¿Notas La Diferencia? [Do You Hear the Difference?]: Perceptual Consequences of Intensive Voice Treatment in Spanish Speakers With Parkinson's Disease.

PURPOSE: The primary objective of this study was to explore the effects of intensive voice-focused treatment on speech parameters in Spanish speakers with dysarthria associated with Parkinson's disease (PD) as perceived by naïve listeners. METHOD: Fifteen Spanish speakers with dysarthria associated with PD received the Lee Silverman Voice Treatment (LSVT LOUD) for a month. Voice and speech recordings were conducted pretreatment, posttreatment, and at a 1-month follow-up. Thirty naïve adult listeners rated the perceptual dimensions of ease of understanding (EoU), resonance, articulatory precision, prosody, and voice quality from sentences extracted from an emotional monologue on a visual analogue scale. RESULTS: EoU, resonance, articulatory precision, and voice quality significantly improved pre- to posttreatment, but gains were not maintained at follow-up. Speech severity was a significant source of variance in mean listener response for all perceptual dimensions, although the interaction between speech severity and time was only significant for resonance and voice quality. CONCLUSIONS: LSVT LOUD may be beneficial to improve perceptual speech domains affected by PD in Spanish speakers with dysarthria. Its impact on the different speech subsystems may reflect a universal distribution of effects when directly targeting the glottal source. Language-specific contributions of each perceptual domain to speech intelligibility should be explored in further research to determine linguistically sensitive treatment targets.

Automatic Assessment of Intelligibility in Noise in Parkinson Disease: Validation Study.

BACKGROUND: Most individuals with Parkinson disease (PD) experience a degradation in their speech intelligibility. Research on the use of automatic speech recognition (ASR) to assess intelligibility is still sparse, especially when trying to replicate communication challenges in real-life conditions (ie, noisy backgrounds). Developing technologies to automatically measure intelligibility in noise can ultimately assist patients in self-managing their voice changes due to the disease. OBJECTIVE: The goal of this study was to pilot-test and validate the use of a customized web-based app to assess speech intelligibility in noise in individuals with dysarthria associated with PD. METHODS: In total, 20 individuals with dysarthria associated with PD and 20 healthy controls (HCs) recorded a set of sentences using their phones. The Google Cloud ASR API was used to automatically transcribe the speakers' sentences. An algorithm was created to embed speakers' sentences in +6-dB signal-to-noise multitalker babble. Results from ASR performance were compared to those from 30 listeners who orthographically transcribed the same set of sentences. Data were reduced into a single event, defined as a success if the artificial intelligence (AI) system transcribed a random speaker or sentence as well or better than the average of 3 randomly chosen human listeners. These data were further analyzed by logistic regression to assess whether AI success differed by speaker group (HCs or speakers with dysarthria) or was affected by sentence length. A discriminant analysis was conducted on the human listener data and AI transcriber data independently to compare the ability of each data set to discriminate between HCs and speakers with dysarthria. RESULTS: The data analysis indicated a 0.8 probability (95% CI 0.65-0.91) that AI performance would be as good or better than the average human listener. AI transcriber success probability was not found to be dependent on speaker group. AI transcriber success was found to decrease with sentence length, losing an estimated 0.03 probability of transcribing as well as the average human listener for each word increase in sentence length. The AI transcriber data were found to offer the same discrimination of speakers into categories (HCs and speakers with dysarthria) as the human listener data. CONCLUSIONS: ASR has the potential to assess intelligibility in noise in speakers with dysarthria associated with PD. Our results hold promise for the use of AI with this clinical population, although a full range of speech severity needs to be evaluated in future work, as well as the effect of different speaking tasks on ASR.

Are zinc oxide nanoparticles safe? A structural study on human serum albumin using in vitro and in silico methods.

With due attention to adsorption of proteins on the nanoparticles surface and the formation of nanoparticle-protein corona, investigation of nanoparticles toxicity on the structure of proteins is important. Therefore, this work was done to evaluate toxicity of Zinc oxide nanoparticles (ZnO NPs) on the structure of human serum albumin (HSA) through in vitro and in silico studies. First, ZnO NPs were synthesized using hydrothermal method and their size and morphology were determined by SEM and TEM methods and then to study its toxicity on the HSA structure were used UV-Vis and fluorescence spectroscopy. Also, in order to investigate interaction mechanism of ZnO NP with HSA at the atomistic level was used molecular dynamics (MD) simulation. The obtained images from SEM and TEM showed that ZnO NPs were nanosheet with size of less than 40 nm. The results of spectroscopic studies showed ZnO NPs lead to significant conformational changes in the protein's absorption and emission spectra. Moreover, MD results indicated the minor structure changes in HSA due to interaction with ZnO NP during the 100 ns simulation, and the formation of nanoparticle-protein corona complex is mainly because of electrostatic interactions between charge groups of HSA and ZnO NP.Communicated by Ramaswamy H. Sarma.

Automatic Speech Recognition in Noise for Parkinson's Disease: A Pilot Study.

The sophistication of artificial intelligence (AI) technologies has significantly advanced in the past decade. However, the observed unpredictability and variability of AI behavior in noisy signals is still underexplored and represents a challenge when trying to generalize AI behavior to real-life environments, especially for people with a speech disorder, who already experience reduced speech intelligibility. In the context of developing assistive technology for people with Parkinson's disease using automatic speech recognition (ASR), this pilot study reports on the performance of Google Cloud speech-to-text technology with dysarthric and healthy speech in the presence of multi-talker babble noise at different intensity levels. Despite sensitivities and shortcomings, it is possible to control the performance of these systems with current tools in order to measure speech intelligibility in real-life conditions.

Determining the rate of achievement of outpatient education standards in major departments of Shiraz medical school from interns' viewpoints, based on ministry's clinical education standards in 2018-2019.

INTRODUCTION: In order to improve outpatient education, it is necessary to carry out formative standard evaluation to reveal the strengths and weaknesses to improve planning the quality of clinical education. Due to numerous challenges in clinical education, the present study was conducted to determine the extent to which outpatient education standards were achieved in the major departments of Shiraz Medical School. METHODS: In this quantitative combined, cross-sectional and practical investigation in the academic year 2018-2019, 178 interns who had passed the outpatient education in the four major wards (internal medicine, pediatrics, gynecology, and surgery) in Shiraz Medical School were randomly selected. A 26-item researcher-made questionnaire, based on the Handbook of the Ministry of Clinical Education (Outpatient Education) for Health and Medical Education's Criteria and indicators, was used in three areas of preparation, timing and implementation; and the psychometric properties of the questionnaire were determined. For quantitative data analysis, SPSS version 22 was used. Furthermore, we performed a qualitative study through semi-structured interviews with 16 interns and analyzed the data using MAXQDA 10 software. RESULTS: The results of the quantitative study showed that 8.4% of interns evaluated the program as poor, 66.3% moderate, and 25.3% good. The qualitative study showed that number and diversity of patients, instructor's educational model, and number of interns had a significant role. CONCLUSION: Although the outpatient teaching in the four major departments of Shiraz Medical School was evaluated relatively acceptable, it is far from the ideal point and need to be improved.

Biocompatible chitosan-zinc oxide nanocomposite based dispersive micro-solid phase extraction coupled with HPLC-UV for the determination of rosmarinic acid in the extracts of medical plants and water sample.

In the present research, a procedure was described for the recovery of rosmarinic acid (RA) from medical extract samples using chitosan­zinc oxide nanoparticles as a biocompatible nanocomposite (CS-ZnO-NC). The dispersive micro-solid phase extraction (D-µ-SPE) of RA from the medical extract samples was investigated by using the prepared biocompatible composite as a solid phase. The HPLC-UV method was used for measuring the extracted RA. The important variables (pH, biocompatible composite mass, contact time, and volume of eluent) associated with the extraction process were analyzed by the application of central composite design (CCD). The achieved optimum values for the mentioned variables were 7.0, 10 mg, 4 min, and 180 µL, respectively. The extraction recovery (99.68%) obtained from the predicted model was in agreement with the experimental data (98.22 ± 1.33%). In addition, under the obtained optimum conditions and over the concentration in the range of 2-3500 ng mL-1, a linear calibration curve was obtained with R2 > 0.993. The limit of detection (LOD) and quantification (LOQ) values were computed, and the obtained ranges were respectively from 0.060 to 0.089 ng mL-1 and 0.201 to 0.297 ng mL-1. In addition, the enrichment factors were obtained in the range of 93.7-110.5 with preconcentration factor of 83.3. Therefore, the D-µ-SPE-HPLC-UV method could be used for analyzing RA in the samples of the extracts obtained from the medical plants and water with the recovery values of the analyte in the range of 96.6%-105.4% and the precision with relative standard deviation <5.7%.

Ultrasound combined with manganese-oxide nanoparticles loaded on activated carbon for extraction and pre-concentration of thymol and carvacrol in methanolic extracts of Thymus daenensis, Salvia officinalis, Stachys pilifera, Satureja khuzistanica, and mentha, and water samples.

A dispersive micro solid-phase extraction (DMSPE) technique was developed using manganese-oxide nanoparticles loaded on activated carbon (Mn3O4-NPs-AC) as an effective sorbent combined with ultrasound for the extraction and determination of a trace amount of thymol and carvacrol in methanolic extracts of Thymus daenensis, Salvia officinalis, Stachys pilifera, Satureja khuzistanica and mentha, and water samples. Thymol and carvacrol phenolic compounds were extracted from real samples using acetonitrile (ACN) as the desorption solvent. Using central composite design (CCD), the effects of pH, ionic strength (NaCl), nano-sorbent mass, contact time, and desorption volume were investigated. Additionally, based on five-level variables, response surface methodology was used to determine the individual and interactive effects between factors on the process. The optimized extraction conditions included 12 mg of Mn3O4-NPs-AC as the sorbent, 300 µL of ACN as the desorption solvent, pH 3.0, 0.5 w/v% of NaCl, and 4.5 min sonication time. Under the optimized conditions, for all the samples, the limits of detection were 0.054-0.104 ng mL-1 and the limits of quantification were 0.178-0.345 ng mL-1. The correlation coefficients of the calibration curves were >0.985, i.e. in the range of 0.4-6000 ng mL-1. To validate the effects of the matrix, the recovery, reproducibility, repeatability, and overall uncertainty were calculated for the five methanolic extracts, at 50, 100, and 500 ng mL-1. The recovery ranged between 94.5% and 109.0% with a relative standard deviation of <8.0% for the repeatability and reproducibility precision, which strongly supports the favorable repeatability and reproducibility of the method. The presented method also has the excellent sorbent features of NPs for the sorption of the analyte, which is due to the use of ultrasound for dispersion of the material in the sample matrix.

Cu- and S- @SnO2 nanoparticles loaded on activated carbon for efficient ultrasound assisted dispersive µSPE-spectrophotometric detection of quercetin in Nasturtium officinale extract and fruit juice samples: CCD-RSM design.

A simple, rapid, and efficient method of dispersive micro solid phase extraction (D-µ-SPE) combined with UV-Vis spectrophotometry via ultrasound-assisted (UA) was applied for the determination and preconcentration of quercetin in extract of watercress (Nasturtium officinale), fruit juice and water samples. The sorbent in this method was synthesized by doping copper and sulfide into the tetragonal structure of SnO2-nanoparticles (Cu- and S- @SnO2-NPs) and subsequently loading it on activated carbon (AC). The D-µ-SPE parameters with direct effect on the extraction efficiency of the targeted analyte, such as sample pH, volume of eluent, sorbent mass and ultrasound time were optimized using central composite design method. Under optimized conditions, the calibration graph for quercetin was linear in the range of 20-4000 ng mL-1; the limit of detection and quantitation were 4.35 and 14.97 ng mL-1, respectively and the enrichment factor was 95.24. Application of this method to analyze spiked extract, fruit juice and water samples resulted in acceptable recovery values ranging from 90.3% to 97.28% with intra-day and inter-day relative standard deviation values lower than 6.0% in all cases. Among the equilibrium isotherms tested, Langmuir was found to be the best fitted model with maximum sorption capacity of 39.37 mg g-1, suggesting a homogeneous mode of sorption for quercetin.

The Effects of Intensive Speech Treatment on Conversational Intelligibility in Spanish Speakers With Parkinson's Disease.

Purpose: The purpose of this study was to examine the effects of intensive speech treatment on the conversational intelligibility of Castilian Spanish speakers with Parkinson's disease (PD), as well as on the speakers' self-perceptions of disability. Method: Fifteen speakers with a medical diagnosis of PD participated in this study. Speech recordings were completed twice before treatment, immediately posttreatment, and at a 1-month follow-up session. Conversational intelligibility was assessed in 2 ways-transcription accuracy scores and intelligibility ratings on a 9-point Likert scale. The Voice Handicap Index (Núñez-Batalla et al., 2007) was administered as a measure of self-perceived disability. Results: Group data revealed that transcription accuracy and median ease-of-understanding ratings increased significantly immediately posttreatment, with gains maintained at the 1-month follow-up. The functional subscale of the Voice Handicap Index decreased significantly posttreatment, suggesting a decrease in perceived communication disability after speech treatment. Conclusion: These findings support the implementation of intensive voice treatment to improve conversational intelligibility in Spanish speakers with PD with dysarthria as well as to improve the speakers' perception of their daily communicative capabilities. Clinical and theoretical considerations are discussed.

Improved adsorption performance of nanostructured composite by ultrasonic wave: Optimization through response surface methodology, isotherm and kinetic studies.

In this work, ultrasound-assisted adsorption of an anionic dye, sunset yellow (SY) and cationic dyes, malachite green (MG), methylene blue (MB) and their ternary dye solutions onto Cu@ Mn-ZnS-NPs-AC from water aqueous was optimized by response surface methodology (RSM) using the central composite design (CCD). The adsorbent was characterized using Field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Energy-dispersive X-ray spectroscopy (EDX) and EDX mapping images. The effects of various parameters such as pH, sonication time, adsorbent mass and initial concentrations of SY, MG and MB were examined. A total 33 experiments were conducted to establish a quadratic model. Cu@ Mn-ZnS-NPs-AC has the maximum adsorption efficiency (>99.5%) when the pH, sonication time, adsorbent mass and initial concentrations of SY, MG and MB were optimally set as 6.0, 5min, 0.02g, 9, 12 and 12mgL-1, respectively. Sonication time has a statistically significant effect on the selected responses. Langmuir isotherm model was found to be best fitted to adsorption and adsorption capacities were 67.5mgg-1 for SY, 74.6mgg-1 for MG and 72.9mgg-1 for MB. Four kinetic models (pseudo-first order, pseudo-second order, Weber-Morris intraparticle diffusion rate and Elovich) were tested to correlate the experimental data and the sorption was fitted well with the pseudo-second order kinetic model.

Cu@SnS/SnO2 nanoparticles as novel sorbent for dispersive micro solid phase extraction of atorvastatin in human plasma and urine samples by high-performance liquid chromatography with UV detection: Application of central composite design (CCD).

Separation and detection of residual drug in biological fluids has directly related to human health in term of their beneficial or side effects. In biological fluid samples (complex matrix which containing drug at very low level) conduction of preliminary efficient technique as good choice for pre-concentration and clean-up of real sample before their instrumental analysis is highly recommended. In this result technique, based on nano-structure material which poses higher available surface area and larger member of reactive sites led to significant improvement in characteristic performance of analytical method. This paper focused on the synthesis and application of novel nano-sorbent for pre-concentration and extraction of atorvastatin (AT) from different biological fluids. Influence of various variables including sorbent dosage, eluent volume and sonication time on present method response was studied and optimized by central composite design under response surface methodology and accordingly an acceptable calibration curves over wide linear ranges (0.3-2000µgL-1) with high coefficient of determination higher than 0.999 strongly confirm high applicability of present method for quantification of analyte while limit of detection and quantification in plasma sample were 0.0608 and 0.2033µg L-1, respectively.

Design and construction of nanoscale material for ultrasonic assisted adsorption of dyes: Application of derivative spectrophotometry and experimental design methodology.

Response surface methodology (RSM) based on central rotatable experimental design was used to investigate the effect of ultrasound assisted simultaneous adsorption process variables on Cu: ZnS-NPs-AC from aqueous solution. Cu: ZnS-NPs-AC was characterized using field emission scanning electron microscopy (FE-SEM), Energy Dispersive X-ray Spectroscopy (EDX) and X-ray diffraction (XRD). To overcome the severe methylene blue (MB) and brilliant green (BG) dyes spectral overlapping, derivative spectrophotometric method were successfully applied for the simultaneous determination of dyes in their binary solutions. Simultaneous determination of the dyes can be carried out using the first-order and second order derivative signal at 664 and 663nm for BG and MB, respectively. The factors investigated were pH (2.5-8.5), adsorbent mass (0.006-0.030g), sonication time (1-5min) and initial MB and BG concentration (3-15mgL-1). Five levels, which were low level, center point, upper level and two axillar points, were considered for each of the factors. The desirability function (DF: 0.9853) on the STATISTICA version 10.0 software showed that the optimum removal (99.832 and 99.423% for MB and BG, respectively) was obtained at pH 8.0, adsorbent mass 0.024g, sonication time 4min and 9mgL-1 initial concentration for each dye. Besides, the results show that obtained data were adequately fitted into the second-order polynomial model, since the calculated model F value (172.96 and 96.35 for MB and BG, respectively) is higher than the critical F value. The values of coefficient of determination (0.9968 and 0.9943 for MB and BG, respectively) and adjusted coefficient of determination (0.9911 and 0.9840 for MB and BG, respectively) are close to 1, indicating a high correlation between the observed and the predicted values. The ultrasonic amplitude and adsorbent mass were found to be the most effective variable influencing the adsorption process. The adsorption equilibrium was well described by the Langmuir isotherm model with maximum adsorption capacity of 185.2 and 151.5mgg-1 for MB and BG respectively on adsorbent. The results indicate that pseudo-second-order kinetic equation and intra-particle diffusion model can better describe the adsorption kinetics.

Screening and optimization of highly effective ultrasound-assisted simultaneous adsorption of cationic dyes onto Mn-doped Fe3O4-nanoparticle-loaded activated carbon.

The ultrasound-assisted simultaneous adsorption of brilliant green (BG) and malachite green (MG) onto Mn-doped Fe3O4 nanoparticle-loaded activated carbon (Mn-Fe3O4-NP-AC) as a novel adsorbent was investigated and analyzed using first derivative spectrophotometry. The adsorbent was characterized using FT-IR, FE-SEM, EDX and XRD. Plackett-Burman design was applied to reduce the total number of experiments and to optimize the ultrasound-assisted simultaneous adsorption procedure, where pH, adsorbent mass and sonication time (among six tested variables) were identified as the most significant factors. The effects of significant variables were further evaluated by a central composite design under response surface methodology. The significance of independent variables and their interactions was investigated by means of the analysis of variance (ANOVA) within 95% confidence level together with Pareto chart. Using this statistical tool, the optimized ultrasound-assisted simultaneous removal of basic dyes was obtained at 7.0, 0.02g, 3min for pH, adsorbent mass, and ultrasonication time, respectively. The maximum values of BG and MG uptake under these experimental conditions were found to be 99.50 and 99.00%, respectively. The adsorption process was found to be followed by the Langmuir isotherm and pseudo-second order model using equilibrium and kinetic studies, respectively. According to Langmuir isotherm model, the maximum adsorption capacities of the adsorbent were obtained to be 101.215 and 87.566mgg-1 for MG and BG, respectively. The value of apparent energy of adsorption obtained from non-linear Dubinin-Radushkevich model (4.348 and 4.337kJmol-1 for MG and BG, respectively) suggested the physical adsorption of the dyes. The studies on the well regenerability of the adsorbent in addition to its high adsorption capacity make it promising for such adsorption applications.

Preparation of nanomaterials for the ultrasound-enhanced removal of Pb2+ ions and malachite green dye: Chemometric optimization and modeling.

Copper oxide nanoparticle-loaded activated carbon (CuO-NP-AC) was synthesized and characterized using different techniques such as FE-SEM, XRD and FT-IR. It was successfully applied for the ultrasound-assisted simultaneous removal of Pb2+ ions and malachite green (MG) dye in binary system from aqueous solution. The effect of important parameters was modeled and optimized by artificial neural network (ANN) and response surface methodology (RSM). Maximum simultaneous removal percentages (>99.0%) were found at 25mgL-1, 20mgL-1, 0.02g, 5min and 6.0 corresponding to initial Pb2+ concentration, initial MG concentration, CuO-NP-AC amount, ultrasonication time and pH, respectively. The precision of the equation obtained by RSM was confirmed by the analysis of variance and calculation of correlation coefficient relating the predicted and the experimental values of ultrasound-assisted simultaneous removal of the analytes. A good agreement between experimental and predicted values was observed. A feed-forward neural network with a topology optimized by response surface methodology was successfully applied for the prediction of ultrasound-assisted simultaneous removal of Pb2+ ions and MG dye in binary system by CuO-NPs-AC. The number of hidden neurons, MSE, R2, number of epochs and error histogram were chosen for ANN modeling. Then, Langmuir, Freundlich, Temkin and D-R isothermal models were applied for fitting the experimental data. It was found that the Langmuir model well describes the isotherm data with a maximum adsorption capacity of 98.328 and 87.719mgg-1 for Pb2+ and MG, respectively. Kinetic studies at optimum condition showed that maximum Pb2+ and MG adsorption is achieved within 5min of the start of most experiments. The combination of pseudo-second-order rate equation and intraparticle diffusion model was applicable to explain the experimental data of ultrasound-assisted simultaneous removal of Pb2+ and MG at optimum condition obtained from RSM.

Optimization of ultrasound-assisted dispersive solid-phase microextraction based on nanoparticles followed by spectrophotometry for the simultaneous determination of dyes using experimental design.

A simple, low cost and ultrasensitive method for the simultaneous preconcentration and determination of trace amount of auramine-O and malachite green in aqueous media following accumulation on novel and lower toxicity nanomaterials by ultrasound-assisted dispersive solid phase micro-extraction (UA-DSPME) procedure combined with spectrophotometric has been described. The Mn doped ZnS nanoparticles loaded on activated carbon were characterized by Field emission scanning electron microscopy (FE-SEM), particle size distribution, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR) analyses and subsequently were used as green and efficient material for dyes accumulation. Contribution of experimental variables such as ultrasonic time, ultrasonic temperature, adsorbent mass, vortex time, ionic strength, pH and elution volume were optimized through experimental design, and while the preconcentrated analytes were efficiently eluted by acetone. Preliminary Plackett-Burman design was applied for selection of most significant factors and giving useful information about their main and interaction part of significant variables like ultrasonic time, adsorbent mass, elution volume and pH were obtained by central composite design combined with response surface analysis and optimum experimental conditions was set at pH of 8.0, 1.2mg of adsorbent, 150µL eluent and 3.7min sonication. Under optimized conditions, the average recoveries (five replicates) for two dyes (spiked at 500.0ngmL(-1)) changes in the range of 92.80-97.70% with acceptable RSD% less than 4.0% over a linear range of 3.0-5000.0ngmL(-1) for the AO and MG in water samples with regression coefficients (R(2)) of 0.9975 and 0.9977, respectively. Acceptable limits of detection of 0.91 and 0.61ngmL(-1) for AO and MG, respectively and high accuracy and repeatability are unique advantages of present method to improve the figures of merit for their accurate determination at trace level in complicated materials.

Synthesis of magnetic γ-Fe2O3-based nanomaterial for ultrasonic assisted dyes adsorption: Modeling and optimization.

γ-Fe2O3 nanoparticles were synthesized and loaded on activated carbon. The prepared nanomaterial was characterized by field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier transforms infrared spectroscopy (FT-IR) and X-ray diffraction (XRD). The γ-Fe2O3 nanoparticle-loaded activated carbon (γ-Fe2O3-NPs-AC) was used as novel adsorbent for the ultrasonic-assisted removal of methylene blue (MB) and malachite green (MG). Response surface methodology and artificial neural network were applied to model and optimize the adsorption of the MB and MG in their individual and binary solutions followed by the investigation on adsorption isotherm and kinetics. The individual effects of parameters such as pH, mass of adsorbent, ultrasonication time as well as MB and MG concentrations in addition to the effects of their possible interactions on the adsorption process were investigated. The numerical optimization revealed that the optimum adsorption (>99.5% for each dye) is obtained at 0.02g, 15mgL(-1), 4min and 7.0 corresponding to the adsorbent mass, each dye concentration, sonication time and pH, respectively. The Freundlich, Langmuir, Temkin and Dubinin-Radushkevich isotherms were studied. The Langmuir was found to be most applicable isotherm which predicted maximum monolayer adsorption capacities of 195.55 and 207.04mgg(-1) for the adsorption of MB and MG, respectively. The pseudo-second order model was found to be applicable for the adsorption kinetics. Blank experiments (without any adsorbent) were run to investigate the possible degradation of the dyes studied in presence of ultrasonication. No dyes degradation was observed.

Ultrasonic enhancement of the simultaneous removal of quaternary toxic organic dyes by CuO nanoparticles loaded on activated carbon: Central composite design, kinetic and isotherm study.

Copper oxide nanoparticles loaded on activated carbon (CuO-NPs-AC) were prepared and fully analyzed and characterized with FE-SEM, XRD and FT-IR. Subsequently, this novel material was used for simultaneous ultrasound-assisted adsorption of brilliant green (BG), auramine O (AO), methylene blue (MB) and eosin yellow (EY) dyes. Problems regard to dyes spectra overlap in quaternary solution of this dyes were omitted by derivative spectrophotometric method. The best pH in quaternary system was studied by using one at a time method to achieved maximum dyes removal percentage. Subsequently, sonication time, adsorbent dosage and initial dyes concentrations influence on dyes removal was optimized by central composite design (CCD) combined with desirability function approach (DFA). Desirability score of 0.978 show optimum conditions set at sonication time (4.2 min), adsorbent mass (0.029 g), initial dyes concentration (4.5 mg L(-1)). Under this optimum condition the removal percentage for MB, AO, EY and BG dyes 97.58, 94.66, 96.22 and 94.93, respectively. The adsorption rate well fitted by pseudo second-order while adsorption capacity according to the Langmuir model as best equilibrium isotherm model for BG, MB, AO and EY was 20.48, 21.26, 22.34 and 21.29 mg g(-1), respectively.

Response surface methodology approach for optimization of simultaneous dye and metal ion ultrasound-assisted adsorption onto Mn doped Fe3O4-NPs loaded on AC: kinetic and isothermal studies.

In the present work, the usefulness of ultrasonic power as a dispersion and mixing tool to accelerate the adsorption of Safranin O (SO), methylene blue (MB), Pb(2+) ions and Cr(3+) ions onto the novel composite Fe3O4-NPs-AC adsorbent was investigated. This new material was extensively characterized and analyzed by different techniques such as XRD, FESEM, Raman spectroscopy and FT-IR. Central composite design (CCD) based on designed runs revealed that adsorbent mass, sonication time, MB concentration, SO concentration, Pb(2+) ion and Cr(3+) ion concentration and some of their interactions have significant contributions to the target compounds removal percentages. A combination of response surface methodology and Design-Expert software was used to qualify and estimate the influence and magnitude of each terms contribution to the response. An optimization study using the following investigated increments of the effective variables, adsorbent mass (0.01-0.03 g), sonication time (2-6 min), initial dye concentration (5-25 mg L(-1)), and initial metal ion concentration (20-60 mg L(-1)), revealed that fixing the experimental variables at 0.025 g of Mn-Fe3O4-NPs-AC, with a 3 min sonication time, and 20 mg L(-1) of MB, 10 mg L(-1) of SO, 38 mg L(-1) of Pb(2+) ions and 42 mg L(-1) of Cr(3+) ions at room temperature lead to the achievement of the best characteristics and performance. Conduction of 32 experiments according to the limitations of CCD and a subsequent analysis of variance (ANOVA) gave useful information about the significant and also approximate contributions of each term (main and interaction of variables) in an empirical equation for the expected response. The results indicate that the R(2) values are more than 0.988 and the adjusted R(2) values are in reasonable agreement with R(2). Under the optimal conditions, the MB, SO, Pb(2+) ion and Cr(3+) ion removal efficiencies reached 99.54%, 98.87%, 80.25% and 99.54% after 3 min, while their equilibrium data with high performance can be represented by Langmuir isotherms and a pseudo second-order kinetic model. The maximum adsorption capacities for the single component system, 229.4 mg g(-1) for MB, 159.7 mg g(-1) for SO, 139.5 mg g(-1) for Pb(2+) ions and 267.4 mg g(-1) for Cr(3+) ions, support the high efficiency of Mn-Fe3O4-NPs-AC as a new adsorbent.

Simultaneous ultrasound-assisted ternary adsorption of dyes onto copper-doped zinc sulfide nanoparticles loaded on activated carbon: optimization by response surface methodology.

The simultaneous and competitive ultrasound-assisted removal of Auramine-O (AO), Erythrosine (Er) and Methylene Blue (MB) from aqueous solutions were rapidly performed onto copper-doped zinc sulfide nanoparticles loaded on activated carbon (ZnS:Cu-NP-AC). ZnS:Cu nanoparticles were studied by FESEM, XRD and TEM. First, the effect of pH was optimized in a one-at-a-time procedure. Then the dependency of dyes removal percentage in their ternary solution on the level and magnitude of variables such as sonication time, initial dyes concentrations and adsorbent dosage was fully investigated and optimized by central composite design (CCD) under response surface methodology (RSM) as well as by regarding desirability function (DF) as a good and general criterion. The good agreement found between experimental and predicted values supports and confirms the suitability of the present model to predict adsorption state. The applied ultrasound strongly enhanced mass transfer process and subsequently performance. Hence, a small amount of the adsorbent (0.04 g) was capable to remove high percentage of dyes, i.e. 100%, 99.6% and 100% for MB, AO and Er, respectively, in very short time (2.5 min). The experimental equilibrium data fitting to Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models showed that the Langmuir model applies well for the evaluation and description of the actual behavior of adsorption. The small amount of proposed adsorbent (0.015 g) was applicable for successful removal of dyes (RE>99.0%) in short time (2.5 min) with high adsorption capacity in single component system (123.5 mg g(-1) for MB, 123 mg g(-1) for AO and 84.5 mg g(-1) for Er). Kinetics evaluation of experiments at various time intervals reveals that adsorption processes can be well predicated and fitted by pseudo-second-order and Elovich models.

Computational capabilities of random automata networks for reservoir computing.

This paper underscores the conjecture that intrinsic computation is maximal in systems at the "edge of chaos". We study the relationship between dynamics and computational capability in random Boolean networks (RBN) for reservoir computing (RC). RC is a computational paradigm in which a trained readout layer interprets the dynamics of an excitable component (called the reservoir) that is perturbed by external input. The reservoir is often implemented as a homogeneous recurrent neural network, but there has been little investigation into the properties of reservoirs that are discrete and heterogeneous. Random Boolean networks are generic and heterogeneous dynamical systems and here we use them as the reservoir. A RBN is typically a closed system; to use it as a reservoir we extend it with an input layer. As a consequence of perturbation, the RBN does not necessarily fall into an attractor. Computational capability in RC arises from a tradeoff between separability and fading memory of inputs. We find the balance of these properties predictive of classification power and optimal at critical connectivity. These results are relevant to the construction of devices which exploit the intrinsic dynamics of complex heterogeneous systems, such as biomolecular substrates.