Current Industry Research Articles

A revolutionary temporary immersion bioreactor method for commercialscale Musa sp. (Tanduk and Berangan) multiplication

Large-scale clonal banana production is required to supply current industry demand. Conventional tissue culture technique needs to be improvised to overcome the high-cost manufacturing and laborious work involved. Therefore, this study aimed to develop an efficient protocol in clonal banana production by using temporary immersion bioreactor (TIB) technology which uses intermittent immersion in liquid nutrients. This study evaluated the optimum protocol pathway for efficient Musa acuminata cv. Berangan and Musa paradisiaca cv. Tanduk propagation with the application of TIB system. M. acuminata and M. paradisiaca optimum initiation medium were developed in MS (Murashige and Skoog) media supplemented with 3 mg/L 6-Benzylaminopurine (BAP) and 0.2 mg/L Thiamine and 0.3 mg/L Thidiazuron (TDZ) respectively. The optimum proliferation medium in TIB developed in MS media supplemented with 3 mg/L BAP for both varieties. The highest number of shoots was achieved in TIB with 20 initial plantlets and a 15-minute immersion with a 6-hour gap along with a 14-day incubation period. Micropropagation of Musa sp. in Temporary Immersion Bioreactor System (RITA ®) has successfully shown significant growth as compared to MS media agar-conventional technique with reduced phenolic content released in both varieties. In conclusion, the production of Musa acuminata cv Berangan and Musa paradisiaca cv. Tanduk by using a temporary immersion bioreactor (TIB) system has huge potential to improve the scale production output with lower-cost plantlets

Real-Time Data Analytics in Customer Experience Management: A Framework for Digital Transformation and Business Intelligence

Real-time data analytics has emerged as a transformative force in customer experience management, enabling organizations to process and respond to customer interactions instantaneously. This article presents a comprehensive framework for implementing real-time analytics in customer-centric business operations, examining both theoretical foundations and practical applications across multiple industries. Through systematic analysis of implementation strategies, technical architectures, and organizational challenges, this article illuminates the critical role of real-time analytics in enabling personalized customer experiences, dynamic pricing optimization, and predictive service delivery. The article synthesizes current industry practices while identifying key success factors and potential barriers in real-time analytics adoption. Findings indicate that organizations leveraging real-time analytics capabilities demonstrate enhanced customer satisfaction, improved operational efficiency, and strengthened competitive positioning. This article contributes to both theoretical understanding and practical implementation of real-time analytics in customer experience management, providing actionable insights for practitioners while identifying promising directions for future research. The article concludes with a structured framework for organizations seeking to implement or enhance their real-time analytics capabilities in pursuit of superior customer experience.

Multiplex CRISPR-Cas Assay for Rapid, Isothermal and Visual Detection of White Spot Syndrome Virus (WSSV) and Enterocytozoon hepatopenaei (EHP) in Penaeid Shrimp.

White spot syndrome virus (WSSV) and Enterocytozoon hepatopenaei (EHP) represent the most economically destructive pathogens in the current shrimp industry. WSSV causes white spot disease (WSD) responsible for rapid shrimp mortality, while EHP stunts growth and therefore reduces overall productivity. Despite the importance of timely disease detection, current diagnostic methods for WSSV and EHP are typically singleplex, and those offering multiplex detection face issues such as complexity, low field compatibility and/or low sensitivity. Here, we introduce an orthogonal, multiplex CRISPR-Cas assay for concomitant detection of WSSV and EHP. This method combines recombinase polymerase amplification (RPA) for target DNA enrichment with Cas12a and Cas13a enzymes for fluorescent detection. This assay produces distinct fluorescent colours for different diagnostic outcomes, allowing naked eye visualisation without ambiguity. Further validation reveals that the assay detects as few as 20 and 200 copies of target DNA from EHP and WSSV, respectively, while producing no false positives with DNA from other shrimp pathogens. Moreover, the assay excellently agrees with established PCR methods in evaluation of clinical samples. Requiring only 37°C and less than an hour to complete, multiplex CRISPR-Cas assay presents a promising tool for onsite diagnostics, offering high accuracy while saving time and resources.

Web Development Framework

Web development frameworks are essential to contemporary web development because they provide an organized method for creating dynamic, feature-rich, and responsive websites. This Research paper delves into the intricacies of web development framework, exploring their significance, functionality, and the diverse landscape they inhabit. The paper further investigates various types of frameworks, including front-end, back-end, and full-stack frameworks, shedding light on their distinct features and suitability for different project requirements. Finally, the paper concludes with a discussion on the feature trends and challenges in web development framework, including the rise of serverless architectures and the increasing demand for real-time applications. By synthesizing current research and industry insights, this paper aims to provide a comprehensive understanding of web development and their impact on modern web development practices

Shear Failure Assessment of a G+3 RC Framed Building via Nonlinear Static Pushover Analysis using SAP2000

Accurately modelling shear behaviour in reinforced concrete (RC) structures is essential for seismic evaluation, as shear failure can lead to catastrophic outcomes. Current industry practices often emphasize nonlinear flexural analysis while underestimating the critical role of shear behaviour. This study aims to develop a nonlinear force-deformation model specifically for shear in RC sections to bridge the gap in existing literature. Standards like IS-456:2000 and ACI-318:2008 provide ultimate strength estimates but may inadequately consider the contribution of concrete under seismic loads. Insights from models by Priestley et al. (1996) and Park and Pauley (1975) are integrated to improve the calculation of shear hinge properties. A comparative nonlinear static (pushover) analysis of an RC framed building, with and without shear hinges, demonstrates the impact of shear modelling. Results show that neglecting shear hinges overestimates base shear and roof displacement capacity, masking non-ductile failure modes. Incorporating shear hinges provides a more realistic assessment of structural strength and ductility, emphasizing their necessity in seismic analysis and design. Keywords: Shear behaviour, Reinforced concrete (RC) structures, Seismic evaluation, Shear failure, Nonlinear analysis, Force-deformation model, Shear hinge.

Colour sorting ROS-based robot evaluation under different lights and camera angles

Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl’s algorithm for the ball’s diameter estimation. The robot’s performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball’s diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%.

Redefining Newsrooms: The Role of Artificial Intelligence in Shaping Journalism's Future

This paper seeks to justify why journalism is at the cutting edge of this revolution with Artificial Intelligence (AI); it has become an essential component of various industries. In this study, the author examines the effects of the integration of AI into the current news media industry with an emphasis on the use of AI in executing analytic functions such as writing and verification of information. As for strong points and prospects, AI contributes improvements in terms of effectiveness and time efficiency, yet it brings into focus certain drawbacks concerning ethical aspects of carrying fake news, distorted information, etc., alongside the problem of algorithms’ credibility of algorism-based journalism. In this empirical qualitative case study, the author examines the benefits and limitations of AI’s implementation in journalism. On this point, it presents literature gaps that exist on the effects of AI on ethical considerations and looks at how such organizations can balance the need to use technology and still produce work of the highest ethical standards. According to the findings, while AI may step up the accuracy and credibility that is required in news dissemination, its abuse may also lead to increased biased and fake news. Overall, this research calls for adopting AI technologies in newsrooms, but only after putting this article’s findings into practice alongside human oversight and ethics. Finally, the paper concludes the future of AI journalism and emphasizes sustainable and more responsible ways to create technological advancement within the field of Journalism to make more truthful and reliable news.

Sustainable alloy design: Fe-enhanced Ti alloys for superior mechanical performance in additive manufacturing

The use of titanium (Ti) in the additive manufacturing (AM) industry has been steadily increasing. However, iron (Fe), a significant impurity originating from the sponge production process, is difficult to remove due to high energy requirements. Consequently, fully eliminating Fe from Ti is challenging and expensive. Understanding the role of Fe in Ti alloys and determining a maximum allowable threshold for AM is therefore critical. This knowledge could potentially relax current industry standards and enable the development of more sustainable Ti alloys. This study investigated the effect of Fe on the microstructure development and mechanical properties of Ti-Fe alloys, with a focus on their suitability for additive manufacturing. Through an in-situ alloying approach, Fe content was systematically increased from 0 to 3 wt%, leading to significant grain size reduction, from over 100 μm to less than 5 μm. These modifications in grain size and morphology, achieved by adjusting the Fe content in laser-based powder bed fusion (LPBF) fabricated Ti alloys, are directly related to substantial enhancements in the mechanical properties. The hardness increased from 171 to 366 HV, and the tensile strength effectively doubled from 426 to 1026 MPa while maintaining ductility. XRD analysis revealed the presence of a full α phase when the Fe content was ≤ 0.75 wt%. However, at higher Fe concentrations, small β peaks were observed. To identify the strengthening mechanisms, theoretical frameworks such as the Hall-Petch relationship and Labusch model were employed. Two significant inflection points were identified at Fe concentrations of 0.1 % and 1 wt%, indicating three important compositional ranges: Region #1 (Fe ≤ 0.1 wt%), Region #2 (0.1–1 wt% Fe), and Region #3 (1–3 wt% Fe). The strengthening mechanism in each region is discussed in detail.

Full-Scale Testing of Corrosion Resistant Alloy-Mechanically Lined Pipes for Submarine Pipelines

Abstract This study evaluates the fatigue performance of Corrosion Resistant Alloy–Mechanically Lined Pipes (CRA-MLPs), focusing on the critical triple point, where the CRA liner, weld overlay, and backing steel pipe intersect. Through Full-Scale Bending Tests (FSBT) and Full-Scale Resonance Fatigue Tests (FSFT), real-world operational and installation conditions were simulated to assess fatigue resistance, particularly at the triple point–an area not adequately addressed in current industry standards. The results reveal that the triple point exhibits excellent fatigue performance, exceeding the criteria of existing guidelines, despite the lack of specific provisions for this critical region. This study provides valuable data to inform updates to industry standards, enhancing the safety and reliability of subsea pipeline operations.

Genetic variability in macadamia rootstocks and their effects on early vigor traits of ‘HAES 741′

Early vigor in tree crops is crucial as it accelerates orchard establishment and enhances resilience to environmental stresses. Rootstocks play an essential role in determining vigor of tree crops. Macadamia is propagated through grafting, however research into rootstocks is rare. This study evaluated a range of macadamia rootstocks including cultivars, standard rootstocks, breeding selections and wild germplasm, focusing on their effects on early vigor (age 4). Phenotypic variability in the scion ‘HAES 741′ was assessed using 24 accessions of seedling and cutting rootstocks over 4 years (2018–2021), analyzing 7 early vigor traits and growth rates. Principal coordinate analysis and cluster analysis revealed high genetic diversity among accessions, clustering into 4 groups. Significant rootstock effects on early vigor were observed. Broad-sense heritability estimates of rootstock effects were moderate for scion height (H2=0.48) and total height (H2=0.44), holding promise for improvement through selection. Positive correlations among scion growth traits, particularly trunk circumference, (rg= 0.50 to 0.99) suggested their potential as indicators of early vigor. The strong genetic correlations for canopy area (rg = 0.84) and canopy volume (rg = 0.63) between scions of grafted trees and the ungrafted trees of the same rootstock accessions reflect the possibility of trait transmission from rootstock to scion. Among the studied rootstocks, ‘BHI1’, ‘BHY2’ and ‘BHI3’ imparted the highest early vigor. Compared with the current industry standard rootstocks ‘H2’ and ‘Beaumont’, the least vigorous rootstocks ‘Mjan1’ and ‘BDW3’ showed reductions of 18 % and 47% in tree height, 54 % and 74% in trunk circumference and 97 % and 210 % in canopy volume respectively. These findings suggest promising rootstock candidates for high performing orchard systems, although ongoing evaluation until maturity is crucial.

A Framework to Integrate Building Information Modelling to the Curriculum of the Quantity Surveying Undergraduate Degree Programme in Sri Lanka

One of the primary sectors that contributes to the national economy of the country is the construction industry. The Quantity Surveyors (QS), offer specific expertise in project management and cost estimation. A revolutionary change is occurring in Sri Lanka (SL)’s construction industry due to the increasing demand for innovative technologies such as Building Information Modeling (BIM). But the Sri Lankan construction industry has been remarkably lagging in adopting these cutting-edge techniques. According to past SL research findings, one of the main obstacles in BIM integration among QS professionals is a severe knowledge gap. By involving BIM approaches into QS education this research aims to develop a framework to implement BIM within the curriculum of quantity surveying undergraduates according to the industrial demand in SL. This paper focuses on the potential difficulties of integrating BIM into SL’s QS curriculum, where graduates may fulfill industry expectations. This research used questionnaire survey, semi- structured interviews for both academic and industrial QS professionals separately, and conducted existing university QS curriculum review to gather data. Purposive sampling is used as the sampling technique and this study uses a mixed-method approach to research, combining qualitative and quantitative evaluations, and analyzes data using descriptive statistics and content analysis. According to data gathered from industrial professionals, it was proved that SL construction industry does not widely use BIM and have a huge demand on knowledgeable BIM of QS in the current industry. Most of the knowledgeable BIM personnel gain knowledge from self-learning and by following some extra courses. Professionals with an academic background proved, that the current QS curriculums are outdated. As a solution, this research develops the framework to integrate BIM to the QS curriculum of the undergraduate degree programmes in SL. A framework suggestion is to be incorporated into the curriculum was the outcome of the researcher's reflections on the interview results. These results contribute to the growth and development of the Sri Lankan construction industry by knowledgeable QS professionals, thereby fostering the advancement of the Sri Lankan construction industry.

Prediction of seam strength of cotton canvas fabric using fuzzy logic

This study investigates the application of fuzzy logic in predicting seam strength in cotton plain canvas fabric, focusing on both warp and weft directions. The precise prediction of seam strength is crucial for manufacturers to uphold quality standards, enhance production efficiency, and minimize waste. The fuzzy logic model in this study uses thread linear density and stitch per inch as input parameters and warp and weft seam strength as output variables. The modeling was conducted using MATLAB, specifically utilizing the Mamdani fuzzy inference system with triangle membership functions. The fuzzy logic model was found to be very accurate, as shown by coefficients of determination (R2) of 0.9841 for the warp way and 0.9888 for the weft way, along with correlation coefficients (R) of 0.992 and 0.9944. The mean absolute percentage error (MAPE) was calculated to be 4.8719 % for the warp way and 4.7561 % for the weft way, each below 5 %, underscoring the model's reliability and robustness in seam strength prediction. This research provides findings with substantial implications for the textile industry, where the application of predictive models is on the rise to enhance production efficiency and product quality. Manufacturers can improve their ability to forecast regarding fabric properties and adjust production processes through the implementation of fuzzy logic models. This approach is consistent with current industry trends emphasizing automation and digitalization, wherein predictive models are essential for facilitating smart manufacturing and quality control.

Pyrolysis and beyond: Sustainable valorization of plastic waste

The recycling of plastics through pyrolysis has garnered significant attention as a waste management approach, particularly given upcoming regulations and the urgent need for absolute sustainability. This review paper evaluates pyrolysis's technological, environmental, and regulatory aspects, emphasizing the need to prioritize environmental impact over purely economic considerations. While it is recognized that financial challenges have constrained many past attempts at scaling pyrolysis systems, this paper argues that a shift in focus toward sustainability criteria is essential for the technology's long-term viability. Prioritizing environmental outcomes is critical given the pressing issues like plastic pollution, which presents significant threats to ecosystems and human health. A comprehensive assessment of environmental impacts is vital for technologies intended for large-scale deployment, ensuring that they align with global sustainability goals. This perspective is especially pertinent in light of the current United Nations Environment Programme (UNEP) global plastic pollution treaty negotiations, where there is a contentious debate over the true circularity of chemical recycling. Environmentalists and several nations advocate for significant reductions in plastic production and bans on single-use plastics. At the same time, oil-producing countries and petrochemical companies push for circularity in the plastic economy. The paper explores the promise and limitations of pyrolysis technology for valorizing plastic wastes, comparing its efficacy with other waste management methods. Biomass pyrolysis is discussed due to its significant synergies with plastic pyrolysis and its potential for carbon sequestration through biochar. However, the discussion on biomass pyrolysis is limited in scope, with the paper's primary focus on waste plastic pyrolysis. The paper discusses the challenges and opportunities in utilizing pyrolysis for waste plastics, which are considered along with regulatory and geopolitical aspects. Environmental impacts, including life cycle analysis and planetary boundary considerations, are examined to understand why pyrolysis has yet to achieve widespread adoption. A case study on Plastic Energy, SABIC, and Unilever highlights current industry status, technological advancements, and information gaps. Finally, a comprehensive framework is proposed to achieve net-zero emissions, improve air quality, and ensure absolute environmental sustainability in plastic recycling. Recommendations are provided for the plastic industry and research institutions to support the transition towards sustainable waste management practices.

Development of a prefabricated construction productivity estimation model through BIM and data augmentation processes

The accuracy of productivity estimates remains a significant challenge due to limited data availability. This research addresses the need for precise productivity estimation in construction by integrating data augmentation techniques, onsite time study data, and Building Information Modeling (BIM) for automated quantity take-offs and design complexity analysis of steel connections. By examining design complexity, the method provides productivity estimates for project zones, sequences, and individual components, improving overall production management. Four data augmentation techniques—normal noise, interpolation, clustering, and Bayesian Linear Regression—were evaluated to enhance time study data. The augmented dataset was used to train an Artificial Neural Network, validated through case studies. The study identified the normal noise method as the most effective, significantly improving time estimation accuracy. Specifically, the proposed approach yielded a 58%–71% enhancement over current industry estimates and a 2.1%–31.1% improvement compared to models without data augmentation. This research enables managers to optimize resource allocation and reduce potential project delays.

Preparing tourism, hospitality and events graduates to be industry ready: extending the three-factor model of authentic learning

ABSTRACT Authentic learning is a critical pedagogy and curriculum requirement in higher education to better prepare students for future workforce requirements. As such, educators adopt a range of authentic learning tasks such as work-integrated learning, industry reports, and field trips to enable student engagement in current industry issues. Although such actions are practical and have valuable impact on students, there has been little effort to narrow the theoretical gap. The present study addresses this by extending the authentic learning three-factor model (construction of knowledge, disciplined inquiry, and value beyond school) to evaluate the perceptions of educators in THE higher education towards their understanding and embeddedness of authentic elements in their teaching curriculum to better prepare industry-ready graduates. In-depth interviews were conducted with higher education THE academics (n = 21) in Australia, New Zealand, The Netherlands, and Scotland. Thematic analysis using NVivo revealed seven key themes within the authentic learning three-factor model: knowledge co-creation and scaffolding, customising knowledge and experiences, student engagement, educator capability, industry readiness, real-world problems, and meaningful connections. The proposed seven standards necessitate a paradigm shift in curriculum design, syllabus structuring, and student assessment within THE institutions. Emphasising industry-readiness as a core objective, educators must align their teaching approaches with the theoretical findings of this research to better prepare students for real-world challenges.

Corpus-Based Word Usage in Social Media Marketing and E-Commerce: Developing Word Lists and a Proposedly Designed Teaching

This study aims to explore the frequently used words in 41 research articles and 12 related books from 2000 to 2024 in the context of social media marketing and e-commerce, focusing on general words, academic words, and specialized words. The investigation also leads to an in-depth examination of collocation words using Antconc software's N-gram feature. Then, the collected words were grouped for designing the proposed design for teaching English for students majoring in social media marketing and e-commerce. In addition, grammatical usage was analyzed to provide a comprehensive picture of frequently used grammatical structure for supporting the effectiveness of a proposedly designed teaching. The investigation focuses on a large dataset of 1,675,230 tokens and 39,931 types, with keyword tokens numbering 818,111 and keyword types numbering 1,791. The vocabulary distribution contains 21.36% K1 words, 7.89% K2 words, and 18.27% from the Academic Word List (AWL), with off-word lists accounting for 52.48%. Key findings emphasize the importance of terms such as social media marketing, e-commerce, and marketing strategies, indicating the key role of social platforms and digital media in contemporary advertising practices. Furthermore, the examination of n-grams and academic vocabulary emphasizes the value of data-driven strategies, technological innovation, and consumer behavior observations. Specialized vocabulary highlights developing trends and innovations in this industry. The findings also enhance the understanding of the growing language of social media and e-commerce marketing, providing insights into current industry trends and terminology. This analysis provides the foundation for developing targeted educational instruments and methods to improve communication and effectiveness in digital marketing environments.

The New Frontier of Ad Analytics: Privacy-Centric Approaches to Campaign Measurement and Optimization

This article explores the transformative shift in digital advertising measurement techniques in response to growing privacy concerns and regulatory pressures. It examines the transition from traditional third-party cookie-based tracking to advanced data modeling approaches that prioritize user privacy. The article provides a comprehensive overview of privacy-preserving ad measurement techniques, including aggregated and anonymized data analysis, probabilistic attribution models, and differential privacy. It delves into Big Tech's advancements in conversion modeling, highlighting the role of machine learning in developing more accurate and privacy-compliant attribution methods. The implications for advertisers and marketers are discussed, addressing the challenges of adapting to this new paradigm and strategies for optimizing campaigns using modeled data. Ethical considerations and the importance of building consumer trust are emphasized, underscoring the need for transparency and responsible data stewardship. By synthesizing current research and industry practices, this article offers insights into the future of ad measurement in a privacy-centric digital ecosystem, demonstrating how the industry can balance effective campaign analytics with robust privacy protection to create a more sustainable and trustworthy advertising landscape.

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review

AbstractThe rapid advancement in energy storage technologies, particularly high‐energy density batteries, is pivotal for diverse applications ranging from portable electronics to electric vehicles and grid storage. This review paper provides a comprehensive analysis of the recent progress in model‐driven manufacturing approaches for high‐energy‐density batteries, highlighting the integration of computational models and simulations with experimental manufacturing processes to optimize performance, reliability, safety, and cost‐effectiveness. We systematically examine various modeling techniques, including electrochemical, thermal, and mechanical models, and their roles in elucidating the complex interplay of materials, design, and manufacturing parameters. The review also discusses the challenges and opportunities in scaling up these model‐driven approaches, addressing key issues such as model validation, parameter sensitivity, and the integration of machine learning and artificial intelligence for predictive modeling, process optimization, and quality assurance. By synthesizing current research findings and industry practices, this paper aims to outline a roadmap for future developments in model‐driven manufacturing of high‐energy density batteries, emphasizing the need for interdisciplinary collaboration and innovation to meet the increasing demands for energy storage solutions.

Centralized Data Warehouse vs. Data Mesh: A Comparative Analysis of Modern Data Management Paradigms

This comprehensive article explores the evolving landscape of enterprise data management, focusing on comparing traditional centralized data warehouses and the emerging data mesh paradigm. As organizations grapple with exponential data growth, projected to reach 180 zettabytes by 2025 [1], the choice of data architecture has become increasingly critical. The article examines both approaches' core principles, advantages, and challenges, highlighting how centralized data warehouses offer strong consistency and governance [2, 8] but face scalability issues [3]. In contrast, data mesh provides enhanced flexibility and domain-specific optimization [4] at the cost of more complex governance [5]. Through a detailed comparative analysis, the study investigates the impact of these architectures on organizational control, scalability, adaptability to business needs, and data quality management. Furthermore, it explores the potential of hybrid approaches that aim to leverage the strengths of both paradigms. The research considers various factors influencing the selection of data management strategies, including organization size, data complexity, and industry-specific requirements. By synthesizing current literature and industry trends, this article provides data professionals and decision-makers with a nuanced understanding of modern data management paradigms, enabling them to make informed choices aligned with their specific business contexts and long-term data strategies.

The Evolution of Identity and Access Management: Integrating Biometric and Behavioral Authentication

This article explores the transformative role of biometric and behavioral authentication in the evolving landscape of Identity and Access Management (IAM). As traditional password-based systems increasingly fail to meet the challenges posed by sophisticated cyber threats, these advanced authentication methods offer a promising solution. The article provides a comprehensive overview of physical and behavioral biometrics, examining their integration into multi-factor authentication systems and their capacity for continuous, passive user verification. It delves into the significant security benefits these technologies offer, including enhanced resistance to phishing, social engineering, and insider threats, while also highlighting the improvements in user experience and convenience. The article critically analyzes the challenges and considerations associated with implementing biometric and behavioral authentication, including privacy concerns, data security, accuracy issues, and scalability. Furthermore, it explores future directions and research opportunities in the field, such as advancements in biometric technologies, the application of machine learning and AI in behavioral analysis, and the development of context-aware authentication systems. By synthesizing current research and industry trends, this article provides valuable insights for cybersecurity professionals, IT decision-makers, and researchers interested in the future of secure and user-friendly authentication methods. It underscores the potential of biometric and behavioral authentication to significantly enhance organizational security postures while improving user satisfaction in an increasingly digital world.