Compact wearable microstrip antenna design using hybrid quasi-Newton and Taguchi optimization.

A novel approach is introduced for designing a miniaturized wearable antenna. Utilizing Taguchi's philosophy typically entails numerous experimentations runs, but our method significantly reduces these by employing a quasi-Newton approach with gradient descent to estimate process parameter ranges. This hybrid technique expedites convergence by streamlining experiments.

Spectroscopic analysis of NIR emitting phosphors based on NaLaMgWO doped with Nd and Er.

While searching for a new host suitable for near infrared (NIR) emission, we explored a new composition NaLaMgWO. The samples were prepared by solid state reaction method. X-ray Diffraction confirms crystallization of NaLaMgWO in monoclinic system.

Broadband Excited YAlO:Cr, Er Phosphors for Enhancing the Spectral Response of GaSb Solar Cell.

We report Er emission in YAlO host, sensitized by Cr. The excitation bands of Cr at 416 and 555 nm are broad and effectively cover the region 400-600 nm. The phosphor can be useful for converting 400-600 nm radiations to infrared (IR) light around 1521 nm.

The impact of leadership on workplace safety climate and employees' well-being with particular focus on transformational leadership.

. Interest regarding the associated impact of leadership style on employee safety and well-being across organizational settings is on the rise. Transformational leadership forms part of the six leadership constructs with significant research-focused settings.

Convective heat transport in a porous wavy enclosure: Nonuniform multi-frequency heating with hybrid nanofluid and magnetic field.

This work investigates the dynamics of the hybrid nanofluidic convective heat transfer in a permeable thermal system under the influence of multifrequency heating and a magnetic field. The geometry comprises a wavy-walled cavity filled with a water-based hybrid nanoliquid (AlO-Cu-HO) in a saturated porous medium. The finite volume approach is applied to scrutinize the hydro-thermal characteristics resulting from bottom heating and side cooling, considering various flow-controlling parameters.

A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.

Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer offers the advantage of a shorter culture duration, which reduces exposure to laboratory conditions, potentially enhancing embryonic development within a more conducive uterine environment and improving the likelihood of successful pregnancies. In this paper, we present a novel ResNet-GRU deep-learning model to predict blastocyst formation at 72 HPI.

Combined LinkNet-MBi-LSTM for brain activity recognition with new Stockwell transform features.

Recognizing brain activity from EEG waves is an important field of study in biomedical engineering and neuroscience. Conventional approaches usually begin with signal processing techniques to extract features from the EEG data, and then machine learning algorithms are applied to classify the data. However, the spatial resolution of these EEG signals is low, which makes it difficult to pinpoint the exact location of the neural activity source in the brain.

ZIF-67 derivatives of NiCoS Decorated in Salen-Complex Amine Functionalization GO Layers for High-Performance Applications in Supercapacitor Devices.

The study develops polyamine-functionalized graphene oxide-supported NiCoS nanomaterial using a metal-organic framework (MOF). This modification adds free amines and oxygen functionality to the graphene oxide electrode surface, resulting in the decrease in the 2 theta value from 11.2 to 7.

A Machine Learning Assisted Non-Enzymatic Electrochemical Biosensor to Detect Urea Based on Multi-Walled Carbon Nanotube Functionalized with Copper Oxide Micro-Flowers.

Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations.

Response surface optimization of a single-step castor oil-based biodiesel production process using a stator-rotor hydrodynamic cavitation reactor.

In order to combat environmental pollution and the depletion of non-renewable fuels, feasible, eco-friendly, and sustainable biodiesel production from non-edible oil crops must be augmented. This study is the first to intensify biodiesel production from castor oil using a self-manufactured cylindrical stator-rotor hydrodynamic cavitation reactor. In order to model and optimize the biodiesel yield, a response surface methodology based on a 1/2 fraction-three-level face center composite design of three levels and five experimental factors was used.

Inorganic nanoparticles: An effective antibiofilm strategy.

Biofilm is a common problem associated with human health. Pathogenicity and increase in resistance of bacteria require urgent development of effective ways for the treatment of bacterial diseases. Different strategies have been developed for the treatment of bacterial infections among which nanoparticles have shown greater prospects in battling with infections.

Molecular Dynamics Computational Study of Sustainable Green Surfactant for Application in Chemical Enhanced Oil Recovery.

Green surfactant (GS) flooding, an environmentally friendly chemical Enhanced Oil Recovery (cEOR) method, is explored in this molecular dynamics (MD) simulation study. This study evaluates the ability of ()-2-dodecanamido-aminobutanedioic as a GS for cEOR, assessing its performance with hexane (C6), dodecane (C12), and eicosane (C20) as representative oils. In the case of the bulk system, a comprehensive molecular-level investigation provides structural details such as the radial distribution function, solvent-accessible surface area, GS adsorption dynamics, diffusivity, and emulsion stability of the GS, oil, and water systems.

Nerve Growth Factor Signaling Modulates the Expression of Glutaminase in Dorsal Root Ganglion Neurons during Peripheral Inflammation.

Glutamate functions as the major excitatory neurotransmitter for primary sensory neurons and has a crucial role in sensitizing peripheral nociceptor terminals producing sensitization. Glutaminase (GLS) is the synthetic enzyme that converts glutamine to glutamate. GLS-immunoreactivity (-ir) and enzyme activity are elevated in dorsal root ganglion (DRG) neuronal cell bodies during chronic peripheral inflammation, but the mechanism for this GLS elevation is yet to be fully characterized.

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules.

Thyroid nodules are commonly diagnosed with ultrasonography, which includes internal characteristics, varying looks, and hazy boundaries, making it challenging for a clinician to differentiate between malignant and benign forms based only on visual identification. The advancement of AI, particularly DL, provides significant breakthroughs in the domain of medical image identification. Yet, there are certain obstacles to achieving accuracy as well as efficacy in thyroid nodule detection.

Synthesis and study of optical properties of a Gd-doped NaYF phosphor for phototherapy lamp application.

In recent trends, radiation falls under the narrowband ultraviolet-B region (305-315 nm) widely used in phototherapy lamp applications in the treatment of skin diseases. In this paper, we report a Gd-doped NaYF luminescent material synthesized for the first time using the low-temperature co-precipitation method. It crystallized into a face-centred cubic structure, as confirmed by X-ray diffraction characterization techniques and Rietveld refinement.

Sensing lead ions in water: a comprehensive review on strategies and sensor materials.

It is well-known fact that elevated lead ions (Pb), the third most toxic among heavy metal ions in aqueous systems, pose a threat to human health and aquatic ecosystems when they exceed permissible limits. Pb is commonly found in industrial waste and fertilizers, contaminating water sources and subsequently entering the human body, causing various adverse health conditions. Unlike being expelled, Pb accumulates within the body, posing potential health risks.

A novel approach for protein secondary structure prediction using encoder-decoder with attention mechanism model.

Computational biology faces many challenges like protein secondary structure prediction (PSS), prediction of solvent accessibility, etc. In this work, we addressed PSS prediction. PSS is based on sequence-structure mapping and interaction among amino acid residues.

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards.

Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios.

Assessment of brain tumor detection techniques and recommendation of neural network.

Objectives: Brain tumor classification is amongst the most complex and challenging jobs in the computer domain. The latest advances in brain tumor detection systems (BTDS) are presented as they can inspire new researchers to deliver new architectures for effective and efficient tumor detection. Here, the data of the multi-modal brain tumor segmentation task is employed, which has been registered, skull stripped, and histogram matching is conducted with the ferrous volume of high contrast.

An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production.

This study aims to optimize the power generation of a conventional Manzanares solar chimney (SC) plant through strategic modifications to the collector inlet height, chimney diameter, and chimney divergence. Employing a finite volume-based solver for numerical analysis, we systematically scrutinize influential geometric parameters, including collector height (h = 1.85 to 0.

Novel energy efficient RND inverter using quantum dot cellular automata in nanotechnology.

Quantum-Dot Cellular Automata (QCA) is a promising technology for designing high-performance and efficient logic circuits, surpassing traditional Complementary Metal Oxide Semiconductor approaches. In today's digital era, the demand for digital circuits with high speed, device density, and energy efficiency is paramount. This paper focuses on the innovative Rotated Normal Cells with Displacement (RND) inverter model, employing normal and rotated cells with a 10 nm displacement through a cell interactive method.

Biosynthesis of Al O and europium-doped Al O nanoparticle using Cymbopogon citratus (lemongrass) extract and its antibacterial property.

Cymbopogon citratus-mediated pure aluminium oxide (Al O ) and europium (Eu)-doped Al O with different amounts of metal ion were prepared using a green synthesis method. Synthesised nanoparticles were characterised by ultraviolet (UV)-visible spectroscopy, photoluminescence (PL), Fourier-transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD) and scanning electron microscopy (SEM). Synthesis of nanoparticles is confirmed by using UV-visible spectroscopy showing maximum absorption at 411 and 345 nm for Al O and Eu-doped Al O , respectively.

Non-enzymatic glucose detection with screen-printed chemiresistive sensor using green synthesised silver nanoparticle and multi-walled carbon nanotubes-zinc oxide nanofibers.

Non-enzymatic screen-printed chemiresistive interdigitated electrodes (SPCIE) were designed and fabricated using a low-cost screen-printing method for detection of the glucose. The interdigitated electrodes (IDE) pattern was printed using conductive graphene ink on the glossy surface of the photo paper. The proposed glossy photo paper-based SPCIE are functionalized with multi-walled carbon nanotubes-zinc oxide (MWCNTs-ZnO) nanofibers to create the chemiresistive matrix.

A Review of Computational Approach for S-system-based Modeling of Gene Regulatory Network.

Inference of gene regulatory network (GRN) from time series microarray data remains as a fascinating task for computer science researchers to understand the complex biological process that occurred inside a cell. Among the different popular models to infer GRN, S-system is considered as one of the promising non-linear mathematical tools to model the dynamics of gene expressions, as well as to infer the GRN. S-system is based on biochemical system theory and power law formalism.