An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

X-ray and electron microscopical studies of thin films of (Cd1−xZnx)S solid solution

A series of (Cd_1–x Zn _x )S powders phosphors and thin layers prepared by thermal evaporation of solid solution were studied. The phosphors used were 41% ZnS: 59% CdS with a cobalt concentration from 0 to 0.325%. The analysis of the structure of films of different thicknesses using X-ray diffraction technique confirms that the calculated relative intensities of the planes show considerable differences from the experimental results. For film thicknesses 70 nm thea axis is normal to the substrate, while at greater thicknesses (273 nm) thec axis is practically normal to the substrate. The effect of the electron beam on the solid solution indicates that layers decomposed leaving the grain boundaries decorated by metallic cadmium and zinc particles.     

Pharmacological Evidence on Augmented Antiallodynia Following Systemic Co-Treatment with GlyT-1 and GlyT-2 Inhibitors in Rat Neuropathic Pain Model

The limited effect of current medications on neuropathic pain (NP) has initiated large efforts to develop effective treatments. Animal studies showed that glycine transporter (GlyT) inhibitors are promising analgesics in NP, though concerns regarding adverse effects were raised. We aimed to study NFPS and Org-25543, GlyT-1 and GlyT-2 inhibitors, respectively and their combination in rat mononeuropathic pain evoked by partial sciatic nerve ligation. Cerebrospinal fluid (CSF) glycine content was also determined by capillary electrophoresis. Subcutaneous (s.c.) 4 mg/kg NFPS or Org-25543 showed analgesia following acute administration (30–60 min). Small doses of each compound failed to produce antiallodynia up to 180 min after the acute administration. However, NFPS (1 mg/kg) produced antiallodynia after four days of treatment. Co-treatment with subanalgesic doses of NFPS (1 mg/kg) and Org-25543 (2 mg/kg) produced analgesia at 60 min and thereafter meanwhile increased significantly the CSF glycine content. This combination alleviated NP without affecting motor function. Test compounds failed to activate G-proteins in spinal cord. To the best of our knowledge for the first time we demonstrated augmented analgesia by combining GlyT-1 and 2 inhibitors. Increased CSF glycine content supports involvement of glycinergic system. Combining selective GlyT inhibitors or developing non-selective GlyT inhibitors might have therapeutic value in NP.     

pHEMA: An Overview for Biomedical Applications

Poly(2-hydroxyethyl methacrylate) (pHEMA) as a biomaterial with excellent biocompatibility and cytocompatibility elicits a minimal immunological response from host tissue making it desirable for different biomedical applications. This article seeks to provide an in-depth overview of the properties and biomedical applications of pHEMA for bone tissue regeneration, wound healing, cancer therapy (stimuli and non-stimuli responsive systems), and ophthalmic applications (contact lenses and ocular drug delivery). As this polymer has been widely applied in ophthalmic applications, a specific consideration has been devoted to this field. Pure pHEMA does not possess antimicrobial properties and the site where the biomedical device is employed may be susceptible to microbial infections. Therefore, antimicrobial strategies such as the use of silver nanoparticles, antibiotics, and antimicrobial agents can be utilized to protect against infections. Therefore, the antimicrobial strategies besides the drug delivery applications of pHEMA were covered. With continuous research and advancement in science and technology, the outlook of pHEMA is promising as it will most certainly be utilized in more biomedical applications in the near future. The aim of this review was to bring together state-of-the-art research on pHEMA and their applications.     

Investigation of transport phenomena in a hybrid ion exchange-electrodialysis system for the removal of copper ions

Hybrid ion exchange electrodialysis processes allow the removal of metal ions from dilute waste liquids and the recovery of more concentrated solutions. The work reported here was aimed at investigating the two steps in the treatment process, namely, adsorption of metal ions onto the packed bed of resin and electromigration (i.e., the transport of these ions in the complex system under the applied electrical field). The case of copper sulfate was investigated. Dowex resins with a cross-linking degree of 2 and 8% were used. The flux of copper through the resin bed and the current efficiency for ion transfer to the cathode compartment were determined as a function of potential gradient and copper ionic fraction in the bed. Apparent diffusion coefficients of Cu²⁺ in the overall system were deduced from the experimental data.     

Improving the Pressurized Flushing Efficiency in Reservoirs: an Experimental Study

Sediment flushing in many reservoirs of the world is accomplished with low efficiency. In this study, a new configuration was proposed for reservoir bottom outlet to increase the pressurized flushing efficiency. In the new configuration, a projecting semi-circular structure was connected to the upstream edge of bottom outlet. It was observed that by employing the projecting bottom outlet, the sediment removal efficiency increased significantly compared to the flushing via typical bottom outlet. In the case of new-configuration bottom outlet with L _sc /D _outlet  = 5.26 and D _sc /D _outlet  = 1.32, the dimensionless length, width and depth of flushing cone increased 280%, 45% and 14%, respectively, compared to the reference test. The proposed structure can ensure the sustainable use of reservoirs.     

Networked event-triggered control: an introduction and research trends

A physical system can be studied as either continuous time or discrete-time system depending upon the control objectives. Discrete-time control systems can be further classified into two categories based on the sampling: (1) time-triggered control systems and (2) event-triggered control systems. Time-triggered systems sample states and calculate controls at every sampling instant in a periodic fashion, even in cases when states and calculated control do not change much. This indicates unnecessary and useless data transmission and computation efforts of a time-triggered system, thus inefficiency. For networked systems, the transmission of measurement and control signals, thus, cause unnecessary network traffic. Event-triggered systems, on the other hand, have potential to reduce the communication burden in addition to reducing the computation of control signals. This paper provides an up-to-date survey on the event-triggered methods for control systems and highlights the potential research directions.     

Rheological and dielectrical characterization of melt mixed polycarbonate-multiwalled carbon nanotube composites

A series of composites of polycarbonate (PC) with 23 different contents of multiwalled carbon nanotubes (MWNT) was produced by melt mixing using the masterbatch dilution method. In dielectric measurements, AC conductivity and complex permittivity data obtained in the frequency range between 10⁻³ and 10⁷ Hz at room temperature indicated the electrical percolation threshold at about 1.0 wt%.

The dynamic mode melt rheological measurements for the same samples at eight temperatures between 170 and 280 °C showed a visible change in the frequency dependence of dynamic moduli and the absolute value of the complex viscosity |η*| particularly at low frequencies. In literature these changes are sometimes related to so called ‘percolation threshold concentration’. Applying this picture to our experimental data we have to assume that the percolation threshold is strongly dependent on the measurement temperature. It changes from about 5 to 0.5 wt% MWNT by increasing the measurement temperature from 170 to 280 °C, respectively. This temperature dependence cannot be explained by a classical liquid-solid transition but may be related to the existence of a combined nanotube-polymer network.     

Temperature-dependent diffusion coefficient of soluble substances during ethanol extraction of clove

The temperature dependence of the diffusion coefficient of ethanol-soluble substances from ground cloves (particle size 250 μm) during extraction was estimated by fitting batch extraction data at several temperatures (27.8, 40, 50, and 60°C) to a previously developed mass transfer model. The model was based on spherical geometry of particles. Nonlinear regression analysis was used to develop an equation that describes the diffusivity as a function of temperature. The temperature dependence ofD _A was of the Arrhenius type.