Cytotoxicity of Newly Synthesized Quinazoline–Sulfonamide Derivatives in Human Leukemia Cell Lines and Their Effect on Hematopoiesis in Zebrafish Embryos

Many quinazoline derivatives with pharmacological properties, such as anticancer activity, have been synthesized. Fourteen quinazoline derivatives bearing a substituted sulfonamide moiety (4a–n) were previously synthesized and fully characterized. These compounds exerted antiproliferative activity against cell lines derived from solid tumors. Herein, the antileukemic activities of these compounds (4a–n) against two different leukemia cell lines (Jurkat acute T cell and THP-1 acute monocytic) were investigated. Our investigation included examining their activity in vivo in a zebrafish embryo model. Remarkably, compounds 4a and 4d were the most potent in suppressing cell proliferation, with an IC₅₀ value range of 4–6.5 µM. Flow cytometry analysis indicated that both compounds halted cell progression at the G2/M phase and induced apoptosis in a dose-dependent manner. RT-PCR and Western blot analyses also showed that both compounds effectively induced apoptosis by upregulating the expression of proapoptotic factors while downregulating that of antiapoptotic factors. In vivo animal toxicity assays performed in zebrafish embryos indicated that compound 4d was more toxic than compound 4a, with compound 4d inducing multiple levels of teratogenic phenotypes in zebrafish embryos at a sublethal concentration. Moreover, both compounds perturbed the hematopoiesis process in developing zebrafish embryos. Collectively, our data suggest that compounds 4a and 4d have the potential to be used as antileukemic agents.     

Genetic algorithm with adaptive and dynamic penalty functions for the selection of cleaner production measures: a constrained optimization problem

This paper presents a new approach of genetic algorithm (GA) to solve the constrained optimization problem. In a constrained optimization problem, feasible and infeasible regions occupy the search space. The infeasible regions consist of the solutions that violate the constraint. Oftentimes classical genetic operators generate infeasible or invalid chromosomes. This situation takes a turn for the worse when infeasible chromosomes alone occupy the whole population. To address this problem, dynamic and adaptive penalty functions are proposed for the GA search process. This is a novel strategy because it will attempt to transform the constrained problem into an unconstrained problem by penalizing the GA fitness function dynamically and adaptively. New equations describing these functions are presented and tested. The effects of the proposed functions developed have been investigated and tested using different GA parameters such as mutation and crossover. Comparisons of the performance of the proposed adaptive and dynamic penalty functions with traditional static penalty functions are presented. The result from the experiments show that the proposed functions developed are more accurate, efficient, robust and easy to implement. The algorithms developed in this research can be applied to evaluate environmental impacts from process operations.     

Particle Swarm Optimization for Solving Sine-Gordan Equation

The term ‘optimization’ refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable ones. The majority of real-world situations can be modelled as an optimization problem. The complex nature of models restricts traditional optimization techniques to obtain a global optimal solution and paves the path for global optimization methods. Particle Swarm Optimization is a potential global optimization technique that has been widely used to address problems in a variety of fields. The idea of this research is to use exponential basis functions and the particle swarm optimization technique to find a numerical solution for the Sine-Gordan equation, whose numerical solutions show the soliton form and has diverse applications. The implemented optimization technique is employed to determine the involved parameter in the basis functions, which was previously approximated as a random number in the work reported till now in the literature. The obtained results are comparable with the results obtained in the literature. The work is presented in the form of figures and tables and is found encouraging.     

IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

Visual impairment is one of the major problems among people of all age groups across the globe. Visually Impaired Persons (VIPs) require help from others to carry out their day-to-day tasks. Since they experience several problems in their daily lives, technical intervention can help them resolve the challenges. In this background, an automatic object detection tool is the need of the hour to empower VIPs with safe navigation. The recent advances in the Internet of Things (IoT) and Deep Learning (DL) techniques make it possible. The current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNet-based object detection (TSOLWR-ODVIP) model to help VIPs. The primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to them. For data acquisition, IoT devices are used in this study. Then, the Lightweight RetinaNet (LWR) model is applied to detect objects accurately. Next, the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR model. Finally, the Long Short-Term Memory (LSTM) model is exploited for classifying objects. The performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects, and the results were examined under distinct aspects. The comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects, enhancing the quality of life of VIPs.     

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on these test images, the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed. To further improve the performance of the Inception v3 model, the thermal exchange optimization (TEO) algorithm is applied to tune the hyperparameters (number of epochs, batch size, and learning rate) showing the novelty of the work. Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer, hyperparameter optimizer, and factorization approach. Additionally, Inception V3 can handle images of different sizes. This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things (IoT)-based devices.