lead electrocardiogram (ECG) resulting in poor accuracy (Ac), or adopting deep-learning (DL) techniques becomes too complex or heavy in architecture, thus hindering their easy implementation in low resource architecture for wearable applications. This work presents a judicious hybridization of both approaches in an end-to-end MCAD using two-stage classifier to achieve higher Ac and low latency while detecting 15 classes of cardiac rhythms [14 types of arrhythmia beats or normal sinus rhythms (NSRs)]. The memory resource saving is achieved through the beat skeletonization process and two-stage feature minimization. The stage#1 classifier uses 14 TDMF features fed to a random forest (RF) regressor and provides probability values for NSR, atrial, or ventricular conduction problem. The stage#2 classifier, another RF, accepts stacked autoencoder (SAE) features, TDMF features, and output of stage#1 to achieve a blind test Ac of 99.21% using only 13 features while evaluated with full Massachusetts Institute of Technology-Beth Israel (MIT-BIH) arrhythmia database (mitdb) records. A stand-alone on-board application of the two-stage MCAD using ARMv6 single-core microcontroller yields a detection latency of 782 ms and consumes 390 kB of memory per cardiac cycle, thus proving a feasible solution for wearable cardiac monitors. It also provides the competitive performance with the State of the art (SOTA) methods and outperforms the limited available research on hardware implementation in terms of number of classes handled and Ac.

Modeling the Behavior of an Aggregate Skeleton during Static Creep of an Asphalt Mixture Based on a Three-Dimensional Mesoscale Random Model

The aggregate skeleton and its evolution under external loads are crucial to evaluate the mechanical properties of the asphalt mixture. This study developed a method to identify the three-dimensionally (3D) aggregate skeleton based on geometry information. The proposed method can identify the evolution process of the aggregate skeleton during loading in the finite element method (FEM). Random models based on the Gilbert–Johnson–Keerthi (GJK) algorithm are used to study the influence of the concave–convex degree of the gradation curve and the magnitude of the creep load on the aggregate skeleton during the creep process. The results indicate that the aggregate skeleton undergoes three stages during creep: aggregate redistribution, skeleton formation, and skeleton failure after reaching the ultimate load, while the increase of load causes the aggregate skeleton to lose its bearing capacity faster. More coarse aggregate raises the strength of the aggregate skeleton, but it is more likely to break down quickly once it reaches the strength. More fine aggregate enhances the stability of the aggregate skeleton. The proposed identification method can serve as a general tool to investigate the evolution process of the internal aggregate structure of the asphalt mixture.

Scalable Biologically-Aware Skeleton Generation for Connectomic Volumes.

As connectomic datasets exceed hundreds of terabytes in size, accurate and efficient skeleton generation of the label volumes has evolved into a critical component of the computation pipeline used for analysis, evaluation, visualization, and error correction. We propose a novel topological thinning strategy that uses biological-constraints to produce accurate centerlines from segmented neuronal volumes while still maintaining biologically relevant properties. Current methods are either agnostic to the underlying biology, have non-linear running times as a function of the number of input voxels, or both. First, we eliminate from the input segmentation biologically-infeasible bubbles, pockets of voxels incorrectly labeled within a neuron, to improve segmentation accuracy, allow for more accurate centerlines, and increase processing speed. Next, a Convolutional Neural Network (CNN) detects cell bodies from the input segmentation, allowing us to anchor our skeletons to the somata. Lastly, a synapse-aware topological thinning approach produces expressive skeletons for each neuron with a nearly one-to-one correspondence between endpoints and synapses. We simultaneously estimate geometric properties of neurite width and geodesic distance between synapse and cell body, improving accuracy by 47.5% and 62.8% over baseline methods. We separate the skeletonization process into a series of computation steps, leveraging data-parallel strategies to increase throughput significantly. We demonstrate our results on over 1250 neurons and neuron fragments from three different species, processing over one million voxels per second per CPU with linear scalability.

Transcriptomics analysis provides insights into metabolisms of sugars and carotenoids of nectarine fruit subjected to different temperature storage

Improper temperature storage of nectarine fruit after harvest could easily lead to rapid fruit softening, decrease of fruit sugar content, loss of flavor substances, and browning of tissues. Transcriptomics and bioinformatic analyses were applied to explore the effect of different storage temperature in regulating the metabolic pathways in nectarine fruit. In this study, we observed that differentially expressed genes (DEGs) were accumulated at high levels to starch and sucrose metabolism and the biosynthesis process of terpenoid skeleton. A detailed analysis of sugar and carotenoid metabolism indicated that the changes of sugars and carotenoids were accurately associated with the gene expressions of invertase, sucrose phosphate synthase, carotenoid cleavage dioxygenase, geranylgeranyl diphosphate synthase and phytoene desaturase. Further analysis found that nectarine fruit stored at low temperature exhibited lower content of sucrose and carotenoids compared with room temperature storage. Low temperature storage mainly inhibited the synthesis of sucrose and carotenoids by down-regulating expressions of invertase and carotenoid cleavage dioxygenase. Moreover, Coexpression network analysis revealed that 3 PpWRKYs, 2 PpMYBs and 1 PpNAC could be identified as putative transcription factors in the regulation of carotenoid metabolism in nectarine fruit. In summary, this research explained the general molecular mechanism of the changes of sugars and carotenoids in nectarine fruit in response to different temperature storage.

Optimization and Evaluation of Enzymatic Hydrolysis Process of Hybrid Broilers Skeleton

Optimization and Evaluation of Enzymatic Hydrolysis Process of Hybrid Broilers Skeleton

Process control for improving dewatering performance of sewage sludge based on carbonaceous skeleton-assisted thermal hydrolysis

The poor dewaterability of sewage sludge is a major obstacle to its disposal and utilization. Our previous study developed a novel method of carbonaceous skeleton-assisted thermal hydrolysis to achieve good performance of sludge dewatering. This work was conducted for further improving the efficiency through investigating the effects of the properties of sludge, skeleton, and key process parameters. A dewatering model was also established based on Darcy's Law and experimental results from a self-designed computer control on-line filter press system. The experimental results showed that the water content can all be reduced by about 36% for sludge with the varying organic content from 35% to 60%. Lignocellulosic skeleton had better assistive capabilities than skeleton with high content of hemicellulose, lipid, and chitin, and the appropriate dosage was 0.2–0.5 g/g DS. Satisfied reduction of about 30% in water content can be obtained when sludge was assisted-hydrolyzed at a moderate temperature of 180 °C only within 5 min and dewatered at 0.4–1.0 MPa for 10–20 min. By using self-developed dewatering model, the filtrate mass with time under any mechanical pressure can be obtained and the theoretical value fit the actual value very well. Based on this, excellent dewatering performance can be achieved through process control of assisted thermal hydrolysis and mechanical dewatering.

Conformational distortion-harnessed singlet fission dynamics in thienoquinoid: rapid generation and subsequent annihilation of multiexciton dark state

Current work unravels the distinctive conformational distortion-harnessed singlet fission process in thienoquinoid skeleton featuring the rapid generation and subsequent annihilation of intramolecular multiexciton dark state with 21A symmetry.

An Effectively Dynamic Path Optimization Approach for the Tree Skeleton Extraction from Portable Laser Scanning Point Clouds

One key step to the tree structure study is skeleton processing. Although there are lots of extraction approaches, the existing methods have paid less attention to extraction effectiveness, which highly use redundant points to formulate the skeleton and bring difficulties to the subsequent 3D modeling. This work proposes a four-step framework for the purpose of skeleton extraction. Firstly, candidate skeleton points are filtered from input data based on the spatial slice projection and grouped using the Euclidean distance analysis. Secondly, a key dynamic path optimization step is used to formulate a tree skeleton using the candidate point information. Thirdly, the optimized path is filled by interpolating points to achieve complete skeletons. Finally, short skeletons are removed based on the distance between branching points and ending points, and then, the extraction skeletons are smoothed for improving the visual quality. Our main contribution lies in that we find the global minimization cost path from every point to the root using a novel energy function. The formulated objective function contains a data term to constrain the distance between points and paths, and a smoothness term to constrain the direction continuities. Experimental scenes include three different types of trees, and input point clouds are collected by a portable laser scanning system. Skeleton extraction results demonstrate that we achieved completeness and correctness of 81.10% and 99.21%. respectively. Besides, our effectiveness is up to 79.26%, which uses only 5.82% of the input tree points in the skeleton representation, showing a promising effective solution for the tree skeleton and structure study.

Brown Tumor of Lower Right Limb in Patients With Primary Hyperparathyroidism: A Case Report

Background.Brown tumor of Hyperparathyroidism is a metabolic disorder that can affect the entire skeleton and reactive process due to bone resorption caused by primary or secondary hyperparathyroidism (HPT). Brown tumors can occur as solitary or multiple lesions in any bone, most often in the pelvis, ribs, clavicle, mandibula, and extremities. Here, we report the Brown tumor in the lower right limb in patients with primary HPT, and the literature is reviewed. Case presentation. Patients was women 30 years old had married and come with main complains of difficulty walking. This condition has been experienced by patients since diagnosis with lunb of tibia last 8 months and caused pain from hip to lower leg. On laboratory results, it showed elevated PTH 1.249 (normal 15-65) pg/dL, elevated phosphatase alkali 1156 (normal 40-150) u/dL, elevated Ca 10,8 (n:8,6 -10,3) mg/dL, phosphor 2,1 (3–4,5) mg/dL. Histology examination of tibia lump was a benign lesion of bone (Brown Tumor). Ultrasonography transabdominal result revealed kidney stones with bilateral renal pelvis dilation, nephrolithiasis non-obstructive was found with size 1 cm & left kidney cyst with size 0.6 cm. On Neck USG showed giant cyst lesion on parathyroid glands. Radiologist pelvic examination results showed bone metastasis disease. Head CT Scan examination concluded as suspect metastatic bone. Body bone scans examination showed pathological bone metastatic process. Conclusion. Brown tumor in right lower limb caused by primary HPT

An Enhanced Retinal vessel segmentation using Deep Convolution Neural Network

Accurate vessel segmentation in retinal images plays a vital role for retinopathy diagnosis and analysis. The presence of very thin vessels in low image contrast, on the other hand makes the segmentation task difficult. In the proposed method retinal vessels are segmented using multiscale Fully Convolved Convolutional Neural Network (FCCNN) architecture. The proposed architecture is trained for pixel classification to cope with the varying width and direction of the vessel structure in the retina. Green channel extraction gives better contrast difference between vessels and background. The skeletonization process is done which prevents the change in structure, thus the vasculature remains unchanged. In addition, an improved class balanced cross entropy loss function is included to avoid misclassification and imbalance problem. The proposed method is verified on public retinal vessel segmentation database (DRIVE). The accuracy of DRIVE FCCNN after 90 epochs was attained as 92.73% and the loss was attained as 0.0632. The experimental results show that segmentation results and accuracy obtained for FCNN are greater compared to the other architecture.

Hunter-gatherer's mortuary practices in artificial mounds of the lower Paraná River basin (Argentina): An approach through funerary taphonomy

The Late Holocene complex hunter-gatherers of the Lower Paraná River Basin (Argentina) settled on fluvial banks of the riparian landscape, and to a much lesser extent, on artificially elevated structures. Although both present a complex variability of burials, those of the artificial mounds are barely known. This work aims to explore the mortuary practices of the oldest artificially elevated sites in the region, Escuela 31 and Cerro Mayor (1800–1500 years BP), through a funerary taphonomic approach. We analyze the burials of 11 adult and subadult individuals of both sexes, focusing on the process of decomposition and skeletonization as consequences of mortuary treatment. The patterns of disarticulation, the presence of insect and animal damage, and cut marks are consistent with the temporal exposure of corpses and the possible use of wraps. For some cases, the spatial arrangement of the remains presented a novel type of secondary burials. The disposal of the dead comprises the ultimate stage of successive events and preparations of the corpse, which manifest complex interactions between the living and the dead. This work contributes to the knowledge about the mortuary program of South American hunter-gatherers in general and societies that built artificial mounds in the southernmost wetlands of South America. It also highlights the importance of funerary taphonomy to understand the variability of mortuary treatments.