Multi-Channel Scheduling for Fast Convergecast in Wireless Sensor Networks

We explore the following fundamental question - how fast can information be collected from a wireless sensor network? We consider a number of design parameters such as, power control, time and frequency scheduling, and routing. There are essentially two factors that hinder efficient data collection - interference and the half-duplex single-transceiver radios. We show that while power control helps in reducing the number of transmission slots to complete a convergecast under a single frequency channel, scheduling transmissions on different frequency channels is more efficient in mitigating the effects of interference (empirically, 6 channels suffice for most 100-node networks). With these observations, we define a receiver-based channel assignment problem, and prove it to be NP-complete on general graphs. We then introduce a greedy channel assignment algorithm that efficiently eliminates interference, and compare its performance with other existing schemes via simulations. Once the interference is completely eliminated, we show that with half-duplex single-transceiver radios the achievable schedule length is lower-bounded by max(2nk − 1,N), where nk is the maximum number of nodes on any subtree and N is the number of nodes in the network. We modify an existing distributed time slot assignment algorithm to achieve this bound when a suitable balanced routing scheme is employed. Through extensive simulations, we demonstrate that convergecast can be completed within up to 50% less time slots, in 100-node networks, using multiple channels as compared to that with single-channel communication. Finally, we also demonstrate further improvements that are possible when the sink is equipped with multiple transceivers or when there are multiple sinks to collect data

Algorithms for Fast Aggregated Convergecast in Sensor Networks

Fast and periodic collection of aggregated data is of considerable interest for mission-critical and continuous monitoring applications in sensor networks. In the many-to-one communication paradigm, referred to as convergecast, we focus on applications wherein data packets are aggregated at each hop en-route to the sink along a tree-based routing topology, and address the problem of minimizing the convergecast schedule length by utilizing multiple frequency channels. The primary hindrance in minimizing the schedule length is the presence of interfering links. We prove that it is NP-complete to determine whether all the interfering links in an arbitrary network can be removed using at most a constant number of frequencies. We give a sufficient condition on the number of frequencies for which all the interfering links can be removed, and propose a polynomial time algorithm that minimizes the schedule length in this case. We also prove that minimizing the schedule length for a given number of frequencies on an arbitrary network is NP-complete, and describe a greedy scheme that gives a constant factor approximation on unit disk graphs. When the routing tree is not given as an input to the problem, we prove that a constant factor approximation is still achievable for degree-bounded trees. Finally, we evaluate our algorithms through simulations and compare their performance under different network parameters

Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Data – Towards a Data Basis for Predictive AI Models

Background Early fatigue mechanisms for various materials are yet to be unveiled for the (very) high-cycle fatigue (VHCF) regime. This can be ascribed to a lack of available data capturing initial fatigue damage evolution, which continues to adversely affect data scientists and computational modeling experts attempting to derive microstructural dependencies from small sample size data and incomplete feature representations. Objective The aim of this work is to address this lack and to drive the digital transformation of materials such that future virtual component design can be rendered more reliable and more efficient. Achieving this relies on fatigue models that comprehensively capture all relevant dependencies. Methods To this end, this work proposes a combined experimental and data post-processing workflow to establish multimodal fatigue crack initiation and propagation data sets efficiently. It evolves around fatigue testing of mesoscale specimens to increase damage detection sensitivity, data fusion through multimodal registration to address data heterogeneity, and image-based data-driven damage localization. Results A workflow with a high degree of automation is established, that links large distortion-corrected microstructure data with damage localization and evolution kinetics. The workflow enables cycling up to the VHCF regime in comparatively short time spans, while maintaining unprecedented time resolution of damage evolution. Resulting data sets capture the interaction of damage with microstructural features and hold the potential to unravel a mechanistic understanding. Conclusions The proposed workflow lays the foundation for future data mining and data-driven modeling of microstructural fatigue by providing statistically meaningful data sets extendable to a wide range of materials

The perception of COVID-19 and avoidance behavior in Turkey: the role of income level, gender and education

Purpose This study aims to reveal both the effect of the perception of COVID-19 on avoidance behaviors and the mediating role of the perception of personal control in this relationship. COVID-19 emerged in December 2019 and since then, it has spread globally in a short period and has affected people socially, economically and culturally. Design/methodology/approach The data for the research was collected from 418 participants during COVID-19, through online questionnaires. The obtained data were analyzed through AMOS and SPSS software using structural equation modeling. Findings The research results show that some perceptions of COVID-19 affect avoidance behavior and that personal control has a mediating role. It has also been found that gender plays a moderating role in the relationship between COVID-19 and avoidance behavior. It has been found that women are especially more sensitive compared to men in perceiving COVID-19. This study also found that perception of COVID-19 changes depending on income. Practical implications After the pandemic is over, people will get in contact with each other less than before, and trade will change accordingly. People will avoid shopping in crowded places, and consumer behaviors will undergo different changes. All of these results considered, it is expected that avoidance behavior will cause some permanent behavioral changes in consumers. Originality/value The study answers the critical question about the effect of the perception of COVID-19 on avoidance behavior. Furthermore, the role of income level, gender and education in this relationship will be highlighted

Dual functionality of conjugated polymer nanoparticles as an anticancer drug carrier and a fluorescent probe for cell imaging

Cataloged from PDF version of article.Multifunctional nanoparticles based on a green emitting, hydrophobic conjugated polymer, poly[(9,9-bis{propeny}fluorenyl-2,7-diyl)-co-(1,4- benzo-{2,1,3}-thiodiazole)] (PPFBT), that acts both as a fluorescent reporter and a matrix to accommodate an anti-cancer compound, camptothecin (CPT), were prepared, characterized and their potential as a fluorescent probe for cell imaging and as a drug delivery vehicle were evaluated via in vitro cell assays. The cell viability of human hepatocellular carcinoma cell line (Huh7) was investigated in the absence and presence of CPT with sulforhodamine B (SRB) and real-time cell electronic sensing (RT-CES) cytotoxicity assays

A deep learning approach for complex microstructure inference

Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing. This, along with deep learning’s seemingly intransparent decision-making process, hampers its breakthrough in this field. We apply a multidisciplinary deep learning approach, devoting equal attention to specimen preparation and imaging, and train distinct U-Net architectures with 30–50 micrographs of different imaging modalities and electron backscatter diffraction-informed annotations. On the challenging task of lath-bainite segmentation in complex-phase steel, we achieve accuracies of 90% rivaling expert segmentations. Further, we discuss the impact of image context, pre-training with domain-extrinsic data, and data augmentation. Network visualization techniques demonstrate plausible model decisions based on grain boundary morphology

A small library of chalcones induce liver cancer cell death through Akt phosphorylation inhibition

Hepatocellular carcinoma (HCC) ranks as the fifth most common and the second deadliest cancer worldwide. HCC is extremely resistant to the conventional chemotherapeutics. Hence, it is vital to develop new treatment options. Chalcones were previously shown to have anticancer activities in other cancer types. In this study, 11 chalcones along with quercetin, papaverin, catechin, Sorafenib and 5FU were analyzed for their bioactivities on 6 HCC cell lines and on dental pulp stem cells (DPSC) which differentiates into hepatocytes, and is used as a model for untransformed control cells. 3 of the chalcones (1, 9 and 11) were selected for further investigation due to their high cytotoxicity against liver cancer cells and compared to the other clinically established compounds. Chalcones did not show significant bioactivity ([Formula: see text]) on dental pulp stem cells. Cell cycle analysis revealed that these 3 chalcone-molecules induced SubG1/G1 arrest. Akt protein phosphorylation was inhibited by these molecules in PTEN deficient, drug resistant, mesenchymal like Mahlavu cells leading to the activation of p21 and the inhibition of NF[Formula: see text]B-p65 transcription factor. Hence the chalcones induced apoptotic cell death pathway through NF[Formula: see text]B-p65 inhibition. On the other hand, these molecules triggered p21 dependent activation of Rb protein and thereby inhibition of cell cycle and cell growth in liver cancer cells. Involvement of PI3K/Akt pathway hyperactivation was previously described in survival of liver cancer cells as carcinogenic event. Therefore, our results indicated that these chalcones can be considered as candidates for liver cancer therapeutics particularly when PI3K/Akt pathway involved in tumor development

Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation models

Crack initiation governs high cycle fatigue life and is sensitive to microstructural details. While corresponding microstructure-sensitive models are available, their validation is difficult. We propose a validation framework where a fatigue test is mimicked in a sub-modeling simulation by embedding the measured microstructure into the specimen geometry and adopting an approximation of the experimental boundary conditions. Exemplary, a phenomenological crystal plasticity model was applied to predict deformation in ferritic steel (EN1.4003). Hotspots in commonly used fatigue indicator parameter maps are compared with damage segmented from micrographs. Along with the data, the framework is published for benchmarking future micromechanical fatigue models

Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation models

Crack initiation governs high cycle fatigue life and is sensitive to microstructural details. While corresponding microstructure-sensitive models are available, their validation is difficult. We propose a validation framework where a fatigue test is mimicked in a sub-modeling simulation by embedding the measured microstructure into the specimen geometry and adopting an approximation of the experimental boundary conditions. Exemplary, a phenomenological crystal plasticity model was applied to predict deformation in ferritic steel (EN1.4003). Hotspots in commonly used fatigue indicator parameter maps are compared with damage segmented from micrographs. Along with the data, the framework is published for benchmarking future micromechanical fatigue models