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Purpose This paper aims to investigate the impact of anodizing time and heat treatment on morphology, phase and corrosion resistance of formed coating. To characterize the anodic oxide layer, X-ray diffraction (XRD) patterns and scanning electron microscopy (SEM) that was equipped with energy dispersive spectroscopy (EDS) was hired. The corrosion behavior of oxide-coated samples was estimated by electrochemical polarization test in simulated body fluid (SBF). Design/methodology/approach Anodic oxidation method is applied to reinforce the corrosion and biological properties of biomaterials in the biomedical industry. In this paper, the alkaline NaOH (1 M) electrolyte was used for AZ31 magnesium alloy anodizing accompanied by heat treatment in the air. Findings It can be concluded that the best corrosion resistance belongs to the 10 min anodic oxidized sample and among the heat-treated samples the 30 min anodized sample represented the lowest corrosion rate. Originality/value In this study, to the best of the authors’ knowledge for the first time, this paper describes the effect of anodizing process time on NaOH (1 M) electrolyte at 3 V on corrosion behavior of magnesium AZ31 alloy with an alternate method to change the phase composition of the formed oxide layer. The morphology and composition of the obtained anodic oxide layer were investigated under the results of SEM, EDS and XRD. The corrosion behavior of the oxide coatings layer fabricated on the magnesium-based substrate was studied by the potentiodynamic polarization test in the SBF solution.

Increased expression of Hypoxia-inducible factor-1α (HIF-1α) in the tumor microenvironment, mainly due to tumor growth, plays a major role in the growth of cancer. Tumor cells induce the expression of cyclooxygenase 2 (COX2) and its product, prostaglandin E2 (PGE2), through overexpression of HIF-1α. It has been shown that ligation of PGE2 with its receptor, EP4, robustly promotes cancer progression. HIF-1α/COX2/PGE2/EP4 signaling pathways appear to play an important role in tumor growth. Therefore, we decided to block the expansion of cancer cells by blocking the initiator (HIF-1α) and end (EP4) of this pathway. In this study, we used hyaluronate (HA), and trimethyl chitosan (TMC) recoated superparamagnetic iron oxide nanoparticles (SPIONs) loaded with HIF-1α-silencing siRNA and the EP4 antagonist (E7046) to treat cancer cells and assessed the effect of combination therapy on cancer progression. The results showed that optimum physicochemical characteristics of NPs (size 126.9 nm, zeta potential 27 mV, PDI

Thick thermal barrier coatings (TTBCs) with vertical cracks deposited by air plasma spray (APS) and solution precursor plasma spray (SPPS) techniques have been widely investigated to achieve good thermal insulation along with reasonable service life. In this study, synthesized unpyrolyzed YSZ powder was air plasma sprayed in order to produce segmentation crack TTBCs. The microstructure and hardness of the deposits were then compared with those of the conventional TTBCs and dense vertically cracked (DVC)TTBCs. In this regard, spraying parameters were optimized to achieve deposits with the appropriate amount of unpyrolyzed particles in them to assist inducing vertical cracks in the deposited layers. The effect of the unpyrolyzed particles on microstructure, porosity, and microhardness of plasma sprayed coatings were also evaluated and compared. The new fabricated coating showed a bimodal structure combining non-molten sub-micron size particles and conventional splats along with segmentation cracks with higher amount of porosity and lower hardness compared to those of the DVC coatings. The results implied that, depositing unpyrolyzed powder by APS, as a new approach for achieving segmentation crack TTBCs, is very promising.

This study investigates the effectiveness of the reduction policies of the apparent and real losses of non-revenue water. For this purpose, using technique for order of preference by similarity to ideal solution (TOPSIS) model, a decision matrix is proposed, involving apparent and real losses risk reduction values induced by the implementation of each non-revenue water reduction policy. First, decision criteria, including technical, economic, and social criteria, were selected and introduced. Then, the technical criterion was calculated using a fuzzy Bayesian model. In the following, using group decision-making and analytic hierarchy process method, the criteria were weighted. In the next step, the bests of decision criteria were determined by the TOPSIS method. This method was applied in the water distribution network of district 4 of Tehran, Iran. The results indicated that the most basic non-revenue water reduction policy by risk-based group decision-making is to identify unauthorized connections in the apparent water loss sector and increase the speed and quality of maintenance and repairs in the real water loss sector.

This paper is devoted to a comparative study on the isothermal oxidation of thick thermal barrier coating (TTBC) with and without segmented cracks produced by atmospheric plasma spray (APS) process. Accordingly, the growth of thermally grown oxide (TGO) and its effect on the degradation of the coating were investigated. Thick top coat in both segmented crack and conventional thick TBC reduced the double layered TGO growth rate slightly. The segmented crack thick TBC demonstrated longer isothermal oxidation life in comparison with that of the conventional thick TBC at 1100 °C. The dominant failure mechanism was spallation due to lateral cracking within the TGO and/or within TBC near the TGO layer, called mixed failure. Stress, and consequently strain, induced on the TTBC due to progressive TGO growth, seems to be primarily responsible for the crack initiation and propagation leading to the coating failure. Increment of elastic energy stored within the top coat due to the increasing of TGO thickness, finally causes thick thermal barrier coating failure in high temperature isothermal oxidation.

The present study aims to investigate the effect of friction stir processing (FSP) conditions on the corrosion characteristic of AZ31B magnesium alloy. Specimens made of AZ31B alloy were friction stir processed under various processing conditions, and their microstructure and corrosion behavior were studied. The corrosion behavior was studied by potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), and immersion test in 3.5% sodium chloride (NaCl) solution. The results showed a substantial improvement in the corrosion resistance of the friction stir processed AZ31B alloy. The improvement is likely a result of more stable corrosion products and microstructure refinement formed after friction sir processing. Keywords: Magnesium, Friction stir processing, Corrosion, Polarization, EIS

Tetracycline is one of the most widely used antibiotics that causes contamination of aqueous environments and has raised serious concern during the past few years. In this work, adsorption of tetracycline on a modified zeolite was studied through a batch system. Synthetic zeolite 13X was modified using Fe(III). The results show that the removal efficiency of tetracycline by modified zeolite has considerably increased. Different experiments were carried out in order to analyze the effect of parameters such as pH, initial concentration of tetracycline, time, etc. The results indicate that tetracycline adsorption on the zeolite strongly depends on the pH of the solution due to amphoteric functional groups of tetracycline and maximum adsorption capacity of tetracycline by modified zeolite with a pH of approximately 6. The Langmuir isotherm shows good agreement with the experimental data suggesting monolayer adsorption. Maximum adsorption capacity of the modified zeolite reached at the experiments is almost 200 mg/g. XRD, XRF and FTIR results confirm the existence of the Fe phase in the zeolite texture. Amide groups of TC were responsible for the complexation with Fe3+. Also, tetracycline removal was studied in a continuous column to simulate an industrial waste water process.

HIF-1α and STAT3 are two of the critical factors in the growth, proliferation, and metastasis of cancer cells and play a crucial role in inhibiting anti-cancer immune responses. Therefore, we used superparamagnetic iron oxide (SPION) nanoparticles (NPs) coated with thiolated chitosan (ChT) and trimethyl chitosan (TMC) and functionalized with hyaluronate (H) and TAT peptide for delivery of siRNA molecules against STAT3 and HIF-1α to cancer cells both in vivo and in vitro. The results indicated that tumor cell transfection with siRNA-encapsulated NPs robustly inhibited proliferation and migration and induced apoptosis in tumor cells. Furthermore, simultaneous silencing of HIF-1α and STAT3 significantly repressed cancer development in two different tumor types (4T1 breast cancer and CT26 colon cancer) which were associated with upregulation of cytotoxic T lymphocytes and IFN-γ secretion. The findings suggest inhibiting the HIF-1α/STAT3 axis by SPION-TMC-ChT-TAT-H NPs as an effective way to treat cancer.

The fundamental site frequency is one of the most important characteristics of the subsoil. Despite the different methods to calculate fundamental frequency, using microtremors resulted from weak ground motion is one of the fastest and cheapest approaches. Unfortunately, the effects of local transient perturbations on microtremor data increase the uncertainty in the estimation of fundamental frequency. In this paper, to solve this problem, the Bayesian data fusion method has been proposed and used to estimate the fundamental frequency of site. In this method the results of the different data windows have been fused to estimate the fundamental frequency more accurately and with less uncertainty. The advantage of this method is that it does not need any extra filtering to remove the transient perturbations’ effects except a simple band-pass filter to remove the out of range frequency components. The method has been applied to microtremor data recorded in the city of Meybod in central Iran. The comparison between the results of this approach and those obtained from the other methods indicate that the proposed method has a better performance and could be applied easily.

Accurate quantification of methane emissions from the natural gas system is important for establishing greenhouse gas inventories and understanding cause and effect for reducing emissions. Current carbon intensity methods generally assume methane emissions are proportional to gas throughput so that increases in gas consumption yield linear increases in emitted methane. However, emissions sources are diverse and many are not proportional to throughput. Insights into the causal drivers of system methane emissions, and how system-wide changes affect such drivers are required. The development of a novel cause-based methodology to assess marginal methane emissions per unit of fuel consumed is introduced. Implications: The carbon intensities of technologies consuming natural gas are critical metrics currently used in policy decisions for reaching environmental goals. For example, the low-carbon fuel standard in California uses carbon intensity to determine incentives provided. Current methods generally assume methane emissions from the natural gas system are completely proportional to throughput. The proposed cause-based marginal emissions method will provide a better understanding of the actual drivers of emissions to support development of more effective mitigation measures. Additionally, increasing the accuracy of carbon intensity calculations supports the development of policies that can maximize the environmental benefits of alternative fuels, including reducing greenhouse gas emissions.

This paper aims at using a kinematic identification procedure in order to enhance the control of a 3-DOF fully decoupled parallel robot, the so-called “Tripteron.” From a practical standpoint, manufacture errors lead to some kinematic uncertainties in the robot which cause real kinematic equations of the robot to be different from the theoretical ones. In this paper, using a white box identification procedure, the independence of degrees-of-freedom in the robot is studied. Considering the fact that the kinematic identification of a robotic manipulator requires the position of its end-effector to be known, in this paper “Kinect” sensor, which is a vision-infra red sensor, is utilized to obtain the spatial coordinates of the end-effector. In order to calibrate the Kinect, a novel approach which is based on a neuro-fuzzy algorithm, the so-called “LoLiMoT” algorithm, is used. Moreover, the results of experimentally performing the identification and calibrating approach are used to the end of implementing a closed-loop classic controller for path tracking purposes. Furthermore, the theoretical unidentified model was implemented in a sliding mode robust controller in order to compare the results with classic controller. The comparison reveals that classic controller which uses identified model leads to a better performance in terms of accuracy and control effort with respect to robust controller which is purely based on theoretical model.

We report the synthesis of mesoporous Ni3S2 nanorod arrays on three-dimensional Ni-graphene foams by a facile hydrothermal method for ultrahigh performance supercapacitors. The graphene foam facilitates unique morphological evolution during the hydrothermal process, from a mesoporous, monolithic structure to nanorod arrays of Ni3S2 with a hollow core-solid shell structure and to entirely solid nanorod arrays of Ni3S2. The Ni3S2 composite electrode delivers a remarkable specific capacitance of 1900 F g−1 at a current density of 1 A g−1, as well as promising cyclic stability with a high capacitance of 940 F g−1 after 2000 cycles at 10 A g−1 and 96% retention of its initial capacitance after the 2001st cycle at 5 A g−1. The symmetric supercapacitor prepared using the Ni3S2 electrodes under an optimum condition operates in a wide potential window of 0–1.6 V and offers a specific capacitance of 190.5 F g-1 at 4.5 mA cm−2 along with a maximum energy density of 67.7 W h kg-1. The promising electrochemical performance along with the facile synthesis strategy offers a new insight into developing high performance supercapacitors for emerging energy storage applications.

Estimated cutting forces are usually mixed up with disturbing forces such as friction and need to be compensated. In common compensation methods, such forces are firstly recorded along machining contours under air-cutting conditions. Then, recorded disturbing forces are recalled for the compensation during the main machining process. This method doubles the process time and needs a precise synchronization. This problem is addressed in this paper. A novel method based on neural networks is introduced to compensate of friction and force ripples during cutting force estimations when signals of permanent magnet linear motors (PMLMs) are used. To this end, a Kalman filter observer was designed and experimentally verified for measuring of friction and force ripples. It was then used to provide target series required for training a neural network. Time series of the translator position along some sinusoidal trajectories were selected as training inputs. Taguchi experimental design method was used to determine the structure of the network (number of layers, nodes, and delays). It can be seen that increasing the complexity of the network does not necessarily lead to a more precise network, and a neural network with a hidden layer,16 nodes in the hidden layer and two time-delays can well model such forces. Experiments showed that the results of both methods are very similar and therefore, the proposed method can be used as well as the recording method. Finally, the designed method was successfully applied to the precise estimation of micro milling forces in order to estimate tool deflections.

Discrete package insulated gate bipolar transistor (IGBT) devices are a popular choice for low-power converters. Although IGBT power modules used in high-power applications have recently been studied in the literature, there are major knowledge gaps regarding reliability and lifetime estimation of discrete devices. In this paper, on-state collector–emitter voltage drop $(V_{{\rm{ce}},{\rm{on}}})$ variations are characterized for discrete IGBT devices exposed to cyclic thermal stresses. Variations in $V_{{\rm{ce}},{\rm{on}}}$ are carefully identified and classified depending on different aging mechanisms, stress levels, and device structures. A probabilistic framework for remaining useful lifetime (RUL) estimation based on the knowledge obtained by accelerated aging experiments for real-time RUL estimation has been proposed. Specifically, the proposed model uses Gaussian process regression (GPR) for applying a Bayesian inference (BI) on RUL estimation of the device under test. Using BI reduces the uncertainty associated with RUL estimation and leads to more accurate results. This concept is also tested by comparing the classical maximum-likelihood estimation and GPR estimation results with the ones obtained by accelerated aging tests.

Thermo-reactive diffusion (TRD) is a high-temperature process for producing hard metal carbide coatings on carbon-containing substrates. In this study, niobium carbide (NbC) coatings were produced on low-alloy special-purpose tool steel (AISI L2) via a thermo-reactive powder-pack treatment. The treatment was done in a sealed box with a powder mixture consisted of ferroniobium, ammonium chloride and alumina powders at different times and temperatures. The ratio of the powders was optimized based on the thickness of coatings. The presence of NbC was confirmed by XRD and the growth kinetics of the coatings was studied, as well; leading to an empirical equation to estimate the thickness of coating based on treatment time and temperature. Moreover, pin-on-disc wear tests were conducted on the coatings at various applied loads. The results showed a significantly higher wear resistance and lower coefficient of friction for the coated metal compared to the bare metal.

The purpose of this paper is to provide an overview of the impact of the microstructural modifications via severe plastic deformation on the corrosion behavior of magnesium and its alloys, especially when they are considered to be biodegradable materials. Mechanical processing involved in grain refinement modifies textures and residual stresses of materials which have their own impacts on corrosion behavior as reported in a large number of studies. However, the existing literature on the influence of microstructure on corrosion resistance is often contradictory, which discloses a lack of knowledge in this area. In this article the effects and contributions of critical factors such as the grain size, texture, residual stress and second phase distribution, affected by severe plastic deformation, on the magne-sium corrosion behavior is reviewed in order to find a relation between the microstructure and corrosion resistance.

As cast pure magnesium was friction stir processed (FSPed) by multiple passes. The treated materials were characterized by scanning electron microscope and X-ray diffraction methods. The corrosion behavior of the materials was also studied using electrochemical impedance spectroscopy. Results showed that FSP refined the structure dramatically to mean grain size value of about 1% of that of in as cast Mg. In addition, FSP dominated basal texture in the treated area of the Mg surface. The grain size was reduced and basal texture intensity increased further by repeating FSP. As a result, the corrosion resistance of the treated material improved considerably to more than 10 times of that of in as cast Mg.

It has been a while since abradable coatings have been used in turbine engines to seal the gap between rotating and stationary components to protect these parts and increase the efficiency of engines. The properties of these coatings should satisfy the need of low wear resistance to seal the gap without any damage to the parts involved, and high erosion resistance against compressed air trying to pass through the gap. Many parameters are involved in order to tailor the coatings' properties to the needs; in this work, the effect of atmospheric plasma spraying (APS) process parameters such as hydrogen flow rate as the secondary plasma gas and spraying distance on wear behavior of nickel-graphite (Ni-G) abradable seal coatings at 400 °C have been investigated. The results showed these parameters have a direct impact on the hardness, adhesion strength, coefficient of friction and consequently wear resistance of the coatings.

Reliability of power converters is crucial for mission critical systems. Among the components that are susceptible to failure, power semiconductor devices are one of the major causes of the power converter failures. This paper focuses on the remaining useful lifetime (RUL) estimation of degraded power MOSFETs, which are stressed by thermal cycling. The relative change in on-state resistance is identified as the fault signature. A data-driven RUL estimation algorithm based on a linear approximation model is proposed. The empirical coefficients are estimated by the classical least squares, where the outliers are removed by random sample consensus (RANSAC) algorithm. A sliding window approach is used to track the nonlinearities. The window size, threshold value, and number of samples used by RANSAC are optimized with the genetic algorithm. The accuracy of the proposed RUL estimation tool is verified on a number of thermally aged discrete power MOSFET data.

In this work, carbonation reaction of CO2 with lime was investigated at both pellet scale and pilot reactor scale. A modified grain model was developed in order to evaluate the kinetic parameters of the reaction. Pellet-scale experiments were carried out in a thermogravimetric analysis (TGA) system providing the data for the model parameters calculation and also confirming the model with experimental data. The activation energy was about 29 kJ/mol for calcined calcium carbonate. Aspen Plus software was used to simulate the packed-bed reactor behavior in order to investigate the accuracy of the kinetic data obtained by the mathematical model.

Background: Silver nanoparticles (Ag-NPs) are the most prominent nanoparticles which are recognized for their high antimicrobial efficacy. Objectives: The aim of this study was to evaluate the reciprocal pharmaceutical effects and antibacterial activity of Ag-NPs and methanolic extract of Crocus sativus L. (saffron) on some bacterial strains. Materials and Methods: For evaluation of antibacterial activity of Ag-NPs and methanolic extract of C. sativus L. (saffron) on some bacteria, agar well diffusion method was used. Minimal inhibition concentration (MIC) and minimum bactericidal concentration (MBC) were determined for saffron extract, Ag-NPs, and their combination on methicillin-resistant Staphylococcus aureus (MRSA) and S. pyogenes and S. epidermidis. Results: The combination of medium concentrations of Ag-NPs (500 μg/mL) and saffron extract (50 mg/mL) was in the optimum mode to eliminate S. epidermidis and S. pyogenes. The results showed that saffron extract, Ag-NPs, and their combined form had antibacterial effects on these bacteria. Conclusion: It is suggested to evaluate the synergistic effects of active components of the extract and antimicrobial preservatives used in food, health, pharmaceutical, and cosmetic industries.

In this study, surface alloying of A2618 aluminum was carried out by pre-placing powder mixture of silicon and iron followed by subsequent tungsten inert gas (TIG) surface melting. This process resulted in the formation of 1.4 to 2.1 mm thick alloyed layers containing Al-Fe-Si intermetallic compounds on aluminum substrate. The fabricated layers were then studied using optical and scanning electron microscopes equipped with energy dispersive X-ray spectroscopy (EDS) analyzer, X-ray diffractometer, microhardness tester and pin on disc wear testing machine. The hardness of the alloyed layer reached values as high as 210 HV0.2, as compared with 60 HV0.2 for A2618 aluminum base material, due to the formation of Al13Fe4, Fe5Si3, Al8Fe2Si, and Al5FeSi compounds in the alloyed region. The wear rate of the surface alloyed specimen was almost half of the wear rate of the untreated base material due to the presence of the aforementioned hard intermetallic compounds in the alloyed layer.

Thermal/power cycles are widely acknowledged methods to accelerate the package related failures. Many studies have focused on one particular aging precursor at a time and continuously monitored it using custom-built circuits. Due to the difficulties in taking sensitive measurements, the reported findings are more on the quantities requiring less sensitive measurements. In this paper, two custom-designed testbeds are used to age a number of power MOSFETs and insulated gate bipolar transistors. An automated curve tracer is utilized to capture parametric variations in I – V curves, parasitic capacitances, and gate charges at certain time intervals. The results suggest that the only viable aging precursors are the on-state voltage drop/on-state resistance, body diode voltage drop, parasitic capacitances, and gate threshold voltage for die attach solder and gate-oxide degradation mechanisms. Based on the experimental results, gate threshold voltage variation is empirically modeled to estimate the remaining useful lifetime of the switches experiencing gate oxide degradation. The model parameters are found by the least squares method applied to inliers determined by the random sample and consensus outlier removal algorithm.

Sewers that constitute the main part of wastewater systems can be used as pretreatment units of wastewater treatment plants. The authors aim to increase the treatment efficiency of sewers b...

Failure of a wind turbine is largely attributed to faults that occur in its gearbox. Maintenance of this machinery is very expensive, mainly due to large downtime and repair cost. While much attention has been given to detect faults in these mechanical devices, real-time fault diagnosis for streaming vibration data from turbine gearboxes is still an outstanding challenge. Moreover, monitoring gearboxes in a wind farm with thousands of wind turbines require massive computational power. In this paper, we propose a three-layer monitoring system: Sensor, Fog, and Cloud layers. Each layer provides a special functionality and runs part of the proposed data processing pipeline.

Plasma brazing of gamma based Ti-45Al-2Nb-2Mn-1B titanium aluminide was investigated using Ti-Ni-Cu filler metals. The joints consisted of irregular or equiaxed grain of α2-Ti3Al phases which contained needle-like or fine particles of τ3-Al3NiTi2 particles. With increasing Ni from 15 to 25 wt% in filler metals, the shear fracture strength of the joints increased from ~120 to ~300 MPa. However, the microhardness of the joints area (410–550 HV) was higher than that of Ti-45Al-2Nb-2Mn-1B base alloy (350–400 HV). The joints and base alloy were compatible and no cracking occurred in the joints.

A novel nanofibrous coating containing chitosan and mineralized bone allograft (MBA) nanoparticles was fabricated on AZ31 magnesium alloy via electrospinning in order to enhance corrosion and biocompatibility behavior in a physiological environment. A number of specimens were initially anodized to assess its effect on the improvement of the adhesion between the electrospun nanofibrous coating and the substrate. The coated materials were characterized by using SEM, EDS, FT-IR, and TG. Wettability and the adhesion of the coatings were also studied. In addition, hydrogen evolution and corrosion resistance were evaluated by conducting the immersion test, electrochemical polarization, and EIS tests, respectively. The results showed that both chitosan and chitosan/ MBA composite nanocoating improved the corrosion resistance. Cytotoxicity examination using fibroblast cells indicated that the presence of MBA nanoparticles in chitosan nanofibrous coating has preferable cell adhesion and proliferation. On the whole, application of both chitosan and chitosan/ MBA nanoparticles composite nanofibrous coatings on pre-anodized magnesium appears to be a promising approach in enhancement of the corrosion and biological behavior of magnesium-based biomaterials.

Fault detection and isolation (FDI) is an important part of modern industrial systems, and plays a vital role in maintainability, safety, and reliability of process. We propose a novel FDI architecture based on a predictive model for fault-free process. We use the least-square support vector machine for identifying a nonlinear system and detecting its faults that may occur. Wavelet analysis on residual is used for fault isolation. An average accuracy of 95% has been achieved for all the abrupt faults of the well-known development and application of methods for actuator diagnosis in industrial control systems benchmark.

In this study, liquid phase surface nitriding of Ti-6Al-4V was carried out by pre-placing of chromium powder on the substrate and subsequent Tungsten Inert Gas (TIG) surface melting. The effect of the application of low and high heat inputs on the microstructure, microhardness and wear resistance of the treated layers were studied. Surface alloying with chromium in a nitrogen containing atmosphere resulted in the formation of hard intermetallic compounds such as TiN, Cr 2 N and TiCr 2 . Moreover, the presence of beta stabilizer chromium together with the application of high heat input during surface treatment resulted in the presence of beta phase at room temperature. However, applying low heat input could not prevent transformation of beta to martensite. The hardness of the layers fabricated at high and low heat inputs were respectively 1050 and 1200 HV 0.3 compared to average 280 HV 0.3 for the as-received material. Liquid phase surface treatment of titanium at the aforementioned conditions improved the wear resistance. The lowest weight loss belonged to the specimen with the beta phase matrix. The formation of the fairly ductile bcc-β phase hindered crack nucleation during wear. The weight loss in this condition was 7 times lower than that of the base material.

In this study, the effect of microstructural modification on mechanical properties and biocorrosion resistance of pure Mg was investigated for tailoring a load-bearing orthopedic biodegradable implant material. This was performed utilizing the friction stir processing (FSP) in 1-3 passes to refine the grain size. Microstructure was examined in an optical microscope and scanning electron microscope with an electron backscatter diffraction unit. X-ray diffraction method was used to identify the texture. Mechanical properties were measured by microhardness and tensile testing. Electrochemical impedance spectroscopy was applied to evaluate corrosion behavior. The results indicate that even applying a single pass of FSP refined the grain size significantly. Increasing the number of FSP passes further refined the structure, increased the mechanical strength and intensified the dominating basal texture. The best combination of mechanical properties and corrosion resistance were achieved after three FSP passes. In this case, the yield strength was about six times higher than that of the as-cast Mg and the corrosion resistance was also improved compared to that in the as-cast condition.

In this study, the surface of AZ91 magnesium alloy plasma sprayed with WC–12 wt-%Co powder was melted by pulsed Nd:YAG laser. Microstructure, chemical composition and phase analyses of the treated layers were studied by utilising a scanning electron microscope equipped with energy dispersive spectroscopy, X-ray diffraction technique and a microhardness testing machine. Laser treated layers showed much finer structure compared to that of the original base material with suitable distribution of WC particles. Microhardness of melted zone achieved at optimum condition was ∼3.5 times higher than that of the base material. In addition, the presence of WC particle and refined structure reduced the wear rate to less than half in the laser treated layer.

In the present study, the feasibility of the formation of surface layers containing hard iron aluminides on AA6061-T6 aluminum via pre-plasma spraying with iron and subsequently double surface melting by pulsed Nd:YAG laser is studied. The effects of single and double laser surface melting on microstructure, phase formation, and hardness of the treated layers are examined. Single-step laser treatment resulted in the presence of undissolved iron particles surrounded by lump-like Al5Fe2 and needle-like Al3Fe intermetallic compounds. Double laser surface melting dissolved the retained undissolved irons and resulted in the formation of Al-Al3Fe eutectic structure. Microhardness profiles along cross section and top surface of the treated layers indicated that laser surface alloying with iron enhanced the hardness of the aluminum to more than twice of that of the base material.

In this study, liquid-phase surface alloying of AZ31 magnesium alloy was conducted by tungsten inert gas (TIG) melting of preplaced ferrosilicon powder. The effects of TIG processing parameters on the formation of a defect-free surface-alloyed layer were investigated. Microstructures of alloyed layers were studied by optical and energy dispersive spectroscopy-enabled scanning electron microscopy. The phases in the resolidified layer were identified by X-ray diffraction analysis coupled with microhardness. The surface hardness increased from 65 HV0.3 for AZ31 magnesium alloy to as high as 330 HV0.3 for alloyed sample due to the formation of Mg2Si and FeSi intermetallic compounds in the alloyed region and structural refinement.

In this paper, liquid-phase surface melting of Ti-6Al-V in atmospheres of argon and a mixture of argon and nitrogen was carried out using tungsten inert gas process. The microstructures of treated layers were investigated by optical microscope, scanning electron microscope, and phases formed were studied by X-ray diffraction pattern. The hardness of these layers was also evaluated using microhardness machine. The results showed that the surface hardness was enhanced from 280 HV for the untreated substrate to 600 HV for the surface-treated layer achieved by surface melting in the argon atmosphere which was due to the formation of martensite microstructures. The surface layer fabricated by surface melting in the mixed atmosphere of argon and nitrogen also resulted in formation of TiN in a martensitic matrix with a hardness of about 1000 HV.

A new cause-based approach was used to estimate the change in methane emissions from the natural gas system resulting from a change in throughput. The analysis builds upon prior work (Mac Kinnon et al., 2018) positing that a cause-based, marginal approach to estimating methane emission impacts of reducing or increasing natural gas use was more accurate than assuming that methane emission vary one-for-one with throughput. The goal of this work is to determine the relationship between methane emissions and changes in throughput both over short time horizons where the gas infrastructure is fixed and over time periods where system expansion (or retirement) and technological improvements via component replacement occur. The results show that methane emissions change with throughput but the relative change in emissions is less than the relative change in throughput. There are many components (emissions sources) in the natural gas system that emit the same amount of methane to the atmosphere regardless of their operational mode; meaning some emissions sources have no or only partial dependence on throughput. As a result, reducing natural gas consumption in the future will not yield a directly proportional reduction in the methane emissions. It is believed that the results of this study will help energy policymakers to understand better the effect of policies aimed at reducing natural gas use on greenhouse gas (GHG) emissions and where such policies should be applied (e.g. system operator or end user).

AlxCoCrFeNi (x = 0.5, 1, and 1.5) high entropy alloys (HEAs) were synthesized on the AISI 304L stainless steel via liquid phase cladding using a tungsten inert gas (TIG) heat source. To do so, elemental powder mixtures were initially ball-milled and preplaced on the steel substrate followed by TIG surface melting. The process parameters were chosen in such a way that the least possible dilution of the substrate into the clad zone was achieved. It was observed that the dilution rate was dependent on the Al content of the powder mixture. Microstructural assessments revealed that alloying occurred in the initial powder mixture during surface melting and a relatively uniform microstructure was achieved within the clad zone. X-ray diffraction analysis showed that the deposited layer had a dual phase FCC and BCC crystal structure. It was also noticed that increasing of Al content in the HEA raised the volume fraction of BCC/ordered BCC (B2) phase. It also led to a change in the morphology of columnar dendrites to equiaxed ones. Segregations of Al-Ni rich phase and Cr-rich phase at the interdendritic regions were evident in the HEA with low (x = 0.5) and high (x = 1.5) Al content, respectively. The hardness of the clad zone was appreciably dependent on the Al content of the HEA, so that various contents of 0.5, 1, and 1.5 resulted in the microhardness values of 170, 510, and 530 HV0.1, respectively.

In this research, microstructure and hot corrosion behaviour of a modified aluminide coating were studied. The coating was formed via a two-step process, Ni/Ni-Co electrodeposition on Ni-base superalloy, Hastelloy-X, followed by slurry aluminising. The obtained coatings with 140 ± 5 μm thickness were characterised by an X-ray diffractometer (XRD) and scanning electron microscope (SEM) equipped with energy-dispersive X-ray spectroscope (EDS) and elemental mapping before and after the corrosion tests. The results showed the β-NiAl for simple and pure Ni pre-plated, and the β-(Ni,Co)Al for Ni Co pre-plated aluminised samples were formed definitely as a predominant phase. The initial electrodeposited layer performed as a Ni reservoir to reinforce aluminide formation. Besides, it reduced outward diffusion of alloying elements such as Cr, Fe, and Mo from substrate alloy as a diffusion retardant. Accordingly, the chemical composition of substrate alloy in pre-plated samples remained almost constant rather than its simple aluminised counterpart during the diffusion coating process. After the hot corrosion test carried out in the salt mixture of Na2SO4/NaCl environment, the results indicated that the presence of Co was beneficial and improved corrosion resistance of the obtained coating even in the Cl-containing salt mixture.

Overexpression of adenosine in the tumor region leads to suppression of various immune cells, particularly T cells through ligation with adenosine 2a receptor (A2aR). In this study, we intended to increase the efficacy of tumor lysate-loaded DC vaccine by silencing the expression of A2aR on T cells through the application of A2aR-specific siRNA-loaded PEG-chitosan-lactate (PCL) nanoparticles (NPs) in the 4T1 breast tumor-bearing mice. Combination therapy by DC vaccine and siRNA-loaded NPs markedly induced tumor regression and increased survival time of mice. These ameliorative effects were partly via downregulation of immunosuppressive cells, increased function of cytotoxic T lymphocytes, and induction of immune-stimulatory cytokines. Moreover, combination therapy could markedly suppress angiogenesis and metastasis processes. These results imply the efficacy of novel combination therapy for the treatment of breast cancer by using A2aR siRNA-loaded NPs and DC vaccine which can be translated into the initial phase of clinical trials in the near future.

In this work, we developed an ultrasensitive and ultraselective electrochemical biosensor for detection of a specific sequence DNA. It was fabricated based on a glassy carbon electrode (GCE) modified with Multi-walled carbon nanotubes (MWCNTs)-Chitosan (CHIT) nanocomposite and a thin film of gold nanoparticles (AuNPs). Immobilization is based on the covalent reaction between thiol group in DNA probe and AuNPs on electrode surface. Cyclic Voltametry (CV) and Differential Pulse Voltammetry (DPV) techniques were used for hybridization detection by methylene blue (MB) as indicator. The surface modification increased significantly DNA immobilization quantity and complementary DNA detection sensitivity. The response signal increased linearly with the increase of the target DNA concentration in the range of (1 × 10-14 M to 9 × 10-14 M) with the detection limit of 7.2 fM (LOD = 3 SX m ). The presented method exhibited excellent specificity and selectivity for complementary and single-mismatch, three-base-mismatch and non-complementary sequences of DNA.

Cochlear Implant (CI) attempts to restore hearing to profoundly deaf patients by stimulating the auditory nerve using electrical signals via an electrode array implanted in the cochlear. With different cochlear size and length of the electrode array, acoustic input frequency allocated for electric stimulation often does not match the characteristic frequency of auditory nerves. Individual tuning process on frequency allocation is time consuming and typically less regarded in busy clinical practice. An objective tuning with electroencephalogram (EEG) could be a preferred alternative method. Statistically, the classical method of averaging over many trials in extracting auditory evoked response from EEG signals is highly subjected to noises. In this paper, we propose to decompose the EEG signal into their scattering coefficients using wavelet scattering transform (WST). The result shows that the scattering coefficients are able to retain the underlying physiological information even after subsequent averaging. We also demonstrated that WST is a crucial pre-process when establishing a cortical correlate to a perceptual task using EEG.

Cochlear implant (CI) restores the hearing sensation in profoundly deafen patients by directly stimulating auditory nerve with electric pulses using an array of tonotopically inserted electrodes. Basal electrodes stimulate in response to high input frequencies while apical electrodes stimulate to low input frequencies. The problem with this electrical stimulation, particularly in unilaterally implanted users who has residual hearing in the contra-lateral ear, lies in the frequency mismatch between characteristic frequency of auditory nerve and input signal. In this paper, we revisit our previously proposed mechanism for tuning intra-cochlear electrode to its pitch matched frequency using a single channel EEG [1]. We apply the wavelet scattering transform to extract a deformation invariant from the EEG signal recorded from each of 10 CI subjects when they were listening to pitch matched electro-acoustic stimulation. Results show that the wavelet scattering transform is able to capture the variability introduced by different subjects, and a more robust alternative to reveal the underlying neuro-physiological responses to this perceptual event.

Around 1% of the people in the world suffer from epilepsy, which is the second most neurological disorder in the human after stroke. The spontaneous recurrence of seizures is the main clinical manifestation of the epilepsy. Real time detecting the seizure in the Electroencephalogram (EEG) signal is a clinical way in the diagnosis and treatment of epilepsy. The unpredicted nature of the epileptic seizures, necessitates continuous monitoring and recording of the brain activities using high-throughput neurophysiological data acquisition systems over extended periods of time. The sheer volume and the velocity of the data generated from continuous monitoring the brain activities make real-time seizure detection a big data analytic problem. In this paper, we present a Spark-based machine-learning approach to the seizure detection problem using linear dimensionality reduction and classification. Using this approach, we achieved an average accuracy, sensitivity, specificity across all patients $(\mathrm{N=24)$ of 99.32%, 99.41%, and 95.25%, respectively. Also, the average Iatency of the Spark-based seizure detection framework is about 0.38 ms.

Bearing faults are one of the main reasons of rotary machines failure. Monitoring vibration signal is an effective method for diagnosing faulty bearings and preventing thus catastrophic failures. However, existing algorithms neither offer satisfactory accuracy nor are efficient for real-time implementation due to complexity in feature extraction part. In this paper, we propose an accurate method for bearing diagnosis customized for real-time implementation. The proposed system estimates power spectral density of vibration signal using an autoregressive model for feature extraction. This is a novel use of autoregressive model for fault diagnosis which reduces the dimensionality of vibration signal and captures its frequency contents simultaneously. The proposed system can diagnose different bearing faults under variable load conditions with above 99 % accuracy.

Monitoring the vibration signal is an effective way for automatic diagnosis of mechanical faults in rotary electric machines. This is done in order to to determine the state of health of electrical machines, especially those used in Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) applications, whose state of health needs to be monitored continuously. Feature extraction is the key step in vibration diagnosis. Usually, the time domain vibration signal is transformed into another domain like Fourier or wavelet for feature extraction. In this paper a dictionary learning method is proposed for finding an optimal transformation for feature extraction. A label consistent sparse representation is applied to vibration data collected from an electric machine to diagnose three classes of bearing faults with a higher accuracy, compared to other extraction methods found in the literature. The trained transform projects the vibration signalfrom different classes offaults to different basis function which improves the classification task.

© 2018, The Author(s). High-pressure torsion (HPT) processing was applied to cast pure magnesium, and the effects of the deformation on the microstructure, hardness, tensile properties and corrosion resistance were evaluated. The microstructures of the processed samples were examined by electron backscatter diffraction, and the mechanical properties were determined by Vickers hardness and tensile testing. The corrosion resistance was studied using electrochemical impedance spectroscopy in a 3.5% NaCl solution. The results show that HPT processing effectively refines the grain size of Mg from millimeters in the cast structure to a few micrometers after processing and also creates a basal texture on the surface. It was found that one or five turns of HPT produced no significant difference in the grain size of the processed Mg and the hardness was a maximum after one turn due to recovery in some grains. Measurements showed that the yield strength of the cast Mg increased by about seven times whereas the corrosion resistance was not significantly affected by the HPT processing.

In this work, in situ formation of Al–Cr intermetallic compound on the surface of AA6061-T6 aluminum pre-placed chromium powder via pulsed Nd:YAG laser surface alloying was investigated. The effect of laser re-melting on microstructure and hardness of the treated layers was examined. Laser re-melting resulted in a significant reduction in the presence of cracks, pores, and undissolved Cr-rich particles along with a more uniform distribution of intermetallic compounds. The results showed that the alloyed layers mainly consisted of fine Al7Cr and Al4Cr intermetallic compounds in an α-Al solid solution matrix with cellular and dendritic morphologies. Hardness profiles along cross section and top surface of the alloyed layers indicated that laser re-melting enhanced homogeneity and increased the hardness value to more than six times of that of the base material.

In this paper, a model based signal processing method is proposed to diagnose two faults under different load condition for permanent magnet synchronous machines. This method captures the spectrum of stator current in a compact representation. Then, its dimensionality is reduced using the discriminant analysis. Finally a support vector machine automatically classifies the health state of the system. For testing the method, a customized test rig is built and data from different loads and faults are collected. An average accuracy of 93% in a 10-fold cross validation is obtained.

Millions of people around the world suffer from epilepsy. It is very important to provide a method to efficiently monitor the seizures and alert the caregivers to help patients. It is proven that EEG signals are the best markers for diagnosis of the epileptic seizures. In this paper, we used the frequency domain features (normalized in-band power spectral density) to extract information from EEG signals. We applied a deep learning technique based on multilayer perceptrons to improve the accuracy of seizure detection. The results indicate that our nonlinear technique is able to efficiently and automatically detect seizure and non-seizure episodes with an F-measure accuracy of around 95%.