Showing papers in "Applied Sciences in 2016"

Metrics for Polyphonic Sound Event Detection

Abstract: This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time The polyphonic system output requires a suitable procedure for evaluation against a reference Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study In parallel, we provide a toolbox containing implementations of presented metrics

Cyclic Crack Monitoring of a Reinforced Concrete Column under Simulated Pseudo-Dynamic Loading Using Piezoceramic-Based Smart Aggregates

Abstract: Structural health monitoring is an important aspect of maintenance for bridge columns in areas of high seismic activity. In this project, recently developed piezoceramic-based transducers, known as smart aggregates (SA), were utilized to perform structural health monitoring of a reinforced concrete (RC) bridge column subjected to pseudo-dynamic loading. The SA-based approach has been previously verified for static and dynamic loading but never for pseudo-dynamic loading. Based on the developed SAs, an active-sensing approach was developed to perform real-time health status evaluation of the RC column during the loading procedure. The existence of cracks attenuated the stress wave transmission energy during the loading procedure and reduced the amplitudes of the signal received by SA sensors. To detect the crack evolution and evaluate the damage severity, a wavelet packet-based structural damage index was developed. Experimental results verified the effectiveness of the SAs in structural health monitoring of the RC column under pseudo-dynamic loading. In addition to monitoring the general severity of the damage, the local structural damage indices show potential to report the cyclic crack open-close phenomenon subjected to the pseudo-dynamic loading.

Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices

Abstract: Dedicated multi-project wafer (MPW) runs for photonic integrated circuits (PICs) from Si foundries mean that researchers and small-to-medium enterprises (SMEs) can now afford to design and fabricate Si photonic chips. While these bare Si-PICs are adequate for testing new device and circuit designs on a probe-station, they cannot be developed into prototype devices, or tested outside of the laboratory, without first packaging them into a durable module. Photonic packaging of PICs is significantly more challenging, and currently orders of magnitude more expensive, than electronic packaging, because it calls for robust micron-level alignment of optical components, precise real-time temperature control, and often a high degree of vertical and horizontal electrical integration. Photonic packaging is perhaps the most significant bottleneck in the development of commercially relevant integrated photonic devices. This article describes how the key optical, electrical, and thermal requirements of Si-PIC packaging can be met, and what further progress is needed before industrial scale-up can be achieved.

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

Abstract: Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.

State of the Art and Trends Review of Smart Metering in Electricity Grids

Abstract: Climate change, awareness of energy efficiency, new trends in electricity markets, the obsolescence of the actual electricity model, and the gradual conversion of consumers to prosumer profiles are the main agents of progressive change in electricity systems towards the Smart Grid paradigm. The introduction of multiple distributed generation and storage resources, with a strong involvement of renewable energies, exposes the necessity of advanced metering or Smart Metering systems, able to manage and control those distributed resources. Due to the heterogeneity of the Smart Metering systems and the specific features of each grid, it is easy to find in the related literature a wide range of solutions with different features. This work describes the key elements in a Smart Metering system and compiles the most employed technologies and standards as well as their main features. Since Smart Metering systems can perform jointly with other activities, these growing initiatives are also addressed. Finally, a revision of the main trends in Smart Metering uses and deployments worldwide is included.

A Brief Description of High Temperature Solid Oxide Fuel Cell’s Operation, Materials, Design, Fabrication Technologies and Performance

Abstract: Today’s world needs highly efficient systems that can fulfill the growing demand for energy. One of the promising solutions is the fuel cell. Solid oxide fuel cell (SOFC) is considered by many developed countries as an alternative solution of energy in near future. A lot of efforts have been made during last decade to make it commercial by reducing its cost and increasing its durability. Different materials, designs and fabrication technologies have been developed and tested to make it more cost effective and stable. This article is focused on the advancements made in the field of high temperature SOFC. High temperature SOFC does not need any precious catalyst for its operation, unlike in other types of fuel cell. Different conventional and innovative materials have been discussed along with properties and effects on the performance of SOFC’s components (electrolyte anode, cathode, interconnect and sealing materials). Advancements made in the field of cell and stack design are also explored along with hurdles coming in their fabrication and performance. This article also gives an overview of methods required for the fabrication of different components of SOFC. The flexibility of SOFC in terms fuel has also been discussed. Performance of the SOFC with varying combination of electrolyte, anode, cathode and fuel is also described in this article.

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Abstract: Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Abstract: (Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 × 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.

Optoelectronic Devices Based on Atomically Thin Transition Metal Dichalcogenides

Abstract: We review the application of atomically thin transition metal dichalcogenides in optoelectronic devices. First, a brief overview of the optical properties of two-dimensional layered semiconductors is given and the role of excitons and valley dichroism in these materials are discussed. The following sections review and compare different concepts of photodetecting and light emitting devices, nanoscale lasers, single photon emitters, valleytronics devices, as well as photovoltaic cells. Lateral and vertical device layouts and different operation mechanisms are compared. An insight into the emerging field of valley-based optoelectronics is given. We conclude with a critical evaluation of the research area, where we discuss potential future applications and remaining challenges.

Influence of Induced Magnetic Field on Free Convection of Nanofluid Considering Koo-Kleinstreuer-Li (KKL) Correlation

Abstract: In this paper, the influence of induced magnetic field on free convection of Al2O3-water nanofluid on permeable plate by means of Koo-Kleinstreuer-Li (KKL) model is reported. Impact of Brownian motion, along with the properties of nanofluid, are also taken into account. The resulting equations are solved utilizing Runge-Kutta integration method. Obtained results are examined for innumerable energetic parameters, namely Al2O3 volume fraction, suction parameter, and Hartmann and magnetic Prandtl numbers. Results indicate that the velocity profile reduces with rise of the suction parameter and magnetic Prandtl and Hartmann numbers but it increases with addition of nanoparticles. Shear stress enhances with rise of suction parameter, magnetic Prandtl and Hartmann numbers. Temperature gradient improves with augment of suction parameter.

Bolt Looseness Detection Based on Piezoelectric Impedance Frequency Shift

Abstract: In this paper, a piezoelectric impedance frequency shift method is developed to estimate the bolt preload for the detection of bolt looseness in engineering structures. An experimental device that allows the precision control of the axial preload force on a bolt is designed and fabricated. A universal testing machine is used to preload accurately on the bolt in the experiments. Under different bolt preload conditions, the impedance analyzer measures the admittance (inverse of the impedance) signal of the PZT (Lead ZirconateTitanate) patches which are bonded on the bolt head. Firstly, a wide frequency band is swept to find a sensitive frequency band of the piezoelectric admittance with the imaginary part. Then in the sensitive frequency band, a specified peak frequency of the admittance signature is chosen to investigate the frequency shift with different bolt preloads. The relationship between the specified frequency shift and the bolt preload is established. The experimental results show that the specified peak frequency decreases as the bolt preload increases for both M16 and M12 bolts, and the frequency shift has a linear relationship with the preload on the bolt. The frequencies of the real and imaginary parts of the admittance signature have the same results. Therefore, the bolt preload can be determined by measuring the specified frequency shift and this method has a good application prospect.

All About Audio Equalization: Solutions and Frontiers

Abstract: Audio equalization is a vast and active research area. The extent of research means that one often cannot identify the preferred technique for a particular problem. This review paper bridges those gaps, systemically providing a deep understanding of the problems and approaches in audio equalization, their relative merits and applications. Digital signal processing techniques for modifying the spectral balance in audio signals and applications of these techniques are reviewed, ranging from classic equalizers to emerging designs based on new advances in signal processing and machine learning. Emphasis is placed on putting the range of approaches within a common mathematical and conceptual framework. The application areas discussed herein are diverse, and include well-defined, solvable problems of filter design subject to constraints, as well as newly emerging challenges that touch on problems in semantics, perception and human computer interaction. Case studies are given in order to illustrate key concepts and how they are applied in practice. We also recommend preferred signal processing approaches for important audio equalization problems. Finally, we discuss current challenges and the uncharted frontiers in this field. The source code for methods discussed in this paper is made available at https://code.soundsoftware.ac.uk/projects/allaboutaudioeq.

Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set

Abstract: Recent years have witnessed an ever-mounting interest in the research of sparse representation. The framework, Sparse Representation-based Classification (SRC), has been widely applied as a classifier in numerous domains, among which Synthetic Aperture Radar (SAR) target recognition is really challenging because it still is an open problem to interpreting the SAR image. In this paper, SRC is utilized to classify a 10-class moving and stationary target acquisition and recognition (MSTAR) target, which is a standard SAR data set. Before the classification, the sizes of the images need to be normalized to maintain the useful information, target and shadow, and to suppress the speckle noise. Specifically, a preprocessing method is recommended to extract the feature vectors of the image, and the feature vectors of the test samples can be represented by the sparse linear combination of basis vectors generated by the feature vectors of the training samples. Then the sparse representation is solved by l 1 -norm minimization. Finally, the identities of the test samples are inferred by the reconstructive errors calculated through the sparse coefficient. Experimental results demonstrate the good performance of SRC. Additionally, the average recognition rate under different feature spaces and the recognition rate of each target are discussed.

Leak Detection in Water-Filled Small-Diameter Polyethylene Pipes by Means of Acoustic Emission Measurements

Abstract: The implementation of effective strategies to manage leaks represents an essential goal for all utilities involved with drinking water supply in order to reduce water losses affecting urban distribution networks. This study concerns the early detection of leaks occurring in small-diameter customers’ connections to water supply networks. An experimental campaign was carried out in a test bed to investigate the sensitivity of Acoustic Emission (AE) monitoring to water leaks. Damages were artificially induced on a polyethylene pipe (length 28 m, outer diameter 32 mm) at different distances from an AE transducer. Measurements were performed in both unburied and buried pipe conditions. The analysis permitted the identification of a clear correlation between three monitored parameters (namely total Hits, Cumulative Counts and Cumulative Amplitude) and the characteristics of the examined leaks.

A Smart Washer for Bolt Looseness Monitoring Based on Piezoelectric Active Sensing Method

Abstract: Piezoceramic based active sensing methods have been researched to monitor preload on bolt connections However, there is a saturation problem involved with this type of method The transmitted energy is sometimes saturated before the maximum preload which is due to it coming into contact with flat surfaces When it comes to flat contact surfaces, the true contact area will easily saturate with the preload The design of a new type of bolt looseness monitoring sensor, a smart washer, is to mitigate the saturation problem The smart washer is composed of two annular disks with contact surfaces that are machined into convex and concave respectively, to eliminate the complete flat contact surfaces and to reduce the saturation effect One piezoelectric patch is bonded on the non-contact surface of each annular disk These two mating annular disks form a smart washer One of the two piezoelectric patches serves as an actuator to generate an ultrasonic wave that propagates through the contact surface; the other one serves as a sensor to detect the propagated waves The wave energy propagated through the contact surface is proportional to the true contact area which is determined by the bolt preload The time reversal method is used to extract the peak of the focused signal as the index of the transmission wave energy; then, the relationship between the signal peak and bolt preload is obtained Experimental results show that the focused signal peak value changes with the bolt preload and presents an approximate linear relationship when the saturation problem is experienced The proposed smart washer can monitor the full range of the rated preload

Raman Spectra of ZrS2 and ZrSe2 from Bulk to Atomically Thin Layers

Abstract: In the race towards two-dimensional electronic and optoelectronic devices, semiconducting transition metal dichalcogenides (TMDCs) from group VIB have been intensively studied in recent years due to the indirect to direct band-gap transition from bulk to the monolayer. However, new materials still need to be explored. For example, semiconducting TMDCs from group IVB have been predicted to have larger mobilities than their counterparts from group VIB in the monolayer limit. In this work we report the mechanical exfoliation of ZrX2 (X = S, Se) from bulk down to the monolayer and we study the dimensionality dependence of the Raman spectra in ambient conditions. We observe Raman signal from bulk to few layers and no shift in the peak positions is found when decreasing the dimensionality. While a Raman signal can be observed from bulk to a bilayer for ZrS2, we could only detect signal down to five layers for flakes of ZrSe2. These results show the possibility of obtaining atomically thin layers of ZrX2 by mechanical exfoliation and represent one of the first steps towards the investigation of the properties of these materials, still unexplored in the two-dimensional limit.

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

Abstract: Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.

A Novel Tunable Multi-Frequency Hybrid Vibration Energy Harvester Using Piezoelectric and Electromagnetic Conversion Mechanisms

Abstract: This paper presents a novel tunable multi-frequency hybrid energy harvester (HEH). It consists of a piezoelectric energy harvester (PEH) and an electromagnetic energy harvester (EMEH), which are coupled with magnetic interaction. An electromechanical coupling model was developed and numerically simulated. The effects of magnetic force, mass ratio, stiffness ratio, and mechanical damping ratios on the output power were investigated. A prototype was fabricated and characterized by experiments. The measured first peak power increases by 16.7% and 833.3% compared with that of the multi-frequency EMEH and the multi-frequency PEH, respectively. It is 2.36 times more than the combined output power of the linear PEH and linear EMEH at 22.6 Hz. The half-power bandwidth for the first peak power is also broadened. Numerical results agree well with the experimental data. It is indicated that magnetic interaction can tune the resonant frequencies. Both magnetic coupling configuration and hybrid conversion mechanism contribute to enhancing the output power and widening the operation bandwidth. The magnitude and direction of magnetic force have significant effects on the performance of the HEH. This proposed HEH is an effective approach to improve the generating performance of the micro-scale energy harvesting devices in low-frequency range.

Evaluation of Antioxidant and Cytotoxicity Activities of Copper Ferrite (CuFe2O4) and Zinc Ferrite (ZnFe2O4) Nanoparticles Synthesized by Sol-Gel Self-Combustion Method

Abstract: Spinel copper ferrite (CuFe2O4) and zinc ferrite (ZnFe2O4) nanoparticles were synthesized using a sol-gel self-combustion technique. The structural, functional, morphological and magnetic properties of the samples were investigated by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Transmission electron microscopy (TEM) and vibrating sample magnetometry (VSM). XRD patterns conform to the copper ferrite and zinc ferrite formation, and the average particle sizes were calculated by using a transmission electron microscope, the measured particle sizes being 56 nm for CuFe2O4 and 68 nm for ZnFe2O4. Both spinel ferrite nanoparticles exhibit ferromagnetic behavior with saturation magnetization of 31 emug−1 for copper ferrite (50.63 Am2/Kg) and 28.8 Am2/Kg for zinc ferrite. Both synthesized ferrite nanoparticles were equally effective in scavenging 2,2-diphenyl-1-picrylhydrazyl hydrate (DPPH) free radicals. ZnFe2O4 and CuFe2O4 nanoparticles showed 30.57% ± 1.0% and 28.69% ± 1.14% scavenging activity at 125 µg/mL concentrations. In vitro cytotoxicity study revealed higher concentrations (>125 µg/mL) of ZnFe2O4 and CuFe2O4 with increased toxicity against MCF-7 cells, but were found to be non-toxic at lower concentrations suggesting their biocompatibility.

An Equivalent Layer-Wise Approach for the Free Vibration Analysis of Thick and Thin Laminated and Sandwich Shells

Abstract: The main purpose of the paper is to present an innovative higher-order structural theory to accurately evaluate the natural frequencies of laminated composite shells. A new kinematic model is developed starting from the theoretical framework given by a unified formulation. The kinematic expansion is taken as a free parameter, and the three-dimensional displacement field is described by using alternatively the Legendre or Lagrange polynomials, following the key points of the most typical Layer-wise (LW) approaches. The structure is considered as a unique body and all the geometric and mechanical properties are evaluated on the shell middle surface, following the idea of the well-known Equivalent Single Layer (ESL) models. For this purpose, the name Equivalent Layer-Wise (ELW) is introduced to define the present approach. The governing equations are solved numerically by means of the Generalized Differential Quadrature (GDQ) method and the solutions are compared with the results available in the literature or obtained through a commercial finite element program. Due to the generality of the current method, several boundary conditions and various mechanical and geometric configurations are considered. Finally, it should be underlined that different doubly-curved surfaces may be considered following the mathematical framework given by the differential geometry.

Review of Laser-Generated Ultrasound Transmitters and Their Applications to All-Optical Ultrasound Transducers and Imaging

Abstract: Medical ultrasound is an imaging technique that utilizes ultrasonic signals as information carriers, and has wide applications such as seeing internal body structures, finding a source of a disease, and examining pregnant women. The most commonly used ultrasonic transducer today is based on piezoelectricity. The piezoelectric transducer, however, may have a limited bandwidth and insufficient sensitivity for reduced element size. Laser-generated ultrasound (LGUS) technique is an effective way to resolve these issues. The LGUS approach based on photoacoustic effect is able to greatly enhance the bandwidth of ultrasound signals and has the potential for high-resolution imaging. High-amplitude LGUS could also be used for therapy to accomplish high precision surgery without an incision. Furthermore, LGUS in conjunction with optical detection of ultrasound allows all-optical ultrasound imaging (i.e., ultrasound is generated and received optically). The all-optical platform offers unique advantages in providing high-resolution information and in facilitating the construction of miniature probes for endoscopic ultrasound. In this article, a detailed review of the recent development of various LGUS transmitters is presented. In addition, a recent research interest in all-optical ultrasound imaging, as well as its applications, is also discussed.

A Review of Time-Scale Modification of Music Signals †

Abstract: Time-scale modification (TSM) is the task of speeding up or slowing down an audio signal’s playback speed without changing its pitch. In digital music production, TSM has become an indispensable tool, which is nowadays integrated in a wide range of music production software. Music signals are diverse—they comprise harmonic, percussive, and transient components, among others. Because of this wide range of acoustic and musical characteristics, there is no single TSM method that can cope with all kinds of audio signals equally well. Our main objective is to foster a better understanding of the capabilities and limitations of TSM procedures. To this end, we review fundamental TSM methods, discuss typical challenges, and indicate potential solutions that combine different strategies. In particular, we discuss a fusion approach that involves recent techniques for harmonic-percussive separation along with time-domain and frequency-domain TSM procedures.

Residential demand response scheduling with consideration of consumer preferences

Abstract: This paper proposes a new demand response scheduling framework for an array of households, which are grouped into different categories based on socio-economic factors, such as the number of occupants, family decomposition and employment status. Each of the households is equipped with a variety of appliances. The model takes the preferences of participating households into account and aims to minimize the overall production cost and, in parallel, to lower the individual electricity bills. In the existing literature, customers submit binary values for each time period to indicate their operational preferences. However, turning the appliances "on" or "off" does not capture the associated discomfort levels, as each appliance provides a different service and leads to a different level of satisfaction. The proposed model employs integer values to indicate household preferences and models the scheduling problem as a multi-objective mixed integer programming. The main thrust of the framework is that the multi-level preference modeling of appliances increases their "flexibility"; hence, the job scheduling can be done at a lower cost. The model is evaluated by using the real data provided by the Department of Energy & Climate Change, UK. In the computational experiments, we examine the relation between the satisfaction of consumers based on the appliance usage preferences and the electricity costs by exploring the Pareto front of the related objective functions. The results show that the proposed model leads to significant savings in electricity cost, while maintaining a good level of customer satisfaction.

Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia

Abstract: Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1) (GM (1,1)), optimized by moth-flame optimization (MFO) algorithm with rolling mechanism (Rolling-MFO-GM (1,1)), was put forward. The parameters a and b of GM (1,1) were optimized by employing moth-flame optimization algorithm (MFO), which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1) for annual electricity consumption prediction when compared with least square regression (LSR), GM (1,1), FOA (fruit fly optimization)-GM (1,1), MFO-GM (1,1), Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1). The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.

The Influence of Process Parameters on the Surface Roughness of a 3D-Printed Co–Cr Dental Alloy Produced via Selective Laser Melting

Abstract: Selective laser melting (SLM), used to fabricate metallic objects with high geometrical complexity, is currently of increasing interest to the fields of medicine and dentistry. SLM-fabricated products should have highly smooth surfaces to minimize the use of post-processing procedures such as finishing and polishing. This study investigated the effect of various laser process parameters (laser power, scan rate, and scan-line spacing) on the surface roughness of a Co–Cr dental alloy that was three-dimensionally (3D) constructed via SLM. Initially, a single-line formation test was used to determine the optimal laser power (200 W) and scan rate (128.6 mm/s) that resulted in beads with an optimal profile. During subsequent multi-layer formation tests, the 3D Co–Cr body with the smoothest surface was produced using a scan-line spacing of 100 μm. The findings of this study show that laser process parameters have crucial effects on the surface quality of SLM-fabricated Co–Cr dental alloys.

On Squeezed Flow of Jeffrey Nanofluid between Two Parallel Disks

Abstract: The present communication examines the magnetohydrodynamic (MHD) squeezing flow of Jeffrey nanofluid between two parallel disks. Constitutive relations of Jeffrey fluid are employed in the problem development. Heat and mass transfer aspects are examined in the presence of thermophoresis and Brownian motion. Jeffrey fluid subject to time dependent applied magnetic field is conducted. Suitable variables lead to a strong nonlinear system. The resulting systems are computed via homotopic approach. The behaviors of several pertinent parameters are analyzed through graphs and numerical data. Skin friction coefficient and heat and mass transfer rates are numerically examined.

Physical and Degradable Properties of Mulching Films Prepared from Natural Fibers and Biodegradable Polymers

Abstract: The use of plastic film in agriculture has the serious drawback of producing vast quantities of waste In this work, films were prepared from natural fibers and biodegradable polymers as potential substitutes for the conventional non-biodegradable plastic film used as mulching material in agricultural production The physical properties (eg, mechanical properties, heat preservation, water permeability, and photopermeability) and degradation characteristics (evaluated by micro-organic culture testing and soil burial testing) of the films were studied in both laboratory and field tests The experimental results indicated that these fiber/polymer films exhibited favorable physical properties that were sufficient for use in mulching film applications Moreover, the degradation degree of the three tested films decreased in the following order: fiber/starch (ST) film > fiber/poly(vinyl alcohol) (PVA) film > fiber/polyacrylate (PA) film The fiber/starch and fiber/PVA films were made from completely biodegradable materials and demonstrated the potential to substitute non-biodegradable films

A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications

Abstract: Double- and triple-walled carbon nanotubes (DWNTs and TWNTs) consist of coaxially-nested two and three single-walled carbon nanotubes (SWNTs). They act as the geometrical bridge between SWNTs and multi-walled carbon nanotubes (MWNTs), providing an ideal model for studying the coupling interactions between different shells in MWNTs. Within this context, this article comprehensively reviews various synthetic routes of DWNTs’ and TWNTs’ production, such as arc discharge, catalytic chemical vapor deposition and thermal annealing of pea pods (i.e., SWNTs encapsulating fullerenes). Their structural features, as well as promising applications and future perspectives are also discussed.

Oxygen Carrier Aided Combustion (OCAC) of Wood Chips in a Semi-Commercial Circulating Fluidized Bed Boiler Using Manganese Ore as Bed Material

Abstract: Oxygen Carrier Aided Combustion (OCAC) is realized by using an active oxygen-carrying bed material in fluidized bed boilers. The active material is reduced in fuel rich parts of the boiler and oxidized in air rich parts. Advantages could be achieved such as new mechanisms for oxygen transport in space and time. Here calcined manganese ore has been used as active bed material in a 12 MWth circulating fluidized bed boiler. The fuel was wood chips and the campaign lasted more than two weeks. From an operational point of view, manganese ore worked excellently. From the temperature profile of the boiler it can be concluded that fuel conversion was facilitated, especially in the dense bottom bed. The effect did not always translate to reduced emissions, which suggests that final combustion in the cyclone outlet was also influenced. Substituting 10% of the sand bed with manganese ore made it possible to reduce the air to fuel ratio without generating large amounts of CO. The use of 100% manganese ore resulted in higher emissions of CO than the sand reference, but, when combined sulphur feeding, dramatic reductions in CO emissions, up to 90% compared to sand reference, was achieved.

Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

Abstract: In this study, an artificial neural network (ANN) model is developed to predict the stability number of breakwater armor stones based on the experimental data reported by Van der Meer in 1988. The harmony search (HS) algorithm is used to determine the near-global optimal initial weights in the training of the model. The stratified sampling is used to sample the training data. A total of 25 HS-ANN hybrid models are tested with different combinations of HS algorithm parameters. The HS-ANN models are compared with the conventional ANN model, which uses a Monte Carlo simulation to determine the initial weights. Each model is run 50 times and the statistical analyses are conducted for the model results. The present models using stratified sampling are shown to be more accurate than those of previous studies. The statistical analyses for the model results show that the HS-ANN model with proper values of HS algorithm parameters can give much better and more stable prediction than the conventional ANN model.