Coaxial Alginate Hydrogels: From Self-Assembled 3D Cellular Constructs to Long-Term Storage

Alginate as a versatile naturally occurring biomaterial has found widespread use in the biomedical field due to its unique features such as biocompatibility and biodegradability. The ability of its semipermeable hydrogels to provide a favourable microenvironment for clinically relevant cells made alginate encapsulation a leading technology for immunoisolation, 3D culture, cryopreservation as well as cell and drug delivery. The aim of this work is the evaluation of structural properties and swelling behaviour of the core-shell capsules for the encapsulation of multipotent stromal cells (MSCs), their 3D culture and cryopreservation using slow freezing. The cells were encapsulated in core-shell capsules using coaxial electrospraying, cultured for 35 days and cryopreserved. Cell viability, metabolic activity and cell–cell interactions were analysed. Cryopreservation of MSCs-laden core-shell capsules was performed according to parameters pre-selected on cell-free capsules. The results suggest that core-shell capsules produced from the low viscosity high-G alginate are superior to high-M ones in terms of stability during in vitro culture, as well as to solid beads in terms of promoting formation of viable self-assembled cellular structures and maintenance of MSCs functionality on a long-term basis. The application of 0.3 M sucrose demonstrated a beneficial effect on the integrity of capsules and viability of formed 3D cell assemblies, as compared to 10% dimethyl sulfoxide (DMSO) alone. The proposed workflow from the preparation of core-shell capsules with self-assembled cellular structures to the cryopreservation appears to be a promising strategy for their off-the-shelf availability.     

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

Electrical studies of nanocomposites consisting of MWNTs and polystyrene

Multi-wall carbon nanotubes (MWNTs)/polystyrene (PS) nanocomposites containing different concentrations of MWNTs were prepared by the solution evaporation method. Dispersion of MWNTs in PS was achieved by using ultrasonic energy. The structure of nanocomposites was investigated using X-ray diffraction (XRD) and transmission electron microscopy (TEM). The dielectric constant (ε′), dielectric loss (ε″), and ac conductivity (σ _ac) were measured from room temperature to 100 °C over the frequency range 10³ Hz–1.2 MHz. The results show that the dielectric properties depend on both frequency and temperature and they are enhanced by increasing the MWNTs content.     

Neuro-fuzzy control strategy for an offshore steel jacket platform subjected to wave-induced forces using magnetorheological dampers

Magnetorheological (MR) damper is a prominent semi-active control device to vibrate mitigation of structures. Due to the inherent non-linear nature of MR damper, an intelligent non-linear neuro-fuzzy control strategy is designed to control wave-induced vibration of an offshore steel jacket platform equipped with MR dampers. In the proposed control system, a dynamic-feedback neural network is adapted to model non-linear dynamic system, and the fuzzy logic controller is used to determine the control forces of MR dampers. By use of two feedforward neural networks required voltages and actual MR damper forces are obtained, in which the first neural network and the second one acts as the inverse dynamics model, and the forward dynamics model of the MR dampers, respectively. The most important characteristic of the proposed intelligent control strategy is its inherent robustness and its ability to handle the non-linear behavior of the system. Besides, no mathematical model needed to calculate forces produced by MR dampers. According to linearized Morison equation, wave-induced forces are determined. The performance of the proposed neuro-fuzzy control system is compared with that of a traditional semi-active control strategy, i.e., clipped optimal control system with LQG-target controller, through computer simulations, while the uncontrolled system response is used as the baseline. It is demonstrated that the design of proposed control system framework is more effective than that of the clipped optimal control scheme with LQG-target controller to reduce the vibration of offshore structure. Furthermore, the control strategy is very important for semi-active control.     

Preparation of organic–inorganic nanocomposites with core‐shell structure by inorganic powders

Organic–inorganic nanocomposites with core‐shell structure were prepared in two steps. In the first step, the latex particles in the semibatch emulsion polymerization of butyl methacrylate (BMA), in the presence of methacrylic acid (MAA), were prepared. Small amounts of acrylic acid incorporated into the latex to have better interaction between the surface of particles and inorganic phase. MAA also increased the latex stability and decreased the amount of coagulum. In the second step, the core‐shell structures were prepared by coating the latex particles with three types of inorganic powders. Pectin coated precipitated calcium carbonate, alumina, and silica. The examinations show that pectin‐coated calcium carbonate has the best response than other types of calcium carbonate. Alumina was the second type of inorganic powder that was used for coating the core particles. Silicagel and fumed silica (Aerosil) were used for coating by silica. Scanning electron microscopy and transmission electron microscopy showed the particle morphology and the core‐shell structure, respectively. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci, 2006     

Learning-based word spotting system for Arabic handwritten documents

The retrieval of information from scanned handwritten documents is becoming vital with the rapid increase of digitized documents, and word spotting systems have been developed to search for words within documents. These systems can be either template matching algorithms or learning based. This paper presents a coherent learning based Arabic handwritten word spotting system which can adapt to the nature of Arabic handwriting, which can have no clear boundaries between words. Consequently, the system recognizes Pieces of Arabic Words (PAWs), then re-constructs and spots words using language models. The proposed system produced promising result for Arabic handwritten word spotting when tested on the CENPARMI Arabic documents database.     

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs. To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model. The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository. The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.     

Simplified AASHTO Load and Resistance Factor Design Girder Live Load Distribution in Illinois

Illinois began full transition to the American Association of State Highway and Transportation Officials load and resistance factor design (LRFD) bridge design specifications from the traditional load factor design code or standard specifications in 2002. To facilitate implementation of the new specification, engineers from the Illinois Department of Transportation undertook a series of investigations. The studies focused on interpretation of LRFD for the design of typical bridges in Illinois and the simplification of its procedures for determination of live load lane distributions to primary superstructure girders. Some important presented results from the conducted investigations are believed not only relevant to bridge design in Illinois, but to other states and jurisdictions which employ or will employ LRFD in the near future. The initial simplifications and interpretations focused on concrete deck-on-steel girder bridges and were subsequently expanded to include concrete deck-on-prestressed concrete girder structures. These types of structures comprise a large portion of Illinois’ inventory. Illinois Department of Transportation engineers continue to build on the studies described in the paper such that policies and procedures for other types of typical bridges can be formulated.     

Antioxidant and antithrombotic effects of green mussels (Perna canaliculus) in rats

In the past decade, the use of marine mussels as seafood is being more popular. They considered being a rich source of various nutritional bioactive compounds that aroused the scientific community's interest. This study investigated the antioxidant and the antithrombotic consequences on Sprague-Dawley male rats after adding dried New Zealand mussel Perna canaliculus in their diet. The biochemical, hematological and histopathological changes were also observed. Forty rats were divided into four groups according to the amount of dried mussels in their diet, in addition to a control group that consumed a basal diet only. Group 1 consumed 25% dried mussels in its basal diet; Group 2 consumed 35% dried mussels in its basal diet, and Group 3 was consumed 45% dried mussels in its basal diet. The biochemical parameters showed improvements in liver function. Interestingly, the lipid profile decreased, especially the low-density lipoprotein cholesterol (LDL-C) levels which were reduced significantly in Group 3 (p < .01). These observations were accompanied by a decrease in iron levels significantly as the amount of dried mussels increased (p < .01). Furthermore, the noticed changes in the hematological profile prove that there is an increase in antithrombotic activity. Dried mussels had potent antioxidant effects in the liver as shown by increased lipid peroxide (p < .05), reduced glutathione (p < .05), and glutathione peroxidase (GSH-Px; p < .05). Additionally, antioxidant activity in the kidney was shown to increase through GSH-Px activity (p < .01). In conclusion, these results indicate that consuming dried mussels resulted in improved biochemical and antioxidants activities and could be used as an antithrombotic agent.