Building an Improved Internet of Things Smart Sensor Network Based on a Three-Phase Methodology

© 2013 IEEE. In recent years, the Internet of Things (IoT) has allowed the easy, intelligent, and efficient connection of many devices used in daily life by means of numerous smart sensors which communicate with each other using wireless signals. The rapid development of the IoT has been a result of recent advances in sensing technology. This paper proposes a three-phase methodology to improve the quality of experience for IoT system technologies. The proposed method employs the concepts of simple routing and two well-known multi-criteria decision-making method (MCDM) techniques: The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, all simple routings are obtained using the proposed depth-first search technology (DFS). AHP is applied to analyze the structure of the problem and to obtain weights for various selected criteria in the second phase. In the third phase, TOPSIS is utilized to rank the simple routings, which are simple paths. A case study example is provided to demonstrate the proposed three-phase methodology. The results from the numerical experiments show that the proposed methodology can successfully achieve the aim of this paper

An artificial intelligence driven multi-feature extraction scheme for big data detection

© 2019 IEEE. The Internet improves the speed of information dissemination, and the scale of unstructured text data is expanding and increasingly being used for mass communication. Although these large amounts of data meet the infinite demand, it is difficult to find public focus in a timely manner. Therefore, information extraction from big data has become an important research issue, and there are many published studies on big data processing at home and abroad. In this paper, we propose a multi-feature keyword extraction method, and based on this, an artificial intelligence driven big data MFE scheme is designed, then an application example of the general scheme is expanded and detailed. Taking news as the carrier, this scheme is applied to the algorithm design of hot event detection. As a result, a multi-feature fusion clustering algorithm is proposed based on user attention with two main stages. In the first stage, a multi-feature fusion model is developed to evaluate keywords, and this model combines the term frequency and part of speech features. We use it to extract keywords for representing news and events. In the second stage, we perform clustering and detect hot events in accordance with the procedure, and during the composition of news clusters, we analyze several variadic parameters in order to explore the optimal effectiveness. Then, experiments on the news corpus are conducted, and the results show that the approach presented herein performs well

Complex graph analysis and representation learning: problems, techniques, and applications

Graph representation learning (GRL) has become a new learning paradigm, supporting a wide range of tasks such as node classification, link prediction, and graph classification. However, the effectiveness of graph analysis heavily depends on the quality of data representation. While existing GRL methods have made significant progress in learning from simple graphs, addressing the challenges posed by complex graph structures remains an active area of research. In many real-world scenarios, graph data usually exhibits characteristics such as complexity, heterogeneity, and dynamicity, where objects and their interactions may be multi-type, multi-modal, and even multi-dimensional, posing challenges to graph-related analysis. To tackle these challenges, GRL has been developed and widely used to model more complex and powerful graphs. In this survey, we provide a comprehensive and structured analysis of the existing literature on GRL from two clear points of view of simple graph and complex graph. We begin by providing a detailed and thorough analysis of state-of-the-art GRL techniques and classify them according to their underlying learning mechanisms. Furthermore, we systematically investigate GRL from the perspective of complex graphs to address the challenges posed by graph complexity. We emphasize the need for specialized GNN models that can handle the complexity of such systems. Finally, we highlight several promising directions for future research

Association Rules Mining among Interests and Applications for Users on Social Networks

Interest is an important concept in psychology and pedagogy and is widely studied in many fields. Especially in recent years, the widespread use of many interest-based recommendation systems has greatly promoted research on interest modeling and mining on social networks. However, the existing studies have rarely tried to explore the relationships among interests and their application value, and most similar studies analyze user behavior data. In this paper, we propose and verify two hypotheses about the interests of social network users. We then use association rules to mine users' interests from LinkedIn users' profiles. Finally, based on the interest association rules and user interest distribution on Twitter, we design an approach to mine interests for Twitter users and conduct two experiments to systematically demonstrate the approach's effectiveness. According to our research, we found that there are a large number of association rules between human interests. These rules play a considerable role in our method of interest mining. Our research work not only provides new ideas for interest mining but also reveals the internal relationship between interest and its application value. The research work has certain theoretical and practical value

Mechanism of Splicing Regulation of Spinal Muscular Atrophy Genes

Spinal muscular atrophy (SMA) is one of the major genetic disorders associated with infant mortality. More than 90% cases of SMA result from deletions or mutations of Survival Motor Neuron 1 (SMN1) gene. SMN2, a nearly identical copy of SMN1, does not compensate for the loss of SMN1due to predominant skipping of exon 7. However, correction of SMN2 exon 7 splicing has proven to confer therapeutic benefits in SMA patients. The only approved drug for SMA is an antisense oligonucleotide (Spinraza™/Nusinersen), which corrects SMN2 exon 7 splicing by blocking intronic splicing silencer N1 (ISS-N1) located immediately downstream of exon 7. ISS-N1 is a complex regulatory element encompassing overlapping negative motifs and sequestering a cryptic splice site. More than 40 protein factors have been implicated in the regulation of SMN exon 7 splicing. There is evidence to support that multiple exons of SMN are alternatively spliced during oxidative stress, which is associated with a growing number of pathological conditions. Here, we provide the most up to date account of the mechanism of splicing regulation of the SMN genes

Probiotic-Derived Polyphosphate Enhances the Epithelial Barrier Function and Maintains Intestinal Homeostasis through Integrin–p38 MAPK Pathway

Probiotics exhibit beneficial effects on human health, particularly in the maintenance of intestinal homeostasis in a complex manner notwithstanding the diversity of an intestinal flora between individuals. Thus, it is highly probable that some common molecules secreted by probiotic and/or commensal bacteria contribute to the maintenance of intestinal homeostasis and protect the intestinal epithelium from injurious stimuli. To address this question, we aimed to isolate the cytoprotective compound from a lactobacillus strain, Lactobacillus brevis SBC8803 which possess the ability to induce cytoprotective heat shock proteins in mouse small intestine. L. brevis was incubated in MRS broth and the supernatant was passed through with a 0.2-µm filter. Caco2/bbe cells were treated with the culture supernatant, and HSP27 expression was evaluated by Western blotting. HSP27-inducible components were separated by ammonium sulfate precipitation, DEAE anion exchange chromatography, gel filtration, and HPLC. Finally, we identified that the HSP27-inducible fraction was polyphosphate (poly P), a simple repeated structure of phosphates, which is a common product of lactobacilli and other bacteria associated with intestinal microflora without any definitive physiological functions. Then, poly P was synthesized by poly P-synthesizing enzyme polyphosphate kinase. The synthesized poly P significantly induced HSP27 from Caco2/BBE cells. In addition, Poly P suppressed the oxidant-induced intestinal permeability in the mouse small intestine and pharmacological inhibitors of p38 MAPK and integrins counteract its protective effect. Daily intrarectal administration of poly P (10 µg) improved the inflammation grade and survival rate in 4% sodium dextran sulfate-administered mice. This study, for the first time, demonstrated that poly P is the molecule responsible for maintaining intestinal barrier actions which are mediated through the intestinal integrin β1-p38 MAPK

Service workload patterns for QoS-driven cloud resource management

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support a continuous approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction technique that combines a workload pattern mining approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques by a hybrid prediction solution. Uncertainty and noise are additional challenges that emerge in multi-layered, often federated cloud architectures. We specifically add log smoothing combined with a fuzzy logic approach to make the prediction solution more robust in the context of these challenges

Thalamic Activation Modulates the Responses of Neurons in Rat Primary Auditory Cortex: An In Vivo Intracellular Recording Study

Auditory cortical plasticity can be induced through various approaches. The medial geniculate body (MGB) of the auditory thalamus gates the ascending auditory inputs to the cortex. The thalamocortical system has been proposed to play a critical role in the responses of the auditory cortex (AC). In the present study, we investigated the cellular mechanism of the cortical activity, adopting an in vivo intracellular recording technique, recording from the primary auditory cortex (AI) while presenting an acoustic stimulus to the rat and electrically stimulating its MGB. We found that low-frequency stimuli enhanced the amplitudes of sound-evoked excitatory postsynaptic potentials (EPSPs) in AI neurons, whereas high-frequency stimuli depressed these auditory responses. The degree of this modulation depended on the intensities of the train stimuli as well as the intervals between the electrical stimulations and their paired sound stimulations. These findings may have implications regarding the basic mechanisms of MGB activation of auditory cortical plasticity and cortical signal processing

Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.Comment: Main text, supplemental inf

General lack of global dosage compensation in ZZ/ZW systems? Broadening the perspective with RNA-seq

Background Species with heteromorphic sex chromosomes face the challenge of large-scale imbalance in gene dose. Microarray-based studies in several independent male heterogametic XX/XY systems suggest that dosage compensation mechanisms are in place to mitigate the detrimental effects of gene dose differences. However, recent genomic research on female heterogametic ZZ/ZW systems has generated surprising results. In two bird species and one lepidopteran no evidence for a global dosage compensating mechanism has been found. The recent advent of massively parallel RNA sequencing now opens up the possibility to gauge the generality of this observation with a broader phylogenetic sampling. It further allows assessing the validity of microarray-based inference on dosage compensation with a novel technology. Results We here expemplify this approach using massively parallel sequencing on barcoded individuals of a bird species, the European crow (Corvus corone), where previously no genetic resources were available. Testing for Z-linkage with quantitative PCR (qPCR,) we first establish that orthology with distantly related species (chicken, zebra finch) can be used as a good predictor for chromosomal affiliation of a gene. We then use a digital measure of gene expression (RNA-seq) on brain transcriptome and confirm a global lack of dosage compensation on the Z chromosome. RNA-seq estimates of male-to-female (m:f) expression difference on the Z compare well to previous microarray-based estimates in birds and lepidopterans. The data further lends support that an up-regulation of female Z-linked genes conveys partial compensation and suggest a relationship between sex-bias and absolute expression level of a gene. Correlation of sex-biased gene expression on the Z chromosome across all three bird species further suggests that the degree of compensation has been partly conserved across 100 million years of avian evolution. Conclusions This work demonstrates that the study of dosage compensation has become amenable to species where previously no genetic resources were available. Massively parallele transcriptome sequencing allows re-assessing the degree of dosage compensation with a novel tool in well-studies species and, in addition, gain valuable insights into the generality of mechanisms across independent taxonomic group for both the XX/XY and ZZ/ZW system