Novel Colorimetry-Based Android Smartphone Application for Detection of Ibuprofen

Sensitive and fast detection of ibuprofen( Ibu) in an aquatic environment often requires costly, time-consuming and sophisticated techniques. To tackle those limitations,a novel android smartphone application was developed based on a colorimetric analysis method using unmodified gold nanoparticles( AuNPs)aptamer probes to quantitatively detect Ibu. Under optimal conditions,it could detect Ibu as low as 0. 25 ng/mL with high selectivity. The determination of Ibu in real water samples was also carried out to confirm the practicability of the application.

Addition of Inhalation Training Devices and Smartphone Application to Verbal Counseling for Asthmatic Patients

Background:One of the main issues with pressurized metered dose inhalers(pMDI)is that some patients find it difficult to use it properly.Methods:This study was carried out to compare the effect of traditional verbal counseling and that of adding an inhalation training device,such as Flo-Tone or Clip-Tone,along with a smartphone application on the incidence of inhalation technique mistakes and the pulmonary function of asthmatic adults and children.Results:The lung function of those in the advanced counseling group significantly improved on the second visit(p<0.001),whereas for those in the verbal counseling group,their lung function only improved on the third visit(p<0.001).For both the groups,the mean number of mistakes in regard to the steps in inhalation technique decreased significantly(p<0.001),with an overall higher percentage in the advanced counseling group.Conclusion:The use of training devices and smartphone applications in addition to traditional verbal counseling for teaching asthmatic adults and children the correct inhalation technique steps using pMDI resulted in a significant improvement in pulmonary function and a significant reduction in the number of inhalation technique mistakes,compared to traditional verbal counseling alone.

CD-FL:Cataract Images Based Disease Detection Using Federated Learning

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time.Automatic cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye treatment.In recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been exponential.Due to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field challenging.To address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this research.The study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient records.The significant features are extracted and normalized using the min-max normalization technique.In the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two clients.Before transferring the attributes to the global model,the suggested method trains the local model.The global model subsequently improves the technique after integrating the new parameters.Every client analyses the results in three rounds to decrease the over-fitting problem.The experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s data.The experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

Drivers’ Reaction of Warning Messages in Work Zone Termination Areas with Left Turn

Work-zone crashes have always drawn public attention. A number of fatalities are recorded every year nationwide within work zone areas. Most existing countermeasures have been dedicated more to the advance warning areas, transition areas, and activity areas of work zone, than the termination areas, where drivers might play less attention to safety threats. In this study, the vehicle-to-vehicle communication based left turn warning system was applied at a work zone termination area, which is immediately followed by a T-intersection. The work-zone is located on the minor road side, while left turn vehicles will be appearing from the major street through the said T-intersection. A smart phone application was designed using Android coding system to provide several types of warning messages to drivers. Corresponding scenarios were designed in a driving simulator, and 20 subjects were recruited to participate in the simulation test followed by a questionnaire survey. The subjects received a warning message when driving to the termination area of a work zone on the coming left turn vehicles. Twenty test drivers’ driving speed, acceleration rates, and break reaction distance to the warning messages were studied in four different scenarios. Results show that the smartphone application has a great impact on driving behaviors, especially the female voice and the beep tone warning, which are recommended for possible field tests. Besides, the developed smartphone applications can be further updated for practical applications of similar needs.

Exploring speeding behavior using naturalistic car driving data from smartphones

The present research aimed to identify critical factors that affect speeding behavior.For that purpose,high-resolution smartphone data collected from a naturalistic driving experiment of 88 drivers were utilized,augmented with data from self-reported questionnaires.Using risk exposure and driving behavior indicators calculated from smartphone sensor data,as well as demographic characteristics and self-reported driving performance,statistical analysis was carried out for modelling the percentage of driving time over the speed limit,namely by means of generalized linear mixed-effects models.More precisely,an overall model was developed for all road environments,and additional separate models were developed for driving on urban and rural roads.The results from the interpretation of the estimated parameters of the models can be summarized as follows:the parameters of trip distance and mobile phone use while driving have been determined as statistically significant and positively correlated with the percentage of speeding time during a driver's trip.In the same context,male drivers and drivers in the age group of18-34 also increase the percentages of speeding instances while driving.Regarding driving behavior as stated on the questionnaire,it seems that low frequencies of self-declared speeding(never or rarely)are statistically significant and negatively correlated with the percentage of speeding time.It is expected that this research can provide considerable gains to society,since the stakeholders including policy makers and industry could rely on the results and recommendations regarding risk factors that appear to be critical for safe driving.

An empirical comparison of four technology-mediated travel survey methods

The increasing demana for advanced modelling methods, which can reflect complex travel activities of individuals, requires enhanced travel data collection methods. The introduction of GPS-assisted data collection methods has provided an alternative to the conventional methods of travel data collection. GPS-assisted data collection methods improve the accu- racy of data collection and enable capturing more details of individuals＇ travel behaviour. Recent technological advancements in smartphone-based positioning technologies and communication facilities have opened up new opportunities to apply smartphones as the media of GPS-assisted data collection. Although, different GPS-assisted methods have been employed recently, their performance has not been widely evaluated in real-world experi- ments compared to traditional data collection methods. Accordingly, this paper evaluates the performance of three GPS-assisted methods, namely handheld GPS tracking, smart- phone-based GPS tracking and smartphone-based prompted-recall data collection methods, in conjunction with the web-based data collection to shed light on different aspects of GPS- assisted data collection methods. These methods are compared in terms of the quality and accuracy of the collected data, the demographic attributes of participants and the specifi- cations of labelled trips. The results show that an appropriate employment of smartphones enhances the accuracy of data collection. It is also found that putting an extra burden on participants during a travel data collection survey results in lower trip-rates and poor data quality. Finally, it is found that the application of smartphone-assisted data collection methods help reporting non-motorised trips more accurately.