Dielectric spectroscopy measurements for moisture prediction in Vidalia onions

Microwave sensing offers an opportunity to determine nondestructively the amount of moisture in materials by sensing the dielectric properties of the material. Dielectric properties of Vidalia onions grown in southeastern Georgia were measured with an open-ended coaxial-line probe and network analyzer in the range from 200 MHz to 20 GHz. Frequency dependence and moisture dependence of dielectric properties were analyzed for moisture contents between 8% and 91%. Moisture content was linearly correlated with the dielectric constant at higher frequencies for the entire moisture range. A density-independent function that incorporates both the dielectric constant and loss factor was tested across multiple frequencies and moisture ranges. Use of this function enabled prediction of moisture content with high accuracy (R² = 0.99) up to 40% moisture content.     

Design framework for robotic surgery wards at hospitals: Computational implementation

Robotic surgery is one of the most recent technologies in healthcare building field. Due to the design complexity of Robotic surgery wards, computational implementations are being developed to either measure the effect of inserting advanced technologies as Electronic medical recorders and tele surgery, or evaluate design alternatives on healthcare building. This paper presents a design framework that responds to the need for coordinating design phases for Robotic Surgery Wards (RSWs) computationally. This proposed design framework for RSWs can generate functional RSW alternatives and more than one solution for each alternative. The framework has been structured based on the main architectural considerations of RSWs which are geometric and topological, the economic considerations, specific developed pools for shape and corridor patterns, and the theory of “Shape Grammars"has been utilized to compute the framework to generate a vast number of design alternatives. Accordingly, a computational implementation has been established to assist designers in early design stages. Numerical validation for the applications of the developed framework and implementation has been conducted by using reference examples of RSWs. The main finding in this paper is providing healthcare building designers with a computational implementation that generates RSW alternative computationally based on specific shape and cost levels.     

An original observer design for reduced sensor control of Packed U Cells based renewable energy system

This paper presents an original Switched Observer (SO) for reduced-sensor control of a grid-connected Packed U Cells (PUC) multilevel inverter. The proposed SO performance is evaluated using a single-phase 7-level PUC inverter connected to the grid through filtering inductor. Based on the actual grid current, the proposed SO estimates accurately the PUC capacitor voltage, which is fed to the Model Predictive Control (MPC) algorithm while making use of a hybrid model considering both discrete and continuous variables. For real-time application, necessary conditions are given to guarantee the practical stability of the proposed SO under system parameters and input voltage variations according to the selected switching pattern. Theoretical analysis and simulation investigations are conducted to prove that the proposed SO-MPC scheme is stable in closed-loop for all system configurations and has good performances even during various disturbances (load change, parameters mismatch, and input voltage variation).     

Model-Driven Approach for Early Power-Aware Design Space Exploration of Embedded Systems

Due to the growing complexity of Systems-on-Chip (SoC) and the increasing cost of their redesign and fabrication, industrials are urgently looking for design methodologies allowing them to identify issues early in the design flow and to explore the largest possible space of solutions. Several aspects should be taken into account in this context, among which power consumption is considered as a major concern. In this paper, we present a Model Driven Engineering (MDE) approach for early power-aware Design Space Exploration (DSE). This approach facilitates designers work by abstracting the energetic behavior of embedded systems through high-level models targeting an automatic generation of power-aware simulation code. It offers also the possibility to model dynamic power management aspects in order to use the corresponding generated code for DSE. This approach was implemented in the DSE toolkit TTool by integrating power concepts in its DIPLODOCUS UML profile and its simulator. This paper illustrates the proposed approach through a Software-Defined Radio (SDR) case study integrating the Dynamic Slack Reclamation (DSR) policy for dynamic power management. The processor power estimates obtained by the generated simulation code were compared to those obtained from physical implementation on the Xilinx Zynq-7000 platform. This comparison showed that our MDE approach allows to take efficient design decisions early in the design flow.     

Cancelable Iris recognition system based on comb filter

This paper presents a novel scheme for cancelable iris recognition based on comb filtering. This scheme begins with a coarse-to-fine iris localization stage. After that, Gabor filtering is applied for feature extraction. The two-dimensional phase pattern of features generated with the LogGabor filter is distorted through comb filtering. The objective of this distortion process is to generate a cancelable feature pattern that represents the iris. The ability to reinitiate a new cancelable pattern is guaranteed through the variation of the comb filter order. The proposed scheme is compared with a cancelable random projection scheme for iris recognition. Experimental results are conducted on CASIA-IrisV3-Interval database for both random projection and comb filtering schemes. Moreover, evaluation metrics are estimated for different comb filter orders of 6, 8, 10, and 12 in addition to the case of original iris features. Hamming distance and Receiver Operating Characteristic (ROC) curve are estimated for both random projection and comb filtering schemes to check robustness and stability. The experimental results show a significant gain in both privacy and performance. Also, the comb filtering scheme achieves a superior performance for all orders compared to the random projection scheme. The proposed comb filtering scheme achieves the highest accuracy of 99.75% for order 6 and a promising Equal Error Rate (EER) of 0.36% for order 10.

     

Thermal Shift Assay for Small GTPase Stability Screening: Evaluation and Suitability

Thermal unfolding methods are commonly used as a predictive technique by tracking the protein’s physical properties. Inherent protein thermal stability and unfolding profiles of biotherapeutics can help to screen or study potential drugs and to find stabilizing or destabilizing conditions. Differential scanning calorimetry (DSC) is a ‘Gold Standard’ for thermal stability assays (TSA), but there are also a multitude of other methodologies, such as differential scanning fluorimetry (DSF). The use of an external probe increases the assay throughput, making it more suitable for screening studies, but the current methodologies suffer from relatively low sensitivity. While DSF is an effective tool for screening, interpretation and comparison of the results is often complicated. To overcome these challenges, we compared three thermal stability probes in small GTPase stability studies: SYPRO Orange, 8-anilino-1-naphthalenesulfonic acid (ANS), and the Protein-Probe. We studied mainly KRAS, as a proof of principle to obtain biochemical knowledge through TSA profiles. We showed that the Protein-Probe can work at lower concentration than the other dyes, and its sensitivity enables effective studies with non-covalent and covalent drugs at the nanomolar level. Using examples, we describe the parameters, which must be taken into account when characterizing the effect of drug candidates, of both small molecules and Designed Ankyrin Repeat Proteins.     

Boron nitride as a processing aid for the extrusion of polyolefins and fluoropolymers

The influence of a new processing additive (fine particles of boron nitride) on the processability of polyolefins and fluoropolymers in extrusion is studied. The equipment used includes an Instron capillary rheometer with two types of dies, namely capillary dies and special annular dies (Nokia Maillefer wire coating crosshead) attached to the rheometer, and an extruder. Two metallocene polyethylenes and several Teflon® fluoropolymers were tested using these two pieces of equipment. The additive had a significant effect on the extrudate appearance of polyethylene and fluoropolymer particularly in the crosshead dies. It was found to eliminate surface melt fracture and to postpone the critical shear rate for the onset of gross melt fracture to significantly higher values depending on resin type, temperature, and additive concentration (typically 0.005% to 0.5%). To explain the possible mechanism for the effect of the additive on the processability of the resins, rheological measurements using both parallel‐plate and sliding‐plate rheometers were carried out. The rheology of the resins did not seem to change significantly with the addition of boron nitride except for the low‐shear‐rate (low‐frequency) range, where the behavior of the filled resin was found to be similar to that of a crosslinked polymer or a phase‐separated entangled blend. Practical wire coating and tubing extrusion studies for these resins were also carried out.