Easwari Engineering College

This study examines how NaOH treatment and alumina filler affect the mechanical properties, water absorption, thermal degradation, and sliding wear of epoxy composites reinforced with pineapple leaf fiber. NaOH treatment greatly improved the composites' tensile, flexural, and impact strengths by strengthening the bond between the fiber and matrix. Furthermore, the incorporation of alumina filler further elevated the mechanical properties. The composite with 10% alumina showed peak values of 41.4 MPa in tensile strength, 63.8 MPa in flexural strength, and 37.6 kJ/m ² in impact strength. Because hygroscopic parts were removed from the treated composites, they absorbed much less water. The 15% alumina composite had the lowest absorption at 18% after 192 h. Thermal degradation analysis showed that NaOH treatment improved thermal stability, with the 15% alumina composite having the highest char residue (15.3%) at 700°C. Sliding wear tests showed that alumina reinforcement significantly reduced specific wear rate (SWR) and coefficient of friction (COF). The improved 15% alumina composite had an SWR of 0.2598 × 10 ⁻⁵ mm ³ /Nm and a COF of 0.103 when sliding at 120 cm/s, with a 45 N load and over 1500 m of distance. A scanning electron microscopy study found that untreated composites experienced severe abrasive wear, while treated and reinforced composites exhibited mild adhesive wear. The study shows that treating PALF composites with NaOH and adding alumina enhance their mechanical, thermal, and tribological properties, making them suitable for high‐performance industrial applications. Highlights Alumina filler improved tensile (41.4 MPa) and flexural strength (63.8 MPa). NaOH‐treated composites absorbed 18% less moisture, enhancing durability. Thermal stability improved, with 15.3% char residue at 700°C for 15% alumina. Optimized composite achieved the lowest wear rate (0.2598 × 10 ⁻⁵ mm ³ /Nm). Artificial neural network and response surface methodology accurately predicted and optimized composite wear behavior.

Fluorescence-based photoinduced electron transfer (PET) has garnered significant attention in the molecular recognition field in recent years because of its unique and desirable photophysical properties. Recent advancements in PET-based chemosensors have demonstrated their potential for real-time monitoring of pollutants such as heavy metals, pesticides, and organic contaminants in various environmental matrices. This review emphasizes the recent advancements in fluorogenic and chromogenic PET-based chemosensors based on Anthracene, Imidazole, Indole, Pyrrole, Thiazole, Naphthalene, Quinoline, Calix[4]arene, Fluorescein, Quantum Dots, Schiff base compounds and also focusing on their molecular design, sensing mechanisms, and photophysical properties reported from the year 2011 to 2024.

Because of the continuous computational and communication growth, the Internet of Things (IoT) plays a significant role in many real-time applications. Hence, huge amount of data are produced by the IoT devices, requires privacy preserving models for securing the data. For preserving the privacy of data, many machine learning models are developed, still, certain models lack in efficiency. For this, an Advanced Privacy Protection (APP) Machine Learning Model is proposed in this paper. The model uses cryptographic techniques for preserving the data privacy in efficient manner. Moreover, the model contains a Secure Data Provider (SDP) for processing the privacy protection-based training with the data on the nodes. The data privacy is ensured and the model factors can be acquired by SDP, where Support Vector Machine (SVM) is the training model employed. The results show that the model significantly increases the accuracy of training model and data privacy, minimizes the communication overhead, computational complexities.

Diffusive coupling plays a crucial role in numerous applications by facilitating the diffusion of energy or information among systems. Typically, this type of coupling is defined by the same variables in coupled systems. However, this paper demonstrates that in circulant systems, diffusive coupling can be applied to cross-variables with cyclical symmetry. This approach results in a unique form of synchronization known as circulant synchronization, occurring among the cross-variables. Furthermore, a dynamic analysis reveals that these systems can exhibit various synchronization manifolds, including periodic, quasiperiodic, and chaotic attractors.

The main objective of this study is to develop an intelligent, resilient event-triggered control method for fractional-order multiagent networked systems (FOMANSs) using reinforcement learning (RL) to address challenges resulting from unknown dynamics, actuator faults, and denial-of-service (DoS) attacks. First, the challenge of unknown system dynamics within their environment must be addressed to achieve desired system stability in the face of unknown dynamics or to optimize consensus in FOMANSs. To address this problem, an adaptive learning law is implemented to handle unknown nonlinear dynamics, parameterized by a neural network, which establishes weights for a fuzzy logic system utilized in cooperative tracking protocols. A novel distributed control policy facilitates signal sharing through RL among agents, reducing error variables through learning. Moreover, this study combines an RL algorithm with the sliding mode control strategy to optimize the parameterization of the distributed control protocol, thereby eliminating its constraints on initial conditions. Second, realizing that DoS attacks typically make the actuator signal inaccessible for distributed control protocols, an innovative intelligent dual-event-triggered control strategy is formulated to reduce the effects of DoS attacks. By coordinating nested event triggers across various channels, the distributed control input is protected from incorrect signals from DoS attacks, thus ensuring its resilience. To address this problem, an intelligent security dual-event-triggered control protocol guarantees Mittag–Leffler stability of the closed-loop system and ensures effective sliding motion conditions. This distributed control protocol ensures robust tracking of control tasks and mitigates “Zeno behavior” during event triggering. The proposed control strategy is validated using a single-link flexible-joint robotic manipulator system.

The scarcity of fine aggregate from natural resources has caused researchers to search for alternative materials to replace conventional river sand and manufactured sand. The disposal of fly ash is one of the challenges thermal power plants face, even though a part has been utilized in cement production. The current study is undertaken to address the issues mentioned above. The novelty of this study is the use of i) high-volume fly ash (Class F) as a replacement for fine sand up to 100% and ii) foam generated using a natural and environment-friendly plant species in foam concrete for design densities ranging from 900 kg/m3 to 1500 kg/m3. The foam was generated using soapnut, a plant-based foaming agent. The paper reports the effectiveness of large-volume fly ash as a replacement for sand in terms of workability, dry density, compressive strength, pore-size distribution, water absorption, sorptivity, and drying shrinkage of foam concrete. The inclusion of fly ash in foam concrete prevented the coalescence of bubbles by providing a protective layer around bubbles and enhanced the concrete properties. Using fly ash as a sand replacement at a constant foam volume enhanced the compressive strength through pore-refinement, which was confirmed through microscopic studies. An increase in the fly-ash replacement leads to an increase in sorptivity and shrinkage due to the rise in paste volume, consequent to the reduction in the requirement of foam volume.

The present research investigates the effects of activation energy and electroosmosis on the peristaltic transport of a pseudoplastic nanofluid flowing through an asymmetric, flexible microchannel. The analysis incorporates crucial factors, such as thermal radiation, magnetic field, thermophoresis, and Brownian motion. The governing mathematical model is simplified using the lubrication approximation. The resulting system of equations is then numerically solved using NDSolve in Mathematica. The impact of key fluid properties on electroosmotic flow is examined through graphical analysis. Results demonstrate that nanofluid velocity decreases with increasing Debye–Hückel parameter. Furthermore, fluid flow is reduced with higher pseudoplastic fluid parameters, while nanoparticle concentration diminishes with increasing temperature ratio parameters. Nanofluid temperature increases with an enhancement in the thermophoresis parameter.

The unconventional machining is an essential role in metal forming process. The dimensional accuracy and super finish are the main features of modern machining methods. Electric discharge machining is a competent process to any materials and composites. In this article is used to machining characteristics of Monel 400 alloy with monitored by computer networks. The response variables are mainly depending on input variables and their levels. Taguchi method is utilized to evaluate the optimal control factors. The mean and SN ratio is evaluated by based on the criteria of the responses. The involvement of control variables to the responses have been validated by variance analysis. The best possible MR was attained at a discharge current of 40 A, pulse on time of 140µs, and spark voltage of 25 V.The finest RTW and SR was occurred at a discharge current of 40 A, pulse on time of 140µs, and spark voltage of 20 V. The scope of the work is to maximize the metal removal and minimize the tool wear.

The present study deals with the dynamics of microelectromechanical system (MEMS) resonators, especially the exploration of strange non-chaotic attractor (SNA) in MEMS resonators. SNAs often arise in systems driven by quasiperiodic forces, where the system is subjected to multiple frequencies that are incommensurate. When we apply the quasiperiodic forces, we identify the presence of SNA regions in the MEMS oscillators through bifurcation and Lyapunov analysis. Subsequently, we analyse the route of SNA in the considered system. In our analysis, the first identified route to SNA is the fractilisation route which is validated through various analyses, such as Poincaré map, distribution of finite-time Lyapunov exponents, Lyapunov variance, singular continuous spectrum and recurrence analysis. Moreover, two additional routes to SNA, namely Haegy–Heamel route and intermittency route, are identified and thoroughly investigated, and the presence of SNA is confirmed using singular continuous spectrum analysis. This work helps to understand SNA that can be important in fields like signal processing, where distinguishing between chaotic and non-chaotic signals is crucial. In particular, the emergence and characterisation of SNAs in MEMS resonators open avenues for further research and applications in nonlinear dynamics and chaotic systems.

This research intends to explore the impact of nanographene on the dielectric, mechanical, and flame retardation behaviour of fiber composites made of snake grass fiber (SF) and Kevlar. The nanographene was evenly dispersed in the epoxy through sonication. The SF/Kevlar hybrid composites with added graphene were fabricated using compression moulding. Mechanical tests were analyzed, comprising impact, flexural, tensile, and interlaminar shear strength. The mechanical test results showed that the hybrid composites containing 3% nanographene showed an improvement of 40.66%, 46.12%, 37.33%, and 26.58%, respectively. The cracked surfaces after the tensile test were subjected to a micrographic analysis. The micrograph images revealed a strong interaction between the SF/Kevlar fiber and the epoxy, with the addition of nanographene. The dielectric test revealed that adding nanographene enhanced the dielectric loss and dielectric constant. The composite containing 5wt.% nanographene showed a 52% increase in dielectric loss compared to the reference sample. The inclusion of nanographene in SF/Kevlar hybrid epoxy composites reduces the flame propagation speed. The sample with 5 wt.% nanographene showed better performance in flammability studies. The presence of nanographene enhances the thermal resistance of the composite, delaying the ignition and reducing the overall flame spread rate.

In this work, a network of Morris–Lecar neurons with electromagnetic induction is imposed with nonlinear magnetic flux diffusion. We study wave propagation in a network of Morris–Lecar (ML) neurons with magnetic flux diffusion, connected to the local nodes of the nearest neighbors in a $110 \times 110$ lattice of neurons with periodic boundary conditions. First, we explore the effect of various initial conditions on the modified ML neuron network without imposing external stimuli. Subsequently, we apply external stimuli at different positions and study wave propagation by changing the amplitude and frequency of the stimuli. The effects of varying Nernst potential of potassium ions, coupling strengths, and flux constants are also analyzed. The resulting collective dynamics of the considered neuronal network are provided in snapshots with different model parameters. This study offers a novel perspective on wave propagation in networks of biological neurons.

The integration of deep learning with remote sensing techniques has emerged as a transformative approach in analyzing carbon flux patterns to mitigate climate change impacts on environmental health. This study introduces a Spatiotemporal Attention Network (SATN) that leverages satellite-derived data and ground-based observations to predict and understand carbon flux variations. Key features such as vegetation indices, soil moisture, and land surface temperature are prioritized, contributing over 60% to the model’s predictive accuracy. The proposed SATN achieved a remarkable R² score of 0.95, outperforming traditional models like ConvLSTM (0.87) and Transformers (0.90). With a low RMSE of 0.045 and MAE of 0.031, the SATN demonstrated robust accuracy and efficiency, achieving a balanced F1-score of 0.91. The results highlight spatial hotspots like the Amazon Rainforest and temporal trends indicating seasonal vegetation growth and human-induced emissions as critical contributors to carbon flux dynamics. By identifying regions and periods of high carbon activity, this study provides actionable insights for reforestation efforts, urban emissions control, and climate resilience planning. The SATN framework sets a new benchmark for integrating machine learning with environmental science, paving the way for innovative climate mitigation strategies.

In recent years, the study of neuronal models has provided significant insights into brain dynamics and neurological disorders. Map-based neuronal models, such as the Rulkov map, have gained considerable popularity due to their computational efficiency and ability to replicate complex neuronal dynamics. We thus here study the collective dynamics of an unidirectional ring network composed of three memristive Rulkov maps, with particular emphasis on synchronization patterns and their dependence on coupling types. By employing electrical and memristive/field couplings, we analyze the emergence of complete synchronization, lag synchronization, and phase synchronization under varying coupling strengths. Our findings highlight how diffusive-based synaptic pathways modulate synchronization and collective behavior in the network. The presented results also offer new perspectives on the role of coupling functions in shaping neuronal synchronization, and they reveal their deeper implications for understanding pathological brain states and for designing neuromorphic systems.

The present study investigates friction stir welded AA6065-10% Al 2 O 3 MMC by incorporating varying percentages of heat treated biosilica. The biosilica is first extracted from waste cassava peel, and it is heated under 1500°C, to get properly arranged crystalline structured biosilica particle. During friction stir welding process, the biosilica particle is dispersed around the welded zone, which in turn impacts load carrying capacity of the material. The study revealed that 3 vol.% of biosilica infused FSW composite 'C' shows maximum tensile strength of 276 MPa, yield strength of 238 MPa, impact energy of 20.8 J, elongation of 5.2%, fatigue strength of 176 MPa. Further, the 5 vol.% of biosilica infused FSW composite 'D' shows hardness strength of 121 Hv. Additionally, it has been discovered under microstructural analysis that the inclusion of fine-grained heat-treated biosilica exhibits the greatest dispersion of biosilica within the nugget zone, heat affected zone, and thermo mechanically affected zone, which affects the composite's overall strength characteristics. Thus, because of their less dense, better thermo mechanical properties, it could be influenced in areas where joint application, load bearing are needed such as aerospace, heavy industrial, infrastructural, transport and military sector.

A Vehicle-to-Grid (V2G) integrated microgrid system, which represents a potential community-scale energy network, is modelled and simulated in this article. A diesel generator for base power generation, renewable energy sources (wind farms and photovoltaic cells), residential loads, and a V2G system with 100 electric cars make up the simulated microgrid. By controlling car battery charging and discharging, the V2G system provides grid support on a day of low demand for around 1,000 homes. While the PV farm and wind farm offer renewable energy based on real-time solar irradiance and wind profiles, the diesel generator maintains power balance by making up for variations in generation and consumption. Electric car charging and grid stability during power outages are the two functions of the V2G technology. Realistic charging patterns and energy availability are reflected in the modelling of five different vehicle-user profiles. Dynamic events like asynchronous machine starting, partial solar shading, and wind farm disconnection are all included in the 24-hour simulation. The system’s reaction to these occurrences is examined in the article, with particular attention paid to grid frequency stability, renewable power variability, and V2G’s function in load balancing and frequency control. The findings demonstrate how V2G integration may improve grid stability and lessen the erratic nature of renewable energy sources in energy systems of the future.

Late gadolinium enhanced-cardiac magnetic resonance (LGE-CMR) images play a critical role in evaluating cardiac pathology, where scar tissue serves as a vital indicator impacting prognosis and treatment decisions. However, accurately segmenting scar tissues and assessing their severity present challenges due to complex tissue composition and imaging artifacts. Existing methods often lack precision and robustness, limiting their clinical applicability. This work proposes a novel methodology that integrates the optimal segmentation algorithm (OSA) for segmentation and Flamingo Gannet search optimization-enabled hybrid deep residual convolutional network (FGSO-HDResC-Net) for severity classification of scar tissues in LGE-CMR images. Initially, the input image is pre-processed by using the adaptive Gabor Kuwahara filter. Then, the approach combines myocardium segmentation via region-based convolutional neural network and scar segmentation using OSA. Subsequently, FGSO-HDResC-Net integrates feature extraction and classification while optimizing hyperparameters through Flamingo Gannet search optimization. The feature extraction stage introduces two sets of techniques: localization features with texture analysis and spatial/temporal features using a deep residual network, complemented by feature fusion using the fractional concept. These features are inputted into a customized 1D convolutional neural network model for severity classification. Through comprehensive evaluation, the effectiveness of FGSO-HDResC-Net in accurately classifying scar tissue severity is demonstrated, offering improved disease assessment and treatment planning for cardiac patients. Moreover, the proposed FGSO-HDResC-Net model demonstrated superior performance, achieving an accuracy of 96.45%, a true positive rate of 95.42%, a true negative rate of 96.48%, a positive predictive value of 94.20%, and a negative predictive value of 94.18%. The accuracy of the devised model is 14.50%, 12.99%, 10.74%, 9.75%, 12.79%, and 11.26% improved than the traditional models.

The objective of this study is to investigate the effects of alumina filler content and NaOH-treated Roselle fibers on mechanical, thermal, biodegradation, and tribological properties while identifying optimal conditions for eco-friendly applications. Compression molding was employed to fabricate composites, and the results revealed significant improvements in performance with chemical treatment and optimal filler content. Mechanical testing showed that the 10% alumina composite exhibited the highest tensile, flexural, and impact strengths due to enhanced interfacial bonding and uniform filler dispersion. Thermal analysis demonstrated improved stability, with the 10% alumina composite offering the best thermal degradation resistance. Biodegradation studies indicated slower weight loss for alumina-filled composites, highlighting their environmental durability. Tribological evaluations revealed that the 10% alumina composite achieved the lowest specific wear rate (SWR) and coefficient of friction (COF), supported by SEM analysis showing minimal wear debris and surface damage. Optimization using a simulated annealing algorithm identified ideal conditions (sliding velocity: 6.6 m/s, sliding distance: 500.33 m, and alumina content: 10.62%) that minimized SWR (13.28 × 10⁻⁵ mm³/Nm) and COF (0.278). These findings provide valuable insights into Roselle fiber composites for sustainable applications in the automotive and packaging industries.

Using quantum chemical calculations, spectroscopic methods, and molecular docking analysis, this work explores the electronic, structural, vibrational, and biological characteristics of CAFI. Intramolecular hydrogen bonding between the methyl and C = O groups (with bond lengths less than 3 Å) was detected, affirming molecular stability. Corresponded with the theoretical expectations, FT-IR and UV spectra corroborating CAFI’s chemical stability. Frontier molecular orbital study indicated HOMO-LUMO energy gaps between 4.227 eV (gas) and 4.792 eV (ethanol), underscoring charge transfer activity. Molecular docking revealed CAFI as the most potent binder to proteins that stimulate kidney function, with a binding energy of -4.08 kcal/mol and sustained hydrogen bonding connections. ADMET analysis confirmed CAFI’s drug-likeness, indicating advantageous absorption, distribution, metabolism, and toxicity characteristics. These findings indicate CAFI as a potential treatment candidate for the regulation of renal function. Supplementary Information The online version contains supplementary material available at 10.1186/s13065-025-01383-8.

In this research, a novel investigation explored the enhancement of nanocomposites by blending styrene-butadiene rubber/acrylonitrile butadiene rubber (SBR/NBR) with both unmodified and [3-(2,3-Epoxypropoxy)-propyl]-trimethoxysilane-modified halloysite nanotubes (pHNTs and mHNTs). The study aimed to improve interfacial interactions and refine the SBR/NBR phase microstructure, leading to superior mechanical properties. Using a two-roll mill, mHNTs were incorporated at varying levels (0–10 phr). Results demonstrated reduced curing times (optimum cure time—t90, scorch time—ts2), increased maximum torque (MH), and substantial enhancements in tensile strength (up to 182%) and abrasion resistance (optimized at 6 phr mHNTs). Additionally, swelling resistance improved with higher mHNTs content, highlighting effective dispersion and interactions of HNTs within the SBR/NBR matrix.