Improved aquila optimizer for swarm-based solutions to complex engineering problems.

The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity.

Insulin Degludec in Critically Ill Patients with Type 2 Diabetes Mellitus: A Prospective Interventional Study.

Objective: Dysglycemia has deleterious outcomes on critically ill patients with diabetes mellitus (DM). Insulin degludec, an ultra-long-acting insulin, is associated with lower rates of hypoglycemia and blood glucose (BG) variability in non-critically ill patients. The experience with insulin degludec in the intensive care units is lacking.

Advanced neural network-based model for predicting court decisions on child custody.

Predicting court rulings has gained attention over the past years. The court rulings are among the most important documents in all legal systems, profoundly impacting the lives of the children in case of divorce or separation. It is evident from literature that Natural language processing (NLP) and machine learning (ML) are widely used in the prediction of court rulings.

ISOLATION, CHARACTERIZATION, AND ANTIHYPERTENSIVE ACTIVITY ALKALOIDS EXTRACTED FROM THE LEAVES OF THE ALSTONIA SCHOLARIS PLANT.

The study aims to investigate the Isolation, Characterization & Antihypertensive Life of Natural Alkaloids out of certain Selected Plants. The Alstonia scholaris papers used in this study are generally available in the tropics and can be obtained in Asia. The plant sample was verified by the pharmacognosy and pharmacology department.

A Dynamic Trust evaluation and update model using advance decision tree for underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) are an emerging research area that is rapidly gaining popularity. However, it has several challenges, including security, node mobility, limited bandwidth, and high error rates. Traditional trust models fail to adapt to the dynamic underwater environment.

HPLC METHOD FOR THE QUANTIFICATION OF SOME ACTIVE FLAVONOIDS IN ETHYL ACETATE EXTRACT OF LEAVES OF BUTEA MONOSPERMA LINN.

Aim: The aim of the present investigation is to study HPLC process to evaluate Some Active Flavonoids in Ethyl Acetate Extract of Leaves of Butea monosperma Linn.

Material And Methods: Using a soxhlation device, the leaves of Butea monosperma Linn. were extracted in stages.

Enhancing Photoelectric Response of Self-powered UV and Visible Detectors Using CuO/ZnO NRs Heterojunctions.

Understanding the development and performance of UV photodetectors is crucial, given their extensive applications in both military and civilian sectors. The evolution of self-powered photodetectors, especially those based on heterojunction nanostructures, has demonstrated significant potential for enhancing both device efficiency and functionality. By exploring the effects of material composition and structural design, can optimize these devices for improved photoelectric response and energy efficiency.

Extended dipeptide composition framework for accurate identification of anticancer peptides.

The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification.

CdInSe@chitosan heterojunction nanocomposite with ultrahigh photocatalytic activity under sunlight driven photodegradation of organic pollutants.

This research focused on synthesizing a CdInSe@Ch nanocomposite by doping CdInSe into chitosan using a photolysis assisted ultrasonic process. The aim was to enhance the photodegradation efficiency of ofloxacin and 2,4-dichlorophenoxyacetic acid under sunlight. The synthesized CdInSe@Ch nanocomposite was investigated via different techniques, including XRD, XPS, FTIR, TEM, DSC, TGA, UV-Vis and PL.

The adoption and use of learning analytics tools to improve decision making in higher learning institutions: An extension of technology acceptance model.

Learning Analytics Tools (LATs) can be used for informed decision-making regarding teaching strategies and their continuous enhancement. Therefore, LATs must be adopted in higher learning institutions, but several factors hinder its implementation, primarily due to the lack of an implementation model. Therefore, in this study, the focus is directed towards examining LATs adoption in Higher Learning Institutions (HLIs), with emphasis on the determinants of the adoption process.

Improvement of the photoelectric dye sensitized solar cell performance using Fe/S-TiO nanoparticles as photoanode electrode.

A sulfur nanoparticles-incorporated iron-doped titanium oxide (Fe/TiO) with different ratio was successfully synthesized by photolysis method and utilized as effective photoanode in dye sensitized solar cell (DSSC) application with N719 dye. The photolysis method was contained the irradiation of the Fe, S and Ti mixture solution with 15 W source irradiation, and then calcined the formed precipitate. The DSSCs fabricated with Fe/S-TiO photoanode appeared an improved solar-to-electrical energy conversion efficiency of 6.

Experimental and theoretical study of improved mesoporous titanium dioxide perovskite solar cell: The impact of modification with graphene oxide.

The present study serves experimental and theoretical analyses in developing a hybrid advanced structure as a photolysis, which is based on electrospun Graphene Oxide-titanium dioxide (GO-TiO) nanofibers as an electron transfer material (ETMs) functionalized for perovskite solar cell (PVSCs) with GO. The prepared ETMs were utilized for the synthesis of mixed-cation (FAPbI3)0.8(MAPbBr3)0.

Dendritic fibromyxolipoma with intramuscular involvement: A case mimicking slow flow vascular malformation on imaging.

Dendritic fibromyxolipoma (DFML) is a benign, very rare, and slow-growing soft tissue tumor commonly involving the muscular fascia of the foot, calf, shoulders, back, or head and neck muscles. Many authors consider dendritic fibromyxolipoma a variant of spindle cell lipoma composed of a plexiform vascular pattern, dendritic cytoplasmic processes, and keloidal collagen. Only a few cases have been reported in the shoulder region, and the presented case represents the second case in English literature whose histopathology showed intramuscular involvement.

CHROMATOGRAPHIC SPECTROPHOTOMETRIC DETERMINATION USING REVERSE PHASE HPLC TECHNIQUE FOR MESALAZINE OR MESALAMINE.

The aim of this paper is to estimate Mesalazine or Mesalamine (MESA) in pharmaceuticals. The reversed-phase HPLC (RP-HPLC) results were used to evaluate the type of Mesalazine. Chromatographic analysis was carried out using an HPLC-UV method along with an Ion Pac column (Arcus EP-C18; 5 m, 4.

Breast Cancer Detection and Prevention Using Machine Learning.

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women.

2-(Benzhydryl sulfinyl)-N-sec-butylacetamide) isolated from fig augmented trastuzumab-triggered phagocytic killing of cancer cells through interface with Fcγ receptor.

The objective of the current study was to extract 2-(benzhydryl sulfinyl)-N-sec-butylacetamide), a novel compound from fig, and then determine its role in enhancing trastuzumab-triggered phagocytic killing of SKOV-3 cancer cells. In this study, Soxhlet was used to extract the compound from the mature and air-dried fig fruits. The production of the isolated extracts was enhanced by using polar and non-polar solvents.

An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal.

Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations.

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems.

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools.

Novel Decision-Making Techniques in Tripolar Fuzzy Environment with Application: A Case Study of ERP Systems.

The intuitionistic fuzzy set (IFS) and bipolar fuzzy set (BFS) are all effective models to describe ambiguous and incomplete cognitive knowledge with membership, non-membership, negative membership, and hesitancy sections. But in daily life problems, there are some situations where we cannot apply the ordinary models of IFS and BFS, separately. Hence, there is a need to combine both the models of IFS and BFS into a single one.

Gender Identification and Classification of Flies Using Machine Learning Techniques.

is an important genetic model organism used extensively in medical and biological studies. About 61% of known human genes have a recognizable match with the genetic code of Drosophila flies, and 50% of fly protein sequences have mammalian analogues. Recently, several investigations have been conducted in Drosophila to study the functions of specific genes exist in the central nervous system, heart, liver, and kidney.

An Efficient Cancer Classification Model Using Microarray and High-Dimensional Data.

Cancer can be considered as one of the leading causes of death widely. One of the most effective tools to be able to handle cancer diagnosis, prognosis, and treatment is by using expression profiling technique which is based on microarray gene. For each data point (sample), gene data expression usually receives tens of thousands of genes.

Intelligent Malaysian Sign Language Translation System Using Convolutional-Based Attention Module with Residual Network.

The deaf-mutes population always feels helpless when they are not understood by others and vice versa. This is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images.

Complex pythagorean fuzzy aggregation operators based on confidence levels and their applications.

The most important influence of this assessment is to analyze some new operational laws based on confidential levels (CLs) for complex Pythagorean fuzzy (CPF) settings. Moreover, to demonstrate the closeness between finite numbers of alternatives, the conception of confidence CPF weighted averaging (CCPFWA), confidence CPF ordered weighted averaging (CCPFOWA), confidence CPF weighted geometric (CCPFWG), and confidence CPF ordered weighted geometric (CCPFOWG) operators are invented. Several significant features of the invented works are also diagnosed.

Analysis of medical diagnosis based on variation co-efficient similarity measures under picture hesitant fuzzy sets and their application.

One of the most dominant and feasible technique is called the PHF setting is exist in the circumstances of fuzzy set theory for handling intricate and vague data in genuine life scenario. The perception of PHF setting is massive universal is compared to these assumptions, who must cope with two or three sorts of data in the shape of singleton element. Under the consideration of the PHF setting, we utilized some SM in the region of the PHF setting are to diagnose the PHFDSM, PHFWDSM, PHFJSM, PHFWJSM, PHFCSM, PHFWCSM, PHFHVSM, PHFWHVSM and demonstrated their flexible parts.