Recent publications
Aluminium-Magnesium alloy has an excellent corrosion resistant property and is used in several aeronautical and maritime applications. Adhesive bonded joints made with adhesives have been evolved as a reliable and environment friendly solution for joining different materials including metals, composites and polymers. There has been tremendous improvement in the quality and bond strength of adhesive based joints due to improvement in adhesive properties. Adhesive joining are specially useful in intricate places were traditional joining methods can't be applied. Surface preparation prior to adhesive application is an important step to have a reliable joint strength. In this research work, nanosecond fiber laser was utilized for pre-treatment of surfaces which will then be adhesively bonded. Simple line patterning type of design was created on Al-Mg alloy (AA5754) surfaces. Texturing was done in three types of processing environment which is open air processing, underwater processing, and under flowing water conditions. Experiments were designed to evaluate the impact of working environment on adhesive joint strength. Also, the effect of texture density and depth were investigated by changing scanning speed and line distances of the fabricated design. Contact angle of surface textures were determined to find surface wettability. Tensile strength of adhesive bonded surfaces were assessed by performing shear strength test. Flowing water condition produced texture depths up to 390 µm and surface roughness up to 84 µm. Surfaces patterned with flowing water environment were observed to have 2.36 times higher adhesive bonding strength compared to untex-tured plain samples. Similarly, open air texturing resulted in 201.3% and static water texturing in 193.5% higher adhesive strength compared to plain untextured samples.
In the present study, we report the synthesis of sodium-ion (Na⁺) conducting blended solid polymer electrolytes (PEO-PEMA) by the standard solution casting technique with SiO2 filler. X-ray diffractometer (XRD) and Field emission scanning electron microscopy (FESEM) confirmed the complete salt dissociation and provided evidence of the composite formation. Furthermore, Fourier transform infrared spectroscopy (FTIR) supported the XRD analysis. Impedance spectroscopy (EIS), linear sweep Voltammetry (LSV), and i-t curve characteristics are used to investigate the electrical properties. The high conductivity value (~ 8× S cm⁻¹ ) was obtainded for a 2% concentration of SiO2 by wt. It also exhibited a high operating voltage range (4.3 V) and a high value of transference number (0.99), which makes it a potential candidate for energy storage devices. The degree of polarization and supports the high conductivity, suggesting that ion migration is mainly due to the segmental motion of the polymer chain. The shifting of loss tangent peaks toward the higher frequency window reflects the reduction of relaxation time. Loss tangent analysis confirmed this decrease in relaxation time with nanofiller addition. Furthermore, complex conductivity analysis showed a strong dependence on nanofiller content. The sigma representation (σ′′ versus σ′) validated the decrease in relaxation time, which agrees with the loss tangent analysis. Ion transport parameters (n, μ, D) were evaluated using the Bruce-Vincent (B-M) method, electrochemical impedance spectroscopy, and FTIR analysis. All the transport parameters showed good agreement with each other. Finally, an ion transport mechanism based on experimental findings was proposed to examine the possible interactions in the polymer nanocomposite matrix.
The Integrated Sensing Digital Framework (ISDF) serves as a transformative force for the Internet of Things (IoT) by facilitating data collection, execution, and computational services. Intelligent transportation, a key application of Cyber‐Physical Systems (CPS), modernizes vehicles through technologies such as GPS, alarms, trackers, and autonomous driving systems. Enhancing real‐time interactions among vehicles, pedestrians, and infrastructure requires advanced wireless communication technologies. While integrating ISDF with IoT offers many benefits, it also presents challenges in the reliability, security, and privacy of data transformation, location tracking, and analysis. This article proposes an efficient decision‐making framework employing augmented algorithms and n‐step bootstrapping learning schemes to identify legitimate devices within ISDF networks. The proposed mechanism is evaluated based on security and privacy metrics, including delivery ratio, accuracy, average trust value, and defense against DoS attacks, validated through simulations.
Water is an essential resource for human life. Safe and pure water is an important component of the ecosystem. Freshwater covers about 2.5% of the earth’s surface, and only 1% of it is usable. River water has a significant proportion of freshwater which is used for various purposes. However, excessive exploitation and inappropriate use of water resources have led to water pollution. The degraded water quality can cause transmission of diseases and it cannot be used for drinking, agricultural and industrial use. Analyzing the water quality has become one of the prime aspects of water management and monitoring. In this work, machine learning techniques are adopted to automate the process of water quality assessment. The complete process is divided into two stages. In the first stage correlation among water parameters is identified and water quality factors are forecasted. During this process, the regression method is applied to forecast the missing water quality parameters. These forecasted parameters along with the original parameters are then used to formulate a Water Quality Index (WQI) which is further used to categorize the water quality using a stacked ensemble classification approach. The proposed approach is implemented on Yamuna River’s data collected from various sampling locations In the Delhi region. The experimental analysis shows that the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) for predicting Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) are 0.4132, 0.1707, and 0.5134, 6.0588 respectively For the next stage classification scenario, a comparative analysis shows that the proposed approach achieves an overall performance as 95.833, 91.66, 92.31, and 92.05% in terms of accuracy, precision, recall and F-score, respectively.
This paper proposed a novel Modified Jordan Recurrent Neural Network (MJRNN) model to identify complex nonlinear dynamical systems. Due to its capabilities, nonlinear dynamic system identification using artificial neural networks is the most commonly used method in control system engineering. The structure of the proposed model is an extended version of the original Jordan recurrent neural network model. The parameter update equations are obtained using the back-propagation optimization algorithm, the most frequently used method as a learning approach for the training of the proposed model’s parameters. The effectiveness of the proposed neural network model is evaluated in comparison to other neural network models such as the Jordan recurrent neural network (JRNN), Elman recurrent neural network (ERNN), Diagonal recurrent neural network (DRNN), and feed-forward neural network (FFNN) models. The robustness of the proposed model is also tested with parameter variation and disturbance signals. The simulation results have shown that the proposed model performs better than the other neural network models.
We present the synthesis, characterisation, and application of a novel R2-based fluorescent probe for selective metal ion detection. Comprehensive studies using UV–Vis absorption and photoluminescence spectroscopy revealed strong binding with cobalt (Co²⁺) and aluminium (Al³⁺) ions, with a 2:1 binding stoichiometry (probe: metal) and with a detection limit as low as 5.5 × 10–8 mol/L for aluminium and 3.2 × 10–8 mol/L for cobalt, highlighting its remarkable sensitivity. With no interference from other metal ions, the probe showed outstanding stability and selectivity over a broad pH range. Analyses of actual water samples verified its usefulness in environmental monitoring. To further demonstrate the probe's potential in sophisticated sensing applications, it was also used to build a molecular logic gate. The experimental results were theoretically supported by Density Functional Theory (DFT) calculations, which also shed light on the binding mechanism. The R2 probe is emphasised in this work as a sensitive, specific, and adaptable instrument for environmental analysis and metal ion detection.
Graphical Abstract
This article emphasizes the influence of pyridine ring substitutions in 2,2’:6’,2″-terpyridine unit, allowing for precise adjustment of ligand properties. This study explores terpyridine-based molecular systems, with its unique coordination properties assessed against a range of competing metal ions, highlighting the specific affinity of the terpyridine ligand towards aluminium (Al) and cobalt (Co) detection in Acetonitrile–water (ACN-H2O) mixed solvent (80:20). The terpyridine-based probe’s structural component is meticulously designed and detailed investigated to understand their influence on sensing performance. Synthesised probes are characterised using NMR, IR, UV–Vis spectroscopy, and mass spectrometry. Such modifications present ample opportunities to customise the attributes and applications of resultant metal complexes. The interaction between the terpyridine-based probes and target metal ions is investigated through various experimental methods, including fluorescence spectroscopy and UV studies along with unassisted discernment of Cobalt through Naked-Eye. Observation turns out that the limit of detection is 4.4 × 10–8 mol/L for cobalt and 4 × 10–7 mol/L for aluminium and coordination features Showing the binding stoichiometry to be 1:1 for R1. ADME Studies have been performed to analyse pharmacokinetic and biological actions. DFT calculations were performed to investigate the molecular probe’s coordination features and its corresponding metal complex.
Graphical Abstract
Cs₂SnI₆ has emerged as a stable and environmentally friendly replacement for lead (Pb)‐based perovskite solar cells (PSCs) due to its air stability, attributed to the Sn⁴⁺ oxidation state, and non‐toxic composition (lead‐free). A key benefit of using Cs₂SnI₆ as an absorber layer is that it enables the elimination of hole transport layers (HTLs) in some device architectures; however, PSCs with HTLs generally outperform those without HTL. Here, the structural, electronic, and optical properties of Cs₂SnI₆ are investigated using first‐principles calculations, and photovoltaic effects by using SCAPS‐1D simulation software. Nine different device configurations have been investigated by combining three electron transport layers (ETLs) with three HTLs to optimize device performance. The impact of HTL thickness, ETL thickness, absorber layer thickness, and operating temperature are studied on the solar cell's efficiency. The optimized PSC demonstrates a fill factor (FF) of 84.683%, a power conversion efficiency (PCE) of 24.0%, the short circuit current density JSC of 28.433 mA cm⁻², the open circuit voltage VOC of 0.998 V, and a quantum efficiency of 99.866%, with optimal operating conditions at 300 K.
This study investigates the relationship between environmental, social, and governance (ESG) practices and return on assets (ROA) in Indian companies, focusing on industry‐specific variations. Using 10 years of panel data across resource‐intensive, consumer‐facing, and service sectors, the analysis employs the system generalized method of moments (GMM) for robust estimation. The results reveal a positive overall effect of ESG practices on ROA, with significant differences across industries. The study uses the categorization of resource‐intensive, consumer‐facing, and service industries to examine the differential effects of ESG factors on ROA. However, environmental scores do not significantly impact ROA, suggesting uniform effects across industries. Social scores enhance ROA without notable industry‐specific differences, while governance scores show varying effects, indicating sector‐specific drivers of profitability. The analysis also highlights the moderating effects of industry categories on the ESG–ROA relationship, suggesting that the impact of ESG practices varies across different sectors. The study also considers the economic effects of COVID‐19, highlighting its marginal impact on ROA and the need for resilient financial strategies. However, the moderating effect of COVID‐19 on this relationship was not significant, indicating limited variation in the ESG–ROA dynamics during the pandemic period except in the governance model. These findings suggest that tailored ESG strategies, aligned with industry‐specific challenges, can optimize financial performance. Policymakers and investors are encouraged to focus on sector‐specific ESG practices to better evaluate company performance and formulate effective regulations. This research contributes to the emerging market context by emphasizing the importance of industry‐specific ESG integration for enhancing financial outcomes.
In the field of image processing, practical applications such as object detection, tracking, and surveillance face significant challenges, particularly in adverse weather conditions like fog. Foggy weather conditions severely reduce object visibility, thereby impeding object detection and tracking processes. To address this issue, various image defogging techniques have been proposed by researchers. The prime motive of this paper is to present a detailed analysis and summary of state-of-the-art single image defogging techniques developed over the past decade. Defogging techniques have been evaluated using both qualitative and quantitative approaches to illustrate their feasibility and effectiveness. This comprehensive review aims to provide researchers with valuable insights into existing techniques so that they can proceed in a particular direction according to their interests and applications.
Primary batteries, or non-rechargeable batteries, are crucial for powering a diverse range of low-drain applications, from household items to specialized devices in medical and aerospace industries. Despite the growth of...
In medical dialogue systems, recent advancements underscore the critical role of incorporating relevant medical knowledge to enhance performance. However, existing knowledge bases often lack completeness, posing a challenge in sourcing pertinent information. We present MedProm, a novel generative model tailored for medical dialogue generation to address this gap. Motivated by the need for comprehensive and contextually relevant responses, MedProm leverages state-of-the-art language models such as BioGPT. Our model is designed to integrate extensive medical knowledge into conversations, facilitating effective communication between patients and healthcare providers. At the core of MedProm lies the MediConnect Graph, a meticulously constructed knowledge graph capturing intricate relationships among medical entities extracted from dialogue contexts. By employing a KnowFusion encoder with a pretraining objective and masked multi-head self-attention, MedProm effectively processes the MediConnect graph, enabling precise control over information flow to capture its underlying structure. Furthermore, MedProm incorporates a sophisticated Curriculum Knowledge Decoder, leveraging transformer-based decoding to generate response utterances conditioned on input representations from the KnowFusion Encoder. The training process is guided through curriculum learning, gradually increasing optimization difficulty based on a coherence-based criterion. Experimental results on two datasets demonstrate the efficacy of MedProm in generating accurate and contextually relevant responses compared to state-of-the-art models.
Vehicular Ad hoc Networks (VANETs) are gaining popularity among academic institutions and industrial fields. The research area of VANET captivates a significant amount of curiosity from authors across the globe. Nonetheless, strong scientific contributions have been presumed through VANETs to explore the clustering concept with VANETs. For this reason, it is essential to outline the current state of study in this discipline. Since 2010, the number of research articles has grown exponentially; the assessment through a tool is needed for authors to comprehend the existing findings and conclusions throughout this field. Using different factors, this article analyzes the social standing of research developments in the discipline of “Clustering in Vehicular Ad Hoc Networks” from 2010 to October 2024 with the help of bibliometric analysis. Since 2024, 17,461 publications have been connected well with the VANETs realm, which was retrieved first from the Scopus database (in BibTeX format). Furthermore, 377 of the collection's articles pertain to the clustering techniques within VANETs. To achieve the best result, the bibliometric tool ‘R-Tool’ using ‘Biblioshiny’ generates and visualizes the specialized area's bibliometric connectivity. Also, this article would provide a visual representation of the scientific data gathered from various authors, countries, and connections to assist authors in identifying the clustering field in the VANETs.
This review paper provides an inclusive overview of the intricate interactions amid ionic liquids (ILs) and essential biomacromolecules, mainly Hemoglobin (Hb), Bovine Serum Albumin (BSA), Human Serum Albumin (HSA), and Calf Thymus‐DNA (CT‐DNA). ILs have recently become a topic of great attention because of their inimitable physicochemical properties and potential uses in different fields. The review systematically explores the binding mechanisms, thermodynamics, and structural changes induced by ILs on Hb, BSA, HSA, and CT‐DNA using spectroscopic, thermodynamic, and computational techniques. The paper highlighted various experimental and computational methodologies to explore the interactions between ionic liquids (ILs) and biomacromolecules. It offers an in‐depth analysis of the techniques employed to decode the intricate nature of these molecular associations and the influence of ILs along with their structural characteristics on the conformational stability, activity, and functionality of biomacromolecules. This foundational understanding is essential for advancing research and developing strategies that exploit the distinctive properties of ILs to foster innovative and sustainable applications within the biomedical field.
A new variant of discrete operators based on the associated (modified) Laguerre polynomials was introduced by Gupta. In the present paper, some direct convergence results for such operators are studied, including a Voronovskaja type result. Some approximation properties were established in weighted spaces, in terms of weighted modulus of continuity.
Herein, we explored the synergistic effect of ionic liquids as anticancer and antibacterial agents to avoid available combination therapies. We synthesized an API-based ionic liquid (IL) designated as [Pro-pip]SAL to meet our objectives. This compound features a cationic moiety derived from procaine and 1,3-benzodioxole, tagged with a salicylate salt as the anionic counterpart. Molecular docking studies showed that interaction is polar involving H-bonding and van der Waal forces. Spectroscopic studies were carried out to validate the results of computational studies for the binding interaction mechanism of [Pro-pip]SAL IL with bovine serum albumin (BSA). The UV–visible spectra showed an increase in absorbance intensity with bathochromic shift, and fluorescence spectra showed static quenching with bathochromic shift revealing that polar interaction played an important role in the interaction between Pro-pip]SAL IL with BSA. CD spectra showed no significant change in the secondary structure of BSA. The antibacterial study revealed that IL showed significant activity against Staphylococcus aureus (gram-positive) bacteria and no effect was observed on E. coli (gram-negative) bacteria. Cytotoxicity study on Vero cell line (non-cancerous cell line; kidney epithelium cells of African green monkey) showed IC50 values 207.95 ± 1.25 μM. Cytotoxicity analysis of [Pro-pip]SAL IL on A549 cell lines revealed antiproliferative properties with an IC50 value of 54.55 ± 0.22 μM. This study interpreted that Pro-pip]SAL IL could be used as an anticancer and antibacterial drug, warranting further exploration and development in these therapeutic areas.
Porous organic polymers (POPs) decorated with specific functional groups possessing high surface area have been extensively utilized for removal of organic dyes from water. However, synthesis of these polymers demands stringent experimental conditions such as high polymerization temperatures, inert atmosphere and the use of transition metal catalysts. Here, a pyrimidine based hydroxyl azo polymer PBHAP has been fabricated via one-pot azo coupling green synthesis in water under ambient conditions in absence of any catalyst. Strategic functionalization of polymer with azo (-N = N-) and free phenolic (-OH) groups has been carried out to impart adsorption properties via hydrogen bonding and electrostatic interactions of peripheral groups between polymer and nitrogen-containing aromatic ring of organic dyes. The adsorbent’s efficacy in removing the hazardous dyes, Methylene Blue (MB), Crystal Violet (CV) and Rhodamine B (RHB) has been investigated. Various characterization methods have been employed to investigate the physicochemical characteristics of polymer and monitor the adsorption kinetics of dye’s eradication from aqueous solution. The adsorption studies have been performed in ambient conditions at room temperature, resulting in maximum removal efficiencies of 98%, 96.2%and 95.7% for cationic dyes CV, RHB and MB respectively. Langmuir isotherm and pseudo-second-order kinetic models have been employed to probe the interactions between adsorbate and adsorbent during the dye removal process. The maximum theoretical adsorptive capacities of 151, 97 and 75 mg/g have been achieved for CV, RHB and MB respectively. Long-term cycle stability experiments have elucidated the regenerative behavior of PBHAP polymer without significant loss in the adsorption efficiency. The polymer displays high adsorptive capacity and preferential selectivity for cationic dyes from a binary mixture of cationic and anionic dyes, monitored using kinetic and Zeta Potential studies (-24 mV) for the polymer. The synthesized polymer has displayed exceptional porosity with a high surface area of 181 m2 /g as confirmed by BET studies and excellent surface wettability validated by water contact angle studies. Owing to these findings, the synthesized polymer has an excellent tendency to remove organic pollutants and holds enormous potential as a versatile adsorbent for wastewater remediation and environmental protection.
Strontium hexaferrite nanoparticles were prepared via polyol-mediated synthesis route for magnetoelectric and resistive switching device applications. X-Ray Rietveld refinement revealed the single-phase formation with space group P63/mmc for the strontium hexaferrite. The hexaferrite nanoparticles possessed improved dielectric properties with high magnetoelectric coupling constant at room temperature. The cross-coupling of magnetic and ferroelectric domains assisted via domain wall resulted in induced ferroelectric polarisation near the magnetic domain wall due to the non-centro symmetry. The BET measurement was used to estimate the surface area of the SrFe12O19 nanoparticles. The polyol-mediated hexaferrite nanoparticles-based materials exhibited battery like characteristics due to the additional redox reaction. With increase in sweep voltage, the battery like characteristics became more prominent due to increase in internal field. These findings suggest strontium hexaferrite nanoparticles possess potential application for resistive switching and non-volatile memory storage devices.
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