A fuzzy quality control-decision support system for improving operational reliability of liquid transfer operations in laboratory automation

Quality control in liquid transfer operations can eliminate false assay results due to transfer failures, monitor the stability of laboratory equipment, provide baselines for continuous improvement, and enable automatic recovery actions for a number of fault conditions. This paper describes the work done to add real-time quality control capability in liquid transfer operations on a closed-loop controlled MEMS based liquid transfer device.Real-time data acquisition enables the interpretation of system variables. Using fuzzy logic as a decision support tool, an overall quality metric and failure mode is deduced from these variables for each channel per operation. Detection of ordinary fault conditions like clogged tips or unexpected empty source wells can activate automatic recovery actions without human intervention, leading to 24/7 utilization of an automated laboratory system. Exported quality data can be used for bio-informatics data analysis as well as documentation for regulatory agencies.     

Trace metal content in nine species of fish from the Black and Aegean Seas,Turkey

Trace metal content of nine fish species harvested from the Black and Aegean Seas were determined by microwave digestion and atomic absorption spectroscopy (MD–AAS). Verification of the MD–AAS method was demonstrated by analysis of standard reference material (NRCC-DORM-2 dogfish muscle). Trace metal content in fish samples were 0.73–1.83 μg/g for copper, 0.45–0.90 μg/g for cadmium, 0.33–0.93 μg/g for lead, 35.4–106 μg/g for zinc, 1.28–7.40 μg/g for manganese, 68.6–163 μg/g for iron, 0.95–1.98 μg/g for chromium, and 1.92–5.68 μg/g for nickel. The levels of lead and cadmium in fish samples were higher than the recommended legal limits for human consumption.     

Determination of trace element contents of baby foods from Turkey

The levels of trace elements in different types of baby foods consumed in Turkey were determined by flame and graphite furnace atomic absorption spectrometry. Dry, wet and microwave digestion procedures were compared and the microwave digestion method was found to be the best. The accuracy of the method was ensured by using a standard reference material (NIST-SRM 8418 Wheat Gluten). The levels of elements in analyzed samples were found to be under legal limits. The range of the investigated elements were 0.52–4.38 μg/g, 0.22–7.20 μg/g, 1.02–67.5 μg/g, 0.92–37.2 μg/g, 0.12–0.32 μg/g, 2.02–68.8 μg/kg, 10.7–66.8 μg/kg, 0.05–10.3 μg/g, 2.67–25.4 μg/kg for Cu, Mn, Fe, Zn, Se, Cr, Al, Ni and Co, respectively.     

Low Cost Scan Test by Test Correlation Utilization

Scan-based testing methodologies remedy the testability problem of sequential circuits; yet they suffer from prolonged test time and excessive test power due to numerous shift operations. The correlation among test data along with the high density of the unspecified bits in test data enables the utilization of the existing test data in the scan chain for the generation of the subsequent test stimulus, thus reducing both test time and test data volume. We propose a pair of scan approaches in this paper; in the first approach, a test stimulus partially consists of the preceding stimulus, while in the second approach, a test stimulus partially consists of the preceding test response bits. Both proposed scan-based test schemes access only a subset of scan cells for loading the subsequent test stimulus while freezing the remaining scan cells with the preceding test data, thus decreasing scan chain transitions during shift operations. The proposed scan architecture is coupled with test data manipulation techniques which include test stimuli ordering and partitioning algorithms, boosting test time reductions. The experimental results confirm that test time reductions exceeding 97%, and test power reductions exceeding 99% can be achieved by the proposed scan-based testing methodologies on larger ISCAS89 benchmark circuits.     

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Water Resources Management - This study develops and applies three hybrid models, including wavelet packet-artificial neural network (WPANN), wavelet packet-adaptive neuro-fuzzy inference system...     

Estimation of Monthly Mean Reference Evapotranspiration in Turkey

Monthly mean reference evapotranspiration (ET ₀ ) is estimated using adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN) models. Various combinations of long-term average monthly climatic data of wind speed, air temperature, relative humidity, and solar radiation, recorded at stations in Turkey, are used as inputs to the ANFIS and ANN models so as to calculate ET ₀ given by the FAO-56 PM (Penman-Monteith) equation. First, a comparison is made among the estimates provided by the ANFIS and ANN models and those by the empirical methods of Hargreaves and Ritchie. Next, the empirical models are calibrated using the ET ₀ values given by FAO-56 PM, and the estimates by the ANFIS and ANN techniques are compared with those of the calibrated models. Mean square error, mean absolute error, and determination coefficient statistics are used as comparison criteria for evaluation of performances of all the models considered. Based on these evaluations, it is found that the ANFIS and ANN schemes can be employed successfully in modeling the monthly mean ET ₀ , because both approaches yield better estimates than the classical methods, and yet ANFIS being slightly more successful than ANN.     

Networking Models in Flying Ad-Hoc Networks (FANETs): Concepts and Challenges

In recent years, the capabilities and roles of Unmanned Aerial Vehicles (UAVs) have rapidly evolved, and their usage in military and civilian areas is extremely popular as a result of the advances in technology of robotic systems such as processors, sensors, communications, and networking technologies. While this technology is progressing, development and maintenance costs of UAVs are decreasing relatively. The focus is changing from use of one large UAV to use of multiple UAVs, which are integrated into teams that can coordinate to achieve high-level goals. This level of coordination requires new networking models that can be set up on highly mobile nodes such as UAVs in the fleet. Such networking models allow any two nodes to communicate directly if they are in the communication range, or indirectly through a number of relay nodes such as UAVs. Setting up an ad-hoc network between flying UAVs is a challenging issue, and requirements can differ from traditional networks, Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs) in terms of node mobility, connectivity, message routing, service quality, application areas, etc. This paper identifies the challenges with using UAVs as relay nodes in an ad-hoc manner, introduces network models of UAVs, and depicts open research issues with analyzing opportunities and future work.     

River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches

This paper demonstrates the application of two different adaptive neuro-fuzzy (ANFIS) techniques for the estimation of monthly streamflows. In the first part of the study, two different ANFIS models, namely ANFIS with grid partition (ANFIS-GP) and ANFIS with sub clustering (ANFIS-SC), were used in one-month ahead streamflow forecasting and the results were evaluated. Monthly flow data from two stations, the Besiri Station on the Garzan Stream and the Baykan Station on the Bitlis Stream in the Firat-Dicle Basin of Turkey were used in the study. The effect of periodicity on the model’s forecasting performance was also investigated. In the second part of the study, the performance of the ANFIS techniques was tested for streamflow estimation using data from the nearby river. The results indicated that the performance of the ANFIS-SC model was slightly better than the ANFIS-GP model in streamflow forecasting.