Investigation of hearing aid users'speech understanding in noise and their spectral-temporal resolution skills

Purpose:Our study aims to compare speech understanding in noise and spectral-temporal resolution skills with regard to the degree of hearing loss,age,hearing aid use experience and gender of hearing aid users.Methods:Our study included sixty-eight hearing aid users aged between 40-70 years,with bilateral mild and moderate symmetrical sensorineural hearing loss.Random gap detection test,Turkish matrix test and spectral-temporally modulated ripple test were implemented on the participants with bilateral hearing aids.The test results acquired were compared statistically according to different variables and the correlations were examined.Results:No statistically significant differences were observed for speech-in-noise recognition,spectraltemporal resolution among older and younger adults in hearing aid users(p>0.05).There wasn’t found a statistically significant difference among test outcomes as regards different hearing loss degrees(p>0.05).Higher performances were obtained in terms of temporal resolution in male participants and participants with more hearing aid use experience(p<0.05).Significant correlations were obtained between the results of speech-in-noise recognition,temporal resolution and spectral resolution tests performed with hearing aids(p<0.05).Conclusion:Our study findings emphasized the importance of regular hearing aid use and it showed that some auditory skills can be improved with hearing aids.Observation of correlations among the speechin-noise recognition,temporal resolution and spectral resolution tests have revealed that these skills should be evaluated as a whole to maximize the patient’s communication abilities.

Estimating Construction Material Indices with ARIMA and Optimized NARNETs

Construction Industry operates relying on various key economic indicators.One of these indicators is material prices.On the other hand,cost is a key concern in all operations of the construction industry.In the uncertain conditions,reliable cost forecasts become an important source of information.Material cost is one of the key components of the overall cost of construction.In addition,cost overrun is a common problem in the construction industry,where nine out of ten construction projects face cost overrun.In order to carry out a successful cost management strategy and prevent cost overruns,it is very important to find reliable methods for the estimation of construction material prices.Material prices have a time dependent nature.In order to increase the foreseeability of the costs of construction materials,this study focuses on estimation of construction material indices through time series analysis.Two different types of analysis are implemented for estimation of the future values of construction material indices.The first method implemented was Autoregressive Integrated Moving Average(ARIMA),which is known to be successful in estimation of time series having a linear nature.The second method implementedwas Non-LinearAutoregressive Neural Network(NARNET)which is known to be successful in modeling and estimating of series with non-linear components.The results have shown that depending on the nature of the series,both these methods can successfully and accurately estimate the future values of the indices.In addition,we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.

Effectiveness of menstruation hygiene skills training for adolescents with autism

BACKGROUND Adolescents with autism spectrum disorder(ASD)may encounter many difficulties with their menstrual cycles.Potential challenges that adolescents with ASD may face include understanding physical changes,coping with symptoms,emotional sensitivity,communication,personal care,and hygiene.AIM To evaluate the effect of menstrual hygiene skills training given to adolescents with ASD on their menstrual hygiene skills.METHODS The study was conducted with 15 adolescents diagnosed with ASD by the single group pre-test and post-test model in three special education centers in Türkiye.Data were collected with the Adolescent and Parent Information Form and the Adolescent-Specific Menstrual Hygiene Skill Registration Form.RESULTS While the mean age of adolescents was 16.06±0.88 years,the mean age of individuals responsible for adolescent care was 43.66±5.56 years.While 60.0%of the adolescents noticed the onset of bleeding before training,this rate was 93.3%after training.The Adolescent-Specific Menstrual Hygiene Skill Registration Form showed a statistically significant increase in the application steps after the training.The difference between the menstrual hygiene skill scores of adolescents CONCLUSION The menstrual hygiene skills training given to adolescents with ASD was beneficial in increasing their menstrual hygiene skills.These individuals must take responsibility during menstruation and independently manage their continuous care activities.

Artificial intelligence and kidney transplantation

Artificial intelligence and its primary subfield,machine learning,have started to gain widespread use in medicine,including the field of kidney transplantation.We made a review of the literature that used artificial intelligence techniques in kidney transplantation.We located six main areas of kidney transplantation that artificial intelligence studies are focused on:Radiological evaluation of the allograft,pathological evaluation including molecular evaluation of the tissue,prediction of graft survival,optimizing the dose of immunosuppression,diagnosis of rejection,and prediction of early graft function.Machine learning techniques provide increased automation leading to faster evaluation and standardization,and show better performance compared to traditional statistical analysis.Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.

Movement of boron from ulexite and colemanite minerals in sapwood and heartwood of Cryptomeria japonica

This study evaluated boron diff usion from raw boron minerals ulexite and colemanite with low water solubility in comparison to disodium octaborate tetrahydrate(DOT).Tests were conducted using sugi(Cryptomeria japonica(L.)f.D.Don)sapwood and heartwood blocks conditioned to 30,60,and 90%target moisture content.The blocks were fi lled with the boron compounds through treatment holes and diff usion was observed at three assay zones across the blocks after 7,30,60 or 90-day incubation period at room temperatures.For comparison,ethylene glycol was also introduced into the holes to elevate boron diff usion.As expected,diff usion increased with increased moisture content and levels were higher at the 60%and 90%moisture levels compared to the 30%level.With some exceptions,boron levels did not follow consistent gradients with distance away from the treatment hole.Incorporation of ethylene glycol helped increase boron levels,even in heartwood blocks.Boron levels were higher from the ulexite source than from colemanite;however,DOT treatments resulted in the highest boron diff usion rates as a result of greater water solubility compared to both raw boron minerals.The results suggest that ulexite together with ethylene glycol may be useful in both sapwood and heartwood materials when kept at high moisture levels for extended periods.

An in-depth review of theory of the TOPSIS method:An experimental analysis

Decision-making is an important part of daily and business life for both individuals and organizations.Although the multi-criteria decision-making methods provide decision makers the necessary tools,they have differences in terms of the assumptions and fundamental theory.Hence,selecting the right decision-making method is at least as important as making the decision.TOPSIS(Technique for Order Performance by Similarity to Ideal Solution)method,which is one of the most widely used multi-criteria decision-making methods,has gained attention of researchers and thus various improved versions of the method have been proposed.This study considers the conventional TOPSIS method and experimentally displays the underlying reasons of the lacks of the conventional TOPSIS method by using a simulation technique.Detailed experimental analysis based on simulation with an application is used to reveal theoretical fundamentals of the TOPSIS method to better understand it and contribute to its improvement.

Carbonatites and Alkaline Igneous Rocks in Post-Collisional Settings:Storehouses of Rare Earth Elements

The rare earth elements(REE)are critical raw materials for much of modern technology,particularly renewable energy infrastructure and electric vehicles that are vital for the energy transition.Many of the world's largest REE deposits occur in alkaline rocks and carbonatites,which are found in intracontinental,rift-related settings,and also in syn-to post-collisional settings.Post-collisional settings host significant REE deposits,such as those of the Mianning-Dechang belt in China.This paper reviews REE mineralization in syn-to post-collisional alkaline-carbonatite complexes worldwide,in order to demonstrate some of the key physical and chemical features of these deposits.We use three examples,in Scotland,Namibia,and Turkey,to illustrate the structure of these systems.We review published geochemical data and use these to build up a broad model for the REE mineral system in post-collisional alkaline-carbonatite complexes.It is evident that immiscibility of carbonate-rich magmas and fluids plays an important part in generating mineralization in these settings,with REE,Ba and F partitioning into the carbonate-rich phase.The most significant REE mineralization in post-collisional alkaline-carbonatite complexes occurs in shallow-level,carbothermal or carbonatite intrusions,but deeper carbonatite bodies and associated alteration zones may also have REE enrichment.

Estimation of optimum design of structural systems via machine learning

Three different structural engineering designs were investigated to determine optimum design variables,and then to estimate design parameters and the main objective function of designs directly,speedily,and effectively.Two different optimization operations were carried out:One used the harmony search(HS)algorithm,combining different ranges of both HS parameters and iteration with population numbers.The other used an estimation application that was done via artificial neural networks(ANN)to find out the estimated values of parameters.To explore the estimation success of ANN models,different test cases were proposed for the three structural designs.Outcomes of the study suggest that ANN estimation for structures is an effective,successful,and speedy tool to forecast and determine the real optimum results for any design model.

The infuence of window size on remote sensing-based prediction of forest structural variables

Background:Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data.Because the value of the reference laser and image metrics that afect the quality of the prediction model depends on window size.However,suitable window sizes are usually determined by trial and error.There are a limited number of published studies evaluating appropriate window sizes for diferent remote sensing data.This research investigated the efect of window size on predicting forest structural variables using airborne LiDAR data,digital aerial image and WorldView-3 satellite image.Results:In the WorldView-3 and digital aerial image,signifcant diferences were observed in the prediction accuracies of the structural variables according to diferent window sizes.For the estimation based on WorldView-3 in black pine stands,the optimal window sizes for stem number(N),volume(V),basal area(BA)and mean height(H)were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.In oak stands,the R^(2) values of each moving window size were almost identical for N and BA.The optimal window size was 400 m^(2) for V and 600 m^(2) for H.For the estimation based on aerial image in black pine stands,the 800 m^(2) window size was optimal for N and H,the 600 m^(2) window size was optimal for V and the 1000 m^(2) window size was optimal for BA.In the oak stands,the optimal window sizes for N,V,BA and H were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.The optimal window sizes may need to be scaled up or down to match the stand canopy components.In the LiDAR data,the R^(2) values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion:This study illustrated that the window size has an efect on the prediction accuracy in estimating forest structural variables based on remote sensing data.Moreover,the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

What Caused the Inconsistency between Rb-Sr and^(40)Ar-^(39)Ar Ages of Authigenic Illites?

Radiogenic isotope dating of illitic clays has been widely used to reconstruct thermal and fluid flow events in siliciclastic sedimentary basins,the information of which is critical to investigate mechanisms of hydrocarbon maturation.This study carried out Rb-Sr and^(40)Ar-^(39)Ar dating of authigenic illitic clay samples separated from the Palaeogene sandstone in the northern South China Sea.Our Rb-Sr data further confirm the previously reported three periods of fluid flow events(at 34.5±0.9,31.2±0.6,and 23.6±0.8 Ma,respectively)in the northern South China Sea,which are related to regional episodic tectonism.However,^(40)Ar-^(39)Ar ages of illite obtained in this study are significantly younger than the corresponding Rb-Sr ages.The significantly younger^(40)Ar-^(39)Ar ages were probably due to ^(40)Ar loss caused by later dry heating events on the Hainan Island that have not affected the Rb-Sr isotopic systematics.The inconsistency between Rb-Sr and^(40)Ar-^(39)Ar data should be attributed to different isotopic behaviors of K-Ar and Rb-Sr isotopic systematics in illite.Our results indicate that Rb-Sr isotopic dating method may be a preferential approach for clay dating in geological settings where exist younger dry heating events.