Reliable Point Research Articles

Object-centric Learning with Capsule Networks: A Survey

Capsule networks emerged as a promising alternative to convolutional neural networks for learning object-centric representations. The idea is to explicitly model part-whole hierarchies by using groups of neurons called capsules to encode visual entities, then learn the relationships between these entities dynamically from data. However, a major hurdle for capsule network research has been the lack of a reliable point of reference for understanding their foundational ideas and motivations. This survey provides a comprehensive and critical overview of capsule networks, which aims to serve as a main point of reference going forward. To that end, we introduce the fundamental concepts and motivations behind capsule networks, such as equivariant inference . We then cover various technical advances in capsule routing algorithms as well as alternative geometric and generative formulations. We provide a detailed explanation of how capsule networks relate to the attention mechanism in Transformers and uncover non-trivial conceptual similarities between them in the context of object-centric representation learning. We also review the extensive applications of capsule networks in computer vision, video and motion, graph representation learning, natural language processing, medical imaging, and many others. To conclude, we provide an in-depth discussion highlighting promising directions for future work.

Image retrieval based on auto-encoder and clustering with centroid update

Abstract This paper suggests a revolutionary deep learning method using a dynamic deep auto-encoder for improving the performance of indexing the feature vectors of images by centroid updation. Feature vectors such as color, semantic, and spatial local binary pattern are extracted from the images for content-based image retrieval. The owner encrypts the images for protection using elliptic curve cryptography before uploading them to the cloud. A black hole entropic fuzzy clustering with Tversky indexing is used to retrieve similar information. When the new training image is matched with any of the centroid then the centroid gets updated by using dynamic deep auto-encoder. During the auto-encoder phase, the conflicted data points are dedicated to reconstruction and the reliable data points are helpful to centroid updation. The suggested BHE fuzzy clustering with dynamic deep auto-encoder approach fared better than the current methods, achieving the best accuracy of 97.605%, the highest $\boldsymbol{F_{1}}$ score of 90.210%, better precision of 90.001%, and the highest recall of 95.149%.

The Reproducibility of Reference Landmarks in the External Acoustic Meatus (EAM) on Cone Beam Computed Tomography (CBCT) Images.

Objective: The aim of the present study is to identify a more reliable reference point in three-dimensional cephalometric analysis to replace the Porion point used in two-dimensional analysis, enhancing the accuracy of assessments. Methods: The methodology assessed potential alternative landmarks for three-dimensional cephalometric analysis. Utilizing a segmenting technique, anatomical landmarks were accurately pinpointed from the external acoustic meatus of 26 Cone Beam Computed Tomography (CBCT) scans. These landmarks were chosen for their clear and unambiguous detectability. To assess reproducibility, each landmark was replicated twice with a one-week interval by a master's student. Reproducibility was quantitatively evaluated by analyzing the absolute difference per axis. Results: Five possible candidate landmarks were identified: the most anterior, posterior, superior, and inferior points of the external acoustic meatus (EAM) and a notch delineating the epitympanic recess. The reproducibility of pinpointing these landmarks ranged from 0.56 mm to 2.2 mm. The absolute mean differences between measurements were 0.46 mm (SD 0.75) for the most anterior point, 0.36 mm (SD 0.44) for the most posterior point, 0.25 mm (SD 0.26) for the most superior point, 1.11 mm (SD 1.03) for the most inferior point, and 0.78 mm (SD 0.57) for the epitympanic notch. Conclusions: The most superior point of the EAM might successfully replace the Porion as an anatomical reference.

Self-supervised multi-echo point cloud denoising in snowfall

Snowfall can cause noise to light detection and ranging (LiDAR) data. This is a problem since it is used in many outdoor applications, e.g., autonomous driving. We propose the task of multi-echo denoising, where the goal is to pick the echo that represents the objects of interest and discard other echoes. Thus, the idea is to pick points from alternative echoes unavailable in standard strongest echo point clouds. Intuitively, we are trying to see through the snowfall. We propose a novel self-supervised deep learning method and the characteristics similarity regularization to achieve this goal. The characteristics similarity regularization utilizes noise characteristics to increase performance. The experiments with a real-world multi-echo snowfall dataset prove the efficacy of multi-echo denoising and superior performance to the baseline. Moreover, based on extensive experiments on a semi-synthetic dataset, our method achieves superior performance compared to the state-of-the-art in self-supervised snowfall denoising. Our work enables more reliable point cloud acquisition in snowfall. The code is available at https://github.com/alvariseppanen/SMEDen.

Peer review of the pesticide risk assessment of the active substance mepiquat (evaluated variant mepiquat chloride).

The conclusions of the EFSA following the peer review of the initial risk assessments carried out by the competent authorities of the rapporteur Member State, Finland, and co-rapporteur Member State, Estonia, for the pesticide active substance mepiquat (evaluated variant mepiquat chloride) are reported. The context of the peer review was that required by Commission Implementing Regulation (EU) No 844/2012, as amended by Commission Implementing Regulation (EU) No 2018/1659. The conclusions were reached on the basis of the evaluation of the representative uses of mepiquat chloride as a plant growth regulator on cereals and grass (field uses). The reliable end points, appropriate for use in regulatory risk assessment, are presented. Missing information identified as being required by the regulatory framework is listed. Concerns are identified.

Peer review of the pesticide risk assessment of the active substance paraffin oil (CAS 8042-47-5, chain lengths C17-C31).

The conclusions of the European Food Safety Authority (EFSA) following the peer review of the initial risk assessments carried out by the competent authorities of the rapporteur Member State, Greece, and co-rapporteur Member State, France, for the pesticide active substance paraffin oil are reported. The context of the peer review was that required by Commission Implementing Regulation (EU) No 844/2012, as amended by Commission Implementing Regulation (EU) No 2018/1659. The conclusions were reached on the basis of the evaluation of the representative uses of paraffin oil as an acaricide and insecticide on potatoes, ornamentals (flower bulbs) and orchards (pear/apple), on pome fruit and stone fruit, on field and permanent protected fruiting vegetables and on field and permanent protected roses and on citrus. The reliable end points appropriate for use in regulatory risk assessment are presented. Missing information identified as being required by the regulatory framework is listed. Concerns are reported where identified.

Peer review of the pesticide risk assessment of the active substance lenacil.

The conclusions of the European Food Safety Authority (EFSA) following the peer review of the initial risk assessments carried out by the competent authorities of the rapporteur Member State, Belgium, and co-rapporteur Member State, Austria, for the pesticide active substance lenacil are reported. The context of the peer review was that required by Commission Implementing Regulation (EU) No 844/2012, as amended by Commission Implementing Regulation (EU) No 2018/1659. The conclusions were reached on the basis of the evaluation of the representative uses of lenacil as a herbicide on sugar and fodder beet (field use). The reliable end points, appropriate for use in regulatory risk assessment, are presented. Missing information identified as being required by the regulatory framework is listed. Concerns are reported where identified.

Redefining the Envelope of Discrepancy in Contemporary Orthodontics: A Narrative Review

Abstract Redefining the envelope of discrepancy with temporary anchorage devices (TADs) refers to the use of mini-implants, miniplates, or miniscrews in orthodontics to expand the range of possible tooth movements and correct complex dental discrepancies. By strategically placing TADs in specific locations, orthodontists can create an augmented anchorage system that enables them to move teeth in ways that were previously not feasible. This expanded envelope of discrepancy allows for more efficient and effective treatment of complex malocclusions, including retraction of teeth, correction of severe crowding, open bites, deep bites, and cases with missing teeth. TADs allow orthodontists to have greater control over tooth movements, as they provide stable and reliable anchorage points. Orthodontists can tackle more challenging cases that were considered difficult or even impossible to treat. TADs minimize the dependence on patient cooperation since they provide additional anchorage independent of patient compliance. They can accelerate treatment by allowing orthodontists to move teeth more efficiently and effectively. Thus, treatment duration can be shortened, reducing the overall time patients spend in braces or aligners. Overall, redefining the envelope of discrepancy with TADs has significantly expanded the possibilities in orthodontic treatment, allowing for more precise and efficient correction of complex dental discrepancies.

H2LWRF-PDR: An efficient indoor positioning algorithm using a single Wi-Fi access point and Pedestrian Dead Reckoning

Indoor localization is challenging in densely populated urban areas due to the multipath effect, attenuation, noise, and difficulty finding reliable Wi-Fi access points. In urban flats, multiple access points are not easily available or require infrastructure setup, which increases cost. To address the above issues, the article proposes a new algorithm called ‘H2LWRF-PDR’ based on Wi-Fi fingerprinting using a single access point with extended features. This algorithm is designed for static and moving objects. The proposed algorithm first converts the received signal strengths from a single access point into five statistical features used as fingerprints. Then, hierarchical clustering is applied to remove outliers from the fingerprints. Then, a random forest classifier is used to predict the cluster values. Finally, the lowess filters are used to smoothen the output values from the classifier. Furthermore, these outputs are combined with the proposed Pedestrian Dead Reckoning (PDR) algorithm. In the end, a Savitzky–Golay filter is utilized to remove the fluctuations in the trajectories. The proposed approach has been validated in urban flat and laboratory environments and compared with state-of-the-art localization algorithms. The proposed algorithm ‘H2LWRF-PDR’ showed 50% improvements in an urban flat dataset and at least 30% in the laboratory dataset.

Nano-silicon embedded in 3D honeycomb carbon frameworks as a high-performance lithium-ion-battery anode

The fabrication of three-dimensional (3D) honeycomb carbon frameworks, incorporating embedded silicon (Si) nanoparticles (NPs), is successfully achieved through a facile method involving mixing, carbonization, and acid pickling. The as-prepared Si@PVPC composites exhibit excellent electrochemical performances due to reasons as follows. (1) Honeycomb channels supply enough space to accommodate the expansion of Si NPs and so structural integrity of 3D honeycomb carbon frameworks are maintained during lithiation/delithiation processes. (2) Honeycomb channels offers fast transfer paths for Li-ion diffusion. (3) 3D carbon frameworks afford reliable and stable electric contact points. As a result, Si@PVPC electrodes show a capacity of 1294.3 mAh g−1 at 2 A g−1 after 1400 cycles with a capacity retention of 85.5 %. Even at an ultrahigh current of 16 A g−1, Si@PVPC electrodes still show a capacity of 619.6 mAh g−1.

Influence of process variations on clinch joint characteristics considering the effect of the nominal tool design

Focusing on upcoming challenges in lightweight design, such as increasing emission targets or novel multimaterial connections, versatile applicable and environmentally friendly production technologies are crucial. In this context, mechanical joining technology clinching offers a fast and energy-efficient procedure for assembling sheet metals, being a proper alternative to established joining methods, such as spot welding. However, the design of clinch points is a challenge, which is partly supported by numerical or data-based approaches for optimal tool dimensions assuring proper joint characteristics. While this is usually done for an ideal environment, real joining processes are characterized by multiple inevitably varying parameters, e.g. of the material, which have a significant impact on the quality of clinch points. Therefore, this contribution addresses the current gap by analyzing the effect of parameter variations or uncertainties on the resulting joint characteristics and studying the impact of the nominal tool design. Thus, an efficient meta-model-based variation simulation procedure is proposed and used for analyzing the effect of different tool design configurations and variation scenarios. Based on the results, it was found that varying process parameters have a strong impact on the resulting joint characteristics, whereby the effect significantly depends on the nominal tool design. This reveals the potential for a robust tool design and implies that the nominal tool design and the tolerancing of parameters should be done simultaneously for a reliable virtual joining point design without extensive iterations and physical tests.

M-GCLO: Multiple Ground Constrained LiDAR Odometry

Abstract. Accurate LiDAR odometry results contribute directly to high-quality point cloud maps. However, traditional LiDAR odometry methods drift easily upward, leading to inaccuracies and inconsistencies in the point cloud maps. Considering abundant and reliable ground points in the Mobile Mapping System(MMS), ground points can be extracted, and constraints can be built to eliminate pose drifts. However, existing LiDAR-based odometry methods either do not use ground point cloud constraints or consider the ground plane as an infinite plane (i.e., single ground constraint), making pose estimation prone to errors. Therefore, this paper is dedicated to developing a Multiple Ground Constrained LiDAR Odometry(M-GCLO) method, which extracts multiple grounds and optimizes those plane parameters for better accuracy and robustness. M-GCLO includes three modules. Firstly, the original point clouds will be classified into the ground and non-ground points. Ground points are voxelized, and multiple ground planes are extracted, parameterized, and optimized to constrain the pose errors. All the non-ground point clouds are used for point-to-distribution matching by maintaining an NDT voxel map. Secondly, a novel method for weighting the residuals is proposed by considering the uncertainties of each point in a scan. Finally, the jacobians and residuals are given along with the weightings for estimating LiDAR states. Experimental results in KITTI and M2DGR datasets show that M-GCLO outperforms state-of-the-art LiDAR odometry methods in large-scale outdoor and indoor scenarios.

Reliability evaluation in the zero‐failure Weibull case based on double‐modified hierarchical Bayes

AbstractHierarchical Bayes, or E‐Bayes, is frequently used to estimate the failure probability when solving a zero‐failure reliability evaluation model; however, the accuracy of the reliability estimation using these methods is not very good in practice. Due to this, a novel double‐modified hierarchical Bayes (DMH‐Bayes) is proposed for Weibull characteristic data in this study to enhance failure probability estimation and improve reliability point estimation accuracy. Meanwhile, in order to guarantee the preservation of the assessment findings' consistency and confidence level, the parametric Bootstrap method (P‐Bootstrap) and the L‐moment estimation method based on point estimation are introduced to obtain reliability confidence interval estimates. Based on Monte–Carlo simulation testing and analysis of a gyroscope bearing, the new model is confirmed to have better applicability and robustness while improving the accuracy of reliability assessment.

Evaluation of the STAT1 mRNA expression in peripheral blood cells as a predictive marker of early pregnancy in Awassi ewes

The primary objective of this study was to measure the expression profile of the signal transducer activator transcription 1 (STAT1) in the circulatory leukocytes of non-pregnant/pregnant ewes and test the possibility that it could be useful for detecting early pregnancy. Whole blood was collected from 20 ewes (control) and another 5 samples were taken on days (15, 25, 35, and 50). Also, blood was collected from 30 ewes on day 15 post insemination (PI) and another 10 samples were taken on days (25, 35, and 50) PI. Total RNA was isolated from peripheral blood leukocytes (PBLs), and STAT1 expression levels were determined by quantitative real-time PCR (qRT-PCR). Gene expression for STAT1 mRNA was 5.8-fold and 5.1 times higher in gravid as opposed to non-gravid ewes. The reliable cut-off point > 1.45 was established using the receiver operating characteristic (ROC) curve, and the accuracy (Acc) for detecting pregnancy was 82%, 93%, 70% and 60%, and the sensitivity (Se) was 83%, 80%, 60% and 33% and Specificity (Sp) 95% on days 15, 25, 35 and 50, respectively. The mRNA expression was lower in ewes that control non-pregnant than pregnant ewes on days 15 and 25. So The best period to detect pregnancy was 15-25 days PI.

Techno-economic assessment of hydrogen-based energy storage systems in determining the optimal configuration of the nuclear-renewable hybrid energy system

Population growth and economic development have significantly increased global energy demand. Hence, it has raised concerns about the increase in the consumption of fossil fuels and climate change. The present work introduced a new approach to using carbon-free energy sources, such as nuclear and renewable, to meet energy demand. The idea of using the Nuclear-Renewable Hybrid Energy System (N-R HES) is suggested as a leading solution that couples a nuclear power plant with renewable energy and hydrogen-based storage systems. For this purpose, using a meta-heuristic method based on Newton's laws, the configuration of the N-R HES is optimized from an economic and reliability point of view. The optimal system is selected from among six cases with different subsystems such as wind turbine, photovoltaic panel, nuclear reactor, electrolysis, fuel cell, and hydrogen storage tank. Furthermore, the performance of hydrogen-based energy storage systems such as high-temperature electrolysis (HTE) and low-temperature electrolysis (LTE) is evaluated from technical and economic aspects. The results of this work showed that using nuclear energy to supply the base load increases the reliability of the system and reduces the loss of power supply probability to zero. More than 70 % of the power is produced by nuclear reactors, which includes more than 80 % of the system costs. The key findings showed that despite HTE's higher efficiency, using LTE as a storage system in N-R HES is more cost-effective. Finally, due to recent developments and the safer design of nuclear reactors, they can play an important role in combination with renewable energies to support carbon-free energy sectors, especially in remote areas, for decades to come.

An Analysis of Relation Among Three Levels of Ala-Tragus Line and Natural Occlusal Plane in Young Dentulous Subjects - A Cross-sectional Photographic Study.

Occlusal plane determination plays an evident role in successful rehabilitation of completely edentulous patients. The most commonly used method is by using Camper's plane. But the posterior reference point of Camper's plane is debatable according to available literature. The aim of this study was to determine the most reliable posterior reference point for Camper's plane. A cross-sectional study was conducted, in which 60 dentulous subjects with age ranging from 18 to 25 years were selected according to the inclusion criteria. The Fox plane was inserted and lateral photographs were taken. A screen protractor was used to measure the angle between the occlusal plane and three ala-tragus lines. All three ala-tragus lines had the inferior border of ala of the nose as the anterior point of reference and for posterior reference, they used superior, middle, or inferior point of the tragus. The ala-tragus line using the inferior point of the tragus had the least mean angle with the occlusal plane. One-way analysis of variance (ANOVA) test obtained a P-value of < 0.0001, and therefore, a statistically significant difference was present among all three lines. Also, post hoc Tukey's honestly significant difference test for intergroup comparison of difference in angle among three different lines with the occlusal plane showed a significant P-value on comparing all three groups between each other. The ala-tragus line formed using the inferior point of the tragus was most parallel to the occlusal plane in dentate patients. Therefore, the inferior point of the tragus should be used for Camper's plane.

Context-based local-global fusion network for 3D point cloud classification and segmentation

3D point clouds have gained much research attention because of their ability to represent the spatial information of real-world environments in a detailed manner. Despite recent progress in point cloud processing with deep neural networks, most of them either implement sophisticated local feature aggregation methods or imitate 2D convolution operations in the range of K nearest neighbors with limited local context information. These methods may struggle to distinguish between similar geometric shapes within the local region of K nearest neighbors, such as doors and walls. To address this issue, we propose a novel local–global fusion network that captures the diverse local geometric shapes with global structure information. The proposed local–global fusion network comprises two main modules. Firstly, we have developed an effective approach for local context learning using incremental dilated KNN (IDKNN) as the neighbor selecting mechanism to enlarge the receptive field and incorporate more reliable points for local geometric shape learning. Secondly, a three-direction region-wise spatial attention (TRSA) algorithm has been developed to explore the global contextual dependencies. For global context learning, we first split the entire 3D space into regions with equal numbers of points, and, then, intra-region context features are extracted to learn the inter-region relations from three orthogonal directions, taking global structural knowledge into account. By fusing the local context information and global contextual dependencies, we establish a Local-Global Fusion Network, end-to-end framework, called LGFNet. Extensive experimental results on several benchmark datasets clearly demonstrate our approach can achieve state-of-the-art (SOTA) performance on point cloud classification, part segmentation, and indoor semantic segmentation. In addition, TRSA and IKDNN can be easily used in a plug-and-play fashion with various existing SOTA networks to substantially improve their performance. Our code is available at https://github.com/jasonwjw/IDKNN

Novel Parameter in Pre-Surgical Orthodontic Preparation: A Retrospective Study on the Role of the Upper Incisor Position and a Morphological Evaluation of the Anterior Nasal Spine.

The position and inclination of the incisors play a crucial role in achieving optimal outcomes in orthodontic and orthognathic surgical treatment, given their impact on facial aesthetics. Background/objectives: Due to numerous distorting factors that affect the reliability of the ANB angle, the aim of the present work is to evaluate a more constant parameter over time, the anterior nasal spine (ANS), and explore whether aligning the incisal margin of the upper incisors with the anterior nasal spine could be a reliable indicator for achieving appropriate labial support in pre-surgical orthodontic preparation. Methods: From a pool of 500 cone beam computed tomography (CBCT) scans, 50 CBCT examinations displaying a Class 1 skeletal pattern (ANB = 2° ± 2°) with an intermediate (3.2-4 mm) or mixed (4-6 mm) sagittal maxillary position (MX), as determined by the 3D multiplanar total face approach (TFA), were selected and compared with CBCT examinations randomly chosen from the initial pool. Moreover, 12 landmarks were identified, and measurements were automatically obtained, using software, and recorded. Mean and standard deviation values were calculated for each sample. A comparison was made between the two samples, aligning the results with the morphological analysis of the anterior nasal spine and the sagittal position of the upper maxilla. Results: In Class 1 subjects, the distance between the incisal margin and the plane passed in relation to the anterior nasal spine should range between -1 mm and 1 mm, aligned with or slightly ahead of the anterior nasal spine or slightly ahead of this limit. Conclusions: The anterior nasal spine can serve as a reliable reference point for planning the position of the upper incisors, with excessive proclination or retroclination from this reference point deemed unacceptable.

Statistical appraisal of geothermal heat flow observations in the Arctic

Abstract. Geothermal heat flow is an important boundary condition for ice sheets, affecting, for example, basal melt rates, but for ice-covered regions, we only have sparse heat flow observations with partly high uncertainty of up to 30 m W m−2. In this study, we first investigate the agreement between such pointwise heat flow observations and solid Earth models, applying a 1D steady-state approach to perform a statistical analysis for the entire Arctic region. We find that most of the continental heat flow observations have a high reliability and agreement to solid Earth models, except a few data points, such as, for example, the NGRIP (North Greenland Ice Core Project) point in central Greenland. For further testing, we perform a conditional simulation with focus on Greenland in which the local characteristics of heat flow structures can be considered. Simple kriging shows that including or excluding the less reliable NGRIP point has a large influence on the surrounding heat flow. The geostatistical analysis with the conditional simulation supports the assumption that NGRIP might not only be problematic for representing a regional feature but likely is an outlier. Basal melt estimates show that such a local spot of high heat flow results in local high basal melt rates but leads to less variation than existing geophysical models.

NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions.

Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous associations between target voxels and their neighbors, which, in turn, adversely impacts FOD accuracy. This study aims to address this issue by introducing a novel neural network, the neighboring voxel attention mechanism network (NVAM-Net), designed to reconstruct high-quality FOD images. The NVAM-Net leverages a Transformer architecture and incorporates two innovative attention mechanisms: voxel attention and surface attention. These mechanisms are specifically designed to capture overlooked features among neighboring voxels. The processed features are subsequently passed through two fully connected layers, further enhancing FOD estimation accuracy by separately estimating spherical harmonics (SH) coefficients of varying orders. The experimental findings, based on the Human Connectome Project (HCP) dataset, reveal that the reconstructed super-resolution FOD images achieve results comparable to those obtained through more advanced dMRI acquisition protocols. These results underscore the NVAM-Net's robust performance in reconstructing multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD). In summary, this research underscores the NVAM-Net's advantages and practical feasibility in reconstructing high-quality FOD images. It provides a reliable reference point for clinical applications in the field of diffusion magnetic resonance imaging.