Search Results (1,600)

The prevalence of osteoporosis, a skeletal disorder characterized by reduced bone mass, microarchitectural deterioration, and increased fracture risk, poses a substantial global healthcare burden. Although animal models and two-dimensional cell cultures have been used to advance bone research, they do not completely replicate the multicellular interactions, extracellular matrix organization, and biomechanical environment of human bone, limiting their translational relevance. This review provides a critical synthesis of recent advances in bone organoid technology, emphasizing biological complexity, technical innovation, and relevance to osteoporosis modeling. Beyond summarizing progress, we distinguish validated capabilities from aspirational claims and identify the methodological gaps that must be addressed before bone organoids can reliably support drug screening, regenerative medicine, and precision approaches. Advances in stem cell biology, tissue engineering, and three-dimensional culture systems have enabled the use of self-organizing, multicellular organoids that reproduce key physiological and pathological features of bone. These systems model estrogen-deficiency-induced bone loss, glucocorticoid-associated osteoporosis, aging-related degeneration, and genetic susceptibility. By integrating osteogenic and endothelial components within biomimetic matrices, bone organoids can support mechanistic studies and pharmacological testing. However, their incomplete vascularization, limited mechanical fidelity, instability, and lack of standardized benchmarks restrict their translational readiness. Overcoming these barriers requires technological refinement, quantitative metrics, and regulatory alignment. Full article

The development of digital supply chains has significantly changed traditional supplier selection models that focus on static and cost-driven criteria. In addition to price, operational standards, service excellence, and contribution to the platform should be taken into account when evaluating sellers operating on dynamic, performance-oriented e-commerce platforms. This study addresses this gap by developing a comprehensive multi-criteria decision-making (MCDM) framework through a systematic literature review according to PRISMA methodology. Searches conducted in Web of Science, ScienceDirect, IEEE Xplore, Google Scholar, and Taylor & Francis yielded 4630 records from 2014 to 2025, of which 123 were analyzed using bibliometric mapping and thematic synthesis. The findings indicate a progressive diversification of evaluation criteria over time: while quality, delivery, and cost remain foundational, recent studies increasingly address customer service, search volume, and refined financial indicators such as profit and markup rate, pointing toward more multidimensional seller evaluation models. Through thematic synthesis of the indicators identified across the reviewed studies, we propose a four-dimensional framework encompassing financial sustainability, operational efficiency, quality and service standards, and market positioning. The study also discusses the implications of integrating artificial intelligence with multi-criteria and hybrid decision-making approaches for developing adaptive seller ranking systems. By synthesizing fragmented research, our framework offers strategic guidance for platform managers designing seller evaluation and allocation mechanisms. Full article

Emerging technologies are increasingly positioned as key enablers of sustainable management; however, existing research largely examines digital transformation and sustainability as parallel rather than integrated processes, particularly within national transformation contexts. Moreover, prior studies tend to focus on individual technologies or environmental outcomes, offering limited insight into how emerging technologies are embedded within ESG-oriented management systems and institutional governance frameworks. To address this gap, this study adopts a structured conceptual literature review methodology guided by a systematic PRISMA-informed selection process. Based on the qualitative synthesis of 76 peer-reviewed studies, the paper develops an integrative framework explaining how emerging technologies enable sustainable management and digital transformation within the context of Saudi Arabia’s Vision 2030. Drawing on sustainability transitions, digital transformation, and ESG management literature, emerging technologies are conceptualized as combinatorial digital capabilities operating through a recursive capability loop (Sense–Analyze–Decide–Act–Verify). These capabilities influence sustainability outcomes through four mediating mechanisms—measurement, optimization, transparency, and institutionalization—and are conditionally shaped by national institutional enablers. The proposed framework positions ESG-oriented management systems as a mediating layer between technological capabilities and multi-dimensional sustainability outcomes, while explicitly addressing the double transition paradox, which recognizes both the sustainability benefits and environmental costs of digital infrastructures. The study advances theory by integrating ESG mediation, institutional moderation, and capability-based mechanisms into a unified analytical architecture and formulates six theoretically grounded propositions to guide future empirical research. The framework also provides actionable insights for managers and policymakers seeking to align digital transformation, ESG integration, and national sustainability agendas in Saudi Arabia and comparable emerging economy contexts. Full article

Quality–Diversity (QD) optimization has emerged as a distinctive paradigm in evolutionary computation, shifting the focus from identifying a single global optimum to illuminating a high-dimensional repertoire of elite solutions that jointly maximize performance and behavioral diversity. While algorithms like MAP-Elites have enabled transformative results in robotics and procedural content generation, their generalization to discrete combinatorial domains remains insufficiently consolidated in the literature. To address this gap, a systematic literature review was conducted strictly following PRISMA 2020 guidelines. The synthesis reveals rapid exponential growth in QD research, accompanied by significant algorithmic diversification toward gradient-informed variations and hardware-accelerated implementations. Despite this maturation, discrete combinatorial applications remain comparatively underrepresented, with only a small fraction (12.5%) of the analyzed corpus explicitly addressing discrete problems using domain-specific representations and heuristics. Based on these empirical findings, a conceptual framework is proposed. This framework positions QD as a vital mechanism for operational resilience in stochastic industrial contexts—specifically mining operations, including predictive maintenance, mineral processing optimization, and blast design—demonstrating its strategic value for complex decision-making. Full article

The growing volume of fiber-reinforced polymer composite waste creates an urgent need for efficient recycling technologies. While solvolysis effectively breaks down thermoset matrices for fiber reinforcement recovery, the process generates hydrocarbon-rich liquid by-products that require further management. This study validates the use of these liquid recycling streams—derived from the solvolysis of unsaturated polyester and epoxy resins—as sustainable carbon precursors for the growth of carbon nanomaterials. Synthesis was performed via catalytic chemical vapor deposition (CVD) at 850 °C using iron nanoparticles impregnated on a zeolite substrate. Morphological analysis confirmed the production of one-dimensional nanostructures (carbon nanotubes/nanofibers), with average diameters below 100 nm. Raman spectroscopy revealed a high degree of graphitization, with I_D/I_G ratios ranging from 0.25 to 0.58, which is comparable to structures synthesized from conventional precursors. Thermogravimetric analysis (TGA) demonstrated high thermal stability and carbon purity reaching up to 90.3%. These findings demonstrate a viable upcycling pathway that enhances the economic attractiveness of composite recycling by transforming waste into advanced nanomaterials. Full article

The rapid adoption of artificial intelligence (AI) across public services and critical infrastructure is reshaping digital governance. While AI promises efficiency and innovation, its reliance on large, high-dimensional datasets introduces privacy, bias, transparency and accountability risks that existing frameworks struggle to address. This study evaluates the maturity of current AI governance frameworks and develops an integrated risk-tiering model that connects ethical principles to auditable technical controls, aligning with Sustainable Development Goal 9 on industry, innovation and infrastructure. A systematic literature review of 450 records from major databases was conducted using PRISMA 2020 guidelines; 95 high-quality studies were analyzed using principal component analysis and k-means clustering. The analysis produced a heat map of governance frameworks, a co-occurrence network of themes, a cluster analysis of framework coverage and an integrated governance risk framework supported by a risk-tiering matrix. Findings reveal a fragmented landscape dominated by ethics/privacy-centric and compliance/risk-focused approaches, with few integrated frameworks and evident tension between privacy and security. This synthesis bridges the gap between values and practice, offering a policy-ready model for secure and sustainable AI governance. Full article

We report the synthesis, spectroscopic characterization, and single-crystal X-ray structures of 5-methyl-3-phenylbenzo[e][1,2,4]triazine (I) and its precursor N′-(3-methyl-2-nitrophenyl)benzohydrazide (IV). Compound IV was obtained by nucleophilic aromatic substitution of 1-fluoro-3-methyl-2-nitrobenzene with benzohydrazide and was converted to I through a reductive cyclodehydration/oxidative aromatization sequence. The present study provides a concise route to the 5-methyl regioisomer together with full structural characterization and examines how methyl substitution at the 5-position influences molecular geometry and crystal packing relative to the previously reported 6- and 8-methyl analogs. X-ray analysis shows that IV adopts a conjugated hydrazide framework with a twisted N–N linkage and an out-of-plane nitro group. In the crystal, it forms one-dimensional N–H⋯O hydrogen-bonded chains further assembled by weaker intermolecular contacts. By contrast, I displays an essentially planar benzo[e][1,2,4]triazine core with an almost coplanar phenyl substituent and packs into slipped π-stacked columns reinforced by secondary C–H⋯N contacts. Comparison with the previously reported methyl regioisomers shows that relocation of the methyl group to the 5-position has little effect on the intrinsic molecular geometry of the benzo[e][1,2,4]triazine scaffold, while subtly modulating the stacking arrangement and secondary packing interactions in the solid state. These results further define the role of methyl-substituent position in shaping the supramolecular organization of 3-phenylbenzo[e][1,2,4]triazines. Full article

As a cornerstone of sustainable hydrogen generation, the hydrogen evolution reaction (HER) demands efficient, earth-abundant electrocatalysts to replace costly platinum benchmarks. Two-dimensional transition metal dichalcogenides (2D-TMDs) represent a highly promising class of non-precious materials for this application. This review provides a comprehensive analysis of recent progress in TMD-based HER catalysis. It begins by elucidating the intrinsic structural properties that underpin their catalytic potential, followed by a summary of key synthesis routes and characterization techniques. The central focus is on strategic engineering approaches to optimize TMD performance. Finally, we discuss persisting challenges and propose future research directions aimed at scalable production, advanced operando studies, and the design of bifunctional TMD catalysts for integrated water-splitting systems. Full article

The Internet of Medical Things (IoMT) has become a core component of modern healthcare infrastructures, enabling continuous patient monitoring, remote diagnostics, and data-driven clinical decision-making. Despite these advances, authentication in IoMT environments remains a critical security challenge, intensified by strict resource constraints of medical devices and the emerging threat posed by quantum computing to classical cryptographic techniques. This systematic review investigates authentication mechanisms in IoMT from both post-quantum and system-level perspectives. A structured literature review was conducted using a PRISMA-informed methodology across major scientific databases, including IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and MDPI. From an initial set of 95 records, 63 studies were selected for qualitative synthesis following screening and eligibility assessment. To organise existing research, this study introduces a multi-dimensional classification framework that categorises authentication solutions according to cryptographic paradigm (classical, hybrid, and post-quantum), deployment architecture, system objectives, and clinical operational constraints. The comparative synthesis demonstrates important trade-offs between security strength, latency, computational overhead, and energy consumption that are frequently underexplored in the existing literature. Furthermore, the analysis identifies key research gaps related to scalability in heterogeneous medical environments, trust establishment across administrative and clinical domains, usability under strict timing constraints, and resilience against quantum-capable adversaries. Based on these findings, future research directions are outlined toward adaptive, lightweight, and context-aware post-quantum authentication frameworks designed for real-world IoMT deployments. Limitations of this review include restriction to English-language publications and selected databases. This study received no external funding, and the review protocol was not formally registered. Full article

This work investigates the extension of StyleGAN2-ADA to three-dimensional prostate T2-weighted (T2W) MRI generation. The architecture is adapted to operate on 3D anisotropic volumes, enabling stable training at a clinically relevant resolution of $256\times 256\times 24$, where a baseline 3D-StyleGAN fails to converge. Quantitative evaluation using Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and generative Precision–Recall metrics demonstrates substantial improvements over a 3D-StyleGAN baseline. Specifically, FID decreased from 114.2 to 27.3, while generative Precision increased from 0.22 to 0.82, indicating markedly improved fidelity and alignment with the real data distribution. Beyond generative metrics, the synthetic volumes were evaluated through radiomic feature analysis and downstream prostate segmentation. Synthetic data augmentation resulted in segmentation performance comparable to real-data training, supporting that volumetric generation preserves anatomically relevant structures, while multivariate radiomic analyses showed strong global feature alignment between real and synthetic volumes. These findings indicate that a 3D extension of StyleGAN2-ADA enables stable high-resolution volumetric prostate MRI synthesis while preserving anatomically coherent structure and global radiomic characteristics. Full article

Previous studies have considered the iridescent feathers of the peafowl as a classic example of structural coloration. The structural color is primarily attributed to a two-dimensional (2D) photonic crystal structure composed of melanin rods and air channels embedded in a keratin matrix. While previous optical models have successfully explained spectral tuning via geometric parameters such as lattice constants and cortex thickness, the potential contribution of auxiliary pigments to these complex hues has been largely overlooked. In this study, we combined high-sensitivity UPLC-MS and transcriptome analysis to elucidate the biochemical and genetic mechanisms underlying peafowl coloration. We identified trace amounts of the Xanthophyll lutein (one of the carotenoids) in iridescent train feathers, challenging the purely structural paradigm. Transcriptome analysis revealed significant differences in the expression of the melanin-related gene ASIP between iridescent and non-iridescent feather follicles. Furthermore, we observed significant expression differences in the carotenoid deposition-related gene GSTA2, correlating with the presence of lutein in iridescent regions. We conclude that while melanin provides the structural foundation for iridescence, lutein acts as an indispensable conditional modulator. The coordinated differential expression of melanin synthesis (ASIP) and carotenoid deposition (GSTA2) genes constitutes the genetic basis for the vibrant iridescent coloration of peafowl feathers. Full article

Eye-tracking methodology represents a young but rapidly growing approach in tourism research, offering a direct window into the cognitive processes driving tourism stakeholders’ behaviour. However, a critical gap remains between the rapid adoption of this tool and the methodological rigour required to interpret its neurophysiological data. This critical review synthesizes 23 empirical studies (2020–2025) from the destination marketing and branding domain to diagnose eye-tracking’s state-of-the-art application. Adopting the SALSA framework (Search, Appraisal, Synthesis, Analysis) augmented by PRISMA 2020 guidelines, this study systematically searched Web of Science and Scopus databases. Studies were appraised using an eight-dimensional quality rubric, assessing from theoretical grounding to experimental design to statistical rigour. Findings revealed a “tool-first” exploratory phenomenon, where the majority of studies relied on basic fixation metrics to infer complex psychological states such as “interest”, when they could imply other cognitive states. Furthermore, most reviewed studies failed to control for stimulus-level confounds (e.g., luminance, AOI size) and utilized inappropriate data-handling procedures and methods, such as the absence of data cleaning and treating count and binary data as continuous data. These, coupled with transparency deficits, undermined the validity of their conclusions. Hence, a Checklist for Eye-Tracking Rigour (CETR) and a methodological decision tree were developed to guide researchers towards confirmatory and neurobiologically grounded research. Findings also provided a framework for managers/practitioners to more accurately interpret eye-tracking studies. Full article

Proton exchange membrane fuel cells (PEMFCs) are promising energy conversion de-vices owing to high efficiency and zero local emissions. Accurate PEMFC performance assessment and control require well-posed models, whose predictive accuracy is largely determined by the correct calibration of key parameters. Metaheuristic algorithms (MHAs) have therefore been widely applied to PEMFC stack parameter estimation, but their rapid proliferation calls for a more systematic and fine-grained synthesis. This review refines the taxonomy of PEMFC mathematical modeling approaches and summarizes Zero-Dimensional PEMFC modeling methods, key parameters, and representative improvement directions aimed at reducing identification difficulty while retaining physical meaning. Newly developed MHAs and enhanced variants of existing methods are then surveyed, and over 40 distinctive optimization approaches are selected for systematic comparison. Modeling approaches and parameter identification methodologies are summarized. In addition, an algorithm selection guide and 26 representative algorithms with their variants are compiled and benchmarked across the five most widely used commercial PEMFC models to enable cross-model comparison. Full article

Background/Objectives: Transverse maxillary deficiency in growing patients can be treated using rapid maxillary expansion (RME) or slow maxillary expansion (SME) with spring-based appliances, such as the Leaf Expander (LE), but their comparative dentoskeletal effects remain debated. This study evaluated the transverse dentoskeletal outcomes of LE-based SME versus conventional RME. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO. Electronic searches were performed in PubMed, Scopus, Embase, Web of Science, and Cochrane Library up to 9 January 2026. Randomized controlled trials (RCTs) comparing LE-based SME and RME in skeletally immature patients were included. Primary outcomes were transverse maxillary change; secondary outcomes included dentoalveolar side effects. Risk of bias was assessed using the RoB 2 tool, and certainty of evidence was evaluated using the GRADE framework. When possible, a meta-analysis was performed using standardized mean differences and a random-effects model. Results: Four RCTs met the inclusion criteria. Both SME and RME achieved significant transverse expansion. Meta-analysis showed no statistically significant differences between protocols for inter-canine distance, inter-second deciduous molar distance, inter-first permanent molar distance, or basal maxillary width. Intergroup differences varied by anatomical site and measurement method: RME showed greater anterior dental and skeletal transverse gains, whereas SME achieved comparable intermolar expansion with greater molar distorotation. Three-dimensional analyses indicated similar morphological enlargement. Risk of bias ranged from low to high; the certainty of evidence was low to very low for most transverse parameters and moderate only for molar distorotation. Conclusions: Both LE-based SME and RME effectively correct transverse maxillary deficiency. Quantitative synthesis showed comparable overall transverse expansion, with differences mainly related to the distribution and biomechanical pattern of dentoskeletal effects rather than the absolute amount of expansion achieved. Appliance selection should be guided by biomechanical features and individual treatment objectives. Further high-quality RCTs with standardized three-dimensional protocols and longer follow-up are needed. Full article