Search Results (7,173)

In recent years, special function theory has played an increasingly important role in the development of advanced mathematical models and statistical distributions. In this paper, a new extension of the Euler Beta function is introduced by employing the Wright function as a kernel, leading to the formulation of the Beta–Wright function. Several fundamental properties of the proposed function are systematically investigated, including summation formulas, functional relations, Mellin transforms, integral representations, and derivative formulas. Furthermore, extended forms of Gauss and confluent hypergeometric functions are constructed within this framework. In addition to its theoretical significance, the proposed function is applied to statistical modeling, and the associated distributions are analyzed using graphical and analytical techniques. The obtained results demonstrate that the Beta–Wright function provides a flexible and effective tool for both analytical investigations and statistical applications. Full article

This study examines the use of artificial intelligence (AI) in organizational decision-making processes (DMPs) related to investments in renewable energy sources (RESs). The research addresses the gap between AI’s technological capabilities and its actual application in investment practice. An empirical two-stage survey was conducted in 2025, and a comparative analysis was conducted to assess the stability of attitudes toward AI adoption. The findings indicate a low level of practical implementation of AI tools in investment decision-making, despite a clear perception of their potential usefulness, particularly for risk analysis and improving decision objectivity. Organizations tend to perceive AI primarily as analytical support rather than an autonomous decision-making mechanism. The results also reveal a persistent level of uncertainty and hesitation associated with trust in AI systems. Comparative analysis confirms that these attitudes remain stable across research stages, suggesting structural rather than temporary barriers to adoption. This study demonstrates that limited adoption of AI in renewable energy investment decisions results mainly from organizational readiness and trust-related factors rather than technological constraints. The paper contributes empirical evidence on the behavioral and organizational determinants of AI implementation in the context of sustainable energy transition. Full article

Umbral theory, formulated in its modern version by S. Roman and G. C. Rota, has been reconsidered in more recent times by G. Dattoli and collaborators with the aim of devising a working computational tool in the framework of special function theory. Concepts like the umbral image and umbral vacuum have been introduced as pivotal elements of the discussion which, albeit effective, lack generality. This article is directed towards endowing the formalism with a rigorous formulation within the context of formal power series with complex coefficients $(\mathbb{C}〚t〛,\partial )$. The new formulation is founded on the definition of the umbral operator error as a functional in the “umbral ground state” subalgebra of analytically convergent formal series $\phi \in \mathbb{C}\left\{t\right\}$. We consider in detail some specific classes of umbral ground states $\phi$ and analyse the conditions for analytic convergence of the corresponding umbral identities, defined as formal series resulting from the action on $\phi$ of operators of the form $f\left(\zeta {error}^{\mu }\right)$ with $f\in \mathbb{C}\left\{t\right\}$ and $\mu ,\zeta \in \mathbb{C}$. For these umbral states, we exploit the Gevrey classification of formal power series to establish a connection with the theory of Borel–Laplace resummation, allowing us to make rigorous sense of a large class of—even divergent—-umbral identities. As an application of the proposed theoretical framework, we introduce and investigate the properties of new umbral images for the Gaussian trigonometric functions, which emphasise the trigonometric-like nature of these functions and enable defining the concept of a “Gaussian Fourier transform”, a potentially powerful tool for applications. Full article

This study presents an optimization tool inspired by bone remodeling principles to address the high computational costs of truss topology optimization. Additionally, a new structural analysis method based on primary forces is proposed to overcome the kinematic stability problem. The strategy developed to obtain the optimal topology optimizes the initial dense ground structure in two stages. In Phase I, unnecessary members in the system are filtered to determine the “primary candidate members”; in Phase II, the final topology is reached through this refined subset. The algorithm performs an effective search in the design space by simulating biological processes that link the rate of mass change in the bone matrix to mechanical stimuli. Numerical results demonstrate high accuracy, as shown by the analytical solution of the 2D Michell truss, with a difference of 1.02%. The results show high consistency with reference studies, providing, in some cases, alternative topologies with the same weight and stiffness as given in the benchmarks. The proposed method achieves significant improvements in computational efficiency, reducing processing times for larger systems by 10 to over 250 times compared to literature benchmarks. Full article

Head and neck squamous cell carcinoma (HNSCC) is biologically and clinically dichotomous according to HPV status, a distinction that fundamentally dictates the design, implementation, and interpretation of liquid biopsy strategies. Conventional anatomical imaging lacks sufficient sensitivity for minimal residual disease (MRD) detection, contributing significantly to treatment failure and suboptimal clinical outcomes. This review provides a critical, evidence-based synthesis of the three principal circulating analytes, circulating tumor DNA (ctDNA), exosomes, and circulating tumor cells (CTCs), and their evolving roles in real-time, non-invasive molecular monitoring. Critically, the clinical readiness of these analytes differs substantially: while ctDNA, particularly HPV-related ctDNA, is approaching clinical validation for MRD detection and recurrence surveillance in HPV-positive HNSCC, exosomes and CTCs remain investigational tools hindered by ongoing technical challenges including lack of standardized assays, limited reproducibility across platforms, and insufficient prospective validation. We review how the presence of a clonal, virally derived DNA target in HPV-positive HNSCC contrasts with the heterogeneous somatic mutational landscape of HPV-negative tumors, necessitating divergent analytical platforms and yielding distinct clinical utility profiles for MRD detection and recurrence surveillance. We further outline a pragmatic translational pathway focused on assay standardization, particularly for exosomes and CTCs where this foundational work is most urgently needed, integration of complementary multimodal liquid biopsy approaches, and rigorously designed prospective interventional clinical trials to establish clinical utility. Collectively, these efforts aim to transition HNSCC management from reactive, anatomy-based surveillance to proactive, molecularly guided precision oncology, with the potential to improve therapeutic decision-making and patient outcomes. Full article

Amid the accelerating waves of global digitalization and the deepening interplay of cultural diversity, urban creative spaces have become pivotal arenas for the digital creative industry—yet a systematic, cross-culturally robust tool for assessing their sustainability remains conspicuously absent. Here, we address this gap by constructing a multi-dimensional evaluation framework derived from a systematic literature review, comprising five primary dimensions—AIGC technology integration, cultural heritage preservation, the economic benefits of the digital cultural industry, ecological synergy and social inclusiveness, and governance and policy support—along with 20 secondary indicators. To enhance methodological rigor, we integrate the Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) to determine indicator weights while mitigating the subjective biases inherent in traditional approaches and employ the TOPSIS method to quantitatively assess and rank the creative spaces of five representative cities: London, Shanghai, Los Angeles, Tokyo, and Berlin. Our findings reveal that London leads in comprehensive sustainability, followed closely by Shanghai, with sensitivity analysis confirming the high robustness of the rankings. The originality of this work lies in reconceptualizing AIGC not as a conventional digital instrument but as a core transformative driver embedded within the evaluation architecture, while the application of IFAHP substantially enhances the scientific validity and methodological reliability of the assessment. This research provides an operational diagnostic tool and actionable optimization pathways for advancing the sustainability of urban creative spaces worldwide, offering practical implications for fostering cultural innovation, bridging the digital divide, promoting social inclusiveness, and informing evidence-based urban governance policies. Full article

Background: Liquid biopsy using circulating tumor DNA (ctDNA) is rapidly reshaping gastrointestinal (GI) oncology. The highest-impact applications are molecular residual disease (mRD) detection after curative-intent therapy and early recognition of progression or resistance during systemic treatment. Methods: We performed a structured, clinically oriented narrative synthesis by using explicit search, eligibility, evidence prioritization, and clinical interpretation rules, integrating landmark prospective cohorts, randomized ctDNA-guided strategy trials where available, meta-analyses, key methodological research (e.g., pre-analytics, assay design, and clonal hematopoiesis (CH)/clonal hematopoiesis of indeterminate potential (CHIP)), and selected trial registries. Results: In resected colorectal cancer (CRC), postoperative ctDNA positivity is among the strongest known biomarkers of recurrence risk; large prospective studies demonstrate clear separation of disease-free survival (DFS)/overall survival (OS) between mRD+ and mRD− patients. In stage II colon cancer, randomized data (DYNAMIC) show that a ctDNA-guided strategy reduces adjuvant chemotherapy exposure without compromising long-term outcomes. In metastatic CRC, ctDNA supports early response monitoring and resistance tracking; ctDNA-selected anti-EGFR rechallenge provides a model of biomarker-driven actionability (CHRONOS). In gastroesophageal cancers, longitudinal ctDNA dynamics correlate with relapse risk and treatment efficacy, and in esophageal squamous cell carcinoma, ctDNA after neoadjuvant chemoradiotherapy informs residual disease risk and adjuvant stratification. In pancreatic ductal adenocarcinoma and hepatobiliary malignancies, sensitivity is constrained by low shedding and background cell-free DNA (cfDNA), yet ctDNA positivity remains clinically meaningful, and emerging data in resected extrahepatic cholangiocarcinoma (STAMP-linked analyses) show that ctDNA dynamics during adjuvant therapy predict recurrence. Conclusions: ctDNA is a clinically validated biomarker for mRD in CRC, whereas in other GI cancers, it remains a promising but methodologically heterogeneous tool whose clinical utility is tumor- and context-dependent. The next phase requires interventional trials demonstrating outcome improvement, harmonized sampling and reporting standards, and rigorous control of confounders (notably CH/CHIP). Full article

Ion-selective electrodes (ISEs) are widely used analytical tools for the determination of specific ions in a variety of analytical applications due to their simplicity, selectivity, and low cost. Recent developments in materials science and digital fabrication have opened new opportunities for redesigning ISEs using modern manufacturing techniques. Here, we present a new application of 3D printing for fabricating potassium-selective electrodes using a simplified membrane composition. The 3D printing cocktail was prepared by mixing potassium tetraphenylborate, silver sulfide or graphite, and industrial ABS (acrylonitrile Butadiene Styrene) polymer. Membranes were tested both without and with the addition of ZnO nanoparticles. Incorporation of ZnO NPs significantly enhanced the electrode slope, while graphite-based membranes exhibited faster response, with potential stabilizing within 3–7 s across a concentration range of 4.88 × 10⁻⁵ mol L⁻¹ to 1.00 × 10⁻² mol L⁻¹. The optimized 3D printed membrane containing 0.6% ZnO NPs showed near-Nernstian behaviour (slope: 59.178 mV per decade and R² = 0.9989), a limit of detection of 2.06 × 10⁻⁵ mol L⁻¹ and high selectivity against common interfering ions. These results demonstrate that 3D printing combined with a suitable membrane composition and nanoparticle incorporation provides a versatile platform for rapid, reproducible, and high-performance potassium ISEs. Full article

Evaluating the aquatic ecological and environmental consequences of dredging projects with precision is essential for reconciling engineering objectives with the long-term health of aquatic ecosystems. This study establishes an evaluation system for the aquatic ecological and environmental impacts of dredging engineering based on the Pressure–State–Response (PSR) analytical framework, and constructs a comprehensive assessment system through Velocity Pausing Particle Swarm Optimization–Projection Pursuit (VPPSO-PP) coupled with fuzzy pattern recognition. Taking the Pinglu Canal project as a case study, the objective weights of indicators are obtained via the VPPSO-PP method, and the impact levels are determined by combining the fuzzy pattern recognition model. Case studies show that the quality of discharged residual water is the most critical factor affecting the aquatic ecological environment, ranking highest with a weight of 0.0839, followed by the proportion of aquatic ecological restoration investment at 0.0685. Among the five typical dredging sections of the Pinglu Canal, the Shaping River section and the Offshore Estuary Section were rated as having a “mild impact.” In contrast, the Main Stream of Qinjiang River section, the Watershed section, and the Qinzhou urban section were rated as having a “moderate impact.” These evaluation results are consistent with the actual engineering conditions. The model developed in this study enables a quantitative and objective assessment of the aquatic ecological impacts of dredging projects. It provides a scientific basis and a practical tool for ecological management and decision-making in dredging operations. Full article

Adaptive reuse has emerged to become a tool for implementing the understanding of sustainability in the domain of architectural conservation, as it encourages the continued usage of old buildings as means of reducing environmental impact, as well as preserving socio-cultural capital while generating economic income. However, in its practice, the decisions regarding granting meanings, interpretation, and preserving memories within adaptation processes are dominated by expert-driven approaches that inadequately incorporate stakeholder values or intangible heritage dimensions. To this end, this study aims to contribute to the current debate by adopting a participatory co-evaluation framework that integrates both authenticity perspectives and sustainability dimensions using Multi-Criteria Decision Analysis (MCDA) for evaluating adaptive reuse alternatives for an abandoned prefabricated wooden heritage building. Stakeholder priorities were drawn through a workshop and transformed into normalized weights using the Simos technique. Four design alternative typologies—namely, Continuity, Cultivation, Differential, and Optimization—were assessed and compared against 20 performance indicators across heritage, social, ecological, and economic criteria using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Indicator-level analyses and sensitivity tests (±10% and ±20% weight variations) were applied to confirm the robustness of rankings. The results from the best-performing alternative demonstrated the trade-offs between heritage authenticity and sustainability objectives, as well as demonstrating how combining participatory methods with quantitative evaluation can support evidence-based decision-making for adaptive reuse. The applied integrated framework helps bridge the gap between heritage theory and practice by combining authenticity, participation, and sustainability in one analytical approach, supporting evidence-based decisions for adaptive reuse. Full article

Data-driven approaches are increasingly required to optimize biofertilization strategies in forage systems. Machine learning (ML) provides an efficient tool for identifying functional drivers in complex plant–soil–microbe systems, offering important perspectives for precision data-driven agriculture. However, despite its potential, ML remains data-driven in studies involving diazotrophic inoculation using biochar as a pelletizing material, particularly in forage grasses. This study applied ML to predict the key drivers controlling Brachiaria brizantha performance and soil quality under biochar-pelletized diazotrophic bacteria (DB). Five isolates were inoculated with or without biochar, and plant traits and soil attributes, including pH, potassium, phosphorus, sodium, and urease activity were evaluated. These data were integrated into multivariate analyses and ML algorithms, including Linear Discriminant Analysis, Random Forest, and Support Vector Machine, to identify the functional drivers that best discriminate treatment performance and uncover mechanistic functional drivers. All isolates increased soil potassium content, with the highest values in the biochar amended treatments, and a 39% increase. Soil pH and urease activity were significantly modulated by isolate identity, while biomass allocation patterns differed among treatments. Overall, the results highlight that biochar pelletization can enhance the effectiveness of DB inoculants. ML revealed that dry foliar biomass, soil pH, and fresh root weight were the most predictive variables, highlighting consistent signatures explaining plant–soil responses to biochar-pelletized DB. These findings demonstrate that interpretable ML can disentangle complex plant–soil–microbe interactions, support precision biofertilization design, and serve as an efficient decision-support tool for sustainable pasture management. Beyond the present system, this study establishes a transferable and scalable analytical framework for precision biofertilization strategies in forage systems and other biochar-mediated agroecosystems, advancing predictive and data-driven approaches in sustainable agricultural engineering. Full article

Urban blue–green infrastructure (BGI), comprising vegetation and aquatic elements, is fundamental to city resilience and climate adaptation. Effective BGI management necessitates high-resolution, spatially accurate data for which Unmanned Aerial Vehicles (UAVs) have emerged as versatile monitoring tools. This study provides a critical synthesis and analytical evaluation of UAV-based technologies for BGI management from 2018 to 2025. Following a PRISMA-guided methodology, the review evaluates dominant research themes, sensor technologies (RGB, multispectral, thermal, LiDAR, and water and air quality sensors), and analytical methods. Departing from traditional descriptive reviews, this study appraises the operational maturity of these technologies using an adapted Technology Readiness Level (TRL) framework. The analysis identifies a significant “maturity gap” between standardized structural mapping (TRL 9) and experimental functional assessments of environmental conditions (TRL 4–6). Notably, the article includes a detailed analysis of specific UAV platforms and sensors, providing specifications of technological capabilities. By identifying critical technical, regulatory, and economic bottlenecks, this review provides a robust, evidence-based foundation for the deployment of drones in enhancing urban resilience and sustainable environmental governance. Full article

The Logistics Performance Index (LPI) is a widely used benchmarking tool for assessing national logistics capabilities. However, its role in sustainability-oriented research remains unclear. This study reconceptualizes the LPI as a multidimensional analytical framework for examining the structural associations between logistics performance and sustainability outcomes. Using cross-country data from 2023, the analysis evaluates the alignment of the six disaggregated LPI dimensions with economic (GDP per capita), social (Human Development Index), and environmental (CO₂ emissions) indicators across approximately 120 countries. The analysis applies an integrated framework combining linear models, ensemble learning techniques, explainable artificial intelligence (SHAP), and clustering analysis to assess the consistency and interpretability of these relationships. The results indicate that logistics performance is more strongly aligned with economic and social outcomes than with environmental indicators. Infrastructure quality, tracking and tracing, and timeliness emerge as key logistics dimensions associated with higher income levels and human development. In contrast, the moderate alignment observed for CO₂-related outcomes highlights the influence of broader structural factors, such as energy systems and industrial composition, beyond logistics performance. Clustering analysis further reveals distinct logistics–environmental configurations, underscoring substantial heterogeneity in sustainability trajectories among countries with similar logistics capabilities. Overall, these findings establish the LPI as a system-level lens for diagnosing logistics–sustainability relationships and for designing context-sensitive policies aligned with the Sustainable Development Goals (SDGs), particularly SDGs 8, 9, 11, and 13. Full article

This study investigates how parents and students interpret and form continued engagement intentions with a radar visualisation tool designed to present multi-subject academic performance. While data visualisation is increasingly used in education, limited empirical attention has been given to whether parents and students, who share the same performance information but hold distinct roles, respond to visualised reports through similar behaviours. To address this gap, an interactive radar visualisation was developed to present secondary school students’ achievement across subjects with peer reference points. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) as an analytical framework, this study examines the determinants of continued intention to use the visualisation tool. Questionnaire data were collected from 706 parents and 264 students in a Macao secondary school. Structural equation modelling (SEM) revealed fundamentally different ideas of continued engagement. For parents, continued intention was significantly associated with performance expectancy (PE) and effort expectancy (EE), social influence (SI) and facilitating conditions (FC), suggesting the tool functioned as a decision support system for academic planning. For students, only social influence (SI) and facilitating conditions (FC) emerged as significant predictors, indicating that peer comparison and external expectations may not fit their needs. Parents also reported significantly higher continued intention than students. The finding extended UTAUT by demonstrating that core acceptance relationships are moderated by different roles, reframing technology acceptance in educational visualisation from system adoption to information interpretation. The study provides empirical evidence that visualised performance reporting functions not merely as a data display but also as a communication medium whose meaning is actively constructed by users. These insights highlight the need for role-sensitive design, emphasising actionable planning support for parents and personally meaningful, agency-oriented feedback for students, in order to foster productive home–school communication and sustained engagement with learning information. Full article

Eye movement research has emerged as a powerful tool in architectural and environmental design, offering insights into how people visually engage with built and natural surroundings. Eye tracking technology enables the study of visual attention, user engagement, and navigation patterns, thereby informing user-centered design. This paper reviews a wide and vast body of research that demonstrates eye tracking’s capacity to inform architectural and environmental design decisions by providing objective, data-driven insights into human perception and interaction with the built world. Key methodologies are discussed, including desktop, mobile, and VR-based systems, as well as recent advances in software analytics and artificial intelligence. Beyond summarizing the existing literature, this review critically evaluates methodological approaches, identifies key challenges, and outlines future research directions. The key findings indicate increased integration of immersive technologies, diversification of analytical paradigms, and expanded application in sustainable and user-centered design. However, methodological heterogeneity, limited ecological validation, and insufficient integration with design optimization frameworks remain significant limitations. This review provides a structured foundation for advancing interdisciplinary research and enhancing evidence-based architectural design. The paper concludes by outlining a forward-looking research agenda for creating more responsive, intuitive, and human-centered environments. Full article