The Role of the pH in the Impregnation of Spherical Mesoporous Silica Particles with L-Arginine Aqueous Solutions

In the context of the development of carriers for amino acids delivery, Spherical Mesoporous Silica Particles (SMSP), characterized by particles size ranging from 0.15 µm to 0.80 µm and average pore diameter of 2.4 nm, were synthesised and loaded with L-arginine (ARG), a basic amino acid involved in several physiological processes. The loading was performed using water as a solvent through the wet impregnation method (with a final arginine content of 9.1% w/w). The material was characterized before and after impregnation by means of X-Ray Diffraction (XRD), nitrogen sorption analysis, Field Emission Scanning Electron Microscopy (FESEM) and Fourier Transform Infrared (FT-IR) spectroscopy. SMSP are shown to suffer degradation upon impregnation, which dramatically affects their porosity. To elucidate the role of the pH of the ARG impregnating solution (originally set at pH ≈ 11) on SMSP degradation, the loading was performed under different pH conditions (5 and 9) keeping constant the ARG concentration. The impregnation performed with acidic solution did not modify the carrier. All samples displayed ARG in amorphous form: zwitterionic species were present in SMSP impregnated at basic pH whereas positive protonated species in that impregnated at acidic pH.     

Temperature sensor integrated curl resonator chipless RFID tag for high capacity data identification applications

Microsystem Technologies - This paper proposes a compact multi-bits chipless RFID tag using fully printed resonators with dual performance of tagging and sensing temperature. Each bit is modelled...     

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors (KNN) classifier. The proposed method makes advantage of the UCI repository, which has a 279-attribute high-dimensional cardiac arrhythmia dataset. The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features. The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients. This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature. The achieved classification accuracy using the proposed approach is 99.8%.     

Thermal and Molecular Characterization of Aspergillus awamori Glucoamylase Catalytic and Starch‐Binding Domains

Aspergillus awamori glucoamylase catalytic domain, linker, and starch‐binding domain, the first and third expressed from yeast, have molecular masses of 56.2, 12.6 and 12.9 kDa, respectively, as determined by MALDI‐TOF mass spectroscopy, and have 10.2, 73.2 and 7.0 % (w/w) carbohydrate, respectively, showing overglycosylation by yeast. Unfolding of the starch‐binding domain monitored by circular dichroism is reversible at pH 6.0—8.0, with the unfolding T_m and ΔH increasing from 49.7 to 58.5 °C and from 284 to 351 kJ/mol, respectively, as pH decreases from 8.0 to 6.0. The catalytic domain unfolds irreversibly at pH 7.5, producing a single asymmetric endotherm by differential scanning calorimetry, with T_m and ΔH at a 1 °C/min heating rate being 60.9 °C and 1720 kJ/mol, but with both increasing as the heating rate increases. This suggests that unfolding is partially under kinetic control, while various tests show that it does not follow a simple two‐state irreversible model. Values of ΔH from calculated solvent‐accessible surface areas of unglycosylated catalytic and starch‐binding domains are about 100 kJ/mol lower than experimentally determined ΔH values of the corresponding glycosylated domains, showing the effect of glycosylation on unfolding enthalpies.     

Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time. In such problems, it is commonly assumed that all problem instances are feasible. In reality some instances can be infeasible due to various practical issues, such as a sudden change in resource requirements or a big change in the availability of resources. Decision-makers have to determine whether a particular instance is feasible or not, as infeasible instances cannot be solved as there are no solutions to implement. In this case, locating the nearest feasible solution would be valuable information for the decision-makers. In this paper, a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is infeasible. To judge the performance of the proposed algorithm, 13 well-known dynamic test problems were solved. The results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40% over all the environments and it can also find a good, but infeasible solution, when an instance is infeasible.