AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation

Wireless Personal Communications - Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To...     

An innovative hybrid approach for extracting named entities from unstructured text data

Named entity recognition (NER) is the core part of information extraction that facilitates the automatic detection and classification of entities in natural language text into predefined categories, such as the names of persons, organizations, locations, and so on. The output of the NER task is crucial for many applications, including relation extraction, textual entailment, machine translation, information retrieval, etc. Literature shows that machine learning and deep learning approaches are the most widely used techniques for NER. However, for entity extraction, the abovementioned approaches demand the availability of a domain‐specific annotated data set. Our goal is to develop a hybrid NER system composed of rule‐based deep learning as well as clustering‐based approaches, which facilitates the extraction of generic entities (such as person, location, and organization) out of natural language texts of domains that lack generic named entities labeled domain data sets. The proposed approach takes the advantages of both deep learning and clustering approaches but separately, in combination with a knowledge‐based approach by using a postprocessing module. We evaluated the proposed methodology on court cases (judgments) as a use case since it contains generic named entities of different forms that are poorly or not present in open‐source NER data sets. We also evaluated our hybrid models on two benchmark data sets, namely, Computational Natural Language Learning (CoNLL) 2003 and Open Knowledge Extraction (OKE) 2016. The experimental results obtained from benchmark data sets show that our hybrid models achieved substantially better performance in terms of the F‐score in comparison to other competitive systems.     

Optimal position and rating of DG in distribution networks by ABC–CS from load flow solutions illustrated by fuzzy-PSO

In this work, load flow problems of both radial distribution networks (RDNs) and mesh distribution networks (MDNs) have been solved using hybrid fuzzy-PSO algorithm. A new voltage stability index (VSI) is also indicated. Based on the suggested load flow, distributed generation (DG) is ready to conduct through the requirement; and with the support of inserting the optimal-sized DG unit in an exact way, the distribution system’s stability is also studied. The exact position of each DG unit has been computed using “loss sensitivity analysis,” whereas the optimal sizing of each DG unit has been done with the help of hybrid artificial bee colony and Cuckoo search algorithm. The suggested method is tested in the regular 33-node and 69-node RDNs as well as in 85-node and 119-node MDNs. The transcendence of the proposed operation has been centered with the aid of comparison to the other existing methods. The suggested VSI is also correlated with other two existing VSIs before and after placement of DG unit(s).

     

Identification of Drought Occurrences Using Ensemble Predictions up to 20-Days in Advance

In the present study, skill of an extended range forecast system has been evaluated for identifying droughts over central India 20-days in advance. Rainfall forecasts from 44 ensemble members of the forecast system developed Indian Institute of Tropical Meteorology (IITM), Pune have been used to prepare probabilistic rainfall forecasts. It is seen that the uncertainties in the forecasts (in terms of ensemble spread) increases from day-5 to day 20. As the focus of the study is for drought predictions, forecasts in the bins 0-5 mm/5 day and 5-25 mm/5 day (no rain or less rain) were studied in detail. It is found that the modeling system has a tendency to over-forecast rainfall probabilities. With bias correction, the forecasts become more reliable. Various drought indices were computed using the mean of the forecast distribution up to 20-days in advance. Standardized precipitation index (SPI) computed using Gamma and Pearson type-III distributions are similar in the study region. It was found that these are in reasonable agreement with those from observations. Probabilistic forecasts of standardized precipitation index (SPI) were made and the relative operating characteristics (ROC) scores indicate that the forecasted SPI values are suitable for application.     

Highly birefringent large negative dispersion-flattened photonic crystal fibre for broadband residual dispersion compensation

A triangular lattice photonic crystal fibre is presented in this paper for residual dispersion compensation. The fibre exhibits a flattened negative dispersion of ?992.01 ± 6.93 ps/(nm-km) over S+C+L wavelength bands and ?995.83 ± 0.42 ps/(nm-km) over C-band. The birefringence is about 4.4 × 10^?2 at the excitation wavelength of 1550 nm which is also very high. Full vector finite element method (FEM) with a perfectly matched absorbing layer (PML) boundary condition is applied to numerically investigate the guiding properties of this PCF. The fibre operates at fundamental mode only. All these properties endorse this fibre as a suitable candidate for compensating residual dispersion and polarization maintaining applications.     

Copper@ZIF-8 Core-Shell Nanowires for Reusable Antimicrobial Face Masks

SARS-CoV-2 and other respiratory viruses spread via aerosols generated by infected people. Face masks can limit transmission. However, widespread use of disposable masks consumes tremendous resources and generates waste. Here, a novel material for treating blown polypropylene filtration media used in medical-grade masks to impart antimicrobial activity is reported. To produce thin copper@ZIF-8 core-shell nanowires (Cu@ZIF-8 NWs), Cu NWs are stabilized using a pluronic F-127 block copolymer, followed by growth of ZIF-8 to obtain uniform core-shell structures. The Cu@ZIF-8 NWs are applied to filtration media by dip coating. Aerosol filtration efficiency decreases upon exposure to ethanol (solvent for dip-coating), but increases with addition of Cu@ZIF-8 NWs. Cu@ZIF-8 NWs shows enhanced antibacterial activity, compared to Cu NWs or ZIF-8 alone, against Streptococcus mutans and Escherichia coli. Antiviral activity against SARS-CoV-2 is assayed using virus-infected Vero E6 cells, demonstrating 55% inhibition of virus replication after 48 h by 1 µg of Cu@ZIF-8 NWs per well. Cu@ZIF-8 NWs’ cytotoxicity is tested against four cell lines, and their effect on inflammatory response in A549 cells is examined, demonstrating good biocompatibility. This low-cost, scalable synthesis and straightforward deposition of Cu@ZIF-8 NWs onto filter media has great potential to reduce disease transmission, resource consumption, and environmental impact of waste.     

Intelligent heart disease prediction in cloud environment through ensembling

Cloud computing is the delivery of on‐demand computing resources. Cloud computing has numerous applications in fields of education, social networking, and medicine. But the benefit of cloud for medical purposes is seamless, particularly because of the enormous data generated by the health care industry. This colossal data can be managed through big data analytics, and hidden patterns can be extracted using machine learning procedures. In particular, the latest issue in the medical domain is the prediction of heart diseases, which can be resolved through culmination of machine learning and cloud computing. Hence, an attempt has been made to propose an intelligent decision support model that can aid medical experts in predicting heart disease based on the historical data of patients. Various machine learning algorithms have been implemented on the heart disease dataset to predict accuracy for heart disease. Naïve Bayes has been selected as an effective model because it provides the highest accuracy of 86.42% followed by AdaBoost and boosted tree. Further, these 3 models are being ensembled, which has increased the overall accuracy to 87.91%. The experimental results have also been evaluated using 10,082 instances that clearly validate the maximum accuracy through ensembling and minimum execution time in cloud environment.     

Effect of birch (Betula pendula) bark and food protein level on root voles (Microtus oeconomus): I. food consumption,growth, and mortality

The effect of protein and birch bark powder (BBP) content of forage on food consumption and growth of root voles (Microtus oeconomus) was studied by feeding young voles with eight different diets for two weeks. Voles ate more when the protein content of forage was moderate (6%) or low (3%), and when BBP was added to the diets, as compared to a high (12%) protein diet without BBP addition. On high and moderate protein diets voles gained weight, whereas on low protein diets they lost weight. Addition of BBP caused reduced growth or weight loss. Consuming a diet with moderate (6%) protein and high BBP (20%) resulted in high mortality (9 of 10 died). These results show that root voles need at least 6% protein for growth and that the effect of birch bark depends on the protein content of the forage.     

Intensified and safe ozonolysis of fatty acid methyl esters in liquid CO2 in a continuous reactor

We demonstrate a continuous reactor for performing the ozonolysis of fatty acid methyl esters (FAMEs) using liquid CO₂ as solvent. The fast reaction kinetics allows the use of small‐volume reactors to completely convert the FAMEs, forming secondary ozonides as the primary products. The short residence times also help maximize the yields of the secondary ozonides by minimizing over‐oxidation and the formation of oligomeric products. The liquid CO₂ medium promotes safe reactor operation by providing an essential fraction of overall reactor cooling and by diluting the vapor phase organics. We also demonstrate a continuous stirred reactor for the safe thermal decomposition of the secondary ozonides to their corresponding acids and aldehydes. Using a lumped kinetic model for the thermal decomposition of the ozonolysis products, we estimate activation energy values of 108.6 ± 0.6 kJ mol^?1 for the decomposition of secondary ozonides and 122 ± 3 kJ mol^?1 for the decomposition of the undesired oligomeric species. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2819–2826, 2017     

Organizational Media Affordances: Operationalization and Associations with Media Use

The concept of affordances has been increasingly applied to the study of information and communication technologies (ICTs) in organizational contexts. However, almost no research operationalizes affordances, limiting comparisons and programmatic research. This article briefly reviews conceptualizations and possibilities of affordances in general and for media, then introduces the concept of organizational media affordances as organizational resources. Analysis of survey data from a large Nordic media organization identified six reliable and valid organizational media affordances: pervasiveness, editability, self-presentation, searchability, visibility, and awareness. Eight media scales based on frequency of use of 10 media within each of three organization levels were differentially associated with these affordances. The conceptualization, measurement approach, and results from this study provide the foundation for considerable future organizational communication and ICT research.