Challenges, applications and future of wireless sensors in Internet of Things: a review

The addition of massive machine type communication (mMTC) as a category of Fifth Generation (5G) of mobile communication, have increased the popularity of Internet of Things (IoT). The sensors are one of the critical component of any IoT device. Although the sensors posses a well-known historical existence, but their integration in wireless technologies and increased demand in IoT applications have increased their importance and the challenges in terms of design, integration, etc. This survey presents a holistic (historical as well as architectural) overview of wireless sensor (WS) nodes, providing a classical definition, in-depth analysis of different modules involved in the design of a WS node, and the ways in which they can be used to measure a system performance. Using the definition and analysis of a WS node, a more comprehensive classification of WS nodes is provided. Moreover, the need to form a wireless sensor network (WSN), their deployment, and communication protocols is explained. The applications of WS nodes in various use cases have been discussed. Additionally, an overlook of challenges and constraints that these WS nodes face in various environments and during the manufacturing process, are discussed. Their main existing developments which are expected to augment the WS nodes, to meet the requirements of the emerging systems, are also presented

Finding central decompositions of p-groups

Polynomial-time algorithms are given to find a central decomposition of maximum size for a finite p-group of class 2 and for a nilpotent Lie ring of class 2. The algorithms use Las Vegas probabilistic routines to compute the structure of finite *-rings and also the Las Vegas C-MeatAxe. When p is small, the probabilistic methods can be replaced by deterministic polynomial-time algorithms. The methods introduce new group isomorphism invariants including new characteristic subgroups.Comment: 28 page

Comprehensive mechanism of gene silencing and its role in plant growth and development

Gene silencing is a negative feedback mechanism that regulates gene expression to define cell fate and also regulates metabolism and gene expression throughout the life of an organism. In plants, gene silencing occurs via transcriptional gene silencing (TGS) and post-transcriptional gene silencing (PTGS). TGS obscures transcription via the methylation of 5′ untranslated region (5′UTR), whereas PTGS causes the methylation of a coding region to result in transcript degradation. In this review, we summarized the history and molecular mechanisms of gene silencing and underlined its specific role in plant growth and crop production

A roadmap to develop dementia research capacity and capability in Pakistan: a model for low- and middle-income countries

Objective To produce a strategic roadmap for supporting the development of dementia research in Pakistan. Background While global research strategies for dementia research already exist, none is tailored to the specific needs and challenges of low- and middle-income countries (LMIC) like Pakistan. Methods We undertook an iterative consensus process with lay and professional experts to develop a Theory of Change-based strategy for dementia research in Pakistan. This included Expert Reference Groups (ERGs), strategic planning techniques, a “research question” priority survey, and consultations with Key Opinion Leaders. Results We agreed on ten principles to guide dementia research in Pakistan, emphasizing pragmatic, resource sparing, real-world approaches to support people with dementia, both locally and internationally. Goals included capacity/capability building. Priority research topics included raising awareness and understanding of dementia, and improving quality of life. Conclusion This roadmap may be a model for other LMIC health ecosystems with emerging dementia research cultures

A Multi-task Network to Detect Junctions in Retinal Vasculature

Junctions in the retinal vasculature are key points to be able to extract its topology, but they vary in appearance, depending on vessel density, width and branching/crossing angles. The complexity of junction patterns is usually accompanied by a scarcity of labels, which discourages the usage of very deep networks for their detection. We propose a multi-task network, generating labels for vessel interior, centerline, edges and junction patterns, to provide additional information to facilitate junction detection. After the initial detection of potential junctions in junction-selective probability maps, candidate locations are re-examined in centerline probability maps to verify if they connect at least 3 branches. The experiments on the DRIVE and IOSTAR showed that our method outperformed a recent study in which a popular deep network was trained as a classifier to find junctions. Moreover, the proposed approach is applicable to unseen datasets with the same degree of success, after training it only once.Comment: MICCAI 2018 Camera Ready Versio

Performance analysis and optimization of in-situ integration of simulation with data analysis: zipping applications up

This paper targets an important class of applications that requires combining HPC simulations with data analysis for online or real-time scientific discovery. We use the state-of-the-art parallel-IO and data-staging libraries to build simulation-time data analysis workflows, and conduct performance analysis with real-world applications of computational fluid dynamics (CFD) simulations and molecular dynamics (MD) simulations. Driven by in-depth performance inefficiency analysis, we design an end-to-end application-level approach to eliminating the interlocks and synchronizations existent in the present methods. Our new approach employs both task parallelism and pipeline parallelism to reduce synchronizations effectively. In addition, we design a fully asynchronous, fine-grain, and pipelining runtime system, which is named Zipper. Zipper is a multi-threaded distributed runtime system and executes in a layer below the simulation and analysis applications. To further reduce the simulation application's stall time and enhance the data transfer performance, we design a concurrent data transfer optimization that uses both HPC network and parallel file system for improved bandwidth. The scalability of the Zipper system has been verified by a performance model and various empirical large scale experiments. The experimental results on an Intel multicore cluster as well as a Knight Landing HPC system demonstrate that the Zipper based approach can outperform the fastest state-of-the-art I/O transport library by up to 220% using 13,056 processor cores