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Background: Ketamine is known to be an effective factor in reducing pain without significant side effects.Objective: One of the limited side effects of Ketamine is agitation. Due to the reduction of this symptom with Haloperidol, we decided to design a randomized clinical trial to compare the analgesic effect of Ketamine with Haloperidol and Fentanyl in reducing acute pain and its complications.Methods: In this study, 200 adult patients who presented to the emergency department with acute pain are examined. They are randomly divided into two groups. One group received intravenous Ketamine with Haloperidol and the other group received intravenous Fentanyl. Patients are then compared for their pain score before and after administration of the drugs, as well as the side effects they experienced.Results: There was no significant difference between the mean scores of initial pain in the two groups, but at all intervals of 5, 10, 15 and 30 min after injection, the mean of pain scores of patients in the group receiving Ketamine and Haloperidol were lower. The need for injection of rescue analgesic was 9% in the Ketamine and Haloperidol group and 34% in the Fentanyl group. The mean agitation score did not differ between the two groups except in the tenth minute. At tenth minute, the mean agitation score of the Ketamine group was higher.Conclusion: Ketamine works better than fentanyl in controlling acute pain, and limited side effect of agitation can be controlled if injected with haloperidol. Due to its better function and fewer side effects, it seems that in controlling acute pain, Ketamine along with Haloperidol can be a good alternative to opioids. [ABSTRACT FROM AUTHOR]

• Time-reassigned adaptive Fourier synchrosqueezing transform is introduced. • Optimal window length is chosen by Bayesian optimization of a novel cost function. • Multicomponent synthetic and real-life signals are used for the validation. • The proposed transform is apt for the fast- and slowly-varying components together. • The proposed transform outperforms adaptive Fourier synchrosqueezing transform. Most of the multicomponent real-life signals, such as electroencephalogram (EEG), are non-stationary having fast- and slowly-varying components. Their dynamics can precisely be measured by time–frequency transforms (TFTs), especially by adaptive TFTs (ATFTs). Adaptive Fourier synchrosqueezing transform (AFSST) is a prime ATFT. It generates sharp and energy-concentrated time–frequency representations (TFRs) for multicomponent signals, especially for those having slowly-varying components. Moreover, time-reassigned Fourier synchrosqueezing transforms work well mostly for signals having fast-varying components. To enhance the TFR for fast- and slowly-varying components together, this work introduces an adaptively time-reassigned AFSST with optimal window length (TAFSSTOL). The optimal window length is selected through Bayesian optimization of an innovative cost function. Previously, the cost functions of ATFTs were either the Rényi entropy (RE) or the error of instantaneous frequency (IF) estimation. However, our cost function includes a fraction containing the quality factor of the entire signal reconstruction and the RE. This process for optimal window length selection generates novel time- and frequency-reassignment operators that are used together in the transform. TAFSSTOL is validated using a three-component synthetic signal, a real newborn EEG signal, a real R-to-R peak interval (RRI) in electrocardiogram (ECG) signal, a real bat echolocation signal, and a real possible freak wave event in the Draupner wave. Our results reveal that in both clean and noisy situations, TAFSSTOL outperforms AFSST in terms of TFR energy concentration and quality of the entire signal reconstruction. Hence, TAFSSTOL can pave the way for time–frequency decomposition of real-life signals comprising fast- and slowly-changing components together. [ABSTRACT FROM AUTHOR]

Hypospadias is a congenital disorder of urethra in which meatus is not at its correct place and occurs in 1 of 250 live male birth. Many techniques have been used for the repair of hypospadias and to decrease the incidence of fistula. Alloderm has been recently used for the repair of urologic congenital defects, and reconstructive surgeries, but not used in hypospadias in a randomized controlled trial. Therefore, the aim of this study was to assess the efficacy of Alloderm® (Regen) to reduce fistula rate in hypospadias repair. This was a randomized controlled trial. Sixty patients were divided into two groups, 30 children underwent surgery using Alloderm® (Regen) as the Alloderm group and 30 without using it as controls. In the Alloderm group, 21 underwent primary surgery (12 mid-shaft hypospadias who underwent concurrent chordee correction and urethroplasty, and 9 penoscrotal who underwent 2 stage surgery, chordee was first corrected and then 6 months later TIP was performed), and 9 underwent fistula repair due to previous surgeries. In the control group, 24 patients underwent primary surgery (15 mid-shaft, and 9 penoscrotal) and 6 fistula repair the same as the Alloderm group but without using Alloderm. There was no meaningful difference between the two groups regarding age (P = 0.634). There was no meaningful difference regarding the operation type between the two groups (P = 0.371). There was no meaningful difference regarding the meatal location between the two groups (P = 0.781). There were no significant post-operative complications in the both groups. No bleeding or diverticulum occurred. Recurrence of fistula occurred in one patient in the Alloderm group after fistula repair and in 2 in the control group. Overall, In the Alloderm group, 3 (2 after TIP and 1 after fistula repair) patients and in the control group 8 (6 after TIP, 2 after fistula repair) patients developed fistulas after surgery (10% vs 26.7%). There was a statistically meaningful difference regarding fistula formation between the two groups using Chi-square test (P = 0.014). Alloderm® (Regen) can be used for hypospadias and fistula repair with very few complications and good results. Fistula was less probably occurred in the Alloderm group compared to the standard repair of hypospadias. [ABSTRACT FROM AUTHOR]

Despite a lot of research on the flow past cylinders in an infinite medium, there is a great deal that is unknown about flow structure affected by the free surface at high Froude numbers (F r). Additionally, at high F r , the study of vortex-induced vibrations (VIV) is very important, which has been underrepresented in previous studies. In the present study, the free-surface flow and VIV are investigated at high F r. Our study has the privilege of performing a comprehensive parametric study on the free-surface parameters. Based on the results, F r has a prominent effect on flow parameters, as a 59 % reduction in the drag coefficient, and an increase of 100 % and 30 % in the lift coefficient and in the Strouhal number are recorded compared to low F r. Additionally, with a 4-fold increase of the free-surface distance to the cylinder, the lift coefficient drops by 27 %, and the Strouhal number increases by 42 %. Also, by approaching the free surface, the vortex shedding is suppressed. Eventually, study of the VIV indicates the substantial impact of the free surface on lock-in phenomenon, i.e., the resonance occurs earlier. Findings of the present study can significantly improve marine design considerations to prevent heavy and irreparable damages. [Display omitted] • The novelty of including free surface effect on flow structure at high Froude numbers. • An inclusive parametric study on free-surface parameters. • The novelty of evaluating vortex-induced vibrations for severe free surface deformation. • A 100 % increases in the lift coefficient and a 30 % enhancement in the Strouhal number compared to low Froude numbers. • Substantial impact of the free surface on lock-in phenomenon and reducing the oscillations. [ABSTRACT FROM AUTHOR]

The practical application of Artificial Intelligence (AI) approaches in estimating the mechanical properties of fiber-reinforced concrete (FRC) subjected to high temperatures was initiated by developing a dataset including concrete mixtures, geometrical and mechanical properties of fiber, and temperatures. The dataset contains compressive strength, tensile strength, and modulus of elasticity of concrete with two distinct types of fiber, i.e., steel and polypropylene with an extensive range of temperatures from 100 to 1200 ℃. The dataset determined the gaps in the literature and showed FRC with high fiber aspect ratios and higher content of fiber (>0.8%) are not studied enough. The AI-based models showed that steel fiber-reinforced concrete (SFRC) has higher residual compressive strength compared with polypropylene fiber-reinforced concrete (PFRC). An increase in steel fiber diameter and length resulted in a higher compressive strength ratio at all temperatures. Moreover, higher PP fibers content and longer PP fibers decrease the rate of tensile strength degradation. A large probabilistic analysis was performed, and it showed that the failure probability (P f) for PFRC is independent of W/B ratios and is almost 50%;. In contrast, for SFRC, W/B ratios between 0.4 to 0.5 have lower failure probability in comparison with other W/B ratios. Moreover, P f for both f ibers at a W/B ratio of 0.5 is almost independent of fiber content. Furthermore, the critical temperature resulting in failure (defined as the residual strength ratio less than 0.5) for SFRC and PFRC is 550 ℃ and 430 ℃, respectively. • The practical Implementation of Artificial Intelligence (AI) in optimizing fiber-reinforced concrete (FRC) behaviour at high temperatures. • An increase in steel fiber diameter and length resulted in a higher compressive strength ratio at all temperatures. • The critical temperature resulting in failure (defined as the remined strength ratio less than 0.5) for SFRC and PFRC is 550 ℃ and 430 ℃, respectively. [ABSTRACT FROM AUTHOR]

• This study proposed a new hybrid architecture based on deep learning for the purpose of reconstructing visual images. • To the best of our knowledge, this research is the first to combine a nonlinear type-2 fuzzy system with a GAN structure. • This research demonstrated that the proposed architecture can reconstruct the visual stimulus content based on the brain response, which is an image-EEG-image transformation. The decoding of the brain stimulated by visual images has been discussed in this study in order to classify and reconstruct the stimulating images. The dataset utilized was given by Stanford University and included the EEG signal recorded from ten healthy people. In the first stage, the proposed approach combines the ability of the Long-Short-Term Memory (LSTM) network to extract sequence (EEG signals) features with the ability of fuzzy network to extract features in high-uncertainty contexts. The features extracted in the previous stage, which is a noise-free and compressed form of the EEG data, were employed as a conditional vector in the CGAN network in the following step. As a result, this network was used to reconstruct visual stimuli that evoke specific brain responses. When compared to earlier research, the evaluation results produced utilizing the suggested approach demonstrate a greater classification accuracy, precision, recall, and F1 score. Also, the similarity of produced and real images was measured quantitatively using two parameters, FID and SSIM; the SSIM results for 10 participants were on average 0.37, 0.35, 0.35, 0.34, 0.42, and 0.42 for groups (1) to (6), and the FID results were 0.44, 0.45, 0.51, 0.50, 0.39 and 0.39, respectively. Therefore, it can be stated that the method suggested in this study can reconstruct visual stimuli using brain response (EEG signal) with acceptable accuracy, so taking a significant step towards the objective of mind reading. [ABSTRACT FROM AUTHOR]

The aim of this study was to perform a genome-wide association study (GWAS) based on Bayes A and Bayes B statistical methods to identify genomic loci and candidate genes associated with body weight gain, feed intake, and feed conversion ratio in Japanese quail. For this purpose, genomic data obtained from Illumina iSelect 4K quail SNP chip were utilized. After implementing various quality control steps, genotype data from a total of 875 birds for 2,015 SNP markers were used for subsequent analyses. The Bayesian analyses were performed using hibayes package in R (version 4.3.1) and Gibbs sampling algorithm. The results of the analyses showed that Bayes A accounted for 11.43, 11.65, and 11.39% of the phenotypic variance for body weight gain, feed intake, and feed conversion ratio, respectively, while the variance explained by Bayes B was 7.02, 8.61, and 6.48%, respectively. Therefore, in the current study, results obtained from Bayes A were used for further analyses. In order to perform the gene enrichment analysis and to identify the functional pathways and classes of genes that are over-represented in a large set of genes associated with each trait, all markers that accounted for more than 0.1% of the phenotypic variance for each trait were used. The results of this analysis revealed a total of 23, 38, and 14 SNP markers associated with body weight gain, feed intake, and feed conversion ratio in Japanese quail, respectively. The results of the gene enrichment analysis led to the identification of biological pathways (and candidate genes) related to lipid phosphorylation ( TTC7A gene) and cell junction ( FGFR4 and FLRT2 genes) associated with body weight gain, calcium signaling pathway ( ADCY2 and CAMK1D genes) associated with feed intake, and glycerolipid metabolic process ( LIPC gene), lipid metabolic process ( ADGRF5 and ESR1 genes), and glutathione transferase ( GSTK1 gene) associated with feed conversion ratio. Overall, the findings of this study can provide valuable insights into the genetic architecture of growth and feed consumption traits in Japanese quail. [ABSTRACT FROM AUTHOR]

• Implementation of Artificial Neural Network (ANN) to study the critical factors of bridge fire incidents. • Defining I-girder steel bridges as the most susceptible structural system. • Defining hydrocarbon tankers and trucks with solid combustible cargo as the most hazardous sources. • Potential hazard prevention and protection measures to improve structural and human life safety, bridge durability, and reduce maintenance costs. Fire poses a major risk to the structural integrity of a bridge or progressively to the bridge's failure. This study uses an Artificial Neural Network (ANN) to investigate the determining factors in bridge fire incidents. Implementation of this powerful model produces an estimation of the damage levels. The basis of the proposed model is determined by critical factors, including bridge location, materials, structural systems, annual average daily traffic (AADT), ignition source, combustible type, and bridge face exposed to fire. Results show that steel I-girder bridges are the most susceptible structural system in a fire. Moreover, a fire involving tankers containing hydrocarbon fuel and trucks with solid combustible cargo is the most dangerous to a bridge. The accuracy of the proposed model is verified, and its outcome can be utilized to determine fire risk based on discrete characteristics. Based on the proposed model, a combination of hazard prevention and protection measures may be utilized to improve structural integrity, human life safety, and durability, and to reduce maintenance costs. [ABSTRACT FROM AUTHOR]

Nowadays, end-of-life (EoL) reverse osmosis (RO) membranes are considered as waste, hence they are incinerated or discarded in landfills. Due to the environmental problem of disposed membranes, their reuse as nanofiltration (NF) membranes was investigated in this study. At first, the fouling of EoL membrane was cleaned and then the effect of sodium hypochlorite (NaOCl) on removing the polyamide (PA) layer and pure water permeability (PWP) of EoL membranes was investigated. With regard to NaOCl exposure intensities, three groups of EoL membranes with PWP of about 10, 20, and 30 L/(m2h bar) were selected as substrates to convert them to NF membranes by deposition of polyelectrolyte (PE) multilayers. The effects of PE type, substrate permeability, and the charge of outer layer on the performance of the resultant NF membranes were studied. As one of the best results, NF membrane composed of eight bilayers of SC498/KE253 polyelectrolytes on substrates with PWP of 18 L/(m2h bar) had salt water permeability (SWP) of 11 L/(m2h bar) and 94% rejection of MgSO 4. Other results are also competitive with commercial NF membranes. The maximum NaCl and MgSO 4 rejection of prepared membranes were 92% and 98%, respectively, at a feed pressure of 10 bar. In addition, the stability test showed that the prepared membranes are stable over a long period of filtration. • EoL RO membrane was successfully converted to NF membranes. • It was demonstrated that bulk polyelectrolytes can be used in LbL deposition. • Performance of prepared membranes were even better than commercial NF membranes. • This would allow economical exploitation of EoL membranes' still valuable parts. • EoL membrane properties can be tailored towards NF or RO. [ABSTRACT FROM AUTHOR]

In the present study, the effect of SiC nanoparticles on the microstructure and texture evolution of friction stir welded AA2024/AA6061 dissimilar joint investigated. The results showed that the SiC nanoparticles homogeneously dispersed in the aluminum matrix. The Al/SiC interfaces had a good quality of bonding between particles and matrix. It was found that the new recrystallized aluminum grains tend to nucleate and grow on the 1 1 ¯ 1 crystal plane of SiC nanoparticle ( 1 ¯ 1 ¯ 1 Al facet on 1 1 ¯ 1 SiC surface). The average grain size in the stirred zone (SZ) decreased to 7.4 μm owing to the grain refinement through the occurrence of both dynamic and static recrystallization. Most grains in the center of the stirred zone grown due to the occurrence of continuous grain growth (CGG) mechanism after the stirring. Moreover, the incorporation of SiC nanoparticles led to a reduction of 26% in the mean grain size of the SZ. The average size of the precipitate particles in the stirred zone was much smaller than the thermo-mechanically affected zone (TMAZ) owing to the dissolution of precipitates during stirring and reprecipitation during cooling. In addition, the grain size of the stirred zone on the advancing side was lower than the retreating side (2.6 μm vs. 9.7 μm). The results revealed a weaker texture after FSW compared to the initial samples. The SiC nanoparticles largely suppressed the rotation of the {001}〈100〉 oriented new grains through particle pinning effect. Unlike the advancing side, there was no recrystallization texture component on the retreating side. • The crystallographic relationship between aluminum and silicon carbide was 1 ¯ 1 ¯ 1 Al 1 1 ¯ 1 SiC. • The addition of SiC nanoparticles has reduced the grain size of the SZ to 7.4 μm. • The average size of particle in the stirred zone was much smaller than TMAZ. • The nanoparticles prevented the rotation of the {001}〈100〉 oriented new grains. [ABSTRACT FROM AUTHOR]

Highlights • Employing a novel approach for attention and consciousness investigation. • Using source signals, obtained from ICA, to avoid volume conduction effect. • Clustering source signals to calculate connectivity. • Connectivity investigation in 2 frameworks for more accuracy: general and detailed. • Detecting significant difference between AC-AuC and iAC-iAuC. Abstract Evidence from recent studies on attention and consciousness has raised many questions about their inherent natures and the existence of a possible relationship between them. The main goal of this study is to investigate the significant difference among different states of attention and consciousness, through the connectivity method. These two cognitive phenomena have been studied in recent years with different approaches, but none of them has employed connectivity methods to study the attention and consciousness relationship. Therefore, the connectivity approach is a novel effort in this field. The 8-channel Electroencephalogram signal (EEGs) was used from 45 participants performing a psychophysical visual task. To track the variation of connectivity during time, the EEG recorded data were organized in a particular way, providing 9 1 s signals, including four stimuli classes: attention-consciousness class, attention-unconsciousness class, inattention-consciousness class, and inattention-unconsciousness class. In order to reduce the volume conduction effect, source signals were extracted with the ICA method. The effective connectivity network was computed using the Granger causality method. For the statistical analysis, connectivity indices were presented. This approach suggests that attention-consciousness and attention-unconsciousness classes, and attention-unconsciousness and inattention-unconsciousness classes are the most and least distinctive combinations, respectively. In a more general view provided by averaging the connectivity matrices, all the presented connectivity indices showed significant differences with p-value<0.05 for two combinations: 1-attention-consciousness and attention-unconsciousness classes, and 2- inattention-consciousness and inattention-unconsciousness classes. [ABSTRACT FROM AUTHOR]

In this paper, we have theoretically investigated the behaviour of an Airy beam (AiB) with an optical vortex (OV) in atmospheric turbulence. The obtained splitting on each line of the phase pattern indicates the OV. The results show that the OV position changes when the power of the fractional Fourier transforms (FrFT) ( p ) changes. Moreover, uniformity of the spot beam disappears with the presence of the vortex in the AiB. The characteristics of an AiB, such as the effective beam size, number, width, height, and uniformity of the spot beam, change by changing the value of p . The position of the OV in the beam with scintillation and without scintillation was changed by changing the value of p . [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Quantitative cryoTEM analysis reveals several different nanocapsule structures. • Crosslinking affects, but does not cause, protrusion formation. • The extent of crosslinking is affected by the type of crosslinker used. • Protrusions are an integral part of the nanocapsule shell. Abstract The effect of crosslinking on the morphology of nanocapsules synthesized by vesicle-templated reversible addition-fragmentation chain transfer (RAFT) polymerization was investigated and it was found that although crosslinking increases the fraction of protruded capsules, it is not the origin of the protrusions. Starved-feed emulsion polymerization of methyl methacrylate and tert -butyl acrylate with varying amounts of a range of crosslinkers using a seed of cationic vesicles covered by anionic co-oligomers containing a RAFT functional group, results in several different structures, with true spherical capsules and protruded capsules being the main products under the used reaction conditions. Quantitative cryoTEM studies allowed the counting of the different morphologies and it was found that the final fraction of protruded structures strongly depended on the used crosslinker. In the case of using 10 mol% of ethylene glycol dimethacrylate, ∼90% of the observed structures were protruded capsules, whereas this was only ∼4% in the case of 10 mol% ethylene glycol diacrylate. Finally, tomography showed that the protrusions are an integral part of the polymer shell. [ABSTRACT FROM AUTHOR]

A novel systematic practical approach to simulate the performance of heat pumps precisely is presented. In the proposed method, by conducting a number of experiments as well using soft computing techniques, the operation of all of the components is modeled comprehensively. The application of the method is examined for an experimental setup. The effective adjustable parameters are selected in a way that enabled them to be changed freely in the experimental setup. The Mean absolute error of prediction of coefficient of performance for validation measured experimental data is only 2.95%. In addition, to provide a comprehensive insight, an extensive sensitivity analysis considering nine performance parameters, such as mass flow rate of refrigerant and pressure drop of condenser, is conducted. It is found that the minimum possible is the best value of inlet temperature of water of condenser. Moreover, for inlet temperature of water of evaporator, frequency of compressor and degree of superheating which are the other adjustable effective parameters trade-offs among performance parameters are observed. For example, increasing the degree of superheating led to improve required work and isentropic efficiency of compressor as well as coefficient of performance and pressure drop of condenser, while it decreased the delivered heat. [ABSTRACT FROM AUTHOR]

A new application for used reverse osmosis (RO) membranes as gas separation membranes was studied. In this regard, firstly, three pretreatment procedures were used to remove the foulants from the surface of used membrane and then they were coated with polydimethylsiloxane (PDMS). The results indicated that PDMS-coated used RO membranes were capable of separating O 2 /N 2 and CO 2 /N 2 . The maximum O 2 /N 2 and CO 2 /N 2 selectivities of coated membranes were 5.9 and 32.5, respectively. The O 2 /N 2 and CO 2 /N 2 selectivities of PDMS membrane were reported in the range of 2.1–2.2 and 11–12, respectively. Finally, an economic assessment was carried out to compare prepared PDMS coated RO membranes with commercial PPO membrane. This showed that coated membranes are less expensive than PPO membrane for CO 2 /N 2 gas separation. The outcome of the research was a simple method for converting used RO membranes to cost effective gas separation membranes. [ABSTRACT FROM AUTHOR]

Locating the source of voltage sags in order to identify the events causing the problem and improving mitigation strategies is a major issue. When two utilities manage interconnected networks or manage a micro grid and an upstream grid, an occurred fault in one area may cause voltage sags that affect sensitive customers/critical loads in another area. From this viewpoint, this paper first reviews and analyses with detail different methods for voltage sag source location. Then eleven generalized method are introduced. A comparative analysis is performed between some of the discussed methods by applying extensive numerical simulations in a Brazilian regional utility by using PSCAD/EMTDC and MATLAB. The location of each monitor represents a difference kind of system topology (radial, interconnected, one source, two sources and interconnected microgrids). In the following, the effectiveness of methods was obtained and comprised for each monitor location and for the whole system.. The results obtained show that the effectiveness of methods based on instantaneous positive sequence components and Clarke's components is equal in many methods except distance relay and resistance sign methods, and that the effectiveness is lower, higher or equal than methods based on positive sequence phasors. The phasor based methods had less effectiveness. The generalized methods based on reactive power using instantaneous positive sequence components and Clarke's components gave the right location in 88% of total simulated cases. The results also provided a comprehensive background to propose future works and to select the best method especially at interconnection point of a micro grid with the presence of renewable distributed energy sources. [ABSTRACT FROM AUTHOR]

Due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) load demand, large scale penetration of these resources in the power systems can stress the reliable network performance, such as reducing power quality, increasing power losses, and voltage deviations. These challenges must be minimized by optimal planning based on the variable output from RES to meet the additional demand caused by EV charging. In this paper, a novel method for optimal allocation and sizing of RES and EV charging stations simultaneously and managing vehicle charging process is provided. A multi-objective optimization problem is formulated to obtain objective variables in order to reduce power losses, voltage fluctuations, charging and demand supplying costs, and EV battery cost. In this optimization problem, the location and capacity of RES and EV charging stations are the optimization variables. Coefficients which are dependent on wind speed, solar radiation, and hourly peak demand ratio for the management of the EV charging pattern in off-peak load hours are introduced. Genetic Algorithm-Particle Swarm Optimization (GA-PSO) hybrid improved optimization algorithm is used to solve the optimization problem in five different scenarios. The performance of the proposed method on IEEE 33-bus system has been investigated to validate the effectiveness of the novel GA-PSO method to optimal sitting and sizing of RES and EV charging stations simultaneously. [ABSTRACT FROM AUTHOR]

In an islanded Micro-Grid (MG), the use of conventional power flow analysis is not effective as the voltage of slack bus and the frequency of the MG are assumed to be constant. Such assumption fails to consider the real characteristics of the islanded MG as all DGs are involved in providing the demand of active and reactive power as well as maintaining the frequency of the MG. In this paper, a review of power flow methods in power system is shown. Furthermore a novel algorithm, named GPSO-GM (Guaranteed convergence Particle Swarm Optimization with Gaussian Mutation), for the power flow analysis problem in an islanded MG is proposed. The problem is modeled without any slack bus by considering steady state frequency as one of the power flow variables. To model different control modes of DGs, such as droop, PV and PQ, in an islanded MG, a new formula for power flow equations is developed. PSO is adopted to minimize the mismatch of total active and reactive power. Two operators, mutation and guaranteed convergence, are added to PSO in order to help in finding an optimal solution and to assist in increasing the speed of the proposed algorithm as well as the accuracy of the results. [ABSTRACT FROM AUTHOR]

• SSVEP-related sources' temporal activities are estimated using the TRCA method. • The criteria are defined based on the task protocol. • The membership function is defined based on the SSVEP physiological knowledge. • Using optimization to calculate the final weights of the membership function. • Introducing the isolated groups to determine the target stimulus frequency. BCI systems are recognized as a means of interaction between the brain and the computer. In this research, a subject-independent BCI target detection system based on SSVEP was developed using the information in the task-related components and their fuzzy memberships to the regarding task. To assess the proposed system, an online available SSVEP data with four stimulus frequencies of 5, 6, 7 and 8 Hz from five participants were used. First, the task-related components of the SSVEP data were extracted using the TRCA algorithm. Then the membership of each task-related component was determined to each stimulus frequency, and the components were ordered fuzzily based on these membership values. Finally, a new concept named " Isolated Group " was defined and the final data label was decided by the identification of the isolated groups. Based on the results, observation of the detected isolated groups, showed that the isolated groups' components were mostly related to the stimulus frequency, based on their FFT plots. The classification results obtained using Leave-One-Subject-Out method were 100 %, 79.17 %, 70.83 %, 70.83 % and 95.83 % (mean: 83.33 %) for five participants, respectively. In summary, the fuzzy ordering of task-related components was used to pick the components with the most important information for target detection. Although the detection of stimulus frequency in BCI applications has been widely studied, most current target detection systems require an intensive training process for every individual. In contrast, in this research's method, only a concise training process for determining criteria weights in terms of an optimization step, is needed. [ABSTRACT FROM AUTHOR]

This paper gives a novel hybrid optimization method to find optimal sitting and operation of an autonomous MG at the same time. The operation is optimized via finding the optimal droop gain parameters of DGs. The optimization problem is formulated as a multi-objective problem where the objectives are applied to minimize the fuel consumption of DGs and to improve the voltage profile and stability of MG subject to operational and security constraints. A hybrid algorithm, named HS-GA, is developed to solve the paper optimization problem. A new formulation of power flow is derived to run the proposed algorithm where the steady state frequency of system, reference frequency, reference voltage and droop coefficients of DGs, based on a droop controller, are considered as optimization variables. The performance of the paper approach is compared with other optimization and non-optimization methods in MG with 33and 69 buses using MATLAB. The performance of the proposed method is compared with a method that the parameters of DGs are pre-determined without conducting any optimization process. The results show, which optimized droop parameters improves the operation of the MG. [ABSTRACT FROM AUTHOR]

Thin film composite (TFC) reverse osmosis (RO) membranes are semipermeable membranes that are utilized in water purification or water desalination systems. Discarding these membranes after end-of-life leads to environmental problems. Reusing old TFC-RO membranes is one way to solve this problem. For this reason, in this study, used TFC-RO membranes were coated with polydimethylsiloxane (PDMS) for CO 2 /N 2 gas separation application. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) was utilized to confirm the crosslinking of coated PDMS. The morphology of PDMS/TFC-RO membranes was characterized using scanning electron microscopy (SEM). The parameters that can affect performance of prepared membranes (N 2 permeance and CO 2 /N 2 selectivity) are concentration of PDMS solution, coating time, solvent evaporation time and curing temperature and time. Given that the used membranes don't have uniform surfaces, the first step of this study was to investigate the effect of the above mentioned factors on virgin membranes using fractional factorial design (FFD) of experiments. The results obtained showed that PDMS concentration is the most significant factor that has a negative effect on N 2 permeance and positive effect on CO 2 /N 2 selectivity. The reported CO 2 /N 2 selectivity of PDMS membranes was 11–12, but this selectivity for prepared PDMS/TFC-RO membranes was in the range of 6.7–22.5. After determining optimum conditions, the gas separation performance of PDMS coated used TFC-RO membrane under these conditions was finally determined. The results showed that the used membranes had a better performance than virgin membranes. [ABSTRACT FROM AUTHOR]

Because of the strategic importance of gasoline in Iran's political economy, any decision to reform its demand has been met with strong economic, social, and political reactions. Therefore, careful consideration of the demand prospect and supply balance can provide the basis for planning for less costly management. Drivers affecting the demand for gasoline are here the same as drivers affecting the demand for automobiles and their use, which include the basic drivers of economic growth, population growth, general inflation, and ultimately gasoline price increase. The effects of these four major drivers were assessed under the reference scenario to examine the possible effects on gasoline demand, GHG emissions, and ultimately the balance of supply and demand. The results show that with every 0.25% change in the country's population growth, the amount of gasoline demand in 2035 is expected to change by 1.45 billion liters per year. The results also show that for every 1% annual increase in gasoline prices between 2021 and 2030, gasoline and GHG emissions in 2035 are expected to fall by 0.547 billion liters per year and 1287 million tons of carbon dioxide equivalence, respectively. • Economic, population, fuel price growth, and inflation drive gasoline consumption. • Unless demand is reduced or refineries built, Iran will import gasoline by 2032. • The average growth rate of GHG emissions of gasoline demand is expected to be 2.7%. • The share of private cars in fuel demand will rise from 54% in 2010 to 70% in 2035. [ABSTRACT FROM AUTHOR]