Bifunctional NiCo-CuO Nanostructures: A Promising Catalyst for Energy Conversion and Storage.

This investigation explores the potential of co-incorporating nickel (Ni) and cobalt (Co) into copper oxide (CuO) nanostructures for bifunctional electrochemical charge storage and oxygen evolution reactions (OER). A facile wet chemical synthesis method is employed to co-incorporate Ni and Co into CuO, yielding diverse nanostructured morphologies, including rods, spheres, and flake. The X-ray diffraction (XRD) and Raman analyses confirmed the formation of NiCo-CuO nanostructure, with minor phases of nickel oxide (NiO) and cobalt tetraoxide (CoO).

Enhancing glaucoma diagnosis: Generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2.

The disease affects the optic nerve and represents the principle reasons of irreversible vision loss, mostly asymptomatic and uncontrolled. Consequently, early and accurate diagnosis is critical to prevent or reduce its effect, however, conventional diagnostic techniques often fail to provide concrete results. In this regard, we present a new approach built on Generative Adversarial Networks (GAN) and MobileNetV2 pretrained architecture for diagnosing glaucoma.

Integration of silver nanostructures in wireless sensor networks for enhanced biochemical sensing.

Integrating noble metal nanostructures, specifically silver nanoparticles, into sensor designs has proven to enhance sensor performance across key metrics, including response time, stability, and sensitivity. However, a critical gap remains in understanding the unique contributions of various synthesis parameters on these enhancements. This study addresses this gap by examining how factors such as temperature, growth time, and choice of capping agents influence nanostructure shape and size, optimizing sensor performance for diverse conditions.

Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning.

Glaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness, and other ocular deformities. Recent advancements in artificial intelligence have introduced new opportunities for enhancing glaucoma detection.

A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.

The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional Near Infra-red Spectroscopy (fNIRS). Classifying contralateral and ipsilateral motor movements is found challenging among the other mental activity signals.

Leveraging artificial intelligence for advancements in reproductive health.

We are writing to address the growing interest in the role of artificial intelligence (AI) within healthcare, particularly in the field of reproductive health. As technology continues to evolve, AI offers an unprecedented opportunity to transform how we diagnose, treat, and improve access to reproductive services, especially in underserved communities. AI-driven tools, supported by machine learning and big data analytics, are already demonstrating their potential in enhancing outcomes in reproductive health.

SVM directed machine learning classifier for human action recognition network.

Understanding human behavior and human action recognition are both essential components of effective surveillance video analysis for the purpose of guaranteeing public safety. However, existing approaches such as three-dimensional convolutional neural networks (3D CNN) and two-stream neural networks (2SNN) have computational hurdles due to the significant parameterization they require. In this paper, we offer HARNet, a specialized lightweight residual 3D CNN that is built on directed acyclic graphs and was created expressly to handle these issues and achieve effective human action detection.

Elimination of nickel ions in a packed column using clamshell waste as an adsorbent.

The present investigation assessed the viability of utilizing a powdered clam shell in continuous adsorption to eliminate nickel ions from simulated wastewater. The breakthrough curves (BTC) were analyzed by altering the Q (inlet flow rate) in a glass column (ID 5 cm, H 35 cm) with a multi-port and filled with the powdered clamshell adsorbent (PCSA). The PCSA's nickel adsorption efficiency was maximum (87.

Fluorescent Staining-Assisted Evaluation of the Anticancer Potential of Nyctanthes arbor-tristis Extracts in HepG2 Liver Cancer Cells.

The current study's initial goal was to investigate the anticancer activity of the extracts from the flowering plant Nyctanthes arbor-tristis. The ethanol extracts' phytochemical analysis was qualitatively determined. It was determined that the various phytochemical substances had antibacterial, antioxidant, and anticancer effects.

Iron-Doped Cadmium Sulfide/Graphene Oxide (CdFeS/GO) Nanocomposites for Photocatalytic Degradation of Toxic Pollutants.

Heterogeneous photocatalysis has been widely explored as a promising solution for dye degradation due to its cost-effectiveness, ease of recovery, and use of green technology. Precisely, iron-doped cadmium sulfide (CdS) exhibits greater efficiency because of its higher charge separation, more electron transfer efficiency, and adsorption under visible region. In this study, two different ratios of iron-doped cadmium sulfide (CdFeS/GO) nanocomposites such as CdFeS/GO and CdFeS/GO were synthesized using the coprecipitation method and characterized with UV-Vis DRS, XRD, FT-IR, Raman, XPS, and HR-TEM.

Sensorless vector-controlled induction motor drives: Boosting performance with Adaptive Neuro-Fuzzy Inference System integrated augmented Model Reference Adaptive System.

The Model Reference Adaptive System (MRAS) is effective for speed control in sensorless Induction Motor (IM) drives, particularly at zero and very low speeds. This study enhances MRAS's resilience and dynamic performance by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. The research addresses challenges related to parameter uncertainties, load variations, and external disturbances through the combination of MRAS and ANFIS.

An improving secure communication using multipath malicious avoidance routing protocol for underwater sensor network.

The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis.

Dual Multi Scale Attention Network Optimized With Archerfish Hunting Optimization Algorithm for Diabetics Prediction.

Diabetes is a chronic disease that occurs when the body cannot regulate blood sugar levels. Nowadays, the screening tests for diabetes are developed using multivariate regression methods. An increasing amount of data is automatically collected to provide an opportunity for creating challenging and accurate prediction modes that are updated constantly with the help of machine learning techniques.

Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis.

In recent years, the research on abnormal events detection is a significant work in surveillance video. Many researchers have been attracted by this work for the past two decades. As a result, several abnormal event detection approaches have been developed.

Delineation of Optimized Single and Multichannel Approximate DA-Based Filter Design Using Influential Single MAC Strategy for Trans-Multiplexer.

In this paper, a multichannel FIR filter design based on the Time Division Multiplex (TDM) approach that incorporates one multiply and add unit, regardless of the variable coefficient length and varying channels, by associating the resource sharing doctrine is suggested. A multiplier based on approximate distributed arithmetic (DA) circuits is employed for effective resource optimization. Although no explicit multiplication was conducted in this realization, the radix-8 and radix-4 Booth algorithms are utilized in the DA framework to curtail and optimize the partial products (PPs).

Synthesis and characterization of Borassus flabellifer flower waste-generated cellulose fillers reinforced PMC composites for lightweight applications.

The development of eco-friendly materials is a challenging one in the research field. Natural fibers are more accessible, biodegradable, inexpensive, and less dense. They offer fewer health risks and are eco-friendly compared to synthetic fibers.

Development of rare earth element-based hybrid aluminium composites using stir casting process.

Aerospace and marine industries are constantly in search of materials that can provide good strength and durability while also being lightweight. Aluminium composites with adequate reinforcements have been proven to be effective in achieving incredible mechanical properties while also maintaining a good strength to weight ratio. Numerous studies have been done to study the various possibilities of matrix reinforcement combinations in aluminium composites.

A versatile conformal circularly polarized quad-element antenna for X-band applications.

This article presents a flexible four-element antenna for X-band applications. The proposed antenna covers the spectrum ranging from 10.6 to 11.

Automatic screening of retinal lesions for detecting diabetic retinopathy using adaptive multiscale MobileNet with abnormality segmentation from public dataset.

Owing to the epidemic growth of diabetes, ophthalmologists need to examine the huge fundus images for diagnosing the disease of Diabetic Retinopathy (DR). Without proper knowledge, people are too lethargic to detect the DR. Therefore, the early diagnosis system is requisite for treating ailments in the medical industry.

Thermal performance prediction of a V-trough solar water heater with a modified twisted tape using ANFIS, G.L.R., R.T. and SVM models of machine learning.

Four distinct neural models were used to evaluate the efficiency of a V-trough solar water heater (VTSWH) equipped with square-cut twisted tape (SCTT) and V-cut twisted tape (VCTT) at two different twist ratios, 3 and 5. The objective of this study was the use of ANFIS (Adaptive Neuro-Fuzzy Inference System), G.L.

A novel Chua's based 2-D chaotic system and its performance analysis in cryptography.

In this study, the chaotic behavior of a second-order circuit comprising a nonlinear resistor and Chua's diode is investigated. This circuit, which includes a nonlinear capacitor and resistor among its components, is considered one of the simplest nonautonomous circuits. The research explores various oscillator characteristics, emphasizing their chaotic properties through bifurcations, Lyapunov exponents, periodicity, local Lyapunov region, and resonance.

Advanced optimal control approaches for immune boosting and clinical treatment to enhance dengue viremia models using ABC fractional-order analysis.

Introduction: This work focuses on the Dengue-viremia ABC (Atangana-Baleanu Caputo) fractional-order differential equations, accounting for both symptomatic and asymptomatic infected cases. Symptomatic cases are characterized by higher viremia levels, whereas asymptomatic cases exhibit lower viremia levels. The fractional-order model highlights memory effects and other advantages over traditional models, offering a more comprehensive representation of dengue dynamics.

Characterisation of AZ31/TiC composites fabricated via ultrasonic vibration assisted friction stir processing.

In this study, the effect of ultrasonic vibration during Friction Stir Vibration Processing (FSVP) on the microstructure and mechanical behaviour of AZ31/TiC surface composites was investigated. Specifically, Titanium Carbide (TiC) particles were introduced as a reinforcement (15 vol%) into the magnesium alloy AZ31 using both Friction Stir Processing (FSP) and FSVP. Comprehensive examinations were carried out to analyse the microstructure, hardness, and tensile behaviour of the resulting composites.

Exploring the potential of diosgenin as a promising antitumor agent through comprehensive spectroscopic characterization, solvent-solute interactions, topological properties, Hirshfeld surface, and molecular docking interactions with 2NZT and 2I1V proteins.

This study characterizes the steroidal saponin diosgenin by theoretical and experimental spectroscopic techniques. Theoretical simulations were performed using the DFT/B3LYP/6-311++G(d,p) basis set to simulate spectroscopic, structural and other properties. Optimized geometries from simulations and experiments showed strong agreement, with R value of 0.