Aggregation Points Research Articles

A novel pipeline based on line-structured features and cross-camera perspectives for coarse aggregate point cloud registration

A novel pipeline based on line-structured features and cross-camera perspectives for coarse aggregate point cloud registration

Distribution characteristics of stress on the vertebrae following different ranges of excision during Modified Anterior Cervical Discectomy and Fusion: A correlation study based on finite element analysis

BackgroundModified Anterior Cervical Discectomy and Fusion with specific resection ranges is an effective surgical method for the treatment of focal ossification of the posterior longitudinal ligament (OPLL). Herein, we compare and analyse the static stress area distribution by performing different cuts on an original ideal finite element model.MethodA total of 96 groups of finite element models of the C4–C6 cervical spine with different vertebral segmentation ranges (width: 1–12 mm, height: 1–8 mm) were established. The same pressure direction and size were applied to observe the size and distribution area of stress following various ranges of excision of the C5 vertebral body.ResultsDifferent cutting areas had similar stress aggregation points. As the contact area decreased, the stress and the bearing above area increased. The correlation of stress area variation was highest between the 1–2 MPa and 6 MPa–Max regions (Rho = − 0.975). In the surface visualisation model fitting, the width and height were of different ratios in different stress regions. The model with the best fitting degree was the 1–2 MPa group, and the equation fitting (Rho = 0.966) was as follows: Area = 908.80 − 25.92 × Width + 2.71 × Height.ConclusionModified Anterior Cervical Discectomy and Fusion with different resection ranges exhibited different stress areas. In a specific resection range of the cervical spine (1–12 mm, 0–8 mm), area conversion occurred at a threshold of 4 MPa. Additionally, the stress was concentrated at the contact points between the vertebral body and the rigid fixator.

Unraveling polycarboxylate superplasticizer (PCE) compatibility in muscovite-blended cement paste through aggregation mechanisms

Muscovite is a common harmful mineral in manufactured sand, and has always led to the poor compatibility of superplasticizers in fresh concrete. Currently, the failure mechanism is usually explained by competitive adsorption and interlayer adsorption. In addition to the adsorption point of view, as a kind of surfactant, the self-aggregation conformation of superplasticizers in solutions also requires attention. This study explored the compatibility of polycarboxylate superplasticizer (PCE) in cement paste containing muscovite and explained the failure mechanism from the PCE aggregation point of view. Results showed that at the saturated dosage of PCE (0.14 wt%), the introduction of 6 wt% muscovite resulted in a complete loss of fluidity. In the pore solution of cement paste blended with muscovite, the PCE exhibited a lower critical micelle concentration (CMC) and larger aggregation size. The aggregation of PCE is due to the high ionic concentration in cement paste with muscovite, which increases the hydrophobic interaction between PCE and counter ion.

Collaborative Pricing Strategy of Distribution Network and Electric Vehicle Aggregator Based on Integral System

Scaled EVs as a new type of load have great potential to participate in the standby market. However, the uncertainty of EV users' participation in aggregator regulation affects the optimal strategy for aggregators to participate in the market. Aiming at the uncertainty of users' willingness and the conflict of interest between aggregators and the distribution network, this paper proposes an optimal scheduling strategy of master-slave game for EV aggregators to improve users' willingness with a point system. First, the second-order cone optimal current model of the distribution network is established, and then the distribution network node marginal tariff is obtained. Second, the aggregator revenue model with energy storage is established, and the price-guided demand response model is proposed, while the aggregator point system is established to guide the charging behavior of EV users, so that the cooperative operation optimization problem between the distribution grid and the aggregator can be solved, and the economic interests among EV users, EV aggregators and distribution grid operators can be effectively balanced.

Global Warming Reduction Proposal Assessment

The aim of this work, is to find out if there is a convergence of different scientific considerations into a common aggregation point, that can be used as a key point for a new global approach to the problem of global warming, even using a heuristic method of evaluation.

Detection of fluorescent low-energy proton tracks in lithium fluoride crystals

The exploitation of visible radiophotoluminescence in lithium fluoride (LiF) crystals, due to local aggregate point defects produced by ionizing radiation in the crystal lattice, is demonstrated for fluorescent imaging of single tracks of protons at low energies. LiF crystals were irradiated perpendicularly with respect to nearly monochromatic, collimated proton beams at the energies of about 1 MeV in a fluence range from ∼5.8 × 105 p/cm2 up to ∼3.4 × 107 p/cm2 and of about 2.6 MeV in a fluence range from ∼9.6 × 105 p/cm2 up to ∼4.7 × 106 p/cm2. The fluence values were estimated by detecting and automatically counting the tracks on the images acquired with a fluorescence microscope at high magnification; they were found to be in agreement with those obtained using CR39 plastic detectors irradiated under the same experimental conditions. Despite the very short range in matter of these charged particles, by focusing the excitation blue light at discrete depths in the irradiated LiF crystals, an estimate of the proton energies was also obtained. These first results are encouraging for the utilization of LiF crystals as fluorescent nuclear track detectors under typical conditions employed in ion radiobiology.

New Dual Algorithm to Placement the Data Aggregation Point for Smart Grid Meters

New Dual Algorithm to Placement the Data Aggregation Point for Smart Grid Meters

Prevalence and gender difference in depression in primary health care in India: A systematic review and meta-analysis.

Depression contributes to the major burden of mental illness in India. Assessment of burden is essential to develop interventions to address the problem at the primary care level. We carried out a systematic review and meta-analysis of studies documenting the prevalence of depression in primary care in India. A wide literature search strategy was developed using keywords and Medical Subject Headings. The literature search was done in MEDLINE (via PubMed), IndMed, and major Indian psychiatric journal websites. The protocol was registered at PROSPERO. Bias assessment was carried out using a Cochrane risk of bias tool. A total of 186 studies were identified after an initial search, of which 17 were included in the final analysis using pre-specified inclusion and exclusion criteria. The aggregate point prevalence of depression at the primary care level of the 17 studies using the random-effect model was 23.0% (95% CI: 16.0-30.0%). Significant heterogeneity was reported among the studies attributed majorly to a variety of study tools for assessing depression. Sub-group analysis revealed the higher aggregated prevalence of depression among females as compared to males at the primary care level. The study provided updated evidence of higher and gender differential burden of depression at the primary care level in India.

Systematic review for the development of a core outcome set for monofocal intraocular lenses for cataract surgery.

The aim of the study was to define a core outcome set (COS) to be measured following cataract surgery for the postoperative evaluation of monofocal intraocular lenses (IOLs). Compared to current COSs, the present work provides updates considering the advances in the technology due to the development of new generation monofocal IOLs, which are characterized by a safety profile comparable to standard monofocal IOLs but with an extended range of intermediate vision. Healthcare professionals (ophthalmologist surgeons) and patients were involved in the selection of outcomes to be included in the COS, starting from a list of indicators retrieved from a systematic literature search. The search considered observational studies with both a retrospective or prospective design, case studies and classic randomized controlled trials (RCTs). A mixed methodology integrating a Delphi-driven and an expert panel approach was adopted to reach an agreement among clinicians, while patients were involved in the completion of a questionnaire. The final COS included 15 outcomes. Eleven outcomes, all clinical, were considered for inclusion after a joint discussion among ophthalmologists; seven outcomes were linked to visual acuity, while the remaining to contrast sensitivity, refractive errors, aberrations and adverse events. Measurement metrics, method of aggregation and measurement time point of these outcomes were specified. The most important aspects for the patients were (1) quality of life after cataract surgery, (2) the capacity to perform activities requiring good near vision (e.g., reading), (3) spectacle independence, and (4) safety of movements without fear of getting hurt or falling (intermediate vision). In a context with limited healthcare resources, it is important to optimize their use considering also the preferences of end-users, namely patients. The proposed COS, developed involving both ophthalmologists and patients, provides an instrument for the postoperative evaluation of different technologies in the context of monofocal IOLs, which can be used not only in clinical trials but also in clinical practice to increase the body of real-world evidence.

Orientational effects in the polarized absorption spectra of molecular aggregates.

We present a detailed theoretical analysis of polarized absorption spectra and linear dichroism of cyanine dye aggregates whose unit cells contain two molecules. The studied threadlike ordered system with a molecular exciton delocalized along its axis can be treated as two chains of conventional molecular aggregates, rotated relative to each other at a certain angle around the aggregate axis. Our approach is based on the general formulas for the effective cross section of light absorption by a molecular aggregate and key points of the molecular exciton theory. We have developed a self-consistent theory for describing the orientational effects in the absorption and dichroic spectra of such supramolecular structures with nonplanar unit cell. It is shown that the spectral behavior of such systems exhibits considerable distinctions from that of conventional cyanine dye aggregates. They consist in the strong dependence of the relative intensities of the J- and H-type spectral bands of the aggregate with a nonplanar unit cell on the angles determining the mutual orientations of the transition dipole moments of constituting molecules and the aggregate axis as well as on the polarization direction of incident light. The derived formulas are reduced to the well-known analytical expressions in the particular case of aggregates with one molecule in the unit cell. The calculations performed within the framework of our excitonic theory combined with available vibronic theory allow us to quite reasonably explain the experimental data for the pseudoisocyanine bromide dye aggregate.

Macromolecular crowding in chiral assembly of ellipsoidal nanoparticles

Anisotropic colloidal particles have the ability to self-assemble into cholesteric structures. We used molecular dynamics to simulate the self-assembly of ellipsoidal particles with the objective to establish a general framework to reveal the primary factors driving chiral interactions. To characterize these interactions, we introduce a characteristic parameter following the crowding factor (CF) theory. Our simulations and statistical analysis showed good agreement with the CF theory; at the early stages of the assembly process, the ellipsoidal particles go through a critical aggregation point followed by further clustering toward nematic order. Furthermore, we demonstrate that under high CF conditions, small initial clusters may induce a chiral twist, which subsequently forms a cholesteric structure with no directional preference in higher organization states.

Inhibition of the ITGB1 gene attenuates crystalline silica-induced pulmonary fibrosis via epithelial-mesenchymal transformation.

Silicosis is a systemic disease caused by long-term exposure to high concentrations of free silica dust particles in the workplace. It is characterized by a persistent inflammatory response, fibroblast proliferation, and excessive collagen deposition, leading to pulmonary interstitial fibrosis. Epithelial interstitial transformation (EMT) can cause epithelial cells to lose their tight junctions, cell polarity, and epithelial properties, thereby enhancing the properties of interstitial cells, which can lead to the progression of fibrosis and the formation of scar tissue. Integrin 1 (ITGB1) is considered an important factor for promoting EMT and tumor invasion in a variety of tumors and also plays an important role in the progression of fibrotic diseases. Therefore, ITGB1 can be used as a potential target for the treatment of silicosis. In this study, we found that silica exposure induced epithelial-mesenchymal transformation in rats and that the expression of integrin ITGB1 was elevated along with the EMT. We used CRISPR/Cas9 technology to construct integrin ITGB1 knockdown cell lines for in vitro experiments. We compared the expression of the EMT key proteins E-cadherin and vimentin in the ITGB1 knockdown cells and wild-type cells simultaneously stimulated by silica and detected the aggregation point distribution of E-cadherin and vimentin in the cells using laser confocal microscopy. Our results showed that ITGB1 knockout inhibited the ITGB1/ILK/Snail signaling pathway and attenuated the EMT occurrence compared to control cells. These results suggested that ITGB1 is associated with silica-induced EMT and may be a potential target for the treatment of silicosis.

Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus.

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti etal. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE.

Multi-Edge Server-Assisted Dynamic Federated Learning With an Optimized Floating Aggregation Point

We propose cooperative edge-assisted dynamic federated learning ( CE-FL). CE-FL introduces a distributed machine learning (ML) architecture, where data collection is carried out at the end devices, while the model training is conducted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperatively</i> at the end devices and the edge servers, enabled via data offloading from the end devices to the edge servers through base stations. CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users’ mobility. CE-FL considers the heterogeneity of network elements in terms of communication/computation models and the proximity to one another. CE-FL further presumes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic</i> environment with online variation of data at the network devices which causes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">drift</i> at the ML model performance. We model the processes taken during CE-FL, and conduct analytical convergence analysis of its ML model training. We then formulate network-aware CE-FL which aims to adaptively optimize all the network elements via tuning their contribution to the learning process, which turns out to be a non-convex mixed integer problem. Motivated by the large scale of the system, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed optimization solver</i> to break down the computation of the solution across the network elements. We finally demonstrate the effectiveness of our framework with the data collected from a real-world testbed.

Nanoscale structural characterization of transthyretin aggregates formed at different time points of protein aggregation using atomic force microscopy-infrared spectroscopy.

Transthyretin (TTR) amyloidosis is a progressive disease characterized by an abrupt aggregation of misfolded protein in multiple organs and tissues TTR is a tetrameric protein expressed in the liver and choroid plexus. Protein misfolding triggers monomerization of TTR tetramers. Next, monomers assemble forming oligomers and fibrils. Although the secondary structure of TTR fibrils is well understood, there is very little if anything is known about the structural organization of TTR oligomers. To end this, we used nano-infrared spectroscopy, also known as atomic force microscopy infrared (AFM-IR) spectroscopy. This emerging technique can be used to determine the secondary structure of individual amyloid oligomers and fibrils. Using AFM-IR, we examined the secondary structure of TTR oligomers formed at the early (3-6 h), middle (9-12 h), and late (28 h) of protein aggregation. We found that aggregating, TTR formed oligomers (Type 1) that were dominated by α-helix (40%) and β-sheet (~30%) together with unordered protein (30%). Our results showed that fibril formation was triggered by another type of TTR oligomers (Type 2) that appeared at 9 h. These new oligomers were primarily composed of parallel β-sheet (55%), with a small amount of antiparallel β-sheet, α-helix, and unordered protein. We also found that Type 1 oligomers were not toxic to cells, whereas TTR fibrils formed at the late stages of protein aggregation were highly cytotoxic. These results show the complexity of protein aggregation and highlight the drastic difference in the protein oligomers that can be formed during such processes.

Input‐response space‐filling designs incorporating response uncertainty

AbstractTraditionally space‐filling designs have focused on the characteristics of the design in the input space ensuring uniform spread throughout the region. Input‐response space‐filling designs considered scenarios when having good spread throughout the range or region of the responses is also of interest. This paper acknowledges that there is typically uncertainty associated with the values of the response(s) and hence proposes a method, Input‐Response Space‐Filling Designs with Uncertainty (IRSFwU), to incorporate this into the design construction. The Pareto front of designs offers alternatives that balance input and response space filling, while prioritizing input combinations with lower associated response uncertainty. These lower uncertainty choices improve the chances of observing the desired response values. We describe the new approach with an uncertainty‐adjusted distance to measure the response space filling, the Pareto aggregate point exchange algorithm to populate the set of promising designs, and illustrate the method with three examples of different input and response relationships and dimensions.

Formation Transformation Method for UUV Group to Approach a Static Target

The unmanned underwater vehicle (UUV) group composed of UUVs carrying different kinds of detection equipment is powerful for underwater target searching and detection. In this paper, a formation transformation method, used while the mission of the UUV group transformed from searching to detecting, is proposed. Firstly, a new formation transformation strategy, in which the UUVs converged to their detection points followed by the aggregation points achieved, is proposed to improve the safety of UUVs during transformation. Following the proposed strategy, particle swarm optimization is employed to find the optimal aggregation points. Finally, possible collisions between UUVs and collisions between UUVs and targets are avoided by adding a collision avoidance algorithm based on a virtual torque field. The model prediction is used to solve the delay problem of underwater communication, and the communication results are used to correct the model prediction results. The test shows that the method is effective. The effectiveness of the proposed method is demonstrated using two simulation examples.

Geometric Deep-Learning-Based Spatiotemporal Forecasting for Inverter-Based Solar Power

Conventional power grids dominated by synchronous generators gradually shift to variable renewable-energy-integrated grids. With the growth of building-level photovoltaic (PV) panels and other inverter-based resource (IBR) deployments in recent years, market retailers and distribution operators have had to deal with the additional operational and management challenges posed by unobserved energy flow if its behind-the-meter (BTM) configuration. Also, the intermittent and stochastic nature of IBR-based solar power introduces uncertainty into the net load forecasting of electric distribution systems. Hence, management and control with high uncertainty in load prediction due to unobservable PV is a technical challenge. This article presents deep-learning-based algorithms for BTM PV power generation using a limited number of sensors in a given distribution system and extending to adjacent geographical areas. The proposed BTM PV forecasting method is based on geometric deep learning—a spatiotemporal graph neural network by processing the relationship between data in a non-Euclidean graph structure. The predictions of short-term BTM PV are aggregated with loss estimation at the data aggregation point with the net load forecasting to compute the true load forecasting with superior performance. The developed BTM PV forecasting method significantly improved true load forecasting results, which were validated and analyzed using a collection of actual BTM PV and load measurements in a test distribution feeder.

Designing an Energy-Efficient Transportation Network to Transport Perishable Crops: An Aggregated VRP and X-means Clustering Approach

Almost all perishable crops deteriorate due to improper and tardy transportation and storage. Vehicle Routing Problem, or VRP, might be of great aid since it takes into account a number of aspects of any transportation and storage issues and optimizes them in such a way as to reduce the overall cost of the carrier. This study attempts to widen the scope of the commonly used VRP model by including traffic and energy consumption features and transforming it into the Aggregated Vehicle Routing Problem (AVRP). Traditional VRP focuses on minimizing distance. Generally, it is unable to find out the optimal number of aggregation points required to serve a system. So, cost optimization of the AVRP approach was designed with two specialized steps. Firstly, the destination data are divided into multiple clusters employing the X-means clustering. And then the best route was found to execute the delivery thus minimizing cost, required time, and carbon footprint. The study was implemented on the Chattogram zone and discovered that the optimal number of aggregation points (AP) required to serve Chattogram is only three namely- AP 1, AP 2, and AP 3. VRP analysis was stretched further with AVRP model using AP 1 and found to reduce the operating cost by 10.96%.

Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach.