Browsing by Type "journal-article"

This review provides detailed information about the extraction and characterization of different cellulosic plant fibers, which are primarily composed of biological macromolecules such as cellulose, hemicellulose, and lignin. The properties of plant fibers vary based on parameters such as extraction methods, plant maturity, the part of the fiber-yielding plant, and soil conditions, which drive researchers to study the various properties of cellulosic fibers before using them as reinforcement in polymer composites. Properties such as density, diameter, functional groups, thermal stability, tensile properties, crystallography, and surface morphology were investigated using various characterization methods discussed in this article. Different surface modification techniques, especially chemical methods, are discussed, and their impacts on various properties of the fiber are also reviewed. The mechanical properties of fiber-reinforced plastics depend on the weight percentage of the fiber, fiber surface condition, fiber length, fiber orientation, secondary filler usage, and manufacturing method. By optimizing the mechanical properties of plant fiber–reinforced composites, this research paves the way for their application in the automotive and construction sectors, where lightweight and durable materials are required. This review analyzes the mechanical properties of composite materials in relation to varying manufacturing parameters, with a particular focus on cellulose-rich fibers and their contribution to the performance of polymer composites. © 2025 Elsevier B.V.

This paper defines the development of an enhanced and accurate real-time environment monitoring and Air quality control system utilizing smart Nano-sensors. The sensors were given improved nanomaterials for instance, graphene and more metal oxides for a better response when identifying various gaseous pollutants as well as gases in the air including Nitrogen Dioxide (NO2), Carbon Dioxide (CO2) and Volatile Organic Compounds (VOC’s). These sensors were incorporated to a system in form of an air pollution monitoring and control system that could immediately analyze data and respond to it. Before the outcome was obtained, field tests were carried out in many cities, and all of them recorded low pollution levels better air quality. Also, there is an environmental and sustainability perspective which proved that this idea can be used as the further strategy of regulating air quality and would meet the requirements of the law. Thus, the conclusions that can be drawn from the analysis of the results demonstrate the advantage of this approach in terms of systematic environmental management and predictive air quality control.

Self-healing is the technique to extend the life of any material and is in key demand around the globe. This article focuses on self-healing and self-assembling monomers and polymers as they are used widely in biomedical applications. Sustainable and efficient solutions across industries are offered by self-assembly in conjunction with techniques such as microencapsulation, reversible photo-cross-linking, and the use of composites. Data mining of patents shows an increasing desire to put these ideas to use in the real world, especially in the fields of nanotechnology, energy, and healthcare. The breadth of these innovations can be increased through ongoing multidisciplinary research, which should lead to discoveries that solve global problems. Advanced materials and their biomedical applications will be driven by the future of these technologies' synergy. The current review analyses various synthesis methods of the self-healing and self-assembling monomers and polymers, their biomedical applications, challenges in implementation and envisage these techniques with the aid of patent landscape analysis for predicting the potential growth of these materials over the existing materials and techniques. This review caters the need of materialists to assess the current state and the future directions of self-healing materials in biomedical applications like tissue engineering, bone regeneration and wound healing. © 2025 The Author(s)

Purpose: Over the past 28 years, studies relating to human values in the Journal of Human Values (JHV) have grown significantly both in numbers and impact. This study aims to map the major intellectual structure of research published in the JHV and suggest a way forward for prospective researchers to make meaningful contributions to the study of human values. Design/Methodology/Approach: A bibliometric analysis of 431 articles and reviews published in the JHV since the inception of the journal in 1995 till date of this study was carried out. The list of publications was sourced from the Scopus database. VOSviewer, an open-source software was used for analysis that included citation analysis, cluster analysis and keyword analysis. Findings: The findings include the identification of key themes and impactful researchers contributing to the JHV . Science mapping was performed using a co-occurrence analysis of 942 keywords, across 431 articles. One-hundred six articles met the threshold of at least two similar keywords. The analysis reveals seven clusters and major themes. Fifty percent of the publications are from India, where the journal is based; however, significant global contributions are made from across the United States, United Kingdom, Europe, Asia, Africa and Oceania. Originality/Value: This analysis offers insights on the themes and trends of research published in the JHV using bibliometric techniques and can be a useful resource for prospective researchers.

Research into natural fibre-reinforced polymer composites (NFRCs) has intensified because society demands sustainable biodegradable substitutes for synthetic composites. This paper investigates plant-based natural fibres from cultivation up until extraction, followed by chemical treatments before manufacturing stages, while analyzing their resulting mechanical and thermal characteristics. This paper specifically investigates NFRCs crashworthiness through examinations of failure modes and energy absorption mechanisms together with investigations of structural geometry and fibre volume fraction and orientation effects. The study explains different manufacturing processes, including hand lay-up and compression molding, as well as vacuum bagging and bladder molding, because their influence on mechanical performance needs assessment. Current research approaches the following issues: weak bond strength between fibre and matrix, and moisture sensitivity, while outlining future perspectives that focus on advanced chemical transformation and bio-resin combination approaches. The comprehensive guide provides instructions for selecting materials along with designing structures for energy-absorbing lightweight solutions and environmentally friendly applications in the transportation and aerospace industries. © 2025 The Author(s). Published with license by Taylor & Francis Group, LLC.

Innovations in additive manufacturing (AM) methods represent a significant advancement in manufacturing technology, opening new avenues for creating objects in various shapes and sizes. Fused deposition modeling (FDM) is a specialized AM technique in which computers build layers upon each other to form a complete 3D object. The feasibility of producing metal parts using these methods has been thoroughly analyzed, but the design process has yet to catch up with manufacturing capabilities. Biodegradable aliphatic polyester PLA is derived from lactic acid. To enhance its strength, PLA is combined with metal particles, resulting in versatile property improvements and applications. While the aesthetic and functional qualities of PLA–metal composite filaments are intriguing, they also present difficulties related to extrusion, equipment wear, and maintaining consistent print quality. These challenges could be mitigated, to some extent, with careful tuning and specialized hardware. However, the inferior mechanical properties of bioresorbable PLA filaments highlight the need for the development of infilled PLA filaments to improve strength and other characteristics. This review discusses the 3D printing of PLA infilled with metal particles, various materials used, and their properties as a matter of interest in AM technology. Additionally, the applications of PLA–metal composites, along with their implications, limitations, and prospects, are comprehensively examined in this article. This sets the stage for the development of high-strength, sustainable materials for use in a range of engineering and technology fields. © 2025 by the authors.

Machine tool chatter adversely affects tool life and surface quality, making early detection essential in machining processes. However, vibration signals collected during machining are often contaminated by noise, hindering accurate chatter prediction. This study presents an advanced chatter detection framework integrating Adaptive Threshold Wavelet De-noising (ATWD), Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Hilbert–Huang Transforms (HHT), and an Adaptive Probabilistic Neural Network (APNN). The novelty of this work lies in the introduction of an adaptive noise term, β2E2(η(t)), within the ICEEMDAN process, which mitigates mode mixing and ensures precise resonance frequency identification. The ATWD dynamically adjusts noise thresholds based on signal decomposition levels, achieving significant noise suppression for non-stationary signals while preserving critical chatter features. Using the Normalised Energy Rati; (NER), the most responsive Intrinsic Mode Functions (IMFs) are selected for feature analysis, leading t; improved signal decomposition. The APNN further enhances classification accuracy by dynamically adjusting network parameters, outperforming traditional PNN classifiers. Comparative analysis demonstrates that the proposed APNN achieves a classification accuracy of 99.5%, representing a substantial improvement over baseline methods. Internal turning experiments using a flexible boring bar validate the proposed methodology, showcasing its practical effectiveness and reliability. The integration of ATWD, ICEEMDAN–HHT fusion, and APNN provides a novel solution for chatter detection, offering significant advancements in noise filtering, feature extraction, and real-time classification accuracy. This methodology is particularly suited for challenging machining environments and Industry 4.0 applications, where precise and rapid chatter detection enhances tool life, reduces production costs, and improves overall machining productivity. © 2025 The Society of Manufacturing Engineers

Smart food packaging technologies are driving sustainable transformations in the food processing and packaging industries by enhancing food safety, improved supply chain efficiency and traceability, increased shelf life, and reducing food wastage. This review aims to highlight recent advances in smart food packaging, including active, intelligent, and biodegradable packaging technologies, its role in enhancing sustainability, the challenges and barriers to its adoption, and future research opportunities in the field. This work also addresses how these innovations contribute to the United Nations' Sustainable Development Goals (SDGs), particularly SDGs 2, 3, 12, 13, 14, and 15. This work identifies and brings out the key smart food packaging technologies, their advantages and disadvantages, most viable applications, and suitable geographical regions. For instance, time-temperature indicators (TTIs) and gas sensors are particularly effective for packaging perishable goods, especially in tropical climates. The impact of smart food packaging is further illustrated through real-world case studies, such as RipeSense® and FreshChain. The work identifies the major challenges in smart food packaging technology adoption as technological limitations, high costs, current food safety regulations, and hesitance in customer acceptance. The work also identifies future research avenues, including integration of artificial intelligence (AI), sustainable innovations, and cross-industry collaborations, in this domain.

The question with national and global administrators is how to control and manage waste. It is well established that global waste mismanagement has been contributing to global warming as more than 2.5 billion tons of waste is produced every year. It is thereby the global community that has come together to minimize the impacts of waste through waste management policies. India’s responsibilities under the World Trade Organization’s Agriculture Agreement are critical for global trade activities. Balancing these duties with the need to decrease agricultural waste is a tough legal task. India must negotiate the WTO’s rules and commitments to avoid unduly distorting international commerce using its agricultural waste management policy. India has guaranteed to decrease greenhouse gas emissions and strengthen its climate resilience. Effective agricultural waste management is related to attempts to lessen and acclimate to environmental change. India is a party to various agreements, treaties, and conventions relating to environmental protection, yet on many occasions implementation concerning waste management is questioned. Presently agricultural waste in India is more than 500 million tonnes, and this farm waste includes huge volumes of garbage such as crop remnants, stubble, and post-harvest waste. The waste products are not appropriately handled, which contributes to air pollution, soil degradation, and greenhouse gas emissions, worsening the environment at large. India’s immense and diversified agricultural industry has been facing a challenging dilemma at this junction of agriculture waste management and international commerce. © 2025, Academic Science Publications and Distributions. All rights reserved.

The demand for advanced coating materials in high-temperature and wear-resistant applications has led to the exploration of multicomponent systems that offer superior mechanical properties. Among these, AlCrSiN has emerged as a promising candidate owing to its ability to blend the thermal stability and hardness of AlCrN aided by grain refinement along with the presence of amorphous phase as a result of silicon addition. In this study, SS 304 substrates were used to deposit AlCrSiN thin films through magnetron sputtering process. This work focuses on the structural, hardness, scratch, wear and adhesion properties of AlCrSiN thin films as a function of substrate temperature. The AlCrSiN coating deposited at 400°C exhibited a hardness of 23 GPa and an (H/E) ratio of 0.08, which is an apparent measure of good wear-resistance coating. The work of adhesion significantly increased from 18 to 150 Jm−2 as the substrate temperature was gradually boosted from 30 to 500°C. Coatings facricated at room temperature and 300°C displayed an amorphous structure, while those deposited at 400°C and 500°C developed CrN/AlN crystallites averaging 16 nm in size within the amorphous matrix. Furthermore, the coefficient of friction (COF) was maintained between 0.27 and 0.30. These findings underscore the potential of AlCrSiN coatings to enhance the wear resistance and mechanical performance of substrates in demanding industrial applications, with substrate temperature playing a key role in optimizing coating properties.

The growing use of algorithmic and biometric technologies in border control is part of a larger trend in global security governance that has significant legal and ethical implications for their effect on individual rights and procedural justice. As central features in the EU's shifting security regime, ETIAS and facial recognition technologies deploy algorithmic profiling and biometric risk assessment to screen visa-exempt third-country nationals. The research systematically examines the decision-making processes of ETIAS and the overall facial recognition system, demonstrating the interplay between algorithmic risk assessments and discretionary human discretion by national authorities. It contends that the algorithmic profiling lack of transparency, combined with sweeping national security exceptions, produces a procedural void, in which the right to reasoned decisions and effective remedies is compromised. Second, the use of interoperable databases and risk indicators puts core data protection principles into jeopardy, notably purpose limitation and the right to be forgotten. This paper also argues that ETIAS and the application of facial recognition technologies represent a larger trend toward “techno-regulatory assemblages” in EU governance, where technological infrastructures increasingly influence legal and administrative decisions. It critically assesses whether the human oversight mechanisms incorporated within ETIAS National Units are adequate to prevent the risks involved in automated decision-making, especially in the face of strict time pressures and security requirements. The study detects a latent paradox: though these systems aim to strengthen a “Security Union,” they might inadvertently lead to an “Insecurity Union” by undermining transparency, procedural protections, and citizen rights.

A novel class of magnetism has been discovered as altermagnetism because of opposite spin in real space. Altermagnetism exhibits total net magnetization zero and symmetry-induced spin splitting, which separating it in contrast to both antiferromagnetism and ferromagnetism. This review discusses the theory of altermagnetism including core symmetry mechanism and first principle approaches in theoretical models. Material candidates (e.g. MnTe, Mn₅Si₃, RuO₂, CrSb etc.) and predictions for altermagnetism were demonstrated through criteria for identifying altermagnetic materials, role of machine learning and artificial intelligence (AI) in this discovery, and databases with high-throughput screening efforts. The experimental realization and characterization were discussed via techniques for detecting altermagnetic order (e.g., angle-resolved photoemission spectroscopy (ARPES), neutron scattering, transport), challenges in confirming altermagnetism, and case studies of experimental breakthroughs. A comprehensive functional properties and device applications were described with the help of anomalous Hall effect without net magnetization, spin transport, spin torque, and spin filtering in altermagnets including integration with ferroelectrics, superconductors, and topological materials. A comparison of altermagnetism was illustrated with antiferromagnetism, spin glasses, and topological magnetic phases. The role of computational tools was highlighted by AI-driven materials discovery, inverse design approaches, and data-driven modelling of magnetic behaviour. Some of the significant headwinds were underlined by demonstrating stability and tunability of altermagnetic states, precise detection and control, need for experimental benchmarks, and integration into real-world devices. At the end outlook and future directions were deliberated about, with opportunities for interdisciplinary research, advances in instrumentation and computation, and the roadmap toward altermagnetic-based spintronic platforms.

The modern world demands sophisticated materials by modifying every aspect of each atom for further acceleration in any specific domain. Fire resistant materials are also the most researched topic of materialists as this open avenue for various fire-related applications. Development of sustainable materials might reduce the harmful environmental effects of the synthetic materials. Thermosensitive hydrogels are specifically used for high temperature and fire-retardant applications. The synthesis of thermosensitive hydrogels from a natural material has been focused on obtaining a sustainable biomaterial. For a better understanding of its thermal behavior, various temperature-related properties were also reviewed. This review focuses on synthesis of thermosensitive hydrogel from natural sources, thermal properties, and future scope through patent landscape analysis. From the review, it was inferred that the thermosensitive hydrogels synthesized from the natural materials cannot only be effectively employed in high temperature and fire retardance but also act as sustainable materials for such applications. © 2025 The Author(s)

In this study, Co2+ and Ce3+ substituted BaCoxCexFe12-2xO19 hexaferrites, combined with polyaniline (PANI), were synthesized via the sol-gel combustion method to explore their structural, dielectric, magnetic, and microwave absorption properties. Substitution levels (x = 0.0 (XP1), 0.6 (XP2), and 1.0 (XP3)) were investigated, revealing significant modifications in structural and electromagnetic characteristics with increasing Co-Ce doping. Field Emission Scanning Electron Microscopy (FESEM) confirmed a transition from well-separated grains in XP1 to densely packed and fused grains in XP3. Dielectric analysis showed a frequency-dependent decrease in real part of permittivity and imaginary part of permittivity, aligning with Maxwell-Wagner polarization. XP1 exhibited the highest real part of permittivity (ε′) of 12,791.13 at low frequencies, while XP3 demonstrated reduced imaginary part of permittivity, attributed to enhanced grain boundary effects. Magnetic measurements revealed a reduction in saturation magnetization (Ms) from 45.92 emu/g in XP1 to 33.42 emu/g in XP3, reflecting the impact of Co2+ and Ce3+ doping on Fe3+ superexchange interactions. Impedance spectroscopy highlighted significant grain boundary effects, with XP3 exhibiting the highest grain boundary resistance, while XP1 showed superior conductivity. Microwave absorption analysis demonstrated effective reflection loss (REL), with XP3 achieving the highest REL of −33.29 dB at 11.48 GHz at 9.9 mm thickness, attributed to superior impedance matching (|Zin| = 364.37 Ω). XP1 displayed effective broadband absorption, with a maximum REL of −36.34 dB at 10.05 GHz and broader frequency coverage. These results establish Co-Ce substituted BaCoxCexFe12-2xO19/PANI composites as promising materials for electromagnetic interference (EMI) shielding and broadband microwave absorption applications. © 2025 Elsevier Ltd and Techna Group S.r.l.

The escalation of occupational and environmental pollutants poses a significant threat to human health, particularly exacerbating chronic respiratory diseases (CRD). Globally, CRD account for 4.0 million reported deaths (Momtazmanesh et al. EClinicalMedicine 59, 2023). This study investigates the repercussions of exposure to unfavourable Air Quality Index (AQI) levels on respiratory health, focusing on asymmetric spirometry flow during natural inspiration at a flow rate of Qin = 10 l/min. Here, employing digital imaging techniques, we developed an in-silico human respiratory tract model, encompassing up to the 7th bifurcation of a healthy male individual. The results reveal that the dynamics of inspired airflow and particles, particularly in turbulent regions, influence particle deposition in the airways. Thus, the upper airways and bifurcations region have higher deposition efficiency of fine particles ~ 2.5 and 10 μm, consequently creating hotspots for respiratory illnesses. Moreover, to quantify the internal flow characteristics, we utilised a set turbulence model, and the trajectory of fine particles was computed by discrete phase model (DPM). The localised quantitative quantification of particle physics focuses on deposition efficiency at different time instants, t = 1.5 s, 2.1s and 2.5 s, complemented by insights into internal flow features, particles are depicted and quantified through regional deposition efficiency, while flow physics is presented by, surface streamlines, turbulent kinetic energy, turbulence intensity and Q-criterion. These findings have a significant implication in effective diagnosis and management of chronic respiratory diseases (CRD), providing valuable insights into the intricate interplay between air quality, airflow dynamics, and respiratory health.

The current work describes the ecofriendly and compatible synthesis of indium sulphide (In₂S₃:InS)-g-C₃N₄ (GCN) heterostructure. Chrysophyceae, commonly known as golden brown algae extract, was used as the reducing agent in a hydrothermal fabrication of InS/GCN. InS, GCN and InS/GCN were used to decorate a glassy carbon electrode (GCE) and evaluate its electrochemical performance using cyclic voltammetry (CV) and amperometric techniques for the detection of the carbamazepine (CBZ) drug. The amperometric method was more sensitive than cyclic voltammetry, detecting concentrations from 0.01 to 240 μM and having a limit of detection of 0.0139 μM. The CBZ recovery studies were conducted for tablets, suspensions, and urine samples, and the recovery percentage was in the range of 97.3 to 99.2 %, with the standard deviation less than 2.6 %. Additionally, CBZ was subjected to light-driven degradation studies in the presence of InS/GCN and found to degrade 81, 93, and 98 % under UV, visible, and sunlight sources, respectively. Various factors affecting degradation, like pH (pH 7), catalyst amount (40 mg) and concentration (10 mg L−1) of drug, were optimized to achieve maximum degradation. Liquid chromatography–mass spectroscopic (LC–MS) results helped predict the degradation pathway. Enhanced activity in InS/GCN could be attributed to the formation of a type II heterostructure with enhanced electron mobility and decreased charge transfer resistance. This new approach serves as a versatile material for use in electrochemistry, the pharmaceutical industry, and environmental remediation. © 2025 Elsevier B.V.

The Internet of Things (IoT) is emerging as a new trend mainly employed in developing numerous vital applications. These applications endure on a federal storage framework primarily concerned with multiple issues. Blockchain technology (BC) is one of the supportive methods for developing IoT-based applications. It is employed to solve the problems encountered in IoT applications. The attack Distributed Denial of Service (DDoS) is one of the leading security attacks in IoT systems. Attackers can effortlessly develop the exposures of IoT gadgets and restrain them as fragments of botnets to commence DDoS threats. The IoT devices are said to be resource-constrained with computing resources and restricted memory. As a developing technology, BC holds the possibility of resolving security problems in IoT. This paper proposes the Metaheuristic-Optimized Blockchain Framework for Attack Detection using a Deep Learning Model (MOBCF-ADDLM) method. The main intention of the MOBCF-ADDLM method is to deliver an effective method for detecting DDoS threats in an IoT environment using advanced techniques. The BC technology is initially applied to mitigate DDoS attacks by presenting decentralized security solutions. Furthermore, data preprocessing utilizes the min-max scaling method to convert input data into a beneficial format. Additionally, feature selection (FS) is performed using the Aquila optimizer (AO) technique to recognize the most relevant features from input data. The attack classification process employs the deep belief network (DBN) technique. Finally, the red panda optimizer (RPO) model modifies the hyper-parameter values of the DBN model optimally and results in higher classification performance. A wide range of experiments with the MOBCF-ADDLM approach is performed under the BoT-IoT Binary and Multiclass datasets. The performance validation of the MOBCF-ADDLM approach portrayed a superior accuracy value of 99.22% over existing models.

This study presents a novel approach to enhance the performance of ducted rockets by using a hybrid gas generator (HGG) as an alternative to conventional solid propellant-based gas generators. The HGG (primary combustor) consists of a single perforated solid fuel through which the oxidizer is passed during combustion, akin to a hybrid rocket. However, by intentionally maintaining the fuel-rich combustion enables the byproducts/effluents of the gas generator to be combusted again in a secondary combustor (ram-combustor) along with the incoming ram air, thus improving the overall performance. The primary benefit of this type of gas generator is its ability to be actively throttled. This study aims to test the proof of concept and feasibility of the HGG for its potential application in ducted rockets (DRs). Hence, in this investigation, the emphasis is made on characterizing the gas generator without ram-combustor, i.e. hybrid rocket mode, with a comparative analysis between hydroxyl-terminated polybutadiene (HTPB) and 10% boron-laden HTPB (HB10) solid fuels with gaseous oxygen. The equivalence ratio is maintained above one during its operation for all the cases, aligning with ducted rocket requirements. The results indicate that HB10, operated with gaseous oxygen as the primary oxidizer, generally demonstrates slightly improved performance in regression rate (2–7%) and chamber pressure (3–8%). Also, successful ignition and combustion of boron within the HGG are observed. Additionally, the study evaluates static motor performance in hybrid rocket mode (HGG), revealing HB10’s marginal superiority over HTPB in terms of specific impulse (for the equivalence ratio greater than 1.2). The trend of characteristic velocity (c*) for the HB10 fuel increases with increasing equivalence ratio and outperforms HTPB after reaching 1.4 (approximately), whereas the c* efficiency shows the reverse trend. The HGG operating on HB10s also has relatively higher thrust (0.6–10%). Although these parameters do not directly determine ducted rocket performance, they offer insights into HGG feasibility and potentiality. These modest performance enhancements of the boron-laden solid-fueled gas generator hold significant promise for enhancing ducted rocket capabilities, particularly due to the possibility of two-stage combustion, a promising technique for enhancing boron combustion efficiency.

With the increasing demand for sustainable and non-toxic alternatives, bio-based plasticizers derived from renewable sources are being developed as environmentally friendly replacements for conventional synthetic plasticizers such as phthalate esters, adipates, trimellitates, benzoates, sebacates, etc. This study investigated the extraction of solid plasticizers from the leaves of the abundantly available Millettia pinnata plant (MPL). It was chemically treated through processes including phytoremediation, slow pyrolysis, alkylation, and filtration to extract the plasticizers. Scanning electron microscopy revealed a porous, smooth surface, while atomic force microscopy further supported the morphological suitability of these materials for biofilm and composite preparation. Fourier transform infrared spectroscopy identified functional groups such as alcohol, amine, amide, hydrocarbon, alkene, and aromatic compounds, while UV analysis confirmed the presence of alcoholic, amino, and carboxyl constituents. The primary phytoconstituents detected in the MPL were molecularly docked to determine binding affinity. Thermal analysis demonstrated that the extracted plasticizer can withstand temperatures up to 267 °C. Furthermore, X-ray Diffraction analysis yielded a high crystallinity index (47.5%) and a low crystalline size (11.3 nm), desirable characteristics in plasticizers. These findings suggest that plasticizers extracted from MPL leaves could serve as a viable, eco-friendly alternative to conventional synthetic plasticizers, offering a sustainable replacement with considerable functional benefits. © The Author(s) 2025.

Predicting human behaviour is a complex task. Traditional methods often rely on explicit user input or external observation, which can be restrictive and impractical in real-world scenarios. As an alternative, Brain-Computer Interfaces (BCIs) offer a more direct and specific means of accessing cognitive and emotional states, providing valuable insights into human intentions and decision-making processes. This paper proposes a novel method that predicts and suggests personalised emotion-based activities for individual users based on multi-modal sensory data collected from the brain, body, and environment. Our method overcomes the limitations of conventional systems by incorporating a multi-modal data collection set throughout the day to understand user context and intent better. By analysing this data, we predict the emotions-based practice of the user's day. We train our method using state-of-the-art, nature-inspired reinforcement learning algorithms and agent technology to optimise its optimisations and personalised continuously. The performance evaluation shows that the accuracy and F1 score for the proposed method achieved 95.6% and 84%, respectively, achieving 2 to 3% more accuracy than AI-based emotion state-of-the-art detection methods. ©2025 The Authors. Published by AnaPub Publications.