In this work, analytical study for simulating a Fabry-Perot bistable etalon (F-P cavity) filled with a dispersive optimized nonlinear optical material (Kerr type) such as semiconductors Indium Antimonide (InSb). Because of a trade off between the etalon finesse values and driving terms, an optimization procedures have been done on the InSb etalon/CO laser parameters, using critical switching irradiance (I_c) via simulation systems of optimization procedures of optical cavity. in order to achieve the  minimum switching power and faster switching time, the optimization parameters of the finesse values and driving terms on optical bistability and switching dynamics must be studied.

In this study, the optimum conditions for COD removal from petroleum refinery wastewater by using a combined electrocoagulation- electro-oxidation system were attained by Taguchi method. An orthogonal array experimental design (L18) which is of four controllable parameters including NaCl concentration, C.D. (current density), PH, and time (time of electrolysis) was employed. Chemical oxygen demand (COD) removal percentage was considered as the quality characteristics to be enhanced. Also, the value of turbidity and TDS (total dissolved solid) were estimated. The optimum levels of the studied parameters were determined precisely by implementing S/N analysis and analysis of variance (ANOVA). The optimum conditions were found to be NaCl = 2.5

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

Background: MRSA (methicillin-resistant Staphylococcus aureus) is a global health problem. Many people are looking for new ways to combat MRSA, for example by using bacteriophages (phages). It has been extremely challenging to isolate a sufficient quantity of lytic anti-MRSA phages. Therefore, new techniques for separating, refining, and reworking anti-MRSA phages were sought in this study.

Methods: Of 437 S. aureus isolates, nine clinical MRSA isolates were obtained from three hospitals in Baghdad, Iraq and two ATCC MRSA strains were used to separate wild anti-MRS

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

Antibiotics are essential for treating infectious diseases, but their overuse and adverse effects are raising concerns about global public health. The pervasiveness of antibiotic contamination in aquatic environments has drawn increased attention in recent years. The primary concern regarding the release of antibiotics into the environment is the potential for microorganisms to become resistant to antibiotics. This review article summarizes the analytical methods used to determine the presence of trimethoprim and metronidazole in various environmental samples. These antibiotics have traditionally been analyzed using tandem mass spectrometry or high-performance liquid chromatography coupled to mass spectrometry; fluorescence or ultrav

This narrative review focused on research investigating the impact of loneliness on the prevalence of dementia and its relationship with other risk factors. A comprehensive and rigorous search was conducted using a variety of scientific databases with specific keywords to identify all prior studies that examined the correlation between dementia and loneliness. The inquiry was confined to articles published in English from January 2017 to March 2024. The narrative review identified a consensus regarding the role of loneliness in enhancing the risk of all‐cause dementia, with a particular emphasis on the subjective perception of loneliness. This phenomenon may be caused by the sensations of exclusion, discrimination, and alienation that are

Secondary trigeminal neuralgia (STN) results from an identifiable underlying pathology, including tumor compression, multiple sclerosis, arteriovenous malformations, hypertension, structural lesions, inflammation, trauma, or familial genetic conditions. This study, through a comprehensive review of the literature in PubMed, Google Scholar, Web of Science, and the Cochrane Library, explores the multifaceted aspects of STN. This study delves into the diverse etiological factors, focusing on the pathophysiological mechanisms that lead to trigeminal nerve dysfunction. The clinical manifestations of STN often overlap with those of primary trigeminal neuralgia, creating diagnostic challenges and necessitating a thorough evaluation that in