OBJECTIVE: In this study we investigated the stimulant methylphenidate (MPH) effects in Attention deficit hyperactivity disorder (ADHD) from neuroimaging and neurophysiological perspective by simultaneous recording functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) during attention task. METHODS: Using fNIRS we obtained frontal cortex hemodynamic responses and using event related potentials (ERP) we obtained amplitude values of P3 component of 18 children with ADHD and gender matched 18 healthy controls performing an oddball task. Same recordings were repeated 3 months after extended-release MPH (OROS-MPH) administration for ADHD group. Prefrontal cortex oxygenation and P3 amplitude were compared between control and pre-MPH ADHD groups and between Pre-MPH and post-MPH ADHD groups. RESULTS: fNIRS indicated that the healthy controls exhibited higher right prefrontal activation than pre-MPH children with ADHD. Reduced P3 amplitude values were found in children with ADHD compared the control group. Reduced right prefrontal activation and P3 amplitude was normalized in ADHD group after MPH therapy. CONCLUSION: Recently multimodal neuroimaging which combine signals from different brain modalities have started to be considered as a potential to improve the accuracy of diagnosis. The current study provides MPH effect assessment in children with ADHD using multimodal EEG/fNIRS system for the first time. This study suggests combination of neuroimaging and electrophysiological parameters is a promising approach to investigate MPH effect assessment in children with ADHD.

Assembly refers both to the process of combining parts to create a structure and to the product resulting therefrom. The complexity of this process increases with the number of pieces in the assembly. This paper presents the assembly planning system design (APSD) program, a computer program developed based on a matrix-based approach and the discrete artificial bee colony (DABC) algorithm, which determines the optimum assembly sequence among numerous feasible assembly sequences (FAS). Specifically, the assembly sequences of three-dimensional (3D) parts prepared in the computer-aided design (CAD) software AutoCAD are first coded using the matrix-based methodology and the resulting FAS are assessed and the optimum assembly sequence is selected according to the assembly time optimisation criterion using DABC. The results of comparison of the performance of the proposed method with other methods proposed in the literature verify its superiority in finding the sequence with the lowest overall time. Further, examination of the results of application of APSD to assemblies consisting of parts in different numbers and shapes shows that it can select the optimum sequence from among hundreds of FAS.

Advanced techniques in image processing and analysis are being extensively studied to assist clinical diagnoses. Digital colour retinal fundus images are widely utilised to investigate various eye diseases. In this paper, we describe the detection of optic disc (OD), macula and age-related macular degeneration (ARMD) pathologies of the macular regions in colour fundus images. ARMD causes the loss of central vision in older adults. If the disease is detected early and treated promptly, much of the vision loss can be prevented. Eighty colour retinal fundus images were tested using our proposed algorithm. The Hough transform was employed for OD determination. A fundus coordinate system was established based on the macula location. An ARMD pathology detection methodology using a subtraction process after contrast-limited adaptive histogram equalisation operations was proposed. The accuracies of the automated segmentations of the OD, macula and ARMD pathologies obtained were 100%, 100% and 95.49%, respectively. These results show that our algorithm is a useful tool for detecting ARMD in retinal fundus images. The application of our method may reduce the time needed by ophthalmologists to diagnose ARMD pathology while providing dependable detection precision. Integration of our technique into traditional software could be used in clinical implementations as an aid in disease diagnosis and as a tool for quantitative evaluation of treatment effectiveness.