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Application of Fuzzy-NSGA-II for achieving maximum biodiesel yield from waste cooking oil
The increasing demand for renewable energy and efficient waste management has highlighted the need for innovative biodiesel production techniques. This study optimises biodiesel production from waste cooking oil (WCO) using fuzzy modelling and non-dominated sorting genetic algorithm-II (NSGA-II). The optimisation process focuses on key input parameters: methanol quantity, reaction temperature, reaction time, and catalyst concentration, which were normalised and represented using linguistic variables. Fuzzy logic was employed to predict biodiesel yield, expressed in terms of linguistic variables, and defuzzified to yield crisp output values. The developed model achieved a high R2 value of 96.34%, demonstrating a strong correlation between input variables and biodiesel yield. The NSGA-II algorithm was utilised for multi-objective optimisation, determining the optimal conditions for biodiesel production: 150ml of methanol, a reaction temperature of 62C, a reaction time of 63min, and a catalyst concentration of 7.5g. These parameters resulted in a maximum biodiesel yield of 97.36%. The Box-Behnken experimental design validated the models efficiency, achieving a yield of 96.88%. This study emphasises the practical implications of optimised biodiesel production, such as reducing environmental pollution by recycling WCO and minimising reliance on fossil fuels. The optimised process meets ASTM standards and exhibits scalability potential for industrial-level production with minor modifications. The models robustness makes it suitable for integration into intelligent manufacturing systems, ensuring consistent biodiesel quality and yield through automated monitoring and control mechanisms. Despite its success, challenges such as feedstock variability and initial setup costs must be addressed. Future studies should focus on adaptive models and energy-efficient processing technologies to enhance scalability and sustainability. This research demonstrates a significant step towards sustainable biofuel production, combining waste management with renewable energy generation. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. -
Employee relations: a comprehensive theory based literature review and future research agenda
This study aims to conduct a systematic and integrative literature review to consolidate the extensive information on employee relations accumulated over the past century, thereby offering new insights into domain-specific phenomena. The research followed a four-phase search strategy in accordance with the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The keyword search utilized terms such as 'employee relations,' 'employee relation,' 'employment relation,' and 'employment relations' in the Scopus and Web of Science databases. By employing an integrative approach along with specific inclusionexclusion criteria, the researchers synthesized articles from leading journals in the field of employee relations, categorizing them based on geographical region, article types, prominent authors and their affiliations, and the most cited research articles. In the final stage, the researchers presented new insights through a conceptual framework utilizing the ADO-TCCM approach, which encompasses antecedents, outcomes, theories, context, methodology, mediators, and moderators of employee relations. This study synthesizes findings and reorganizes key themes into innovative frameworks, providing fresh perspectives on various aspects of employee relations. Ultimately, it offers valuable insights into the critical factors that strengthen long-term employee-employer relationships. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
A Hybrid Intrusion Detection System for detecting Cross-layer DoS attacks in IoT
The Internet of Things (IoT) is critically prone to Denial of Service (DoS) attacks at multiple layers. If designed carefully, intrusion detection systems (IDS) can detect these attacks effectively. In the proposed study, we develop a Hybrid IDS to detect Cross-Layer DoS attacks in IoT. The proposed Cross-Layer system reduces the false positive rate considerably than a single IDS. The IDS is designed by ensembling multiple machine learning techniques to avoid overfitting or underfitting. The Hybrid IDS works in two stages, the first stage for detection of the attack occurrence (Anomaly detection) followed by a second stage to classify the attack types (Signature of the attacks). The output of the first stage is Correctly Detected Samples (CDS), which are again tested by the second stage to get Correctly Classified Samples (CCS). Another unique aspect of the proposed study is the dataset generation for different attacks considered. Rather than using the existing dataset, we have developed a trace file in NetSim Simulator by designing an attack environment. At the same time, during the feature selection process, a novel and efficient technique is applied to select the best feature set along with the critical component (CF). Simulation results accurately detect CDS of up to 95% and CCS of up to 96% with a weighted average F1 score. The testing time of the proposed model is also considerably lower than that of individual models, which makes the system efficient and lightweight. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
A novel mobile sink placement in wireless sensor network using deep maxout network based energy prediction with adjacent cell score
The majority of Wireless Sensor Networks (WSNs) are made up of energy- and cost-efficient detecting nodes. Traditional wireless sensor networks encounter serious problems, including latency, network failure, and congestion, since they rely on individual base stations (BSs) to gather data from the whole network. Sensor nodes adjacent to the base station will use more energy because of excessive energy consumption and energy-hole constraints, affecting the network's life. Understanding the best place for mobile sink nodes can help alleviate this issue by lowering energy usage and extending the network's lifespan. In this paper, utilizing a deep learning-based energy prediction and neighbour cell score model, we build and construct an efficient method to locate mobile receivers using distance, expected energy, and fairness variables. Furthermore, a Deep Maximum Output Network (DMN) calculates the desired power. However, the minimum length, maximum residual energy, complete normalized right, maximum network lifespan, and maximum normalized throughput for our suggested neighbor-based cell scoring with Deep Maxout Network are 137.364, 30.903, 64.426, and 60.613, respectively. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
L-arginine functionalized NiFe?O? nanoparticles: synthesis, characterization, antimicrobial activity, and biocompatibility evaluation in a zebrafish model
NiFe?O? nanoparticles and L-argininefunctionalized NiFe?O? (NiFe?O4LA) were synthesized, characterized, and evaluated for antimicrobial performance and in vivo biocompatibility in a zebrafish embryo model. The XRD and HRTEM studies confirmed the formation of single-phase cubic spinel NiFe?O? with average crystallite size ? 2030nm. Surface modification with LA preserved the spinel structure while reducing crystallinity and hydrodynamic size 128.7nm. FTIR and XPS verified successful surface functionalization. UV vis measurements showed band-gap narrowing after La conjugation (Eg ? 3.23 ? 3.15eV). NiFe?O?LA exhibited enhanced antimicrobial activity compared with bare NiFe?O? against MRSA and C. albicans an effect attributed to enhanced surface interaction and ROS-mediated oxidative damage. The MIC values of C. albicans is found to 1200g/mL while in the case MRSA is about 900g/mL. The ROS assays with histidine modulation supported a role for redox activity in antimicrobial action. In vivo zebrafish embryo assays showed minimal developmental toxicity at tested exposures with better viability for the LA coated material. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
Mangrove area classification in Pichavaram using Hyperspectral Imaging and Optimized Channel-Level Residual CNN framework
The Pichavaram mangrove forest in Tamil Nadu is one of Indias most ecologically significant regions, supporting coastal health and local communities. However, effective mangrove area classification remains challenging due to field inaccessibility and inefficiency of traditional assessment methods, highlighting the demand for advanced solutions. As the existing remote sensing-based studies suffer from limited classification accuracy and high computational complexity, this study combined Hyperspectral Image (HSI) with an Optimized Channel Level Residual CNN (OC-LRCNN) model for improved results in mangrove-related research. The proposed model employs unsupervised feature extraction to capture essential patterns with minimal training data while channel-level residual connections enhance discriminative feature selection and reduce spectral redundancy. Utilizing the Pichavaram EO-1 Hyperion and AVIRIS-NG datasets, the proposed model is compared with traditional CNN, state-of-the-art deep learning architectures (VGG, ResNet, DenseNet) and machine learning methods like SVM and RF. The OC-LRCNN achieved classification accuracies of 98.2% and 99.0% for the Hyperion and AVIRIS-NG datasets with consistently high precision, recall, F1-score and kappa values. These findings demonstrate the models effectiveness in reliable mangrove classification and monitoring applications. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
A Physics-guided Unsupervised Learning Framework for High-impact Heavy Rainfall Prediction in Data-sparse Environments
High-Impact Weather (HIW) events, particularly high-impact heavy rainfall, pose significant risks to urban infrastructure in Australia. Traditional forecasting approaches often struggle to resolve the complex, non-linear thermodynamic interactions that drive these infrequent events, while standard supervised machine learning models are hindered by severe class imbalance. This study presents a novel, multi-disciplinary framework that integrates synoptic climatology with unsupervised anomaly detection to classify and predict high-impact heavy rainfall events in Darwin, Sydney, Brisbane, and Perth. Using daily meteorological observations (20242025), we developed a multi-phase analytical framework comprising precursor, thermodynamic, kinematic, and system evolution phases to isolate the physical signatures of storm genesis. Exploratory analysis using Danger Rose polar histograms revealed a strong anisotropic risk pattern, with heavy rainfall predominantly associated with South-South-East (SSE) and West-South-West (WSW) vectors. Bivariate Kernel Density Estimation (KDE) revealed a distinct Thermodynamic Lock-in mechanism, where severe events are confined to narrow regimes of low pressure (< 1010 hPa), high humidity (> 60%), and compressed diurnal temperature ranges. To address the limited representation of severe events data (12.1%), we benchmarked five unsupervised anomaly detection algorithms. The results indicate that DBSCAN (Density-Based Spatial Clustering) yields the optimal performance (F1-Score: 0.319; Recall: 67.5%), significantly outperforming Isolation Forest and PCA. Topological validation via t-SNE projection confirms that high-impact heavy rainfall events form dense, cohesive clusters within the phase space rather than appearing as randomly distributed stochastic outliers. These findings prove that hybridizing physical phase-space analysis with density-based machine learning offers a robust pathway for early warning systems in data-sparse environments. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
HEDP-WBAN: Homomorphic Encryption and Differential Privacy for Secure Edge-Based WBANs
In the current era, healthcare is precious for all, so recent technology helps monitor the patients health via sensors and nodes, collect the same data, and analyze the patients condition. This is a technology known as a wireless body area network (WBAN). Nano-sensor devices are attached to the human body as part of WBAN to collect and monitor patient data, but have limited processing power and battery life. So, it cannot perform heavy computations within the node. Edge computing addresses this issue by processing data and reducing response times (in a near-edge node), which also helps mitigate delays and offloads work from WBAN nodes, but creates a privacy risk. Sensitive patient health information can be exposed through cyberattacks, unauthorized access, or profiling from edge nodes. This research proposes that PrivEdge-WBAN (Privacy-Preserving Edge Computing for WBANs) is integrated with edge computing, creates a framework for authentication and secure data processing, and supports new privacy-preserving and energy-efficient techniques. The proposed model combines lightweight Homomorphic Encryption (HE) and Differential Privacy (DP) to enable privacy-preserving computations and security at the edge node while maintaining energy efficiency. Moreover, the proposed framework combines an adaptive security engine that dynamically regulates cryptographic processes and authentication complexity derived from real-time energy levels and device workload. PrivEdge-WBAN aims to provide a comprehensive solution for security, privacy, and battery conservation in the real-world applications of WBAN. The outcome of this research can significantly influence the design of sustainable and secure surveillance systems for the healthcare sector, particularly for chronic illnesses, aging care, and other emergencies. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Tribo-catalytic Dye Degradation Driven by Mechanical Friction Using ZnS Microparticles with Different Morphologies
This work examines the tribocatalytic properties of zinc sulphide (ZnS) microparticles for dye degradation using mechanical energy. ZnS microparticles were synthesized into four distinct morphologies: microrods, spherical aggregates, microflakes, and microflowers using the solvothermal method. These morphologies were characterized using XRD, FESEM, EDS analysis, UVVis spectroscopy, XPS, and BET analysis. The tribocatalytic activity was assessed by degrading methylene blue (MB) dye under magnetic stirring in a dark setting. The experiment was carried out at the neutral pH of MB solution (~ 6.5). Among the prepared ZnS morphologies, the micro flakes displayed the largest surface area (120m/g) and exhibited enhanced dye degradation efficacy, achieving 57% MB elimination after 15h of agitation at 800rpm, corresponding to a pseudo-first-order rate constant of 0.054min?. By analyzing the degradation kinetics as pseudo- first-order kinetics, we elucidated the crucial significance of surface morphology and contact area in facilitating effective electron transfer during tribocatalysis. Additionally, we investigated the influence of PTFE bar size, material concentration, stirring speed and initial dye concentration on degradation efficiency. Reusability test demonstrated stable performance over four consecutive cycles with a minor decrease (~ 5%). The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Eco-friendly AgZnO Nanocomposites Synthesis and Their Role as Photocatalyst for Textile Dye Degradation
Recent research in the field of nanotechnology revealed that plant extract and their derivatives are good stabilizing and reducing agents. Artemisia stelleriana (Dusty Miller) is widely used as an ornamental plant. The current study, explores one-pot method to synthesise A. stelleriana-mediated silver/ zinc oxide nanocomposites (AS-Ag/ZnONCs). Using UV-visible spectrophotometer, scanning electron microscopy, energy-dispersive X-ray, Transmission Electron Microscope, X-ray diffraction, and Fourier transform infrared spectroscopy the characterisation of the synthesised AS-Ag/ZnONCs was examined. The crystalline size of the AS-Ag/ZnONCs was determined to be 45.39nm using the Williamson-hall equation. Irregular-shaped nanocomposites were observed from AS-Ag/ZnONCs, exhibiting an average size of 35.2nm. To check the activity of AS-Ag/ZnONCs as photocatalysts to degrade RY145, RY86, RB222A and RB220 dyes was determined. The order of photocatalytic activity of AS-Ag/ZnONCs was as follows: RY145 > RB220 > RB222A > RY86. Low toxicity was observed when Vigna radiata (Mung bean) and Artemia salina (Brine shrimp) were exposed to treated dye solutions using AS- Ag/ZnONCs when compared with untreated dye solutions. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Surfactant-Guided Synthesis of Mn?O? Nanostructures for Photocatalytic, Photoelectrochemical Applications and Sustainable Water Reuse
Currently, the global community is confronting water contamination from several sources, which poses significant environmental challenges. The reutilisation of polluted water constitutes a feasible strategy for sustainable wastewater management. In the present study, Mn3O4 was synthesised via chemical precipitation using polyvinyl alcohol (PVA), cetyltrimethylammonium bromide (CTAB) and glycine as surfactants and without any surfactants. The synthesised Mn3O4 using Glycine(MNG) showed the best photocatalytic efficacy compared to other synthesised materials, about 93 0.35% disintegration of Rhodamine B in 150min when illuminated with visible light. The progress of photodegradation was in conformity with the pseudo-first-order kinetic model with a velocity of 0.017min?1. Photoelectrochemical investigations assessed charge transfer, stability, and light-harvesting behaviour of Mn3O4 catalysts, showing their improved performance beyond photocatalysis. Seed germination in the treated water, controlled and uncontrolled conditions, was compared to ensure the agricultural and environmental viability. Additionally, ~ 84.54% of total organic carbon removal was achieved. A conceivable degradation mechanism was suggested after the degraded intermediates were examined using high-performance liquid chromatography (HPLC) and the elution patterns and retention periods. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
White LED Light-Mediated Eosin Y-Photocatalyzed One-Pot Synthesis of Novel 1,2,4-Triazol-3-Amines By Sequential Addition
Abstract: A facile and proficient, eco-friendly multicomponent synthesis of 12 novel biologically essential 1,2,4-triazol-3-amines via the sequential addition of substituted phenacyl bromide, aromatic aldehyde, hydrazinecarbothioamide, and urea under white LED with eosin Y as a photocatalyst has been developed. The intrinsic advantages are methodology is cost-effective, non-toxic, generates a high yield of product, is column chromatography free and does not need the use of a specific instrument. Surprisingly, our methodology uses moderate conditions and can count the tolerance of a wide variety of electron-donating and electron-withdrawing groups. The analysis and early conclusions give more value and context for the future development of organic synthesis using photocatalysts. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. -
(Mes-Acr-Me)+ClO4 Catalyzed Visible Light-Supported, One-Pot Green Synthesis of 1,8-Naphthyridine-3-Carbonitriles
Abstract: A novel, four-component one-pot green synthesis of biologically active 1,8-naphthyridines by a reaction of diverse aromatic aldehyde, malononitrile, 4-hydroxy substituted 1,6-dimethylpyridin-2(1H)-one, corresponding aniline in EtOH catalyzed by 9-Mesityl-10-methylacridinium perchlorate [(Mes-Acr-Me)+ClO4] under visible light generated from a 24W Blue LED wavelength 450460nm at 26C is reported. In contrast with the reported procedure, our methodology is diverse, versatile and has several favourable factors such as metal-free, excellent yields, shorter reaction durations, chromatography free and straightforward extraction process. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. -
Visible Light Mediated Organophotoredox-Catalyzed One-Pot Domino Synthesis of Novel 6,7 Disubstituted 1H-Pyrroles
The development of environmentally benign protocols to synthesize novel N-heterocycles is vital in the field of synthetic organic chemistry. We herein report a successful one-pot domino synthesis of novel 6,7 disubstituted 1H-pyrroles using substituted phenacyl bromide, barbituric acid/Meldrums acid, aromatic amines catalysed by 5mol% Fluorescein in presence of visible light. This procedure is a useful and adaptable method for the synthesis of pyrroles since it is compatible with a wide range of sensitive functional groups, does not require column chromatography purification. During the reaction, Fluorescein may catalyse the formation of enamine leading to amino alcohol which subsequently undergoes dehydration to give 6,7 disubstituted 1H-pyrroles. All the synthesized derivatives were obtained in 9095% yields and were characterized by 1H, 13C NMR and HRMS (ESI) analysis. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. -
Pt Nanospheres Decorated Graphene-?-CD Modified Pencil Graphite Electrode for the Electrochemical Determination of Vitamin B6
An electrochemical sensor for Vitamin B6 determination has been prepared by the electrochemical deposition of Pt nanospheres on graphene-?-CD coated Pencil Graphite Electrode (PGE). Cyclic voltammetric (CV) and electrochemical impedance spectroscopic (EIS) studies were employed to explore the electrochemical properties of the modified electrode. The physicochemical properties of the modified electrodes were characterized by X-ray photoelectron spectroscopy (XPS), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and optical profilometric studies. The experimental conditions such as effect of scan rate, concentration and pH were optimized. The linear dynamic range for the determination of Vitamin B6 was found to be 5nM to 205nM. The low level of detection limit (1.2nM) implies the high sensitivity of the process. The suggested method was effectively employed for the electrocatalytic evaluation of Vitamin B6 in different juice samples. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. -
The Effect of Viscous Dissipation on the DarcyBard Problem: Weakly Nonlinear Analysis and Strongly Nonlinear Computations
We consider the effect of viscous dissipation on the onset and nonlinear development of two-dimensional convection in a unit enclosure heated from below. First, we show that the linear theory is unchanged from that which arises when viscous dissipation is absent. Second, a weakly nonlinear analysis shows that convection becomes weaker with increasing values of Ge~, a modified Gebhart number. In addition, the rate of heat transfer at the lower and upper sufaces differ from one another. Third, nonlinear convection is found to lose both up/down and left/right symmetry as both Ge~ and Ra (the DarcyRayleigh number) increase. It is also found that once viscous dissipation increases in strength to unphysically large amounts, then the maximum temperature migrates from the lower boundary to the interior of the enclosure. The Author(s) 2025. -
A Study on the Stability of DarcyBrinkmanBard Convection in a Binary Fluid-Saturated Porous Medium: RigidRigid Boundaries
The linear stability analysis of DarcyBrinkmanBard convection (DBBC) in a binary fluid-saturated porous layer is studied numerically using n term Galerkin approach for rigidrigid, isothermal boundaries. The occupied binary fluid and porous medium are assumed to be in thermal non-equilibrium. Thus, two energy equations are used for each phase. The critical values of the DarcyRayleigh and wave numbers for theonset of convectionare obtained by considering ten terms in the Galerkin solution. The effect of the five parameters of the model, namely the Darcy number, Da, the modified ratio of thermal conductivity ?, theLewis number Le, theseparation ratio coefficient, ?, and the inter-phase heat transfer coefficient, H, on the stability of the system is discussed in detail and presented with the aid of plots and tables. The onset of convection in a binary fluid-saturated porous medium is delayed for realistic boundary conditions compared with ideal boundary conditions (stress-free, isothermal boundary conditions). Increasing the values of theDarcy number, inter-phase heat transfer coefficient, and the separation ratio coefficient stabilizes DBBC. In contrast, the thermal conductivity ratioand Lewis number aredestabilize the system. Furthermore, convective cell size remains unaltered with increasing ?. Convection is delayed in thepure fluid medium compared to thebinary fluid medium. Local thermal non-equilibrium ceases for small and large inter-phase heat transfer coefficient values. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Development and Validation of the Brief Financial Resilience Scale
Financial resilience refers to the ability to withstand and recover from financial adversity. Despite its increasing relevance, there is a notable lack of well-constructed and psychometrically validated tools to measure this construct. This study aimed to develop and validate the Brief Financial Resilience Scale (BFRS), based on Salignac et al. (Social Indicators Research, 145(1), 1738, 2019) multidimensional framework. The process involved content validation using Lawshes method, followed by item analysis, exploratory factor analysis, and confirmatory factor analysis using data from a community sample of 433 Indian millennials. The results support a two-dimensional structure: (a) financial resources and access, and (b) financial knowledge and behavior. The final 10-item BFRS demonstrated acceptable reliability (Cronbachs alpha = 0.885) and construct validity. The scale would be useful for policymakers, practitioners, and researchers in identifying vulnerabilities and design targeted interventions within diverse socioeconomic contexts. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Translation and Validation of the Malayalam Version of the Subjective Happiness Scale
The subjective happiness scale (SHS) is a brief instrument used to measure global subjective happiness that has been translated from its original English to many other languages. To date, there is no reported translation of this scale into Malayalam, a language spoken by over 32 million people especially in the southern state of Kerala, India. In the present study, 656 community-dwelling older adults participating in the Kerala Einstein study (KES) completed the Malayalam version of the SHS. The Malayalam version demonstrated high internal consistency and good convergent validity, as assessed by comparison to measures of depression and anxiety. We also used factor analysis to determine that the Malayalam version of the SHS has a unidimensional structure, akin to the original English as well as other language adaptations. Our study adds to the repertoire of tools to measure happiness in non-English-speaking populations, enabling future research to explore the foundations of well-being across diverse cultures. The Author(s) 2024.