Marine microorganisms live in severe environments, which causes them to create extracellular polysaccharides (EPS) in reaction to external stimuli. In this study, a marine bacterium was isolated from the Parangipettai Vellar estuary and sequenced to identify the organism as Salinococcus roseus, which can produce EPS. The ideal pH, carbon supply and nitrogen source for maximal EPS synthesis were identified as 7, glucose and ammonium sulfate, respectively. The FTIR examination of the EPS revealed polysaccharides, proteins, lipids and nucleic acid functional groups. The XRD study revealed that the EPS molecules are amorphous, whereas the surface morphology shows a continuous flat shape with fissures and folds. The created EPS was loaded into the agar bridge of the two-compartment microbial fuel cell (MFC). The voltage generated by the agar EPS bridge is just slightly higher than that of the ordinary agar salt bridge. The agar EPS bridge produced the most power (12mW/m2) at a current density of 101mA/m2. The electrical output and treatment performance observed in this work arise from the nonlinear coupling between microbial reaction kinetics, charge transfer processes and variable-coefficient ion transport across the EPS bridge. Such nonlinear behavior is consistent with recent developments in nonlinear wave and transport analysis in inhomogeneous forced media, which provide a mathematical foundation for modeling EPS-mediated MFC systems. Similarly, the pH, conductivity and chemical oxygen demand (COD) removal rates were found to be higher in the EPS-loaded agar bridge, indicating that it is a better electron transfer medium. As a result, the EPS-driven MFC might be investigated further for use as a sustainable source of combined wastewater treatment and energy generation.

In this study, an analytical investigation of the squeezing flow of a blood-based, couple-stress ternary hybrid nanofluid between parallel horizontal channels, with the effect of thermal radiation, is presented. The ternary hybrid nanofluid is the combination of silver (Ag), alumina (Al2O3), and titania (TiO2) nanoparticles in a non-Newtonian blood base fluid (Casson). Appropriate similarity transformations are applied to the continuity, momentum, and energy equations to obtain nonlinear ordinary differential equations, which are then solved numerically using the Homotopy Analysis Method (HAM) available in the Mathematica packages BVPh 1.0 and BVPh 2.0. The effects of the squeezing parameter, the Casson parameter, the couple stress parameter, the thermal radiation parameter, and the volume fraction of the nanoparticles on the velocity and temperature profiles, the skin friction coefficient, and the local Nusselt number are investigated through a comprehensive parametric study. The results show that the fluid velocity decreases as the Casson and couple stress parameters increase, whereas the squeezing parameter increases. The temperature distribution becomes more uniform with higher thermal radiation and nanoparticle volume fraction because of increased radiative and conductive heat transport, and lower squeezing due to the squeezing force. Besides, the convergence analysis proves the stability and accuracy of HAM solutions. The results confirm that the blood-based ternary hybrid nanofluids exhibit excellent thermal characteristics that could be used in biomedical thermal management, microfluidic devices, cooling systems, and advanced heat transfer systems.

In this work, a sustainable and cost-effective approach was developed to synthesize a ternary composite (NiMn2O4/TiO2-Chitosan) via an eco-friendly method using coconut cotyledon extract as a reducing agent. This environmentally friendly synthesis route successfully developed a nanocomposite with distinct structural and morphological characteristics. XRD analysis confirmed the formation of a highly crystalline phase, while FESEM revealed mixed and irregular morphologies. EDAX spectrum qualitatively verified the presence of Ni, Mn, Ti, C and O in the composite. HRSTEM further displayed a disordered cubic structure, indicating nanoscale complexity. The optical band gap of the composite was determined to be 2.93[Formula: see text]eV, suggesting potential for semiconducting applications. Electrochemical evaluation in a three-electrode system using KOH electrolyte demonstrated a remarkable specific capacitance of 1089.41[Formula: see text]F g[Formula: see text] at a current density of 1[Formula: see text]mA g[Formula: see text]. Furthermore, the electrode exhibited an excellent energy density of 45.77[Formula: see text]Wh kg[Formula: see text] with a power density of 275[Formula: see text]W kg[Formula: see text], underscoring its superior energy storage performance. These results confirm the successful fabrication of highly efficient electrode material through a green, economical and facile method. The unique architecture and outstanding supercapacitor behavior of NiMn 2 O 4 /TiO 2 -Chitosan composite make it a promising candidate for advanced energy storage applications.

First-principles calculations were performed to investigate the pressure-dependent structural, electronic, mechanical, optical, and thermal properties of ASiF 3 ([Formula: see text], K, Rb) fluoroperovskites from 0 to 12 GPa using DFT within the GGA-PBE functional. Band-structure analysis shows that NaSiF 3 , KSiF 3 , and RbSiF 3 are direct bandgap semiconductors at ambient pressure, with GGA-PBE bandgaps of 0.609, 0.805, and 0.918[Formula: see text]eV, respectively. The bandgaps decrease strongly under compression, and additional GGA[Formula: see text]U and HSE06 calculations confirm pressure-induced bandgap narrowing. Within HSE06, the bandgaps decreased from 1.198, 1.730, and 1.782[Formula: see text]eV at 0[Formula: see text]GPa to 0, 0.642, and 0.671[Formula: see text]eV at 12[Formula: see text]GPa for NaSiF 3 , KSiF 3 , and RbSiF 3 , respectively. Lattice parameters and bond lengths reduce at higher pressures, confirming lattice compression while preserving the cubic crystal framework. Optical conductivity and absorption are improved with a red shift to lower photon energies under pressure, indicating a pressure-tunable optical response. Mechanical analysis indicates anisotropic and ductile behavior, with KSiF 3 and RbSiF 3 showing better stability under compression than NaSiF 3 . Thermal analysis shows pressure-dependent Debye temperature, melting temperature, and lattice thermal conductivity. These results indicate the possible relevance of lead-free ASiF 3 fluoroperovskites for pressure-tunable optoelectronic and ultraviolet photonic applications.

In medical diagnostics, the accurate classification and analysis of biomedical signals play a crucial role, particularly in the diagnosis of neurological disorders such as epilepsy. Electroencephalogram (EEG) signals, which represent the electrical activity of the brain, are fundamental in identifying epileptic seizures. However, challenges such as data scarcity and imbalance significantly hinder the development of robust diagnostic models. Addressing these challenges, in this paper, we explore enhancing medical signal processing and diagnosis, with a focus on epilepsy classification through EEG signals, by harnessing AI-generated content techniques. We introduce a novel framework that utilizes generative adversarial networks for the generation of synthetic EEG signals to augment existing datasets, thereby mitigating issues of data scarcity and imbalance. Furthermore, we incorporate an attention-based temporal convolutional network model to efficiently process and classify EEG signals by emphasizing salient features crucial for accurate diagnosis. Our comprehensive evaluation, including rigorous ablation studies, is conducted on the widely recognized Bonn Epilepsy Data. The results achieves an accuracy of 98.89% and F1 score of 98.91%. The findings demonstrate substantial improvements in epilepsy classification accuracy, showcasing the potential of AI-generated content in advancing the field of medical signal processing and diagnosis.

The goal of this work is to investigate the early diagnosis and treatment of conjunctivitis viral infections in the eyes by introducing non-medication recovery strategies. To this end, a new mathematical model is developed for better control of conjunctivitis virus. The essential characteristics of an epidemic model bounded-ness and uniqueness are examined for bounded discoveries using Banach space. A recently built system, SEIRuRi, R u R i , is analyzed computationaly and quantitatively to find its stable position. The flip bifurcation of the proposed system has also been verified under different parameters impact. Using the next generation approach, we have generated the reproductive number "R0"and R 0 "and confirmed its sensitivity analysis based on each parameter to determine how sensitive the rate of parameter change is in the proposed eye infection. The created system's solution is found utilizing the Atangana-Toufik technique, a sophisticated method for dependable bounded solution including error analysis, employing various fractional values. In order to confirm that those with strong immune systems were able to recover from acute stage infections without the need for medicine, simulations have been created to mimic the actual behavior and consequences of conjunctivitis virus infections. Due to the patients' strong immune systems and preventative measures, as well as early diagnosis for treatment and better control, we determine the actual state of the conjunctivitis virus control for both those receiving medicine and those who are not receiving medicine.

Consumer electronics are becoming increasingly popular in our daily life. Enabled with Artificial Intelligence (AI) of Things (AIoT), consumer electronics can autonomously analyze user data and learn user preferences. AIoT has empowered various personal consumer applications, such as healthcare and recommendation, in which user sentiment analysis is necessary. This paper studies sentiment analysis by analyzing user data generated from consumer electronics, especially image data. Considering that the images generated by consumer electronics generally have blended emotions, we apply the Label Enhancement (LE) technologies to enhance the emotion labels into fine-grained emotion distributions. To match the need of real-world AIoT scenarios, we put forward in this paper the first trustworthy LE algorithm, called LE-Weighted k-Nearest Neighbors (LE-WkNN). Theoretical analysis shows that the enhanced emotion distributions by LE-WkNN are guaranteed to approach the ground-truth ones, which has strong theory guidance. Second, we train a convolution neural network to learn the enhanced emotion distributions. Finally, we conduct experiments on three large-scale emotion datasets. The experimental results validate that LE-WkNN accurately enhances the emotion distributions and our model achieves the best performance for sentiment analysis. Overall, LE-WkNN is trustworthy and has great potential for consumer electronics applications.

Wind energy is one of the leading renewable sources. Multiple wind turbines are installed at a given site to produce more electrical energy, making wind farm efficiency optimization a vital area of study. The flow field in the wake of the first row of turbines is characterized by wind velocity deficit and high turbulent intensity. For this reason, a downstream turbines in a wind farm can capture less wind energy than the first-row turbine. The present research specifically focuses on intelligently estimating the wake speed in wind farm using well-known five machine learning algorithms. The data used is computationally synthesized from the Jensen wake model. Machine learning models such as Artificial Neural Networks (ANN), Random Forest Regression (RFR), Decision Tree Regression (DTR), Support Vector Machines (SVM), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are implemented to adopt the complex nonlinear relationship for accurately estimating the wake speed. Among the tested models, the Random Forest Model performed the best with an R2 score of 0.9905, Mean Square Error (MSE) of 1.26E-06, and Root Mean Square Error (RMSE) of 0.0011, demonstrating its effectiveness in accurately estimating wake effects. In comparison, the Decision Tree Regression also showed promising results with an R2 score of 0.9646, although it exhibited a slightly higher MSE of 4.75E-06, and RMSE of 0.0021. The outcomes of this research have the potential to revolutionize wind farm optimization by providing more adaptive and faster wake estimation.

Assistive technologies, particularly multi-fingered robotic hands (MFRHs), are critical for enhancing the quality of life for individuals with upper-limb disabilities. However, achieving precise and stable control of such systems remains a significant challenge. This study proposes an Improved Grey Wolf Optimization (IGWO)-tuned Linear Quadratic Regulator (LQR) to enhance the control performance of an MFRH. The MFRH was modeled using Denavit–Hartenberg kinematics and Euler–Lagrange dynamics, with micro-DC motors selected based on computed torque requirements. The LQR controller, optimized via IGWO to systematically determine weighting matrices, was benchmarked against PID and PID-PSO controllers under diverse input scenarios. For step input, the IGWO-LQR achieved a settling time of 0.018 s with zero overshoot for Joint 1, outperforming PID (settling time: 0.0721 s; overshoot: 6.58%) and PID-PSO (settling time: 0.042 s; overshoot: 2.1%). Similar improvements were observed across all joints, with Joint 3 recording an IAE of 0.001334 for IGWO-LQR versus 0.004695 for PID. Evaluations under square-wave, sine, and sigmoid inputs further validated the controller’s robustness, with IGWO-LQR consistently delivering minimal tracking errors and rapid stabilization. These results demonstrate that the IGWO-LQR framework significantly enhances precision and dynamic response.

Parameter identification for a proton exchange membrane fuel cell (PEMFC) entails employing optimisation techniques to discover the best unknown parameter values required to generate an accurate fuel cell performance prediction model. This technique, known as parameter identification, is important since manufacturers' datasheets do not usually disclose these values. To address this, the manuscript examines five optimisation strategies, including the suggested algorithm, Enhanced Tunicate Swarm Optimizer (ETSO), for predicting these parameters in PEMFCs. Each technique uses the six unknown parameters as decision variables, aiming to reduce the sum squared error (SSE) between anticipated and observed cell voltages. The data reveal that the suggested strategy outperforms existing approaches and cutting-edge optimizers. The two models are used to assess the dependability and performance of the PEMFC. The results are also compared to the non-parametric tests, and it is found that the suggested method outperforms the other algorithms in both suggested models.

This paper's goal is to investigate the oscillatory flow of double-diffusive convection in a Voigt fluid. We re-design the basic equations of the channel flow, which initially appear dimensional, in a dimensionless manner using non-dimensional variables. The oscillation technique transforms the governing equations into coupled ordinary differential equations (CODEs). We have derived the analytical expressions for velocity, temperature, and concentration distribution by solving the CODEs using the direct analytical method. The study focuses on how physical factors, like the thermal Grashof number, the solutal Grashof number, the Prandtl number, the Lewis number, and the Voigt fluid parameter, affect different aspects of flow, such as speed, temperature, concentration, rate of heat transfer, and mass transfer. Notably, the study finds that skin friction increases on both channel plates with increasing injection on the heated plate. The significance of double-diffusive oscillatory flow lies in its ability to improve heat and mass transfer rates, as well as its impact on pattern formation and stability, which influences a wide range of applications from engineering design to environmental studies.

With the accumulation of the ionospheric Radio Occultation (RO) measurements provided by Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), it becomes accessible to use the long-term observations provided by their two COSMIC missions, namely COSMIC-1 and COSMIC-2, to exploit the ionospheric climatology research. As such, the spatial and temporal of diurnal, seasonal and annual variations of the electron density peak (NmF2 and hmF2) are investigated. The dataset used encompasses COSMIC observations spanning 16 consecutive years from Jan 2007 to Dec 2023, corresponding to the minimum phase of solar cycle 23 (2007–2008), complete solar cycle 24 (2009–2019) and the ascending phase of the solar cycle 25 (2020–2023). The present study emphasizes some of the preceding features that prescribed the morphology of the ionosphere. It showed that the NmF2 generally becomes fully developed in the afternoon time and enhanced to higher values in the dusktime hours, while the lowest levels are observed around post-sunset until the early morning. Further, the NmF2 variations exhibit semiannual pattern, with peak NmF2 values occurring during equinoxes. Whereas the hmF2 variations exhibit annual pattern peaks around summer in the northern hemisphere but around winter in the southern hemisphere. Results additionally verify that the long-term fluctuations in ionospheric electron density parameters are closely aligned with changes in the solar activity cycle. They also demonstrate that, irrespective of the season, the NmF2 saturates as the solar activity approaches higher levels. Besides, this dusktime NmF2 is always much higher and more pronounced in the northern hemisphere than the southern hemisphere irrespective season. The mechanism of the dusk side hemispheric asymmetry and saturation to be investigated in further studies. Consequently, our study extends previous research studies by identifying the long-term patterns of ionospheric parameters, which would be beneficial for constructing and developing well-defined ionospheric modeling.

The aim of the present work is to discuss the fractional mass-spring system with damping and driving force, considering a simple modification to the fractional derivatives with a non-singular kernel of the Atangana–Baleanu and Caputo–Fabrizio types. We introduce two novel modified fractional derivatives that offer advantages when the fractional differential equations involve higher-order fractional derivatives of order 1+α or α +1 , with 0<α <1 . Previous definitions of fractional derivatives with non-singular kernel do not have a unique definition, leading to significant inconsistencies. One of the main results of the present work is that the proposed modifications provide a unique result for the fractional-order derivatives 1+α and α +1 . Additionally, we apply these two novel fractional derivatives to the fractional mass-spring system with damping and driving force. In the case of the modified Caputo–Fabrizio fractional derivative, novel analytical solutions have been constructed, showing interesting oscillating time evolution with a transient term not previously reported. This transient term features an initial nonzero oscillating return away from the equilibrium position. For the modified Atangana–Baleanu fractional derivative, the numerical solutions also exhibit this nonzero oscillating return away from the equilibrium position. These results are not present when using the Caputo singular kernel derivative, as demonstrated in the comparison figures reported here.

This study investigates the impact of the Stephan blowing and Cattaneo-Christov flux model on bioconvective flow of dusty hybrid nanofluid over a Riga plate in the presence of gyrotactic microorganisms and variable dust particles volume friction. Mass and heat phenomena are explored in the context of Stefan blowing impacts. The hybrid nanofluid consists of nanoparticles of MgO and Ag base fluid water. The Cattaneo-Christov mass and heat flux model has a significant impact on the bioconvective flow. The model predicts a slower temperature distribution and a higher concentration gradient than the traditional Fick's law and Fourier's law, respectively. The occurrence of gyrotactic microorganisms enhance the flow characteristics. This model is important for optimizing mass and heat transfer in a variety of engineering systems, including heating and cooling technologies, where effective thermal control is essential. It can be used by employing the regulated migration of microbes. By maximizing the removal of impurities, it helps with the design of sophisticated water purification systems in environmental engineering. The amalgamation of gyrotactic microorganisms and Stefan blowing effects augments the comprehension of intricate fluid dynamics, maybe resulting in advancements in microfluidic apparatuses and bioinspired technologies. In order to transform the controlling PDEs into nonlinear ODEs, a new set of non-dimensional variables is used. The MATLAB (RKF-45th) technique is then used to resolve the ODEs numerically. As the Stephan blowing parameter (0.1 <= Sb <= 1.9) ) increases, the outcomes show that the flow distributions upsurge for both the dust and fluid phases, but the dust and fluid phase thermal distributions drop.

The optimization of resources and reduction of costs through efficient inventory management are paramount to organizational success. This study undertakes a comparative analysis of two distinct forecasting methodologies, Exponential Smoothing (ES) and Gradient Boosting (GB), within the framework of materials forecasting aimed at inventory minimization. Our study introduces innovation by methodically scrutinizing these approaches within a unified framework, shedding light on their merits and shortcomings. This comparative analysis gives practitioners a practical roadmap for the optimal forecasting strategy to streamline inventory management operations. Methodologies are evaluated based on their efficiency in predicting material demand, encompassing metrics such as accuracy, computational efficiency, and suitability across various inventory management scenarios. Response surface methodology entails refining processes to modify factorial variables’ configurations to attain a desired peak or trough in response. The SPSS results show that the ES method has 43.20%, surpassing the accuracy of the inventory optimization model, which stood at 65.08%. The response surface methodology results show that 45.20% profit was achieved for the variable and operational cost process parameters. This research seeks to unveil the traces of each method, facilitating decision-makers in selecting an optimal forecasting strategy tailored to their specific inventory management requirements. The analysis shows that the ES method surpasses the accuracy of the GB machine learning for material forecasting to minimize inventory.

For sustainable hospitality and tourism, the validity of online evaluations is crucial at a time when they influence travelers’ choices. Understanding the facts and conducting a thorough investigation to distinguish between truthful and deceptive hotel reviews are crucial. The urgent need to discern between truthful and deceptive hotel reviews is addressed by the current study. This misleading “opinion spam” is common in the hospitality sector, misleading potential customers and harming the standing of hotel review websites. This data science project aims to create a reliable detection system that correctly recognizes and classifies hotel reviews as either true or misleading. When it comes to natural language processing, sentiment analysis is essential for determining the text’s emotional tone. With an 800-instance dataset comprising true and false reviews, this study investigates the sentiment analysis performance of three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Among the training, testing, and validation sets, the CNN model yielded the highest accuracy rates, measuring 98%, 77%, and 80%, respectively. Despite showing balanced precision and recall, the LSTM model was not as accurate as the CNN model, with an accuracy of 60%. There were difficulties in capturing sequential relationships, for which the RNN model further trailed, with accuracy rates of 57%, 57%, and 58%. A thorough assessment of every model’s performance was conducted using ROC curves and classification reports.

Orthogonal time frequency selectivity is considered one of the most promising advanced waveforms for high-mobility conditions in beyond-fifth-generation networks. A high peak-to-average power ratio in orthogonal time frequency introduces nonlinearity and degrades the power amplifier performance. In this work, we propose a novel automatic amplitude reduction neural network algorithm combined with partial transmission sequence and selective mapping methods using the Vendermonde matrix for generating phase sequences. In a conventional selective mapping methods or partial transmission sequence, selecting an optimal phase increases the complexity, which is overcome via Vendermonde matrix in a proposed framework. The minimization of the peak-to-average power ratio is obtained at the double-stage process: in the first stage, the threshold amplitude is set, and the amplitude exceeding the threshold is reduced by using an automatic amplitude reduction neural network and then the partial transmission scheme or selective mapping methods. The simulation results reveal that, compared with the conventional algorithms, the proposed algorithms yield a significant peak average-to-power ratio, bit error rate, and power spectrum density performance.

Many existing control techniques proposed in the literature tend to overlook faults and physical limitations in the systems, which significantly restricts their applicability to practical, real-world systems. Consequently, there is an urgent necessity to advance the control and synchronization of such systems in real-world scenarios, specifically when faced with the challenges posed by faults and physical limitations in their control actuators. Motivated by this, our study unveils an innovative control approach that combines a neural network-based sliding mode algorithm with fuzzy logic systems to handle nonlinear systems. This proposed controller is further enhanced with an intelligent observer that takes into account potential faults and limitations in the control actuator, and it integrates a fuzzy logic engine to regulate its operations, thus reducing system chatter and increasing its adaptability. This strategy enables the system to maintain regulation in the face of control input constraints and faults and ensures that the closed-loop system will achieve convergence within a finite-time frame. The detailed explanation of the control design confirms its finite-time stability. The robust performance of the proposed controller applied to autonomous and non-autonomous systems grappling with control input limitations and faults demonstrates its effectiveness.

A proton-exchange membrane fuel cell (PEMFC) generates electricity, heat, and water from oxygen and fuel. Hydrogen is recommended as a fuel because it is a renewable fuel when manufactured, for example, by water electrolysis using renewable energy power. Porous metal has excellent characteristics such as controlled permeability, low density, and high porosity. Corrosion is now the most major hurdle to the use of porous metal in PEMFCs, and owing to the porous metal’s complicated internal structure, additional challenges must be addressed in the coating preparation process. As a result, this article figures out how to successfully handle the porous metal corrosion problem in a PEMFC setting, which increases the porous metal utilization in the fuel cell industry. This article also examined the flow field in PEMFC and important characteristics. The influence of flow field in the fuel cell was also investigated.