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A bibliometric analysis of compact city for sustainable urban development
This work provides a detailed bibliometric review of compact city literature for sustainable urbanism from 1983 to 2023. The compact city concept has received considerable attention as a possible approach towards solving sustainable city issues. Hence, based on the documents obtained from the Scopus database, we studied the research topics and authors, as well as the thematic development of this topic. The research used the bibliometric approach of citation analysis, keyword cooccurrence, and thematic evolution mapping. Research indicates that there is an Increased research productivity over the recent past especially in the last two decades. Among developed countries, China has become one of the most active participants in the provision of new knowledge. The thematic focus has shifted from pure and applied to complex themes like sustainability, GIS and urban design. The current trends show increasing concern about sustainability, development, and the 15-minute city model. The proposed analysis also reveals the unequal Within the group of countries, citation rates vary significantly, and the scope of methodological approaches is insufficient. In summary, this review finds that although compact city research has evolved to a certain extent, typological models are still lacking context sensitivity; international cooperation remains rather limited; and finally, many long-term outcomes have not been adequately investigated in order to unleash the full potential of compact cities as a global model of sustainable urban development. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). -
Valorisation of starfruit waste derived pectin for biodegradable sheet fabrication: A comprehensive study on extraction and characterization
This research work focuses on the extraction and characterization of pectin from starfruit peel and its application for fabrication of pectin film. Starfruit is chosen as the source for pectin extraction as the data regarding pectin extraction starfruit is relatively scarce in the available literature. Conventional organic acid based extraction using citric acid is employed for pectin extraction as it is eco-friendly and cost effective. The yield of pectin was found to be 8.22 1.018 (w/w). Fourier-transform infrared spectroscopy (FT-IR), analysis is used to identify functional groups present in the extracted pectin and X-ray Powder Diffraction (XRD) is done to check its crystallinity. Furthermore, scanning electron microscopy (SEM) characterization was performed to deduce the morphological characteristics of the extracted biopolymer. The particle size was found to be between 1m and 20 m. Fabrication of pectin based film was done using solvent cast method. The biodegradable film developed was found to be transparent and flexible. This work highlights the use of starfruit as a cost effective substrate for pectin extraction. Future studies should aim at exploring various applications of pectin and utilizing its potential in diverse applications. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). -
Revisiting the PushPull Tourist Motivation Model: A Theoretical and Empirical Justification for a ReflectiveFormative Structure
This study introduces a novel reflectiveformative hierarchical model specification for the classic pushpull tourist motivation construct, aligning its measurement with the theoretical distinction between intrinsic push drives and external pull attributes. Unlike the traditional reflective-reflective structuring of tourist motivation we defied the higher order factors (novelty, knowledge and facilities as formative. Using partial least squares structural equation modeling (PLS-SEM) on a purposive sample of 319 international tourists, we empirically validate the reflectiveformative (reflective first-order, formative second-order) model. The reflectiveformative model showed a superior fit and predictive power: it explained substantially more variance in key outcome constructs (social motives (R2 = 53.60) and self-actualization (R2 = 23.10)) than the traditional reflectivereflective specification (social motives (R2 = 49.30) and self-actualization (R2 = 21.70)), which is consistent with best-practice guidelines for theoretically grounded models. In contrast, the incorrectly specified reflectivereflective model showed stronger effects between unrelated constructs, supporting concerns that choosing the wrong type of measurement model can lead to incorrect conclusions. By reconciling the pushpull theory with measurement design, this works main contributions are a theoretically justified reflectiveformative model for tourist motivation, and evidence of its empirical benefits. These findings highlight a methodological innovation in motivation modeling and underscore that modeling pushpull motives formatively yields more accurate insights for theory and practice. 2025 by the authors. -
A Gauss Hypergeometric-Type Model for Heavy-Tailed Survival Times in Biomedical Research
In this study, we introduced and analyzed the SlashLogLogistic (SlaLL) distribution, a novel statistical model developed by applying the slash methodology to loglogistic and beta distributions. The SlaLL distribution is particularly suited for modeling datasets characterized by heavy tails and extreme values, frequently encountered in survival time analyses. We derived the mathematical representation of the distribution involving Gauss hypergeometric and beta functions, explicitly established the probability density function, cumulative distribution function, hazard rate function, and reliability function, and provided clear definitions of its moments. Through comprehensive simulation studies, the accuracy and robustness of maximum likelihood and Bayesian methods for parameter estimation were validated. Comparative empirical analyses demonstrated the SlaLL distributions superior fitting performance over well-known slash-based models, emphasizing its practical utility in accurately capturing the complexities of real-world survival time data. 2025 by the authors. -
Design and Analysis of Reliability Sampling Plans Based on the ToppLeone Generated Weibull Distribution
As part of this study, we design a reliability acceptance sampling plan under the assumption that the lifetime of a product follows the ToppLeone generated Weibull (TLGW) distribution, a model that exhibits structural symmetry in its hazard rate behavior and distributional form. The fundamental procedures for constructing such a plan are described. We compute and tabulate the minimum sample sizes required for given risk criteria using both binomial and Poisson models for the number of failures. We also provide the operating characteristic (OC) values for the proposed sampling plans, and determine the minimum ratios of true mean life to specified mean life needed to satisfy a given producers risk. The role of symmetry in the TLGW distribution is highlighted in its balanced tail properties and shape characteristics, which influence the performance of the acceptance sampling plan. Finally, we illustrate the applicability of the proposed plan with real-world data. 2025 by the authors. -
A Predictive Framework for Sustainable Human Resource Management Using tNPS-Driven Machine Learning Models
This study proposes a predictive framework that integrates machine learning techniques with Transactional Net Promoter Score (tNPS) data to enhance sustainable Human Resource management. A synthetically generated dataset, simulating real-world employee feedback across divisions and departments, was used to classify employee performance and engagement levels. Six machine learning models such as XGBoost, TabNet, Random Forest, Support Vector Machines, K-Nearest Neighbors, and Neural Architecture Search were applied to predict high-performing and at-risk employees. XGBoost achieved the highest accuracy and robustness across key performance metrics, including precision, recall, and F1-score. The findings demonstrate the potential of combining real-time sentiment data with predictive analytics to support proactive HR strategies. By enabling early intervention, data-driven workforce planning, and continuous performance monitoring, the proposed framework contributes to long-term employee satisfaction, talent retention, and organizational resilience, aligning with sustainable development goals in human capital management. 2025 by the authors. -
ESG Narrative Quality in Green Bond Disclosures: Implications for Risk Perception, Transparency, and Market Trust
This research evaluates the extent to which firms green bond disclosures create and convey a meaningful representation of their Environmental, Social, and Governance (ESG) commitments. Additionally, this research explores how investors distinguish between disclosures that represent genuine commitment to sustainability and those that may be indicative of greenwashing, and how such distinctions impact their assessment of an issuers credibility as well as the issuers performance subsequent to the issuance of a green bond. The methodology employed in this research employs a convergent mixed-methods approach that combines quantitative methods (Natural Language Processing (NLP), financial modeling, etc.) with qualitative methodologies (case studies, interviews). The NLP methodology employed in this research includes sentiment analysis, topic modeling, and ambiguity measurement in order to determine the tone, thematic content, and linguistic clarity of the disclosure texts. Subsequently, the results of the NLP methodologies are correlated with firm level outcomes using cross validated partial least squares regression (PLS-R), event study methodologies, and one way ANOVA to test for temporal and industrial variability. Finally, the results of the computational and financial methodologies are supplemented by qualitative case studies and interviews to provide context for the patterns identified in the computational and financial methodologies. In summary, the results of this research demonstrate that firms that communicate in a clear, balanced, and verifiable manner experience better market reaction and more favorable accounting results subsequent to the issuance of a green bond than do firms whose communications are vague, overly optimistic, or lacking in consistency. Conversely, the findings suggest that investors have become increasingly sensitive to potential greenwashing and therefore are less likely to respond favorably to communications characterized by the aforementioned characteristics. 2025 by the authors. -
K???an???am Performance: K???a Devotion, Ritual Ecology, and Colonial Transformation in South India
This paper critically explores K???an???am, a Sanskrit ritual dance-theater tradition from Kerala, as a product of socio-political and religious transformations in early modern South India. Conceived in the mid-17th century by the Zamorin King M?nav?da, author of the Sanskrit text K???ag?ti, K???an???am was both a devotional offering to Lord K???a and a strategic expression of ritual sovereignty. Rooted in K???a bhakti (devotion), the tradition reflects how religious performance was mobilized to assert political legitimacy, particularly amid rivalry with regional powers such as Travancore. The Guruvayur Sri Krishna Temple, situated in the Malabar region of northern Kerala and central to the performance of K???an???am, emerged as a vital sacred space where royal patronage, ritual authority, and caste hierarchy intersected. The performances exclusivity restricted to Hindu audiences within temple premises reinforced patterns of spatial control and caste-based exclusion. Institutional support codified the tradition, sustaining it across generations within a narrow sociocultural framework. With the decline of Zamorin rule and the onset of colonialism, K???an???am faced structural disruptions. Colonial interventions in temple administration, landholding, and religious patronage weakened its ritual foundations. Guruvayurs transformation into a public devotional center reflected wider shifts in ritual ecology and sacred geography under colonial modernity. In both the colonial and postcolonial periods, K???an???am struggled to survive, nearly facing extinction before its revival under the Guruvayur temples custodianship. By examining K???a devotion, royal ambition, caste dynamics, and colonial transformation, this paper offers a critical lens on Keralas evolving religious and cultural landscapes. 2025 by the authors. -
Enhancement of Phenolic and Polyacetylene Production in Chinese Lobelia (Lobelia chinensis Lour.) Plant Suspension Culture by Employing Silver, Iron Oxide Nanoparticles and Multiwalled Carbon Nanotubes as Elicitors
Silver nanoparticles (AgNPs), iron oxide nanoparticles (Fe2O4NPs), and multiwalled carbon nanotubes (MWCNTs) are widely used in various applications, such as biomedicine, environmental remediation, and agriculture. In addition, these nanomaterials can affect the production of bioactive compounds in plants that have pharmacological activities. In the current study, the in vitro plant cultures of Chinese lobelia (Lobelia chinensis Lour.) were established in MS medium and treated with 0, 12.5, 25, 37.5, and 50 mg L?1 AgNPs or Fe2O4NPs, or MWCNTs. Initially, plants were grown for four weeks without any elicitors, and after that, the cultures were treated with nano-elicitors for one week. After five weeks, the effects of nano-elicitors were estimated on growth, total phenolic, flavonoids, polyacetylenes, and ABTS/DPPH/FRAP antioxidant activity was investigated. The results showed that lower levels of AgNPs (25 mg L?1), Fe2O4NPs (25 mg L?1), and MWCNTs (12.5 mg L?1) favored the accumulation of fresh and dry biomass. Whereas, 37.5 mg L?1 AgNPs, 25 mg L?1 Fe2O4NPs, and 37.5 mg L?1 MWCNTs enhanced the accumulation of total phenolics, flavonoids, specific phenolic compounds including chlorogenic acid, catechin, phloretic acid, coumaric acid, salicylic acid, naringin, myricetin, linarin, and polyacetylenes viz. lobetylonin and lobetyolin in higher concentrations. The plant extracts elicited by nanomaterials also depicted very good antioxidant activities according to ABTS, DPPH, and FRAP assays. These results suggest that specific nanomaterials, and at specific levels, could be used for the production of bioactive compounds from shoot cultures of Chinese lobelia. 2025 by the authors. -
AI-Driven Stacking Ensemble for Predicting Total Power Output of Wave Energy Converters: A Data-Driven Approach to Renewable Energy Processes
This study develops and evaluates an AI-driven stacked hybrid machine learning model for predicting the total power output of wave energy converters (WECs) across four Australian coastal locations: Adelaide, Perth, Sydney, and Tasmania. This research enhances prediction accuracy through advanced ensemble learning techniques while addressing spatial variability in wave energy processes. The dataset comprises spatial coordinates and power output readings from 16 fully submerged WECs per location, capturing the variability of wave energy across different coastal regions. Data preprocessing included missing value imputation, duplicate removal, and spatial feature transformation via Euclidean distance calculation. Principal component analysis (PCA) was employed to reduce dimensionality while preserving critical features influencing power generation. To develop an accurate prediction model, we employed a stacking ensemble approach using XGBoost, LightGBM, and CatBoost as base learners, optimized via Optuna hyperparameter tuning with 10-fold cross-validation. A Ridge regression meta-learner combined the outputs of these models, leveraging their complementary strengths to enhance predictive performance. Experimental results demonstrate that the hybrid model consistently outperforms individual models, enhancing predictive accuracy across all locations. Sydney exhibited the highest accuracy (RMSE = 9089.58 W, R2 = 0.8576), while Tasmania posed the greatest challenge (RMSE = 45,032.37 W, R2 = 0.8378). The ensemble approach mitigated overfitting and improved generalization by leveraging the complementary strengths of XGBoost, LightGBM, and CatBoost. By leveraging AI-driven ensemble learning, this study provides a scalable and reliable framework for wave energy forecasting, facilitating more efficient grid integration and resource planning in renewable energy systems. 2025 by the authors. -
StructureProperty Relationships Governing Rheological, Damping, and Thermal Behaviour of Immiscible Natural Rubber/Nitrile Rubber Blend Nanocomposites
Polymer nanocomposites have been attracting significant interest over the last three decades. One of the most intriguing applications is related to the preparation of clay-filled nanocomposites based on rubber blend matrices. Although several studies already exist on the subject, there is limited information available regarding their rheological, thermal, and, particularly, damping behaviour of rubber blend systems. In this work, the rheological, viscoelastic, and thermal behaviour of a natural rubber/nitrile rubber (NR/NBR) blend nanocomposite containing organically modified nanoclay was systematically investigated, and the damping characteristics were also assessed. At a lower nanoclay concentration (5 phr), network formation through fillerfiller and fillerpolymer interactions led to partial immobilization of polymer chains, resulting in a pronounced increase in viscosity and enhanced viscoelastic response. In contrast, at higher nanoclay loading (10 phr), strong agglomeration of filler particles occurred, corresponding to a stacked clay morphology, which hindered effective fillerfiller network formation and weakened fillerpolymer interactions, leading to lower viscosity and reduced damping efficiency. The blend composition and filler content were found to significantly influence the investigated properties, especially the hysteresis loss and the thermal conductivity, which is explained by matrixfiller interactions and the resulting morphology of the system. 2026 by the authors. -
Investigation of Spectroscopic Parameters and Trap Parameters of Eu3+-Activated Y2SiO5 Phosphors for Display and Dosimetry Applications
Using the solid-state reaction technique, varied Y2SiO5 phosphors activated by europium (Eu3+) ions at varied concentrations were made at calcination temperatures of 1000 C and 1250 C during sintering in an air environment. The XRD technique identified the monoclinic structure, and the FTIR technique was used to analyze the generated phosphors. Photoluminescence emission and excitation patterns were measured using varying concentrations of Eu3+ ions. The optimal strength was observed at a 2.0 mol% concentration. Emission peaks were detected at 582 nm and 589 nm for the 5D0?7F1 transition and at 601 nm, 613 nm, and 632 nm for the 5D0?7F2 transition under 263 nm excitation. Because Eu3+ is naturally bright, these emission peaks show how ions change from one excited state to another. This makes them useful for making phosphors that emit red light for use in optoelectronics and flexible displays. Based on the computed (1931 CIE) chromaticity coordinates for the photoluminescence emission spectra, it was determined that the produced phosphor may be used in light-emitting diodes. The TL glow curve was examined for various doping ion concentrations and durations of UV exposure levels, revealing a broad peak at 183 C. Using computerized glow curve deconvolution (CGCD), we calculated the kinetic parameters. 2024 by the authors. -
Bayesian and Non-Bayesian Parameter Estimation for the Bivariate Odd Lindley Half-Logistic Distribution Using Progressive Type-II Censoring with Applications in Sports Data
The Bivariate Odd Lindley Half-Logistic (BOLiHL) distribution with progressive Type-II censoring provides a powerful statistical tool for analyzing dependent data effectively. This approach benefits society by enhancing engineering systems, improving healthcare decisions, and supporting effective risk management, all while optimizing resources and minimizing experimental burdens. In this paper, the likelihood function derived under progressive Type-II censoring is generalized for the BOLiHL distribution. The well-known maximum likelihood estimation method and Bayesian estimation are applied to evaluate the parameters of the distribution. A study utilizing simulation techniques is performed to evaluate the performance of the estimators, using statistical analysis metrics for censored observations under a progressive Type-II censoring scheme with varying sample sizes, failure times, and censoring schemes. Additionally, a real dataset is studied to validate the proposed model, delivering impactful analyses for practical applications. 2025 by the authors. -
Study of Expression of MST3 in Myeloid Leukaemia
Myeloid leukaemia (ML) is a cancer that occurs by the accumulation of abnormally multiplied myeloid cells in bone marrow, peripheral blood, and other related tissue. MST3 is a gene of the GCK family that has a role in apoptosis, along with other cellular functions like cellular differentiation, cell cycle, metabolism, and others. Objectives: The objectives of this study were to count RBCs and WBCs, study MST3 expression in ML and control samples, and perform an in silico correlation study on the KRAS and NRAS genes. Methods: The counting of RBCs and WBCs was carried out using a hemacytometer, the expression of MST3 was studied using RT-PCR, and a correlation study was carried out using GEPIA. Results: RBC and WBC levels in ML differed from the control levels, and the expression of MST3 was found to be upregulated in ML in comparison to controls, with a 2.908.65-fold change, with a significant p-value > 0.05. A positive correlation in expression was also found between MST3 and KRAS and NRAS genes, with a significant r value correlation. Conclusions: From this study, it could be deduced that MST3 might have a role in ML pathogenesis, but further research is needed to study its role in the progression of the disease. 2025 by the authors. -
Climate Performance and Firm Valuation: A Meta-Analysis of Tobins Q in the Post-IPCC AR6 Era
This study examines whether corporate climate performance is reflected in firm valuation by synthesising recent empirical evidence, using Tobins Q as a forward-looking indicator of market expectations. Employing a random-effects meta-analysis of 30 peer-reviewed studies published between 2020 and 2025 across multiple industries and regions, the findings reveal a modest yet statistically significant positive association between stronger climate performance and higher market valuations, suggesting that investors increasingly incorporate climate-related information into firm pricing. Contrary to prevailing assumptions in the literature, proactive climate strategies, such as emissions-reduction initiatives, do not systematically generate greater valuation benefits than disclosure-oriented approaches; both exhibit comparable positive effects. Similarly, valuation outcomes do not differ materially between self-reported and externally verified climate data. Meta-regression analysis identifies data source as the only statistically significant moderator, although its influence remains nuanced. Overall, the results indicate that climate performance enhances firm valuation in a context-dependent manner, challenging the view that only proactive strategies or externally verified data are uniquely rewarded by financial markets. The study contributes to the sustainable and corporate finance literature by clarifying how capital markets price climate-related corporate behaviour under heterogeneous strategic responses. 2026 by the authors. -
Bridging Financial and Operational Gaps in Supply Chain Finance: An Information Processing Theory Perspective
This paper explores the integration of financial and operational flows in Supply Chain Finance (SCF) through the lens of Information Processing Theory (IPT). Despite increasing adoption of SCF solutions like reverse factoring and trade credit, existing literature lacks a unified theoretical framework that captures both financial and organizational complexities. Drawing from 47 peer-reviewed articles in leading supply chain journals, this study identifies key SCF dimensionstask characteristics, environment, and interdependenceas primary sources of uncertainty and information processing needs. It then examines how IT systems, coordination mechanisms, and organizational design enhance processing capacity, enabling firms to build SCF capabilities such as risk assessment, supplier onboarding, and financial process standardization. These capabilities facilitate financial supply chain integration through data connectivity, embedded flows, and collaborative planning. The study contributes a comprehensive conceptual model that connects SCF uncertainties, processing strategies, and performance outcomes, addressing theoretical and managerial gaps. It further provides a foundation for future empirical research and strategic design of SCF systems to enhance supply chain resilience and financial efficiency. 2025 by the authors. -
Comprehensive Study of Silver Nanoparticle Functionalization of Kalzhat Bentonite for Medical Application
The characterization and biomedical modification of bentonite clays from the Kalzhat deposit (Kzh), which is situated in Kazakhstans Zhetysu region, are the main objectives of this work. In order to improve the raw materials structural qualities, the montmorillonite fraction was enriched, and coarse impurities were eliminated using the Salo method. The presence of meso- and micropores that guarantee high dispersity and specific surface area, as well as the prevalence of montmorillonite and kaolinite, was all confirmed by physicochemical analysis. Particle size measurements indicated finely dispersed structures with a propensity to aggregate, whereas thermal analysis demonstrated resilience under heating. After effective functionalization with silver nanoparticles, a porous hybrid system with improved surface reactivity was produced. These enhancements demonstrate the modified bentonites usefulness as a multifunctional carrier for the immobilization and controlled release of pharmaceuticals, with potential uses in drug delivery systems, antimicrobial coatings, and wound-healing materials. The material has potential use in sorption and environmental protection technologies in addition to its biomedical application. Overall, Kzhs structural and functional performance is greatly improved by the combination of purification and functionalization with silver nanoparticles, highlighting its promise as a useful element in the development of next-generation polymercomposite systems. 2025 by the authors. -
AdaptiveNet: A Novel Architecture for Reducing Computation Complexity to Fake Review Classification
The exponential rise of e-commerce platforms has resulted in a dramatic increase in online reviews, which creates a challenge in distinguishing fake reviews that erode consumer confidence and harm commerce ecosystems. Traditional approaches for fake review detection employ computationally expensive deep learning networks which are resource-intensive and difficult to use in practice. In this paper, we describe AdaptiveNet, a new lightweight neural architecture that achieves fake review detection with much lower computational resources while maintaining a higher detection and classification precision. The model proposed in this paper is based on three original innovations: a Multi-Scale Semantic Fusion (MSSF) layer for hierarchical feature extraction, Dynamic Attention Scaling (DAS) with complexity measure attention, and Adaptive Parameter Sharing (APS) context-gated networks. With thorough evaluation on Amazon, Yelp, and TripAdvisor datasets of reviews totalling 1.2 million reviews, AdaptiveNet attains 94.8% accuracy while achieving 65% computational overhead in comparison to traditional models. The architecture outperformed all other state-of-the-art models, BERT-base (92.1%), RoBERTa (91.8%), and other more recent efficient models, requiring 70% lower parameters and 60% lower energy consumption. This work markedly advances the other efficient deep learning architectures for text classification and allows for the practical implementation of fake review detection systems in resource-limited settings as process innovation. 2026 by the authors. -
Rv1899c, an HDAC1ZBTB25-Interacting Protein of Mycobacterium tuberculosis, Promotes Stress Resistance and Immune Evasion in Infected Macrophages
Rv1899c, a previously identified HDAC1ZBTB25-interacting protein of Mycobacterium tuberculosis, plays a crucial role in bacterial adaptation and immune modulation. Recombinant M. smegmatis-expressing Rv1899c (MS_ Rv1899c) showed enhanced survival under acidic and oxidative stress compared to vector controls, along with improved early intracellular growth in THP1-derived macrophages. This was accompanied by reduced reactive oxygen species (ROS), diminished cytokines associated with inflammation and downregulation of autophagy proteins ATG5, Beclin, and LC3, which ultimately skewed the immune response, suppressing the pro-inflammatory M1 macrophage population. Targeting Rv1899c with 3-aminobenzamide (3-AB) impaired intracellular bacterial survival and restored IL-12B expression, while its combination with the HDAC inhibitor C1994 significantly enhanced bacterial clearance. Structural modelling confirmed the high stereochemical quality of the Rv1899c macrodomain, and computational studies identified 3-AB as the strongest ligand (?5.75 kcal/mol), stabilized through hydrogen bonding and hydrophobic interactions with key residues. Molecular dynamics simulations conducted for 200 ns demonstrated stable proteinligand interactions with consistent parameters, while MM/GBSA analysis indicated favourable binding energy (?G_bind = ?6.6 kcal/mol), largely influenced by van der Waals and electrostatic forces. Together, these findings highlight Rv1899c as a mediator of stress resistance and immune evasion and propose it as a potential therapeutic target against M. tuberculosis. 2025 by the authors. -
Enhancement of Phenolic and Polyacetylene Accumulation in Lobelia chinensis (Chinese lobelia) Plantlet Cultures Through Yeast Extract and Salicylic Acid Elicitation
Lobelia chinensis (Lour.) is a medicinal plant that contains phytochemicals, such as phenolics and polyacetylene compounds, with beneficial biological activities. In vitro cultures are typically employed for biomass generation and plant multiplication. However, the current biotechnological approaches for producing these chemicals are ineffective, which is why bioelicitors are being used to boost synthesis of these molecules. Plantlet cultures were established in vitro using Murashige and Skoog medium supplemented with 3% sucrose (w/v). Following 4 weeks of culture initiation, the plantlet cultures were treated with 0, 25, 50, 100, or 200 mg L?1 of yeast extract (YE) or with 0, 25, 50, 100, or 200 M of salicylic acid (SA) for 1 week to boost the synthesis of bioactive compounds. The amounts of total phenolics, total flavonoids, specific phenolics including catechin, phloretic acid, linarin, and polyacetylenes, including lobetyolinin and lobetylin, were considerably elevated in the plantlet cultures treated with 50 mg L?1 YE and/or 25 M SA. The 2,2 Diphenyl 1 picrylhydrazyl (DPPH) radical scavenging assay, 2,2?-azino-bis (3-ethybenzothiazoline-6-sulphonic acid) (ABTS) assay, and ferric reducing antioxidant power (FRAP) assay were performed to assess the antioxidant properties of the plantlets. The elicitor-treated plantlets were found to have higher antioxidant activity. Thus, plantlet biomass produced in vitro can be used as a raw material to produce medicinal and nutraceutical products. 2025 by the authors.