Aggregation Points Research Articles

Soil salinity dynamics in arid oases during irrigated and non‐irrigated seasons

AbstractPreventing soil salinization is key to the healthy development of agroecosystems in arid regions. Notably, long‐term anthropogenic irrigation accelerates secondary salinity accumulation in arid areas; however, the regional‐scale spatial patterns of soil salinity pre‐ and post‐irrigation season remain unclear. Accordingly, field observations over two periods (n = 182 samples), along with environmental covariates and the randomForest algorithm, were used to estimate the horizontal and vertical spatial distributions (12 soil depths) of soil salinity in the Weiku Oasis (0–60 cm) at a 30 m spatial resolution. The results showed that the prediction accuracy was greater for the non‐irrigated season than for the irrigated season. Moreover, soil salinity was low in the interior of the oasis, high in the desert oasis interlacing zone, and significantly decreased with increasing soil depth. Variable contributions indicated that the effects of SoilGrids variables on soil salinity increased with greater soil depth. During the irrigation season, an inflection point for salt aggregation was observed at ~55 cm for farmlands, grasslands, and shrubs, with the greatest variation seen in farmlands. The findings here provide insights into the improvement of irrigation techniques in arid zone agroecosystems.

Local and wide-scale livestock movement networks inform disease control strategies in East Africa

Livestock mobility exacerbates infectious disease risks across sub-Saharan Africa, but enables critical access to grazing and water resources, and trade. Identifying locations of high livestock traffic offers opportunities for targeted control. We focus on Tanzanian agropastoral and pastoral communities that account respectively for over 75% and 15% of livestock husbandry in eastern Africa. We construct networks of livestock connectivity based on participatory mapping data on herd movements reported by village livestock keepers as well as data from trading points to understand how seasonal availability of resources, land-use and trade influence the movements of livestock. In communities that practise agropastoralism, inter- and intra-village connectivity through communal livestock resources (e.g. pasture and water) was 1.9 times higher in the dry compared to the wet season suggesting greater livestock traffic and increased contact probability. In contrast, livestock from pastoral communities were 1.6 times more connected at communal locations during the wet season when they also tended to move farther (by 3 km compared to the dry season). Trade-linked movements were twice more likely from rural to urban locations. Urban locations were central to all networks, particularly those with potentially high onward movements, for example to abattoirs, livestock holding grounds, or other markets, including beyond national boundaries. We demonstrate how livestock movement information can be used to devise strategic interventions that target critical livestock aggregation points (i.e. locations of high centrality values) and times (i.e. prior to and after the wet season in pastoral and agropastoral areas, respectively). Such targeted interventions are a cost-effective approach to limit infection without restricting livestock mobility critical to sustainable livelihoods.

Real-Time Probing of Melamine-Induced Gold Nanoparticle Aggregation Kinetics via Evanescent-Wave Coupled Cavity Ring-down Spectroscopy.

Insight into the aggregation kinetics of gold nanoparticles (GNPs) is critical for developing a colorimetric assay extensively used in chemical and biomolecular sensing. The aggregation of NPs plays a significant role in many natural and industrial processes, demanding comprehensive perceptions of the aggregation kinetics at a solid-liquid interface. However, the direct observation of the melamine-induced aggregation process of GNPs in the time-domain still remains a challenge. There is little to no information on the fundamental mechanisms of such kinetics using evanescent waves. Total internal reflection (TIR) has been applied to generate the evanescent field (EF), exploring aggregation kinetics near the solid-liquid interface. Here, we utilized a precise optical cavity-based method, an evanescent-wave coupled cavity ring-down spectroscopy (EW-CRDS), that can probe the melamine-induced aggregation kinetics of GNPs. The key feature of the present method is that the evanescent field generated by TIR illumination harnesses the power of CRDS to study 2D fractals via the collision and attachment of the GNPs and their melamine-induced aggregates at the interfacial region in real-time. This kinetic study reveals a critical point for diffusion-limited aggregation and provides insights into the design and optimization of colorimetric sensors that exploit the aggregation of GNPs. Furthermore, the EW-CRDS is a unique analytical approach that helps to deepen our understanding in probing the real-time aggregation process, detecting the presence of aggregator as compared to UV-vis and dynamic light scattering (DLS) spectroscopy.

Insights into Early Steps of Decanoic Acid Self-Assemblies under Prebiotic Temperatures Using Molecular Dynamics Simulations.

The origin of life possibly required processes in confined systems that facilitated simple chemical reactions and other more complex reactions impossible to achieve under the condition of infinite dilution. In this context, the self-assembly of micelles or vesicles derived from prebiotic amphiphilic molecules is a cornerstone in the chemical evolution pathway. A prime example of these building blocks is decanoic acid, a short-chain fatty acid capable of self-assembling under ambient conditions. This study explored a simplified system made of decanoic acids under temperatures ranging from 0 °C to 110 °C to replicate prebiotic conditions. The study revealed the first point of aggregation of decanoic acid into vesicles and examined the insertion of a prebiotic-like peptide in a primitive bilayer. The information gathered from this research provides critical insights into molecule interactions with primitive membranes, allowing us to understand the first nanometric compartments needed to trigger further reactions that were essential for the origin of life.

Time distancing to avoid network microbursts from drones’ high-definition video streams

Maintenance of social infrastructure, such as utility poles and bridges, is a major challenge in the era of declining labor force. While remote maintenance with drones is expected to solve this problem, it requires high-quality images for error-free decisions by a remote maintainer and low-latency to prevent many retries of remote operations since they directly affect maintenance efficiency. For that purpose, low-compression video with low encoding delay and small distortion is a leading candidate. However, we found that such traffic with high-burstiness causes a microburst, which is a short-time congestion experienced at the aggregation point even though the network capacity seems to be sufficient. We also propose a congestion avoidance method by designating appropriate timing to the encoders without losing image quality and report latency reduction in 5G-NR simulations.

354 Decreasing Blood Transfusions with a Multidisciplinary Intra-Operative Algorithm in Open Craniosynostosis Surgery

INTRODUCTION: Blood transfusions are common in craniosynostosis surgery. Lack of an intra-operative transfusion protocol is a known risk factor for increased transfusion in these surgeries. METHODS: Quality improvement methodology was used (Institute for Healthcare Improvement model). A multidisciplinary team proceeded with a survey of anesthesiology teams to evaluate readiness for change; creation of an intra-operative algorithm (guiding decision making, timing of measuring hemoglobin, administration of fluid resuscitation, and indications for blood transfusion); and integration of the algorithm into the surgery time out. No modifications were made regarding the type of general anesthesia and surgical technique (1 neurosurgeon, 2 plastic surgeons). Data collected: use of the intra-operative algorithm, administration of blood products, lowest hemoglobin recorded during admission, and other relevant surgical details. Minimally invasive surgeries were excluded. RESULTS: Eighty-two surgeries were included in three cohorts: baseline (2018, n = 6), project initiation (2019-2020, n = 44), and post-intervention (2021-2022, n = 32). After implementation, the algorithm was used in all cases. Transfusions given in stable patients with a hemoglobin >7.5 g/dL decreased from a baseline of 40.0% to 0% (p < 0.01). This improvement was demonstrated with a centerline shift on a statistical process control chart (Aggregate Point Rule). Overall transfusion rate decreased from a baseline of 100% to 59.4% post-intervention (p = 0.07). Those receiving a transfusion did not require more than 1 unit of packed red blood cells. No transfusions were required beyond post-operative day 1. CONCLUSIONS: Implementation of a multidisciplinary intra-operative transfusion algorithm for open craniosynostosis surgery led to meaningful decrease in blood transfusions. This was achieved without changing surgical technique or introducing complex adjuncts.

Zero problems of the Bergman kernel function on the second type of Cartan-Hartogs domain

We give the condition that the Bergman kernel function of the second type of Cartan-Hartogs domain has zeros. When the Bergman kernel function of this type of domain has zeros, the topological properties of the zero cluster are proved, and the conditions are given to judge whether a boundary point on the definition domain of the Bergman kernel function is an aggregation point of the zero cluster.

A coarse aggregate gradation detection method based on 3D point cloud

Coarse aggregate plays the role of skeleton and accounts for the highest proportion in asphalt pavement, and its particle size and gradation distribution have an important impact on pavement performance. Traditional gradation detection methods by square sieves are inefficient and labor-intensive. This paper is a study of automated gradation detection methods based on 3D point clouds and deep learning. A line structured light based acquisition system was built to collect aggregate point cloud data, and a multi-threshold segmentation method is proposed to segment each aggregate from the background. To solve the problem of high aggregate complexity and low discrimination between different particle sizes, we propose an aggregate particle size classification model ISRNet (Improved Structure Relation Network) with pointnet. Based on SA (Set Abstraction), we add adaptive sampling (AS) after Farthest Point Sampling (FPS) to shift the sampling points adaptively. In addition, for the characteristic that the aggregate particle size is affected by different local structures, we propose to add ISRN after SA to learn the relation features. For the quality prediction problem, a GA-LightGBM model was developed with nine 3D features as input and parameter optimization inspired by genetic algorithms. In addition, the effects of different acquisition parameters on the model are discussed. Compared with other classification algorithms, the accuracy of the proposed classification method achieved 99.12% in the range of 0–10 mm and 98.89% in the range of 10–20 mm. Moreover, the maximum error of aggregate gradation evaluation is 1.63%, which can meet the industrial requirement. It proves the advantages of 3D point cloud in determining the aggregate particle size and gradation, improving the automatic detection efficiency and saving labor costs.

An optimal aggregation method for interval grey numbers using on Steiner-Weber point with application

Aiming at the problem of low accuracy of two-dimensional preference information aggregation, this paper takes two-dimensional interval grey numbers as an example to define its preference information mapping rules. This rule maps preference information to preference points on a two-dimensional plane. Based on the theory of plane Steiner-Weber point, we construct a two-dimensional optimal model, and prove the optimality of the model theoretically. Then, adopt plant growth simulation algorithm (PGSA) to solve the proposed model. The obtained optimal aggregation point that can represent the comprehensive opinions. Finally, by analyzing the selection problem of Fangcang shelter hospital and comparing it with the particle swarm optimization (PSO) method, we conclude that the sum of weighted Euclidean distance obtained by our method is minimal. The aggregation precision of our method is higher than that of other aggregation method to a certain extent.

Temperature dependent micellization behavior of as synthesized anionic SAILs in aqueous nonionic polymer solutions: conductivity, UV-visible probe and antimicrobial studies

As synthesized surface active ionic liquids (SAILs), Tetrapropylammonium Dodecylsulfate (TPADS), and Tetrabutylammonium Dodecylsulfate (TBADS) have been characterized by FTIR, 1H-NMR and 13C-NMR spectroscopic techniques. Conductometric and UV-Visible probe investigations have been attempted to study the aggregation behavior of these synthesized SAILs in aqueous solution of polymers Polyvinylpyrrolidone (PVP) and Polyethylene glycol (PEG) at different temperatures and concentrations. Two breaking points were identified from κ vs [SAILs] plots. First, the critical aggregation concentration (CAC) is recognized with the polymer-SAILs binding and the second being regarded as critical micellar concentration (CMC), suggested to represent saturation point for Polymer-SAIL aggregation. Thermodynamic parameters of micellization ( Δ G m o , Δ H m o and Δ S m o ) are rationalized in terms of interactions prevailing between hydrophobic and hydrophilic regions of SAIL-Polymer system. Micellization process for the blend of SAIL-PVP is found to be more favored than SAIL-PEG at all temperatures. Further, the effect of PVP on the antimicrobial activities of TPADS/TBADS has been tested by measuring zone of inhibition which accounts for higher effectiveness of PVP-SAILs system against gram positive bacteria (Bacillus cereus and Staphylococcus aureus) as compared to gram negative bacteria (Pseudomonas aeruginosa).

Building an Equitable Future Through Data Disaggregation.

Building an Equitable Future Through Data Disaggregation.

The functional role of Ire1 in regulating autophagy and proteasomal degradation under prolonged proteotoxic stress.

Inhibition of endoribonuclease/kinase Ire1 has shown beneficial effects in many proteotoxicity-induced pathology models. The mechanism by which this occurs has not been elucidated completely. Using a proteotoxic yeast model of Huntington's disease, we show that the deletion of Ire1 led to lower protein aggregation at longer time points. The rate of protein degradation was higher in ΔIre1 cells. We monitored the two major protein degradation mechanisms in the cell. The increase in expression of Rpn4, coding for the transcription factor controlling proteasome biogenesis, was higher in ΔIre1 cells. The chymotrypsin-like proteasomal activity was also significantly enhanced in these cells at later time points of aggregation. The gene and protein expression levels of the autophagy gene Atg8 were higher in ΔIre1 than in wild-type cells. Significant increase in autophagy flux was also seen in ΔIre1 cells at later time points of aggregation. The results suggest that the deletion of Ire1 activates UPR-independent arms of the proteostasis network, especially under conditions of aggravated stress. Thus, the inhibition of Ire1 may regulate UPR-independent cellular stress-response pathways under prolonged stress.

Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree

Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures.

Data Aggregation-based Transmission Method in Ultra-Dense Wireless Networks

As the Internet of Things (IoT) advances, machine-type devices are densely deployed and massive networks such as ultra-dense networks (UDNs) are formed. Various devices attend to the network to transmit data using machine-type communication (MTC), whereby numerous, various are generated. MTC devices generally have resource constraints and use wireless communication. In this kind of network, data aggregation is a key function to provide transmission efficiency. It can reduce the number of transmitted data in the network, and this leads to energy saving and reducing transmission delays. In order to effectively operate data aggregation in UDNs, it is important to select an aggregation point well. The total number of transmitted data may vary, depending on the aggregation point to which the data are delivered. Therefore, in this paper, we propose a novel data aggregation scheme to select the appropriate aggregation point and describe the data transmission method applying the proposed aggregation scheme. In addition, we evaluate the proposed scheme with extensive computer simulations. Better performances in the proposed scheme are achieved compared to the conventional approach.

Aggregate Point Cloud Geometric Features for Processing

As 3D acquisition technology develops and 3D sensors become increasingly affordable, large quantities of 3D point cloud data are emerging. How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved. The point cloud geometric information is hidden in disordered, unstructured points, making point cloud analysis a very challenging problem. To address this problem, we propose a novel network framework, called Tree Graph Network (TGNet), which can sample, group, and aggregate local geometric features. Specifically, we construct a Tree Graph by explicit rules, which consists of curves extending in all directions in point cloud feature space, and then aggregate the features of the graph through a cross-attention mechanism. In this way, we incorporate more point cloud geometric structure information into the representation of local geometric features, which makes our network perform better. Our model performs well on several basic point clouds processing tasks such as classification, segmentation, and normal estimation, demonstrating the effectiveness and superiority of our network. Furthermore, we provide ablation experiments and visualizations to better understand our network.

DERNNet: Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

A Wireless Sensor Network (WSN) is constructed with numerous sensors over geographical regions. The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy. As sensor nodes are resource constrained in nature, novel techniques are essential to improve lifetime of nodes in WSN. Nodes energy is considered as an important resource for sensor node which are battery powered based. In WSN, energy is consumed mainly while data is being transferred among nodes in the network. Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer. Moreover, this network is threatened by attacks like vampire attack where the network is loaded by fake traffic. Here, Dual Encoding Recurrent Neural network (DERNNet) is proposed for classifying the vampire nodes s node in the network. Moreover, the Grey Wolf Optimization (GWO) algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points; thereby maximizing battery/lifetime of the network nodes. The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing (KIDSASR), Risk-aware Reputation-based Trust (RaRTrust) model and Activation Function-based Trusted Neighbor Selection (AF-TNS) in terms of various parameters. These existing methods may lead to wastage of energy due to vampire attack, which further reduce the lifetime and increase average energy consumed in the network. Hence, the proposed DERNNet method achieves 31.4% of routing overhead, 23% of end-to-end delay, 78.6% of energy efficiency, 94.8% of throughput, 28.2% of average latency, 92.4% of packet delivery ratio, 85.2% of network lifetime, and 94.3% of classification accuracy.

Optical pulse labeling studies reveal exogenous seeding slows α-synuclein clearance

The accumulation of α-synuclein (α-syn) in intracellular formations known as Lewy bodies (LBs) is associated with several neurodegenerative diseases including Parkinson’s disease and Lewy Body Dementia. There is still limited understanding of how α-syn and LB formation is associated with cellular dysfunction and degeneration in these diseases. To examine the clearance and production dynamics of α-syn we transduced organotypic murine brain slice cultures (BSCs) with recombinant adeno-associated viruses (rAAVs) to express Dendra2-tagged human wild-type (WT) and mutant A53T α-syn, with and without the addition of exogenous α-syn fibrillar seeds and tracked them over several weeks in culture using optical pulse labeling. We found that neurons expressing WT or mutant A53T human α-syn show similar rates of α-syn turnover even when insoluble, phosphorylated Ser129 α-syn has accumulated. Taken together, this data reveals α-syn aggregation and overexpression, pSer129 α-syn, nor the A53T mutation affect α-syn dynamics in this system. Prion-type seeding with exogenous α-syn fibrils significantly slows α-syn turnover, in the absence of toxicity but is associated with the accumulation of anti-p62 immunoreactivity and Thiazin Red positivity. Prion-type induction of α-syn aggregation points towards a potential protein clearance deficit in the presence of fibrillar seeds and the ease of this system to explore precise mechanisms underlying these processes. This system facilitates the exploration of α-syn protein dynamics over long-term culture periods. This platform can further be exploited to provide mechanistic insight on what drives this slowing of α-syn turnover and how therapeutics, other genes or different α-syn mutations may affect α-syn protein dynamics.

Identification and evaluation of spalling aggregate in chip seals in three dimensions

In the context of the present lack of aggregate spalling identification methods and the imprecise recognition of images of aggregate spalling points owing to aggregate stacking and coverage in the course of chip-seal paving, this paper proposes an aggregate spalling recognition method based on 3-D laser elevation data points in order to improve the recognition accuracy of spalling aggregate in chip seals. In this study, 3-D point elevation data on the surface of a chip seal specimen were fed into the 3-D laser detection equipment, the raw data were smoothed, the super four-point fast and robust matching method was used on the 3-D laser points for registration, and 3-D reconstructions of the chip seal were built based on the laser point after registration, and 3-D reconstructions before and after spalling were overlaid with the different values of the surface elevations before and after spalling. The spalling plane curve was then obtained using contours from the differences in the values of the surface elevation before and after spalling, and the spalling perimeter Ln, spalling area Sn, and spalling volume Vn were then calculated on the basis of the 3-D coordinates of the spalling curve. The results showed that the relative errors of the spalling area measured by the laser method and the OTSU method (an image segmentation algorithm proposed by Otsu (1979)) were #1 3.1% vs 4.0%, #2 4.4% vs 6.4%, #3 3.3% vs 58.2%, respectively, indicating that the bitumen attached to the aggregate and the overlap of plane images of aggregate due to aggregate stacking are the main reasons for the greater errors in the recognition of image method. The relative errors in the spalling perimeter of the three specimens are 12.61%, 1.60%, and 0.94%, the relative errors of spalling area are 3.13%, 4.36%, and 3.31%, and the relative errors of spalling volume are 6.69%, 6.57%, and 8.71%, which are less than the approximately same level 10% indicating that the accuracy and the stability of aggregate spalling recognition based on 3-D laser elevation data is excellent.

Investigation of mechanical strength and permeability characteristics of pervious concrete mixed with coral aggregate and seawater

The availability of the conventional raw materials required to produce pervious concrete on sea islands remote from the mainland is lacking, which could be alleviated by utilizing marine resources. This study aimed to develop seawater coral aggregate pervious concrete (SCAPC) by utilizing coral waste and seawater and assess the potential application of this method to sea islands. The SCAPC specimens were designed with different coral aggregate sizes (2.36–4.75 mm, 4.75–9.5 mm) and aggregate/binder ratios (2, 2.5, 3), and the addition of 10 % of sea sand. The physical properties (hardened density and porosity), mechanical strengths (compressive and splitting tensile strengths), water permeability, and sulfate attack resistance were investigated. The failure surface under mechanical loading and the microtopography between matrix and coral aggregate of SCAPC samples were also characterized. The results showed that the compressive strength and water permeability coefficient of the resulting SCAPCs at 28 days with a porosity ranging from 16.9 % to 33.5 % could reach 6.2–18.5 MPa and 12.9–26.5 mm/s, respectively. This not only satisfied the ACI 522R-recommended values for pervious concrete applicable to pedestrian road areas, but the samples demonstrated excellent permeability. The SCAPC specimens manufactured with aggregate/binder ratios of 2.5 incorporating 10 % sea sand were found to achieve the required balance between compressive strength (11.8–12.5 MPa) and water permeability coefficient (19.6–20.9 mm/s). The failure patterns of the SCAPC samples presented a markedly different complete fracture of coral aggregate point from that of traditional aggregate pervious concrete, ascribing this variation to the characterized fragility of coral aggregate and intensified interfacial adhesion.

Macro-meso multiscale analysis of asphalt concrete in different laboratory compaction methods and field compaction

The difference between asphalt concrete (AC) compacted in the laboratory and the field significantly influences its gradation design and performance evaluation accuracy. In this research, the effect of the laboratory compaction and field compaction on AC characteristics were compared using the characteristic indexes of surface texture, internal structure, asphalt binder distribution, aggregate distribution, compressive resilience modulus, and the crack resistance index (CRI). Six different compaction methods for AC specimens were studied, and five different types of laboratory compaction equipment were used, namely the Marshall compactor, Steel segmented roller compactor (SSRC), Superpave gyratory compactor (SGC), shear box compactor (SBC), and roller. The results showed that a uniform texture of AC surface was created with vibration and kneading. The aggregate contact distribution of specimens compacted by SGC, SBC, and roller was similar to field compaction as an inverse “bathtub” shape. The aggregate rotation angle of AC compacted by SGC, SBC, and roller showed higher randomness. The coarse aggregate of specimens degraded after Marshall compaction. The fine aggregate coated with more asphalt binder being moved towards the middle height of the specimen, resulting in higher asphalt binder content in the middle layer during the compaction of SGC and SBC. The specimens compacted with SGC and SBC had better mechanical properties, especially in the middle layer of the specimen. The increase of aggregate contact points improved the mechanical properties of the mixture. The free movement of aggregates was helpful for slippage of aggregates and promoted the filling of voids with asphalt mastic to make the skeleton structure more compact. Correlation analysis showed that SGC and the roller could better reproduce the field compaction state.