Aggregation Points Research Articles

Evaluation of the evolution of the structure of cold recycled mixture subjected to wheel tracking using digital image processing

The meso-structural characteristics of cold recycled mixes using asphalt emulsion (CRME) were investigated by wheel tracking tests and two-dimensional digital image processing technology in this study. The trends of contact points, long-axis states, fractional dimensionality, and the rule of aggregate distribution were analysed. Fitted equations of the CRME rutting depth and mesoscopic parameters were established in different loading periods. The number of aggregate contact points appears to increase as the loading time increasing. The long axis of the aggregate tends to be consistent. The long axis inclination and fractal dimension of the aggregate profile gradually decrease over time. The aggregate area ratio increased gradually and then the mixture became dense. The evolution of CRME could be divided into three stages under wheel tracking test: the stage of initial compaction, relative compaction, and destruction. The support vector machine prediction model of rutting depth and mesoscopic parameters was established by K-fold Cross Validation algorithm (K-CV) optimization. The proposed model showed high accuracy for the movement state of the asphalt mixture, which could provide scientific guide for pavement condition evaluation.

Positive charge in the complementarity-determining regions of synthetic nanobody prevents aggregation

In the past, specificity and affinity were the priority for synthetic antibody library. However, therapeutic antibodies need good stability for medical use. Through carefully adjust the chemical diversity in CDRs, one hopes to design a synthetic antibody library with good developability. Here we thoroughly analyzed 296 nanobody sequences and structures, constructed a fully-functional synthetic nanobody library, evaluated the relationship between aggregation and isoelectric point, and found that high-pI nanobodies were more resistant to aggregation than low-pIs. As we used the same framework for constructing the library, CDRs charge played a crucial role in mediating nanobody aggregation. We also analyzed the theoretical pI of 296 nanobodies from PDB, about 75% had basic pI, only 25% were acidic. Those results provided useful guidelines for designing next-generation synthetic nanobody libraries and for identifying potent and safe nanobody therapeutics.

Novel Approach Sizing and Routing of Wireless Sensor Networks for Applications in Smart Cities

Citizens are expected to require the growth of multiple Internet of Things (IoT) -based applications to improve public and private services. According to their concept, smart cities seek to improve the efficiency, reliability, and resilience of these services. Consequently, this paper searches for a new vision for resolving problems related to the quick deployment of a wireless sensor network (WSN) by using a sizing model and considering the capacity and coverage of the concentrators. Additionally, three different routing models of these technology resources are presented as alternatives for each WSN deployment to ensure connectivity between smart meters and hubs required for smart metering. On the other hand, these solutions must reduce costs when this type of wireless communication network is deployed. The present work proposes various optimization models that consider the physical and network layers in order to integrate different wireless communication technologies, thus reducing costs in terms of the minimum number of data aggregation points. Using a heterogeneous wireless network can reduce resource costs and energy consumption in comparison to a single cellular technology, as proposed in previous works. This work proposes a sizing model and three different models for routing wireless networks. In each case, constraints are evaluated and can be associated with different real-world scenarios. This document provides an optimization model that encompasses all of the proposed constraints; due to the combinatorial nature of the problem, this would require a heuristic technique.

Experimental behaviour of road by using plastic waste

The use of plastic and related materials is increasing exponentially due to tremendous growth in population, urbanization and changed life style leads to widespread littering of plastic on the landscape. Disposal of waste plastic is a serious problem globally due to their non-biodegradability and hazardous to human healths, since these are not disposed scientifically and thus, create ground and water. If this curse to mankind in the form of waste plastic is used as a boon for mankind by using it as additives in road construction, it will proved to be a best solution over worst road condition. In the present paper techniques has been developed to use plastic waste for construction of bituminous roads and flexible pavements. In general bitumen is used as binder in road construction. Binding properties of this bitumen can be modified by blending it with waste plastic pieces. It can be used for construction purpose. Keywords— Bitumen, Coarse Aggregate, Ductility Test, Flash and Fire Point test, Plastic Waste

Progressive approaches to flexible group skyline queries

The G-Skyline (GSky) query is formulated to report optimal groups that are not dominated by any other group of the same size. Particularly, a given group $$G_1$$ dominates another group $$G_2$$ if for any point $$p\in G_1$$ , p dominates or equals to points $$p{'}\in G_2$$ ; at the same time, there is at least one point p dominating $$p{'}$$ . Most existing group skyline queries need to calculate an aggregate point for each group. Compared to these queries, the GSky query is more practical because it avoids specifying an aggregate function which leads to miss important results containing non-skyline points. This means the GSky query can get much more comprehensive query results which not only contain the G-Skylines consisting of skyline points but also the G-Skylines including non-skyline points. Here, a non-skyline point is dominated by another point in a given data set. However, the GSky query usually returns too many results, making it a big burden for users to pick out their expected results. To address these issues, we investigate a flexible group skyline query, namely Flexible G-Skyline (FGSky) query, which is flexible and practical for directly computing the optimal groups on the basis of user preferences. In this paper, we formulate the FGSky query, identify its properties, and present effective pruning strategies. Besides, we propose progressive algorithms for the FGSky query where a grouping strategy and a layered strategy are utilized to get better query performance. Through extensive experiments on both synthetic and real data sets, we demonstrate the efficiency, effectiveness, and progressiveness of the proposed algorithms.

Q-learning based routing for in-network aggregation in wireless sensor networks

In-network data aggregation is an inherent paradigm that extends the lifetime of resource-constrained wireless sensor networks (WSNs). By aggregating sensor data at intermediate nodes, it eliminates data redundancy, minimizes the number of transmissions and saves energy. A key component of in-network data aggregation is the design of an optimal routing structure. However, when the monitoring environment is highly dynamic, the conventional in-network aggregation routing algorithms lead to unnecessary redesign, high overhead and inferior performance, and make in-network aggregation a challenging task. This paper proposes a novel adaptive routing algorithm for in-network aggregation (RINA) in wireless sensor networks. The proposed approach employs a reinforcement learning method called Q-learning to build a routing tree based on minimal information such as residual energy, distance between nodes and link strength. In addition, it finds the aggregation points in the routing structure to maximize the number of overlapping routes in order to increase the aggregation ratio. Theoretical analysis proves the feasibility of the proposed approach. Simulation results show that the aggregation tree constructed by RINA increases the network lifetime by achieving optimum data aggregation and outperform other state-of-the-art approaches in terms of different significant features under different simulation scenarios.

Numerical Simulation of Seepage and Deformation in Excavation of a Deep Foundation Pit under Water-Rich Fractured Intrusive Rock

Water is one of the major risk sources in the excavation of deep-large foundation pits in a water-rich area. The presence of intrusive broken diorite porphyrite in the stratum aggravates the risk level of deep foundation pits. Based on a geological survey report and design documents of parameter information, MIDAS/GTS software was used to perform the numerical simulation of an engineering example of a deep foundation pit project of ultradeep and water-rich intrusion into the broken rock station of subway line 4 in a city. The simulation results show the characteristics of seepage path evolution, seepage aggregation areas and points, and the effect of seepage on the deformation of a deep foundation pit during the whole construction of this deep foundation pit. The results show that with the precipitation-excavation of the deep foundation pit, the pore water pressure at the bottom of the foundation pit follows a distribution of three “concave” shapes. High-permeability pressure zones are found around the foundation pit, intruding broken diorite porphyrite zones, and middle coarse sand zones. With further excavation of the foundation pit, the seepage pressure in the middle part of the foundation pit gradually decreases, and the two “concave” distributions in the middle gradually merge together. After excavation to the bottom of the pit, the pore water pressure at the bottom is distributed in two asymmetrical “concave” shapes, and the maximum peak of pore water pressure is found at the intrusion of fractured porphyrites prone to water inrush. The four corners of the foundation pit are prone to form seepage accumulation zones; therefore, suffosion and piping zones are formed. The surface settlement caused by excavation is found to be the largest along the longitudinal axis of the deep foundation pit, whereas the largest deformation is found near the foundation pit side in the horizontal axis direction of the foundation pit. With the excavation of the deep foundation pit, the diaphragm wall converges to the foundation pit with the maximum deformation reaching about 25 mm. After the first precipitation-excavation of the deep foundation pit to the silty clay and the bottom of the pit with the largest uplift, with further precipitation-excavation of the deep foundation pit, the uplift at the bottom of the deep foundation pit changes only slightly.

Intelligent Data Aggregation Framework for Resource Constrained Remote Internet of Things Applications

Internet of Things (IoT) is a technology that can connect everything to the Internet. IoT can be used in a wide range of applications which includes remote applications like Underwater networks. Remote applications involve the deployment of several low-power, low-cost interconnected sensor nodes in the specific region. With a massive amount of devices connected to the IoT and the considerable amount of data associated with it, there remain concerns about data management. Also, the amount of data generated in an extensive IoT-based remote sensing network is usually enormous for the servers to process, and many times data generated are redundant. Hence there is a need for designing a framework that addresses both aggregations of data and security-related issues at various aggregation points. In this paper, we are proposing an intelligent data aggregation mechanism for IoT-based remote sensing networks. This method avoids redundant data transmission by adapting spatial aggregation techniques. The proposed method was tested through simulations, and the results prove the efficiency of the proposed work.

Secure Data Transmission Framework for Internet of Things based on Oil Spill Detection Application

Internet of Things (IoT) is a leading technology which can interlink anything to Internet and makes everything to intelligent and smart. IoT is not just a single technology. IoT is a combination of various technologies like communication, data analytics, sensors and actuators, cloud computing, artificial intelligence, machine learning, etc. Applications of IoT are spread across various domains. IoT is most suitable for remote applications like underwater networks. One such application is oil spill detection in ocean. Oil spill in an ocean is a critical challenge that causes damages to marine ecosystem. Detection of oil spills in a real time manner helps to resolve the problem quickly to minimize the damage. IoT can be used to detect the oil spill by making use of sensors deployed at various locations of ocean. With a massive amount of sensors deployed and the huge amount of data associated with it, there remain concerns about the data management. Also amount of data generated in IoT based remote sensing network is usually enormous for the servers to process and many times data generated are redundant. Hence there is a need for designing a framework which addresses both aggregation of data and security related issues at various aggregation points. In this paper we are proposing a secure data transmission framework for detecting oil spill through IoT, which avoids redundant data transmission through data aggregation and ensures secure data transmission through authentication and light weight encryption.

Clustering Algorithm-Based Network Planning for Advanced Metering Infrastructure in Smart Grid

Nowadays, legacy electrical distribution systems are migrating to a new modern electric grid with the capability of supporting different applications such as advanced metering infrastructure (AMI), distributed energy resources, and electric vehicles. Among these applications, AMI is playing an important role in delivering data from customers to power utilities, supporting reliable real-time monitoring, and remote operation of power quality data and voltage profile. The AMI consists of smart meters, data aggregation points (DAPs), the utility control center, and communication networks. Appropriate network planning plays an essential role in facilitating the exchange of data between consumers and power utilities as well as accommodating new smart grid applications and future growth. This work proposes an optimal placement of DAPs for AMI based on machine learning clustering techniques in residential grids. Network partitioning is introduced to create sub-networks and graph algorithms generate a deployment topology given optimization constraints. A new measurement metric called coverage density is considered to indicate neighborhood area networks (NAN) zones with the appropriate coverage. Three real scenarios of NAN are considered: urban, suburban, and rural. The proposed algorithm is evaluated and compared with conventional heuristic optimization methods with respect to average and maximum distance between smart meters and DAPs, coverage density, and execution time.

Feasibility Study of Power Line Communications for Backhauling Outdoor Small Cells

Small cells are low-power and low-range radio access nodes that can be used to increase network capacity in reduced areas with high traffic demand and to provide coverage to small isolated zones. As their number is expected to be very high, connecting them to the aggregation point in a cost-efficient manner is a key challenge, and there is consensus that none of the current technologies is valid for all scenarios. Since lamp-posts are one of the most convenient locations for outdoor small cells, this work analyzes the use of power line communications (PLC) over outdoor public lighting networks (OPLN) as a backhaul technology for outdoor small cell deployments. To this end, the characteristics of the PLC channels established in OPLN are firstly assessed. The analysis combines noise measurements performed in existing OPLN and channel responses obtained from a multiconductor transmission line (MTL) model. The attenuation, delay spread and spatial correlation of the multiple-input multiple-output (MIMO) channels are investigated and their influence in the physical layer parameters of PLC systems is discussed. Afterward, the performance achieved by state-of-the-art PLC systems is assessed. To this end, estimations obtained by means of simulations and measurements taken in actual networks are included. Data throughput achieved by PLC systems based on the ITU-T G.hn standard, as well as the expected improvements obtained by $3\times 3$ MIMO systems, are given. Results indicate that PLC can be an interesting technology for coverage-driven small cell deployments with backhaul length shorter than 150 m, offering throughput values similar to existing Sub-6 GHz wireless solutions in non-line-of-sight (NLOS) conditions and wired ones like G.fast.

Characterization of pili protein with molecular mass of 85 kDa Escherichia coli as an adhesin and a hemagglutinin

Background: Escherichia coli (E. coli) is a rod-shaped gram-negative bacterium included in Multi Drug Resistant (MDR) bacteria. There are several strategies in place to prevent and treat E. coli bacterial infections to reduce some the incidences of MDR. One of them is the development of a protein-based vaccine. Pili is one of virulence factors in E. coli surface proteins that can mediate attachments of bacteria to host cells (adhesin or hemagglutinin) and can be used as protein-based vaccine candidates. Objective: This study aims to determine characterization of pili protein with molecular mass of 85 kDa Escherichia coli bacteria as an adhesin and a hemagglutinin. Methods: This study is a true experimental laboratories and a descriptive study to determine roles of pili protein with molecular mass of 85 kDa as an adhesin and a hemagglutinin. Samples used in this study were a stock of E. coli in the Microbiology Laboratory Faculty of Medicine, Universitas Negeri Jember (UNEJ). A hemagglutination test in this study used mouse erythrocytes while an adhesion test used mouse enterocytes. Isolation and purification of E. coli pili protein, isolation of mouse erythrocytes, isolation of mouse enterocytes, the hemagglutination test and the adhesion test were methods in this study. A data analysis with a correlation-regression was used to determine relationships between the adhesion index and titer pili 85 kDa with a limit of significance, 0.05 (p Results: The hemagglutination test showed no ability of agglutination with the formation of red aggregate points starting from the first dilution. An analysis with Pearson correlation indicated a relationship between titer pili 85 kDa E. coli and adhesion index (p = 0.009; R = -0.921).Conclusion: Pili protein 85 kDa E. coli plays a role as an adhesin protein and do not have a role as a hemagglutinin protein.

A sensitivity analysis of a mathematical model for the synergistic interplay of amyloid beta and tau on the dynamics of Alzheimer’s disease

We propose a mathematical model for the onset and progression of Alzheimer’s disease based on transport and diffusion equations. We treat brain neurons as a continuous medium and structure them by their degree of malfunctioning. Three different mechanisms are assumed to be relevant for the temporal evolution of the disease: i) diffusion and agglomeration of soluble Amyloid beta, ii) effects of phosphorylated tau protein and iii) neuron-to-neuron prion-like transmission of the disease. We model these processes by a system of Smoluchowski equations for the Amyloid beta concentration, an evolution equation for the dynamics of tau protein and a kinetic-type transport equation for the distribution function of the degree of malfunctioning of neurons. The latter equation contains an integral term describing the random onset of the disease as a jump process localized in particularly sensitive areas of the brain. We are particularly interested in investigating the effects of the synergistic interplay of Amyloid beta and tau on the dynamics of Alzheimer’s disease. The output of our numerical simulations, although in 2D with an over-simplified geometry, is in good qualitative agreement with clinical findings concerning both the disease distribution in the brain, which varies from early to advanced stages, and the effects of tau on the dynamics of the disease. Statement of SignificanceWe propose an in silico study of the onset and progression of Alzheimer’s disease (AD) in the brain by means of a mathematical model formulated in terms of kinetic and macroscopic integro-differential equations. From the biological side, our model takes into account the synergistic effect of Amiloid beta and phosphorylated tau protein and investigates the impact of their interplay on AD dynamics. From the mathematical side, unlike several other models present in the literature, our model does not focus on the detailed description of specific intra-cellular biochemical processes. It takes instead an aggregate point of view and, thanks to a multiscale approach inspired by statistical mechanics, describes the spatio-temporal patterns of the degree of neuronal malfunctioning due to AD in macroscopic portions of the brain tissue.

Factors Influencing the Unemployment Rate in Romania During 1997-2019

"Current macroeconomic theories examine the problem of unemploy-ment rate from the aggregate demand point of view. Rising unem-ployment is always seen as a sign of the weak economy, where is a slow growth and also little spending. These might trigger actions by au-thorities to help reduce unemployment, by increasing the nation's money supply, so it can boost the economy. Among the factors that in-fluence (un-)employment, at least two very important should be taken in consideration: exchange rate and inflation. The research aims to ana-lyse the impact of inflation, the RON / EURO exchange rate and the fi-nancial crisis on unemployment rate in Romania during January 1997- March 2019. Keywords: unemployment rate, exchange rate, inflation rate JEL Codes: E24, F31, E31"

Efficient Positioning of Data Aggregation Point for Wireless Sensor Network

Efficient Positioning of Data Aggregation Point for Wireless Sensor Network

A Local Graph-Based Structure for Processing Gigantic Aggregated 3D Point Clouds

We present an original workflow for structuring a point cloud generated from several scans. Our representation is based on a set of local graphs. Each graph is constructed from the depth map provided by each scan. The graphs are then connected together via the overlapping areas, and careful consideration of the redundant points in these regions leads to a piecewise and globally consistent structure for the underlying surface sampled by the point cloud. The proposed workflow allows structuring aggregated point clouds, scan after scan, whatever the number of acquisitions and the number of points per acquisition, even on computers with very limited memory capacities. To show that our structure can be highly relevant for the community, where the gigantic amount of data represents a real scientific challenge per se, we present an algorithm based on this structure capable of resampling billions of points on standard computers. This application is particularly attractive for simplifying and visualizing gigantic point clouds representing very large-scale scenes (buildings, urban scenes, historical sites...), which often require a prohibitive number of points to describe them accurately.

Self-Assembly of Block Copolymers in an Ionic Liquid and Properties of Resulting Ion Gels

Ionic liquids (ILs), which are salts with low melting points, show unique properties including non-volatility and high ionic conductivity. By combining an ionic liquid (IL) with an amphiphilic ABA-type triblock copolymer, polystyrene (PSt)-b-poly(methyl methacrylate) (PMMA)-b-PSt (SMS), insoluble A blocks aggregate and form cross-linking points, whereas soluble B blocks form a gel network swollen with the IL.[1,2] As a result, a self-standing and highly ion-conductive ion gels can be obtained, and they have been applied to electrochemical devices such as polymer actuators. In these devices, the mechanical toughness and ionic conductivity are important parameters. Thus, we have investigated the correlation between microstructures formed by the block copolymer and the properties of ion gels such as ionic conductivity and mechanical property. In particular, we focused on a gyroid structure in which both insoluble and soluble segments form continuous phases, because this unique microstructure is expected to exhibit high mechanical strength and favorable ionic conductivity, simultaneously. In this study, we directly observed microstructures using AFM to investigate how microstructures affect properties of the ion gel. Herein, we used poly(ethylene oxide) (PEO), which has a lower glass transition temperature (T g) than PMMA, as a middle block (PSt-PEO-PSt, SOS). Consequently, SOS showed various microstructures including the gyroid depending on block ratio and content of IL. It was found that ionic conductivity and mechanical property strongly depended on the connectivity of microstructures. Namely, the gyroid structure showed the enhanced mechanical property and good ionic conductivity.In addition, we observed in-situ change in the microstructure for a stimuli responsive ion gel containing temperature-responsive poly(N-isopropylacrylamide) (PNIPAm) blocks: PNIPAm is known to show lower critical solution temperature (LCST)-type phase behavior in water while showing upper critical solution temperature (UCST)-type phase behavior in ILs.[3] PNIPAm-b-PEO-b-PNIPAm (NON) exhibits gel-to-sol transition in IL with increasing temperature due to the UCST-type phase transition of PNIPAm blocks. We have also reported photo-responsive gel-to-sol transition of ion gels by introducing a photo-responsive azobenzene moiety to either IL or polymer.[2] These transition phenomena have been studied in terms of a macroscopic property such as rheological property. However, little is known about their microstructure during the photo-responsive change. Thus, we observed the photo-responsive change in the microstructure using AFM and investigated how the microstructure change is synchronized with the rheological response. We found that UV irradiation induced the change in the microstructures from a microphase-separated gel to a sol with a vague structure, and it was correlated with the photo-responsive change in the mechanical property and ionic conductivity. Acknowledgements This work was financially supported by the Grant-in-Aid for Scientific Research for Basic Research S (15H05758) from Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.

Fungal Symbionts Enhance N-Uptake for Antarctic Plants Even in Non-N Limited Soils.

Plant-fungi interactions have been identified as fundamental drivers of the plant host performance, particularly in cold environments where organic matter degradation rates are slow, precisely for the capacity of the fungal symbiont to enhance the availability of labile nitrogen (N) in the plant rhizosphere. Nevertheless, these positive effects appear to be modulated by the composition and amount of the N pool in the soil, being greater when plant hosts are growing where N is scarce as is the case of Antarctic soils. Nevertheless, in some coastal areas of this continent, seabirds and marine mammal colonies exert, through their accumulated feces and urine a strong influence on the edaphic N content surrounding their aggregation points. To evaluate if the fungal symbionts (root endophytes), associated to the only two Antarctic vascular plants Colobanthus quitensis and Deschampsia antarctica, act as N-uptake enhancers, even in such N-rich conditions as those found around animal influence, we assessed, under controlled conditions, the process of N mineralization in soil by the accumulation of NH4+ in the rizhosphere and the biomass accumulation of plants with (E+) and without (E−) fungal symbionts. Complementarily, taking advantage of the isotopic N-fractionation that root-fungal symbionts exert on organic N molecules during its acquisition process, we also determined if endophytes actively participate in the Antarctic plants N-uptake, when inorganic N is not a limiting factor, by estimating the δ15N isotopic signatures in leaves. Overall, symbiotic interaction increased the availability of NH4+ in the rhizosphere of both species. As expected, the enhanced availability of inorganic N resulted in a higher final biomass in E + compared with E− plants of both species. In addition, we found that the positive role of fungal symbionts was also actively linked to the process of N-uptake in both species, evidenced by the contrasting δ15N signatures present in E+ (−0.4 to −2.3‰) relative to E− plants (2.7–3.1‰). In conclusion, despite being grown under rich N soils, the two Antarctic vascular plants showed that the presence of root-fungal endophytes, furthermore enhanced the availability of inorganic N sources in the rhizosphere, has a positive impact in their biomass, remarking the active participation of these endophytes in the N-uptake process for plants inhabiting the Antarctic continent.

Controlling the Chain Folding for the Synthesis of Single-Chain Polymer Nanoparticles Using Thermoresponsive Polymers

Synthetic polymer single-chain nanoparticles (SCNPs) are an emerging new class of nanomaterials that possess similar folded structures as natural proteins. However, most SCNPs reported so far are p...

Overview of gene therapy in spinal muscular atrophy and Duchenne muscular dystrophy.

Both 5q-linked spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD) are fatal monogenic neuromuscular disorders caused by loss-of-function mutations. SMA is an autosomal recessive disorder affecting motor neurons that is typically caused by homozygous whole-gene deletions of SMN1. DMD is an X-linked recessive muscle disease most often due to exon deletions, but also duplications and smaller sized variants within the DMD gene. Gene replacement therapy offers the opportunity to correct the underlying genetic defect by the introduction of a functional gene. We review the transformative work from clinical trials to United States Food and Drug Administration approval of onasemnogene abeparvovec-xioi in SMA and its application in clinical practice and the early results of microdystrophin delivery in DMD. We also review the introduction of antisense oligonucleotides to alter pre-messenger RNA splicing to promote exon inclusion (as in nusinersen in SMA) or exclusion (as in eteplirsen in DMD) into neuromuscular therapeutics. There are multiple promising novel genetically mediated therapies on the horizon, which in aggregate point towards a hopeful future for individuals with SMA and DMD.