Search Results (800)

Background/Objectives: The incorporation of the human growth and development literature, an ontogenetic framework, a large virtual sample of individuals across the entire growth period, and a contemporary sample of adult individuals provides a unique opportunity to explore the cranial complex across the entire life cycle. This study (1) assesses cranial variation in postnatal ontogeny to determine the life history stage during which subadult crania can reach comparable levels of phenotypic expression to adult crania and (2) exposes when biological sex can be estimated using craniometric data from immature individuals with accuracy levels comparable to adults. Methods: Contemporary individuals between birth and 102 years of age from one virtual (Subadult Virtual Anthropology Database; SVAD) and one skeletal (Forensic Data Bank; FDB) collection were used in the analyses (n = 2152). Results: Discriminant analysis reveals a clear ontogenetic trajectory across the life history stages, with adolescents, SVAD adults, and FDB adults exhibiting similar cranial dimensions. The analysis also revealed a shift from the growth energetic period into the reproductive energetic period during adolescence. This transition is reflected in the divergence of male and female craniometrics in adolescence, which is also when sex estimation accuracy is comparable to SVAD and FDB adults. Conclusions: The current study argues that skeletal and/or dental maturity is not necessary to estimate sex using the cranium and urges the field to reconsider methodological divisions between subadults and adults. Full article

Background: A promising improvement in orthodontic diagnostics is the use of convolutional neural networks (CNNs) to predict skeletal maturity using dental radiographic data, namely the calcification phases of the lower canine. Objectives: The purpose of this study is to improve the prediction of skeletal growth maturation through the use of sophisticated deep learning techniques, particularly CNNs, in the analysis of orthopantomography developmental markers. Methods: The CNN was trained and validated using the suggested model, which enables precise assessment of skeletal maturity across data collected from patients undergoing orthodontic and dental evaluations. By using a multiclass classification framework to classify the various stages of skeletal maturation. Results: CNN model predicting the development of the lower canine from the second molar provided a test accuracy of 97.50%, the model made it possible to automatically interpret radiographic features that were previously evaluated manually. Conclusions: CNN models can be trained to correctly identify the lower canine calcification stage, this helped clinicians with treatment planning and timing, especially with regard to growth modification, implant, prosthodontics approach and its clinical applicability. It guarantees ethical imaging procedures and optimizes clinical workflows by doing away with the necessity for further imaging. Full article

The Reef 2050 Long-Term Sustainability Plan (Reef 2050 Plan) was crafted to protect, manage and enhance the resilience of Australia’s Great Barrier Reef (GBR). It explicitly recognises that strengthening governance is key to achieving its targeted outcomes. To date, however, the lack of evaluation of the impact of GBR governance (including many complex policies, programmes and plans) under the Reef 2050 Plan has hindered its adaption. This paper presents a first benchmark of the health of the governance system associated with the Reef 2050 Plan. A novel analytical framework was built to do this. It was populated through the gathering of multiple lines of evidence, including global theory and evaluation practice and case studies and primary data from interviews and workshops with Traditional Owners, experts across government, industry, non-government organisations and other governance systems experts. Our assessment has found the health of governance system to be emergent to maturing, yet strong by global standards. Strengths include robust global engagement, the integrative nature of the Reef 2050 Plan, crisis response systems and GBR Marine Park management. Weaknesses include the increased need for (i) power sharing with Traditional Owners; (ii) rebuilding governmental trust with the farming and fishing sectors; (iii) more contemporary spatial planning for GBR and catchment resilience; and (iv) greater subsidiarity to deliver government programmes. In conclusions, we strongly recommend that regular benchmarking and informed refinement of Reef 2050 Plan governance arrangements would mature the system toward better outcomes. Full article

This study explores the integration of Business Intelligence (BI) and Operations Research (OR) as a driver of sustainability within the evolving framework of Logistics 4.0. As logistics systems face pressures from environmental regulations, digital transformation, and stakeholder expectations, the intersection of data analytics and optimization emerges as a critical lever for sustainable operations. Grounded in a Delphi study conducted in a Portuguese logistics firm, this research captures expert consensus across five dimensions of BI implementation: data infrastructure, real-time decision-making, operational transparency, stakeholder coordination, and sustainability performance monitoring. Methodologically, this study employed two iterative Delphi rounds with 61 cross-functional professionals directly engaged with the organization’s BI systems, particularly Microsoft Power BI. Findings indicate that integrating BI with OR models enhances organizational capacity for proactive scenario planning, carbon footprint reduction, and ESG-aligned decision-making. The results also underscore the importance of cross-departmental collaboration, data governance maturity, and user training in fully leveraging BI for sustainable value creation. By providing both theoretical insights and practical guidance, this study advances the emerging discourse on data-driven sustainability in logistics. It offers actionable insights for logistics managers, sustainability strategists, and policymakers seeking to operationalize digital sustainability and embed intelligence-driven approaches into resilient, low-carbon supply chains. Full article

This study examines the digital maturity of small- and medium-sized enterprises (SMEs) in the context of Industry 4.0. Despite growing awareness of the importance of digital transformation, many SMEs encounter structural and strategic challenges that impede their progress. Among their obstacles is the inadequacy of digital maturity models used to diagnose digital maturity levels in SMEs due to their typological, sectoral, geographical, and other specific characteristics. Using a constructivist and qualitative approach, we have developed a simplified, inclusive, and holistic assessment framework comprising six key dimensions (technology, culture, organization, people and human resources, strategic planning), associated with six progressive maturity levels. Our findings reveal that most SMEs studied in 2023 exhibit a beginner level of digital maturity. These enterprises are characterized by small-scale digital initiatives, often lacking a clear strategy, with limited or partial digitization of processes and heterogeneous technology adoption. The resulting self-assessment tool provides SMEs with practical guidance to launch, evaluate, and accelerate their digital transformation. This study contributes theoretically by proposing a practical digital maturity model and offering a tool to support SMEs and public policy. It highlights the need for tailored support, strategic alignment, and continuous training to unlock the full potential of Industry 4.0 in less urbanized and resource-constrained areas. Full article

Transfer RNA-derived small RNAs (tDRs) are increasingly being recognized as versatile regulators, yet their physiological landscape remains poorly charted. We analyzed tDR expression in seven adult mouse tissues to explore tissue-specific tDR enrichment using a tDR-optimized library preparation methodology. We catalogued 26,901 unique nuclear tDRs (ntDRs) and 5114 mitochondrial tDRs (mtDRs). Clustering analysis segregated the tissues, with the spleen and lungs forming a distinct immune cluster. Tissue-versus-all and pairwise differential analysis showed the spleen harboring unique ntDRs and mtDRs. Tissue-enriched tDRs arose from specific isoacceptor and isodecoder tRNAs, independent of mature tRNA abundance, suggesting selective biogenesis rather than bulk turnover. G-quadruplex prediction revealed a pronounced enrichment of potentially quadruplex-forming ntDRs in the kidneys, heart, and spleen, predominantly derived from i-tRFs and tRF3 fragments, suggesting structure-dependent functions in immune regulation. We also benchmarked our library strategy against the PANDORA-seq method. Despite comparable or lower sequencing depth, our method detected ~3–10-fold more unique ntDRs and we observed a clearer representation of tRF-3 fragments and greater isotype diversity. Our tissue atlas and improved tDR sequencing method reveal extensive tissue-specific heterogeneity in tDR biogenesis, sequencing, and structure, providing a framework for understanding the context-dependent regulatory roles of tDRs. Full article

This study evaluates the degree to which small and medium sized enterprises (SMEs) are prepared to adopt smart manufacturing in contrast to large enterprises, a transition that depends on the effective use of the Internet of Things, artificial intelligence (AI), and advanced analytics. While many large multinational companies have already integrated such technologies, smaller firms still struggle because of tight budgets, limited technical expertise, and difficulties in scaling new systems. To capture these realities, the investigation refines the Initiative Mittelstand-Digital für Produktionsunternehmen und Logistik-Systeme (IMPULS) Industry 4.0 readiness model, which was initially developed to help German SMEs, so that it aligns with the circumstances faced by smaller manufacturers. A thorough review of published work first surveys existing readiness and maturity frameworks, highlights their limitations, and guides the selection of new, SME-specific indicators. The framework gauges readiness across six dimensions: strategic planning and organizational design, smart factory infrastructure, lean operations, digital products, data-driven services, and workforce capability. Each dimension is operationalized through a questionnaire that offers clear benchmarks and actionable targets suited to the current resources of each enterprise. Weaving strategic vision, skill growth, and cooperative support, the approach offers managers a direct path to sharper competitiveness and lasting innovation within a changing industrial landscape. Additionally, a separate Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is provided for each dimension based on survey data offering decision-makers concise guidance for future investment. The proposed adaptation of the IMPULS framework, validated through empirical data from 31 SMEs, introduces a novel readiness index, diagnostic gap metrics, and actionable cluster profiles tailored to developing-country industrial ecosystems. Full article

This study evaluated the reproductive toxicity and reversibility of gossypol exposure in female Institute of Cancer Research (ICR) mice using the Targeted Risk Assessment of Environmental Chemicals (TRAEC) framework. Mice treated with gossypol (20 mg/kg/day, 30 days) showed reduced body weight (35.90 ± 3.19 g vs. 30.26 ± 0.91 g, p < 0.001), depletion of primordial follicles (46.2 ± 4.8 vs. 27.5 ± 3.6, p < 0.01), and impaired oocyte maturation, with polar body extrusion decreasing from 65.9% to 22.6% at 60 μM (p < 0.0001). In the human granulosa-like tumor cell line (KGN), apoptosis increased to 91.1% at 20 μg/mL compared with 11.46% in controls (p < 0.0001). Proteomic profiling identified 151 differentially expressed proteins, enriched in steroidogenesis, immune regulation, and mitochondrial metabolism. After one-month withdrawal, partial morphological recovery was observed, but endocrine function remained impaired. The TRAEC risk score of 4.68 classified gossypol as a moderate reproductive toxicant. These findings indicate that gossypol damages ovarian reserve and oocyte competence, with only partial reversibility, highlighting the need for caution in its clinical use. Full article

This paper presents the development and implementation of a shopfloor visualization-oriented digitalization framework for heterogeneous industrial equipment, aimed to enhance sustainable performance in manufacturing environments. The proposed solution addresses a critical challenge in modern industry: the integration of legacy and modern equipment into a unified, real-time monitoring and control system. In this paper, a modular and scalable architecture that enables data acquisition from equipment with varying communication protocols and technological maturity was designed and implemented, utilizing Industrial Internet of Things (IIoT) gateways, protocol converters, and Open Platform Communications Unified Architecture (OPC UA). A key contribution of this work is the integration of various data sources into a centralized visualization platform that supports real-time monitoring, anomaly detection, and performance analytics. By visualizing operational parameters—including energy consumption, machine efficiency, and environmental indicators—the system facilitates data-driven decision-making and supports predictive maintenance strategies. The implementation was validated in a real industrial setting, where the solution significantly improved transparency, reduced downtime, and contributed to measurable energy efficiency gains. This research demonstrates that visualization-oriented digitalization not only enables interoperability among heterogeneous assets, but also acts as a catalyst for achieving sustainability goals. The developed methodology and tools provide a replicable model for manufacturing organizations seeking to transition toward Industry 4.0 in a resource-efficient and future-proof manner. Full article

This study examines patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), with an emphasis on functional diversity across value chain activities. Based on a cross-sectional dataset of 781 enterprises and an entropy-based model, it assesses internal variability in AI use and explores its relationship with strategic perception and dynamic capabilities. The findings reveal predominant partial adoption, alongside high functional entropy in sectors such as mining and services, suggesting an ongoing phase of technological experimentation. However, a significant gap emerges between perceived strategic use and actual functional configurations—especially among microenterprises—indicating a misalignment between intent and organizational capacity. Barriers to adoption include limited technical skills, high costs, infrastructure constraints, and cultural resistance, yet over 70% of non-adopters express future adoption intentions. Regional analysis identifies both the Andean Highlands and Coastal regions as “innovative,” although with distinct profiles of digital maturity. While microenterprises focus on accessible tools (e.g., chatbots), small enterprises engage in data analytics and automation. Correlation analyses reveal no significant relationship between functional diversity and strategic value or capability development, underscoring the importance of qualitative organizational factors. While primarily descriptive, the entropy-based approach provides a robust diagnostic baseline that can be complemented by multivariate or qualitative methods to uncover causal mechanisms and strengthen policy implications. The proposed framework offers a replicable and adaptable tool for characterizing AI integration and informing differentiated support policies, with relevance for Ecuador and other emerging economies facing fragmented digital transformation. Full article

Smart mobility has emerged as a transformative enabler for achieving the United Nations Sustainable Development Goals (SDGs), offering technological and systemic solutions to pressing urban challenges such as congestion, environmental degradation, accessibility, and economic inclusion. Realizing this potential, however, depends not only on technological maturity but also on robust education and capacity-building frameworks. This paper addresses two critical gaps: the absence of a systematic review of structured academic curricula, vocational training programs, and professional development pathways dedicated to smart mobility, and the lack of a formal approach to demonstrate how structured, research-oriented education can effectively bridge theory and practice. The review examines a wide spectrum of initiatives, including academic programs, industry training, challenge-based competitions, and community-driven platforms. The analysis shows significant progress in Europe and North America but also reveals important gaps, particularly the limited availability of structured initiatives in the Global South, the underrepresentation of accessibility and inclusivity, and the insufficient integration of governance, ethical AI, policy, and cybersecurity. A case study of the AI for Smart Mobility course, developed using a design science methodology, illustrates how research-oriented education can be operationalized in practice. Since 2020, the course has engaged hundreds of students and professionals, with project dissemination through the AI4SM Medium hub attracting more than 20,000 views and 11,000 reads worldwide. The findings highlight both the progress made and the persistent gaps in smart mobility education, underscoring the need for wider geographic reach, stronger emphasis on inclusivity and governance, and structured approaches that effectively link theory with practice. Full article

Background: Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives: To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods: A narrative review of recent PubMed-indexed studies (2019–2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results: AI-supported platforms—including remote blood pressure monitoring, machine learning titration algorithms, and digital twins—have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions: AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks. Full article

RNA virus populations exist as quasispecies-complex, dynamic clouds of closely related but genetically diverse variants generated by high mutation rates during replication. Assessing quasispecies structure and diversity is crucial for understanding viral evolution, adaptation, and response to antiviral treatments. However, comparing single quasispecies observations from individual biosamples, especially at different infection or treatment time points, presents statistical challenges. Traditional inferential tests are inapplicable due to the lack of replicate observations, and resampling-based approaches such as the bootstrap and jackknife are limited by biases and non-independence, particularly for diversity indices sensitive to rare haplotypes. In this study, we address these limitations by applying the delta method to derive analytical variances for a set of quasispecies structure indicators specifically designed to assess the quasispecies maturation state. We demonstrate the utility of this approach using high-depth next-generation sequencing data from hepatitis C virus (HCV) quasispecies evolving in vitro under various conditions, including free evolution and exposure to antiviral or mutagenic treatments. Our results reveal that with highly fit HCV quasispecies, sofosbuvir inhibits quasispecies genetic diversity, while mutagenic treatments accelerate maturation, compared to untreated controls. We emphasize the interpretation of results through absolute differences, log-fold changes, and standardized effect sizes, moving beyond mere statistical significance. This framework enables robust, quantitative comparisons of quasispecies diversity from single observations, providing valuable insights into viral adaptation and treatment response. The R code and session info with required libraries and versions is provided in the supplementary material. Full article

Postnatal stem cells are crucial for tissue homeostasis and repair and are regulated by specialized microenvironmental microterritories known as “stem cell niches”. Proposed by R. Schofield in 1978 for hematopoietic stem cells, niches maintain self-renewal, guide differentiation and maturation, and can even revert progenitor cells to an undifferentiated state. Niches respond to injury, oxygen levels, mechanical cues, and signaling molecules. While the niche concept has advanced regenerative medicine, bioengineering, and 3D bioprinting, further progress is hindered by inconsistent interpretations of its core principles. To address this, we proposed a consensus-building initiative among experts in regenerative medicine and bioengineering. We have developed a questionnaire covering the niche topography, hierarchy, dimension, geometry, composition, regulatory mechanisms, and specifically the mesenchymal stem cell niches. This pilot survey, being conducted under the auspices of the National Society for Regenerative Medicine in the Russian Federation, aims to establish a standardized framework on the eve of the 50th anniversary of Schofield’s hypothesis. The resulting consensus will guide future research and innovation in this pivotal field. Full article

Accurate monitoring of canopy nitrogen content in wolfberry (Lycium barbarum L.) is essential for optimizing fertilization management, improving crop yield, and promoting sustainable agriculture. However, the sparse, architecturally complex canopy of this perennial shrub—featuring coexisting branches, leaves, flowers, and fruits across maturity stages—poses significant challenges for canopy spectral-based nitrogen assessment. This study integrates methods across canopy spectral acquisition, transformation, feature spectral selection, and model construction, and specifically explores the potential of hyperspectral remote sensing, integrated with spectral mathematical transformations and machine learning algorithms, for predicting canopy nitrogen content in wolfberry. The overarching goal is to establish a feasible technical framework and predictive model for monitoring canopy nitrogen in wolfberry. In this study, canopy spectral measurements are systematically collected from densely overlapping leaf regions within the east, south, west, and north orientations of the wolfberry canopy. Spectral data undergo mathematical transformation using first-derivative (FD) and continuum-removal (CR) techniques. Optimal spectral variables are identified through correlation analysis combined with Recursive Feature Elimination (RFE). Subsequently, predictive models are constructed using five machine learning algorithms and three linear regression methods. Key results demonstrate that (1) FD and CR transformations enhance the correlation with nitrogen content (max correlation coefficient (r) = −0.577 and 0.522, respectively; p < 0.01), surpassing original spectra (OS, −0.411), while concurrently improving model predictive capability. Validation tests yield maximum R² values of 0.712 (FD) and 0.521 (CR) versus 0.407 for OS, confirming FD’s superior performance enhancement. (2) Nonlinear machine learning models, by capturing complex canopy-light interactions, outperform linear methods and exhibit superior predictive performance, achieving R² values ranging from 0.768 to 0.976 in the training set—significantly outperforming linear regression models (R² = 0.107–0.669). (3) The Random Forest (RF) model trained on FD-processed spectra achieves the highest accuracy, with R² values of 0.914 (training set) and 0.712 (validation set), along with an RPD of 1.772. This study demonstrates the efficacy of spectral transformations and nonlinear regression methods in enhancing nitrogen content estimation. It establishes the first effective field monitoring strategy and optimal predictive model for canopy nitrogen content in wolfberry. Full article