Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT

In recent trends, wireless sensor networks (WSNs) have become popular because of their cost, simple structure, reliability, and developments in the communication field. The Internet of Things (IoT) refers to the interconnection of everyday objects and sharing of information through the Internet. Congestion in networks leads to transmission delays and packet loss and causes wastage of time and energy on recovery. The routing protocols are adaptive to the congestion status of the network, which can greatly improve the network performance. In this research, collision-aware routing using the multi-objective seagull optimization algorithm (CAR-MOSOA) is designed to meet the efficiency of a scalable WSN. The proposed protocol exploits the clustering process to choose cluster heads to transfer the data from source to endpoint, thus forming a scalable network, and improves the performance of the CAR-MOSOA protocol. The proposed CAR-MOSOA is simulated and examined using the NS-2.34 simulator due to its modularity and inexpensiveness. The results of the CAR-MOSOA are comprehensively investigated with existing algorithms such as fully distributed energy-aware multi-level (FDEAM) routing, energy-efficient optimal multi-path routing protocol (EOMR), tunicate swarm grey wolf optimization (TSGWO), and CoAP simple congestion control/advanced (CoCoA). The simulation results of the proposed CAR-MOSOA for 400 nodes are as follows: energy consumption, 33 J; end-to-end delay, 29 s; packet delivery ratio, 95%; and network lifetime, 973 s, which are improved compared to the FDEAM, EOMR, TSGWO, and CoCoA.

Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

MoSBi: Automated signature mining for molecular stratification and subtyping

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de-novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (Molecular Signature identification using Biclustering), an automated multi-algorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We evaluated the performance of MoSBi on transcriptomics, proteomics, and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multi-algorithm integration, MoSBi identified robust group and disease-specific signatures across all scenarios overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that support biological hypothesis generation. MoSBi is available as an R package and web service to make automated biclustering analysis accessible for application in molecular sample stratification.

A Flexible Software/Hardware Adaptive Network for Embedded Distributed Architectures

Embedded platforms are projected to integrate hundreds of cores in the near future, and expanding the interconnection network remains a key challenge. We propose SNet, a new Scalable NETwork paradigm that extends the NoCs area to include a software/hardware dynamic routing mechanism. To design routing pathways among communicating processes, it uses a distributed, adaptive, non-supervised routing method based on the ACO algorithm (Ant Colony Optimization). A small footprint hardware unit called DMC speeds up data transfer (Direct Management of Communications). SNet has the benefit of being extremely versatile, allowing for the creation of a broad range of routing topologies to meet the needs of various applications. We provide the DMC module in this work and assess SNet performance by executing a large number of test cases.

Stakeholders conflict and private–public partnership chain (PPPC): supply chain of perishable product

PurposeThis paper aims to address procurement, logistics management, inventory control and distribution of perishable items, i.e. vegetables, fruits, flowers and fishes, during the social isolation period of the Covid-19 era to identify conflicting interests among the channel members; present inventory and information sharing scenario; and reveal organizational dispute and existence of redundant, nonessential and corrupted members in the supply chain.Design/methodology/approachThis study uses an exploratory investigation to evaluate the relations among the members of the supply chain of perishable food items. In this context, it is designed to investigate the field, observe the members of the existing supply chain from rural and remote places and capture their interviews to accomplish the objectives.FindingsThis study identified that although the supply chain of perishable food items is controlled truly by private parties, from a realistic view, the private–public partnership is essential where the government should play the coordinating role. In this context, continuous interaction, coordination and information sharing among the members to establish an optimum and scalable network and remove any redundant nodal points is a key success factor for managing an efficient supply chain.Research limitations/implicationsTheoretical and managerial implication of this research is enormous. The existence of functional and dysfunctional conflicts in the same supply network and how it can be detrimental to the performance of the members are exposed in this study, which can be an excellent source to be investigated. Practitioners and researchers can gain a greater understanding to identify the root causes of conflicts in the existing structural dynamics, shedding light on organizational interactions, power and group behavior during the Covid-19 era.Originality/valueFrom the light of management and inter-organizational conflicts, this is a pioneer study that has detected the redundant channel members, their source of power and how their removal can present an optimum channel with group coherence and synergistic interest.

Network diffusion for scalable embedding of massive single-cell ATAC-seq data

ABSTRACTWith the rapid development of single-cell ATAC-seq technology, it has become possible to profile the chromatin accessibility of massive individual cells. However, it remains challenging to characterize their regulatory heterogeneity due to the high-dimensional, sparse and near-binary nature of data. Most existing data representation methods were designed based on correlation, which may be ill-defined for sparse data. Moreover, these methods do not well address the issue of excessive zeros. Thus, a simple, fast and scalable approach is needed to analyze single-cell ATAC-seq data with massive cells, address the “missingness” and accurately categorize cell types. To this end, we developed a network diffusion method for scalable embedding of massive single-cell ATAC-seq data (named as scAND). Specifically, we considered the near-binary single-cell ATAC-seq data as a bipartite network that reflects the accessible relationship between cells and accessible regions, and further adopted a simple and scalable network diffusion method to embed it. scAND can take information from similar cells to alleviate the sparsity and improve cell type identification. Extensive tests and comparison with existing methods using synthetic and real data as benchmarks demonstrated its distinct superiorities in terms of clustering accuracy, robustness, scalability and data integration.AvailabilityThe Python-based scAND tool is freely available at http://page.amss.ac.cn/shihua.zhang/software.html.