Search Results (234)

Milk tea is a globally popular new-style tea beverage product. In recent years, the industry has achieved rapid development in terms of scale expansion and quality iteration and upgrading. The flavor quality and product stability have become the focus of attention and research hotspots in this field. The chemical foundation of milk tea flavor, processing methods, and flavor quality evaluation approaches are thoroughly elaborated. The chemical basis of tea-based, milk-based, and milk tea flavors is systematically summarized, primarily including the analysis of key flavor compounds and the interactions between tea-based and milk-based substances. Subsequently, the tea-based production methods, mixed processing techniques, and factors influencing storage and preservation of milk tea are discussed. Furthermore, evaluation methods for milk tea flavor quality, including traditional sensory evaluation and intelligent assessment techniques are systematically outlined. This review not only summarizes the recent research progress but also looks forward to the interdisciplinary work that needs to be carried out in the future. These efforts aim to provide information on the transformation from the research stage of tea milk product formulas to the development of solutions with controllable quality. Thus, they offer valuable theoretical guidance for the formation and regulation of tea milk flavor and quality as well as the development of new products. This work aims to provide theoretical insights and technical support for the translation from laboratory formulations to quality-controlled industrial solutions. Full article

This study proposes a new formalized approach to the stabilization of linear transformations in artificial neural networks, based on discrete algebraic properties. In contrast to existing stability methods that rely on spectral norms, regularization techniques, or empirical heuristics, this work introduces the concept of algebraic stabilization—stability that arises from the structural properties of the matrices defining linear operators. The central object of investigation is the class of integer-valued matrices for which exponentiation to a form of the type ${\mathrm{W}}^{\mathrm{k}}=\mathrm{I}+\mathsf{\mu }\mathrm{D}$ is possible, where $\mathrm{D}\in {\mathbb{Z}}^{\mathrm{n}\times \mathrm{n}},\mathsf{\mu }\in {\mathbb{Z}}_{>1}$. A well-known problem in group algebra is considered that guarantees the existence of such an exponent under the condition that $\mathsf{\mu }$ is coprime with the determinant of $\mathrm{W}$. Within this framework, modular arithmetic, reduction modulo $\mathsf{\mu }$, and the group structure of ${\mathrm{G}\mathrm{L}}_{\mathrm{n}}\left({\mathbb{Z}}_{\mathsf{\mu }}\right)$ are employed, thereby linking the proposed method to the theory of finite groups and linear automata. The advantages of the approach are discussed, including formal control over the iterative behavior of transformations, compatibility with quantized and finitely representable networks, the possibility of embedding stabilizing conditions directly into the network architecture, and the potential to improve model interpretability and reliability. At the same time, limitations are identified, particularly those related to constructive implementation, the selection of suitable hyperparameters, and generalization to broader classes of transformations. Full article

This work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization. More specifically, a visible-band vegetation index (RGBVI) is calculated to enhance canopy regions, followed by morphological filtering to delineate individual tree crowns. The Euclidean Distance Transform is then applied, and the local maxima of the smoothed distance map are extracted as candidate tree locations. The final detections are iteratively refined using the AIC to optimize the number of trees with respect to canopy coverage efficiency. Additionally, this work introduces DTCD-PC, a modified algorithm tailored for point clouds, which significantly enhances detection accuracy in complex environments. This work makes a significant contribution to tree detection in the following ways: (1) by creating a tree detection framework entirely based on an unsupervised technique, which outperforms state-of-the-art unsupervised and supervised tree detection methods; (2) by introducing a new urban dataset, named AgiosNikolaos-3, that consists of orthomosaics and photogrammetrically reconstructed 3D point clouds, allowing the assessment of the proposed method in complex urban environments. The proposed DTCD approach was evaluated on the Acacia-6 dataset, consisting of UAV images of six-month-old Acacia trees in Southeast Asia, demonstrating superior detection performance compared to existing state-of-the-art techniques, both unsupervised and supervised. Additional experiments were conducted in the custom-developed Urban Dataset, confirming the robustness and generalizability of the DTCD-PC method in heterogeneous environments. Full article

This paper presents the Living Lab Assessment Method (LLAM), a context-sensitive framework for assessing impact and value creation in Living Labs (LLs). While LLs have become established instruments for Open and Urban Innovation, systematic and transferable approaches to evaluate their impact remain scarce and still show theoretical and practical barriers. This study proposes a new methodological approach that aims to address these challenges through the development of the LLAM, the Living Lab Assessment Method. This study reports a five-year iterative development process embedded in Ghent’s urban and social innovation ecosystem through the combination of three complementary methodological pillars: (1) co-creation and co-design with lead users, ensuring alignment with practitioner needs and real-world conditions; (2) multiple case study research, enabling iterative refinement across diverse Living Lab projects, and (3) participatory action research, integrating reflexive and iterative cycles of observation, implementation, and adjustment. The LLAM was empirically developed and validated across four use cases, each contributing to the method’s operational robustness and contextual adaptability. Results show that LLAM captures multi-level value creation, ranging from individual learning and network strengthening to systemic transformation, by linking participatory processes to outcomes across stakeholder, project, and ecosystem levels. The paper concludes that LLAM advances both theoretical understanding and practical evaluation of Living Labs by providing a structured, adaptable, and empirically grounded methodology for assessing their contribution to sustainable and inclusive urban innovation. Full article

Problem: Entrepreneurship education continues to expand, yet it remains fragmented across disciplines and loosely connected to the knowledge, innovation, and venture ecosystems that shape entrepreneurial success. At the same time, AI is transforming research, collaboration, and venture development, but its use in education is typically limited to narrow, task-specific applications rather than ecosystem-level integration. Objective: This paper seeks to develop a comprehensive conceptual model for integrating AI into entrepreneurship education by positioning AI as a connective infrastructure that links and activates the knowledge, innovation, and venture ecosystems. Methods: The model is derived through an integrative synthesis of literature, programs, and activities on entrepreneurship education, ecosystem-based learning, and AI-enabled research and innovation practices, combined with an analysis of gaps in current educational approaches. Key Findings: The proposed model defines a progressive learning pathway consisting of (1) AI competency training that builds foundational capacities in critical judgment, responsible application, and creative adaptation; (2) AI praxis labs that use AI-curated ecosystem data to support iterative, project-based learning; and (3) venture studios where students scale outputs into innovations and ventures through structured ecosystem engagement. This pathway demonstrates how AI can function as a structural mediator of problem definition, research design, experimentation, analysis, and narrative translation. Contributions: This paper reframes entrepreneurship education as an iterative, inclusive, and ecosystem-connected process enabled by AI infrastructure. It offers a new theoretical lens for understanding AI’s educational role and provides actionable implications for curriculum design, institutional readiness, and policy development while identifying avenues for future research on competency development and ecosystem impacts. Full article

Background/Objectives: New vaccine platforms that rapidly yield low-cost, easily manufactured vaccines are highly desired, yet current approaches lack key features. We developed the Killed Whole-Cell/Genome-Reduced Bacteria (KWC/GRB) platform, which uses a genome-reduced Gram-negative chassis to enhance antigen exposure and modularity via an autotransporter (AT) system. Integrated within a Design–Build–Test–Learn (DBTL) framework, KWC/GRB enables rapid iteration of engineered antigens and immunomodulatory elements. Here, we applied this platform to the HIV-1 fusion peptide (FP) and tested multiple antigen engineering strategies to enhance its immunogenicity. Methods: For a new vaccine, we synthesized DNA encoding the antigen together with selected immunomodulators and cloned the constructs into a plasmid. The plasmids were transformed into genome-reduced bacteria (GRB), which were grown, induced for antigen expression, and then inactivated to produce the vaccines. We tested multiple strategies to enhance antigen immunogenicity, including multimeric HIV-1 fusion peptide (FP) designs separated by different linkers and constructs incorporating immunomodulators such as TLR agonists, mucosal-immunity-promoting peptides, and a non-cognate T-cell agonist. Vaccines were selected based on structure prediction and confirmed surface expression by flow cytometry. Mice were vaccinated, and anti-FP antibody responses were measured by ELISA. Results: ELISA responses increased nearly one order of magnitude across design rounds, with the top-performing construct showing an ~8-fold improvement over the initial 1mer vaccine. Multimeric antigens separated by an α-helical linker were the most immunogenic. The non-cognate T-cell agonist increased responses context-dependently. Flow cytometry showed that increased anti-FP-mAb binding to GRB was associated with greater induction of antibody responses. Although anti-FP immune responses were greatly increased, the sera did not neutralize HIV. Conclusions: Although none of the constructs elicited detectable neutralizing activity, the combination of uniformly low AlphaFold pLDDT scores and the functional data suggests that the FP region may not adopt a stable native-like structure in this display context. Importantly, the results demonstrate that the KWC/GRB platform can generate highly immunogenic vaccines, and when applied to antigens with well-defined native tertiary structures, the approach should enable rapidly produced, high-response, very low-cost vaccines. Full article

Traditional route optimization frameworks often suffer from “spatial blindness,” addressing the problem through abstract matrices devoid of geographical context. To address this fundamental methodological gap, this study proposes the Iterative Score Propagation Algorithm (ISPA), a transparent, GNN-inspired framework that reframes optimization as a holistic corridor problem. ISPA’s robustness and superiority were tested against established Multi-Criteria Decision-Making (MCDM) methods (WLC, TOPSIS, VIKOR) across three diverse engineering scenarios (Rural Highway, Pipeline, Trekking Trail) and two distinct weighting philosophies (Entropy and AHP). The holistic analysis reveals that ISPA achieves the highest final score (0.815) across all six test conditions, demonstrating both the highest overall mean performance (0.629) and the greatest stability (1.000). Furthermore, its flexible cost function successfully modeled unconventional objectives, such as a “climbing reward,” enabling a paradigm shift from cost minimization to experience maximization. ISPA’s superior performance stems from its structural advantage in contextualizing spatial data. This work introduces a new, spatially-aware approach that transforms route planning from a static calculation into a dynamic design and scenario analysis tool for planners and engineers. Full article

This paper presents closed-form solutions for a three-dimensional system of nonlinear difference equations with variable coefficients. The approach employs functional transformations and leverages generalized Fibonacci sequences to construct the solutions explicitly. These solutions reveal a profound connection to generalized Fibonacci recursions. The proposed method is based on sophisticated mathematical transformations that reduce the complex nonlinear system to a solvable linear form, followed by the derivation of general solutions through iterative techniques and harmonic analysis. Furthermore, we extend our results to a generalized class of systems by introducing flexible functional transformations, while rigorously maintaining the required regularity conditions. The findings demonstrate the effectiveness of this methodology in addressing a broad class of complex nonlinear systems and open new perspectives for modeling multivariate dynamical phenomena. The analysis further reveals two distinct dynamical regimes—an unbounded oscillatory growth phase and a bounded cyclic equilibrium—arising from the relative magnitude of the variable coefficients, thereby highlighting the method’s capacity to characterize both amplifying and self-regulating behaviors within a unified analytical framework. Full article

Recently, the convergence of advanced sensor technologies and innovations in artificial intelligence and robotics has highlighted facial emotion recognition (FER) as an essential component of human–computer interaction (HCI). Traditional FER studies based on handcrafted features and shallow machine learning have shown a limited performance, while convolutional neural networks (CNNs) have improved nonlinear emotion pattern analysis but have been constrained by local feature extraction. Vision transformers (ViTs) have addressed this by leveraging global correlations, yet both CNN- and ViT-based single networks often suffer from overfitting, single-network dependency, and information loss in ensemble operations. To overcome these limitations, we propose ArecaNet, an assembled residual enhanced cross-attention network that integrates multiple feature streams without information loss. The framework comprises (i) channel and spatial feature extraction via SCSESResNet, (ii) landmark feature extraction from specialized sub-networks, (iii) iterative fusion through residual enhanced cross-attention, (iv) final emotion classification from the fused representation. Our research introduces a novel approach by integrating pre-trained sub-networks specialized in facial recognition with an attention mechanism and our uniquely designed main network, which is optimized for size reduction and efficient feature extraction. The extracted features are fused through an iterative residual enhanced cross-attention mechanism, which minimizes information loss and preserves complementary representations across networks. This strategy overcomes the limitations of conventional ensemble methods, enabling seamless feature integration and robust recognition. The experimental results show that the proposed ArecaNet achieved accuracies of 97.0% and 97.8% using the public databases, FER-2013 and RAF-DB, which were 4.5% better than the existing state-of-the-art method, PAtt-Lite, for FER-2013 and 2.75% for RAF-DB, and achieved a new state-of-the-art accuracy for each database. Full article

This paper is devoted to the consensus control of a networked system constituted by multiple underactuated marine surface vessels (MSVs) with input saturation. Compared with the related works on this topic, two remarkable features are involved in the system under investigation: (1) Input saturation of each follower MSV is considered in the paper but ignored in most of the related works. (2) More coarse information is allowed about the network since more severer uncertainties (external disturbance joint with unknown system parameters) are involved in each follower MSV, and moreover, the output of the leader MSV is not necessarily second-order continuously differentiable while its time derivatives are not necessarily available for feedback. The above two aspects lead to the incapability of the traditional control schemes on this topic. To solve the control problem, a novel adaptive control scheme is proposed by adaptive dynamic compensation technique combining with certain methods for the handling of saturation input and under-actuation. Specifically, a smooth function is introduced to approximate the saturation input, by which and a couple of state transformations, a new system is obtained with a skillful injection of an auxiliary input for the handling of under-actuation. Then, an iterative procedure is given to derive an adaptive controller which ensures that all the signals of the closed-loop system are bounded while the output of the follower MSVs practically tracks that of the leader. Finally, simulation results are provided to validate the effectiveness of the proposed theoretical results. Full article

E-learning systems that support personalized learning require sophisticated decision-making methods to adapt content to students optimally. This paper deals with applying multi-criteria decision-making methods in assigning learning objects in an e-learning system to students based on relevant customization criteria. The novelty of this study lies in the application of ANP and DEMATEL to improve content adaptation for students. Structuring the decision-making problem according to the DEMATEL and using ANP for prioritization has made the entire selection of learning objects better with respect to cognitive and learning styles and Bloom’s taxonomy levels. The method consists of various forms. In the first, DEMATEL has identified dependencies between criteria and clusters, mentioning their influence values on a 0–4 scale. A linear transformation model quantified the compatibility level of a student profile to a learning material. The transformed DEMATEL results were incorporated in all the interdependencies among criteria. The unweighted supermatrix was normalized by cluster weights assigned by experts before the iterative computation led to the converging weighted supermatrix. The outcome was that the individual students made these final priority rankings for learning materials. A pilot experiment was carried out to validate the system, and the results revealed that in the experimental group, the personalized learning environment showed the maximum statistical improvement over the control group. The research was conducted in Croatia, and the participants were students (N = 77) from two public universities and one polytechnic. Ultimately, the newly developed combined ANP-DEMATEL approach was effective in an instantaneous result-optimized dynamic learning path generation, ensuring knowledge acquisition. This research further contributes to developing intelligent educational systems by demonstrating how ANP and DEMATEL can be used synergistically to improve e-learning personalization. Future work could include optimizing weight assignment strategies or using new learning contexts to further adaptivity. Full article

The rapid advances in artificial intelligence (AI) and high-performance computing (HPC) are transforming the landscape of engineering design, and the concentrated solar power (CSP) tower sector is no exception. As these technologies increasingly penetrate the energy domain, they bring new capabilities for addressing the complex, multi-variable nature of receiver design and optimisation. This review explores the application of AI-driven generative design techniques in the context of CSP tower receivers, with a particular focus on the use of metaheuristic algorithms and machine learning models. A structured classification is presented, highlighting the most commonly employed methods, such as Genetic Algorithms (GAs), Particle Swarm Optimisation (PSO), and Artificial Neural Networks (ANNs), and mapping them to specific receiver types: cavity, external, and volumetric. GAs are found to dominate multi-objective optimisation tasks, especially those involving trade-offs between thermal efficiency and heat flux uniformity, while ANNs offer strong potential as surrogate models for accelerating design iterations. The review also identifies existing gaps in the literature and outlines future opportunities, including the integration of high-fidelity simulations and experimental validation into AI design workflows. These insights demonstrate the growing relevance and impact of AI in advancing the next generation of high-performance CSP receiver systems. Full article

This work investigates how fractionality affects the dynamical behavior of dust-acoustic shock waves that arise and propagate in a depleted-electron complex plasma. This model consists of inertial negatively charged dust grains and inertialess nonextensive distributed ions. Initially, the fluid model equations that govern the propagation of nonlinear dust-acoustic shock waves are reduced to the integer Burgers-type equations using the reductive perturbation method. Thereafter, the integer Burgers-type equations are converted to the fractional cases using a suitable transformation. For analyzing this fractional family, both the Tantawy technique and the new iterative method are implemented within the Caputo sense framework. These methods can produce highly accurate analytical approximations without necessitating stringent assumptions or intricate computational processes, in contrast to other similar methods. Numerical examples and the calculation of the absolute error demonstrate the efficacy of the suggested methodologies, emphasizing their superior precision and swift convergence. Full article

With access to new energy sources, the problem of reactive power optimization and dispatching has become increasingly important for research. However, the reactive power optimization problem is a mixed integer nonlinear optimization problem. In order to solve the integer variables and nonlinear conditions existing therein, a method for coordinated reactive power optimization and dispatching based on semidefinite programming is proposed. Firstly, a reactive power optimization model considering discrete variables and continuous variables is established with the minimization of total operating cost as the objective function; secondly, the discrete variables are transformed into equality constraints by quadratic equations, and then a solvable semi-definite programming problem is obtained; thirdly, the rank-one constraint is restored by the Iterative Optimization based Gaussian Randomization Method (IOGRM), and the optimal solution equivalent to the original problem is obtained. Finally, the correctness and effectiveness of the proposed model and solution method are verified by analyzing and comparing with the second-order cone programming (SOCP) through the modified IEEE standard example. Full article

This paper investigates the design potential of post-consumer plastic waste through the Waste Driven Design (WDD) method, developed at IUAV University of Venice and implemented in both experimental and semi-industrial contexts. WDD proposes a situated and transdisciplinary approach, where waste is no longer regarded as a material to be discarded, but as a resource to be explored, transformed, and valorised. Using the Marble CAP case study—a new material derived from non-recyclable food packaging—the paper presents an iterative and scalable design process that combines technical experimentation, material storytelling, and application potential. The stages of the process are examined, from waste collection and cataloguing to the production of pressed sheets, which are tested under various conditions and finishes. The results demonstrate how, in design, material can become a catalyst for new aesthetics, languages, and production chains. Rather than concluding with the formal outcome, the project opens up spaces for critical and operational interventions along the supply chain, highlighting how design can contribute to imagining and activating alternative trajectories for waste transformation. Full article