Frequency regularization of a linked wind–diesel system using dual structure fuzzy with ultra-capacitor

Microgrids (μ-grids) are gaining increased interest around the world for supplying cheap and clean energy. In this paper, a μ-grid comprising a wind turbine generator (WTG) and diesel generator (DG) is considered. It is one of most practical and demanding systems suitable for the present energy crisis in isolated or rural areas. However, wind energy is intermittent in nature while load demand changes frequently. Therefore, a µ-grid can experience large frequency and power fluctuations. The speed governor of the DG tries to minimize the frequency and power deviations in µ-grid though its operation is slow and cannot adequately minimize system deviations. The paper proposes a novel arrangement based on a dual structured fuzzy (DSF) whose structure changes according to the switching limit with a reduced rule base. It has the capability to switch between proportional and integral actions and hence improves the frequency regularization of the μ-grid. The proposed strategy is tested in a μ-grid and the results considering step load alteration, load alteration at different instants of time, nonstop changing load request are compared with some of the well published methods to validate the effectiveness and simplicity of the present design. In addition, it shows that ultra-capacitor establishment in a μ-grid has a positive impact in minimizing system deviations with DSF for the studied cases.     

CanarDeep: a hybrid deep neural model with mixed fusion for rumour detection in social data streams

Neural Computing and Applications - The unrelenting trend of doctored narratives, content spamming, fake news and rumour dissemination on social media can lead to grave consequences that range from...     

Parametric optimization of multiwalled carbon nanotube-assisted electric discharge machining of Al-10%SiCp metal matrix composite by response surface methodology

The demand for miniaturized products having a glossy surface or nano-level surface is increasing exponentially in automobile, aerospace, biomedical, and semiconductor industries. The mirror-like surface finish has generated a need to develop advanced machining processes. The addition of powder particle into electric discharge machining (EDM) oil is considered a promising technique to achieve surface integrity at the miniaturization level. In this research, the Al–10%SiC_p metal matrix composite (MMC) has been machined after mixing the appropriate amount of multiwalled carbon nanotubes (MWCNTs) into the EDM dielectric fluid. An advanced experimental setup has been designed and fabricated in the laboratory for conducting the experiments. This proposed technology is called nano powder mixed electric discharge machining (NPMEDM). The input parameters of NPMEDM are also optimized using central composite rotatable design (CCRD) based on response surface methodology (RSM) in order to obtain the best surface finish and material removal rate (MRR). The MRR has been increased by 38.22% and surface finish has been improved by 46.06% after mixing the MWCNTs into the EDM dielectric fluid. The results indicate that the combination of parameters A5, B5, C5, and D5 might have produced maximum MRR, whereas A1, B1, C1, and D3 have produced minimum surface roughness (SR).     

Mechanical and Thermophysical Properties of Cerium Monopnictides

The ultrasonic attenuation due to phonon–phonon interaction, thermoelastic relaxation and dislocation damping mechanisms has been investigated in cerium monopnictides CeX (X: N, P, As, Sb and Bi) for longitudinal and shear waves along \({\langle }100{\rangle }\), \({\langle }110{\rangle }\) and \({\langle }111{\rangle }\) directions. The second- and third-order elastic constants of CeX have also been computed in the temperature range 0 K to 500 K using Coulomb and Born–Mayer potential upto second nearest neighbours. The computed values of these elastic constants have been applied to find out Young’s moduli, bulk moduli, Breazeale’s non-linearity parameters, Zener anisotropy, ultrasonic velocity, ultrasonic Grüneisen parameter, thermal relaxation time, acoustic coupling constants and ultrasonic attenuation. The fracture/toughness ratio is less than 1.75, which shows that the chosen materials are brittle in nature as found for other monopnictides. The drag coefficient acting on the motion of screw and edge dislocations due to shear and compressional phonon viscosities of the lattice have also been evaluated for both the longitudinal and shear waves. The thermoelastic loss and dislocation damping loss are negligible in comparison to loss due to Akhieser damping (phonon–phonon interaction). The obtained results for CeX are in qualitative agreement with other semi-metallic monopnictides.     

An improved harmony search algorithm with dynamically varying bandwidth

The present work demonstrates a new variant of the harmony search (HS) algorithm where bandwidth (BW) is one of the deciding factors for the time complexity and the performance of the algorithm. The BW needs to have both explorative and exploitative characteristics. The ideology is to use a large BW to search in the full domain and to adjust the BW dynamically closer to the optimal solution. After trying a series of approaches, a methodology inspired by the functioning of a low-pass filter showed satisfactory results. This approach was implemented in the self-adaptive improved harmony search (SIHS) algorithm and tested on several benchmark functions. Compared to the existing HS algorithm and its variants, SIHS showed better performance on most of the test functions. Thereafter, the algorithm was applied to geometric parameter optimization of a friction stir welding tool.     

Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.     

Physiatric research fellows'' perceptions of the quality of their training

After being poisoned by eating the mushroom species Cortinarius speciosissimus, a twin developed interstitial nephritis with acute renal failure. He received a renal transplant from his living twin brother, who was presumed dizygotic on phenotypic grounds. Fifteen years later, the twins were zygosity tested by DNA "fingerprint analysis" and found to be monozygotic, despite important phenotypic discordances. The recipient has discontinued immunosuppression therapy and remains well after 9 months. We suggest that, for medical and other reasons, zygosity should be determined at birth on all like-sexed twins.     

PVC-Based 2,2,2-Cryptand Sensor for Zinc Ions

A PVC-based membrane of 2,2,2-cryptand exhibits a very good response for Zn(2+) in a wide concentration range (from 2.06 ppm to 6.54 × 10(3) ppm) with a slope of 22.0 mV/decade of Zn(2+) concentration. The response time of the sensor is <10 s, and the membrane can be used for more than 3 months without any observed divergence in potentials. The proposed sensor exhibits very good selectivity for Zn(2+) over other cations and can be used in a wide pH range (2.8-7.0). It has also been possible to use this assembly as an indicator electrode in potentiometric titrations involving zinc ions.     

Graph theoretic foundations of multibody dynamics

This second part of a two part paper uses concepts from graph theory to obtain a deeper understanding of the mathematical foundations of multibody dynamics. The first part (Jain in Graph theoretic foundations of multibody dynamics. Part I. Structural properties, 2010) established the block-weighted adjacency (BWA) matrix structure of spatial operators associated with serial- and tree-topology multibody system dynamics, and introduced the notions of spatial kernel operators (SKO) and spatial propagation operators (SPO). This paper builds upon these connections to show that key analytical results and computational algorithms are a direct consequence of these structural properties and require minimal assumptions about the specific nature of the underlying multibody system. We formalize this notion by introducing the notion of SKO models for general tree-topology multibody systems. We show that key analytical results, including mass-matrix factorization, inversion, and decomposition hold for all SKO models. It is also shown that key low-order scatter/gather recursive computational algorithms follow directly from these abstract-level analytical results. Application examples to illustrate the concrete application of these general results are provided. The paper also describes a general recipe for developing SKO models. The abstract nature of SKO models allows for the application of these techniques to a very broad class of multibody systems.