Towards Design of an Efficient Sensing Data Acquisition scheme for UAVs-assisted Wireless Sensor Networks

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

Mandatory coupling of zygotic transcription to DNA replication in early Drosophila embryos

Collisions between transcribing RNA polymerases and DNA replication forks are disruptive. The threat of collisions is particularly acute during the rapid early embryonic cell cycles of Drosophila when S phase occupies the entirety of interphase. We hypothesized that collision-avoidance mechanisms safeguard the onset of zygotic transcription in these cycles. To explore this hypothesis, we used real-time imaging of transcriptional events at the onset of each interphase. Endogenously tagged RNA polymerase II (RNAPII) abruptly formed clusters before nascent transcripts accumulated, indicating recruitment prior to transcriptional engagement. Injection of inhibitors of DNA replication prevented RNAPII clustering, blocked formation of foci of the pioneer factor Zelda, and largely prevented expression of transcription reporters. Knockdown of Zelda or the histone acetyltransferase CBP prevented RNAPII cluster formation except at the replication-dependent (RD) histone gene locus. We suggest a model in which the passage of replication forks allows Zelda and a distinct pathway at the RD histone locus to reconfigure chromatin to nucleate RNAPII clustering and promote transcriptional initiation. The replication dependency of these events defers initiation of transcription and ensures that RNA polymerases transcribe behind advancing replication forks. The resulting coordination of transcription and replication explains how early embryos circumvent collisions and promote genome stability.

Effect of Cold Rolling on Cluster(1) Dissolvability during Artificial Aging and Formability during Natural Aging in Al-0.6Mg-1.0Si-0.5Cu Alloy

Natural aging after solution treatment has a negative effect on the precipitation strengthening of Al–Mg–Si alloys since Cluster(1) formed at a room temperature cannot be dissolved or transformed into precipitates during artificial aging at 170 °C. In this study, cold rolling is focused on as an alternative solution to pre-aging, which is a conventional method to prevent Cluster(1) formation. It is known that excess vacancies are necessary for cluster formation. Cold rolling suppresses cluster formation because excess vacancies disappear at dislocations introduced by cold rolling. In addition, it is expected that cold rolling accelerates the precipitation behavior because the diffusion of solute atoms is promoted by introduced lattice defects. The transition of Cluster(1) was evaluated by Micro Vickers hardness tests, tensile tests, electrical conductivity measurements and differential scanning calorimetry analyses. Results showed the negative effect of natural aging was almost suppressed in 10% cold-rolled samples and completely suppressed in 30% cold-rolled samples since Cluster(1) dissolved during artificial aging at 170 °C due to lowering of the temperature of Cluster(1) dissolution by cold rolling. It was found that the precipitation in cold-rolled samples was accelerated since the hardness peak of 10% cold-rolled samples appeared earlier than T6 and pre-aged samples.

Assembling Nanoparticle Clusters by Kinetic Control Using Weakly Interacting DNA

In this paper, we describe the use of weakly interacting DNA linkages to assemble nanoparticles into defined clusters. Gold nanoparticles (AuNPs) were synthesized and functionalized with thiol modified single-stranded DNA (ssDNA) and hybridized with ssDNA linkers of a defined length (L). The self-assembly kinetics were altered by manipulating interparticle energetics through changes to linker length, rigidity, and sequence. The linker length regulated the hybridization energy between complementary AuNPs, were longer L increased adhesion, resulting in classical uncontrollable aggregation. In contrast, L of six complementary bases decreased adhesion and resulting in slower nucleation that promoted small cluster formation, the growth of which was studied at two assembly temperatures. Results indicated that a decrease in temperature to 15 oC increased cluster yield with L6 as compared to 25 oC. Finally, the clusters were separated from unassembled AuNPs by sucrose gradient ultracentrifugation (UC) and studied via UV-visible spectrophotometry (UV-vis), dynamic light scattering (DLS) and transmission electron microscopy (TEM).

VDPC: Variational Density Peak Clustering Algorithm

<div>Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of data distribution but may not well handle the other types of distributions. For example, K-means identifies convex clusters well \cite{bai2017fast}, and DBSCAN is able to find clusters with similar densities \cite{DBSCAN}. </div><div>Therefore, most clustering methods may not work well on data distribution patterns that are different from the assumptions being made and on a mixture of different distribution patterns. Taking DBSCAN as an example, it is sensitive to the loosely connected points between dense natural clusters as illustrated in Figure~\ref{figconnect}. The density of the connected points shown in Figure~\ref{figconnect} is different from the natural clusters on both ends, however, DBSCAN with fixed global parameter values may wrongly assign these connected points and consider all the data points in Figure~\ref{figconnect} as one big cluster.</div>

VDPC: Variational Density Peak Clustering Algorithm

<div>Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of data distribution but may not well handle the other types of distributions. For example, K-means identifies convex clusters well \cite{bai2017fast}, and DBSCAN is able to find clusters with similar densities \cite{DBSCAN}. </div><div>Therefore, most clustering methods may not work well on data distribution patterns that are different from the assumptions being made and on a mixture of different distribution patterns. Taking DBSCAN as an example, it is sensitive to the loosely connected points between dense natural clusters as illustrated in Figure~\ref{figconnect}. The density of the connected points shown in Figure~\ref{figconnect} is different from the natural clusters on both ends, however, DBSCAN with fixed global parameter values may wrongly assign these connected points and consider all the data points in Figure~\ref{figconnect} as one big cluster.</div>

Multi-physics study of acoustofluidic delivery agents' clustering behavior

Acoustofluidicly manipulated microbubbles (MBs) and echogenic liposomes (ELIPs) have been suggested as drug delivery systems for the ‘on demand’ release of drug in target tissue. This requires a clear understanding of their behaviour during ultrasonication and after ultrasonication stops. The main focus of this study is to investigate the behaviour of MBs and ELIPs clusters after ultrasonication stops and the underlaying cause of cluster diffusion considering electrostatic repulsion, steric repulsion and Brownian motion. It also examines the capability of existing models used to predict MBs’ attraction velocity due to secondary radiation force, on predicting ELIPs’ attraction velocity. Tunable resistive pulse sensing (TRPS) and phase analysis light scattering (PALS) techniques were used to measure zeta potentials of the agents and the size distributions were measured using TRPS. The zeta potentials were found to be -2.43 mV and -0.62 mV for Definity™ MBs, and -3.62 mV and -2.35 mV for ELIPs using TRPS and PALS, respectively. Both agents were shown to have significant cluster formation at pressures as low as 6 kPa. Clusters of both agents were shown to diffuse as sonication stops at a rate that approximately equals the sum of the diffusion coefficients of the agents forming them. The de-clustering behaviours are due to Brownian motion as no sign of electrostatic repulsion was observed and particles movements were observed to be faster for smaller diameters. These findings are important to design and optimise effective drug delivery systems using acoustofluidically manipulated MBs and ELIPs.

Semantic Verbal Fluency in Youth with Down Syndrome: Analysis of Conventional and Contextual Cluster Formation

Expressive language delays and executive functioning challenges are common in youth with Down syndrome (DS). Verbal fluency is one method to investigate these constructs. We examined semantic verbal fluency responses to determine patterns in response generation and the psychometric properties of coded cluster formations. Participants were 97 children and adolescents with DS ranging in age from 6 to 19 years old. The semantic verbal fluency task was administered at two time points, two weeks apart. Heterogeneity in performance was observed for responses when coded either with conventional or contextual classifications. Overall, the number of switches in conventional classifications was greater than contextual classifications. This implies that participants did not use traditional (conventional) categories to organize their semantic verbal fluency responses, but may have been using contextual strategies. However, the number of switches and cluster size variables had poor to moderate test–retest reliability, which indicated that participants did not stay consistent with their performance over the two-week testing interval, regardless of the strategies used. Therefore, conventional and contextual clusters and switches as a measure of executive control may not be appropriate for all individuals with DS and additional attention is warranted to determine the utility of response coding in this population.