Search Results (199)

This study analyzes formative research as a pedagogical resource for teaching scientific logic in higher education from a constructivist perspective. The purpose of the article is to examine how formative research contributes to the development of scientific reasoning, critical thinking, and analytical skills among university students through active, reflective, and contextually grounded learning processes. The study is an exploratory narrative/documentary literature review. The initial bibliographic search identified 105 scientific documents published between 2000 and 2025 in indexed databases such as Scopus, Web of Science, SciELO, Taylor & Francis, MDPI, ResearchGate, Redalyc, and RENATI. After duplicates were removed and inclusion and exclusion criteria were applied, 54 studies were selected for the final analysis. A two-way documentary analysis matrix was used to identify conceptual relationships among constructivism, reflection-in-action, mental representations, induction and deduction, and their contributions to scientific logic. The findings show that formative research strengthens scientific logic by promoting active knowledge construction, critical reflection, problem-solving, and argumentative reasoning. The contributions of Piaget, Vygotsky, Bruner, Schön, and Fosnot demonstrate that scientific thinking develops through interaction, inquiry, contextualized learning, and reflective practice. Inductive and deductive reasoning were also identified as complementary mechanisms for developing analytical and interpretive competencies in university education. The study proposes that formative research should be considered a central pedagogical strategy in higher education because it facilitates the integration of scientific reasoning, reflective learning, and research-based teaching. Finally, an operational formative research program based on a holistic student development approach is proposed to foster scientific reasoning, intellectual autonomy, and the formation of more critical, reflective, and scientifically competent university students. Full article

Decommissioned airfields are increasingly recognized as strategic sites for ecological regeneration, climate adaptation, and the creation of new public spaces. However, research on their transformation has predominantly focused on the environmental performance of Nature-based Solutions (NBS), often overlooking the role of inherited spatial morphology in structuring regeneration processes and outcomes. This paper proposes an AI-assisted, morphology-based proto-ontological framework for analyzing and designing post-airfield architecture. The framework was developed through the inductive and comparative analysis of a corpus of 32 urban post-airfield regeneration projects, from which recurrent inherited morphologies, transformation actions, spatial devices, and NBS were identified and structured into a relational sequence. The framework was then applied to two contrasting case studies: Maurice Rose Airfield Park (Frankfurt) and Xuhui Runway Park (Shanghai); these were selected for their different transformation logics. The results show that similar airfield morphologies can generate markedly different climatic, ecological, social, and memory-related outcomes depending on how they are transformed and linked to NBS. The study demonstrates that inherited airfield morphologies are not passive remnants but operative spatial structures, and that NBS should be understood as spatially embedded and form-generating design components. The proposed proto-ontology offers a transferable analytical model and a basis for future computational and generative design applications. Full article

Research Objective: To explore governance pathways for small-property-right housing from the perspective of land appreciation revenue distribution, thereby promoting high-quality urban renewal. Research Methods: The study employs theoretical analysis, inductive summarization, and logical reasoning. Research Findings: (1) Land appreciation revenue consists of absolute rent, differential rent I, and differential rent II, corresponding respectively to land ownership, land development rights, and land management rights; (2) A framework for the distribution of land appreciation gains that “balances public and private interests and promotes multi-stakeholder sharing” is established, clarifying the revenue boundaries for entities such as the government, village collectives, and housing operators; (3) Two governance pathways are proposed: converting retained collective property rights into affordable rental housing, and categorizing and disposing of properties after government expropriation and conversion to state ownership. These are further refined into five implementation models. Research Conclusions: The rational distribution of land appreciation gains is key to resolving the governance challenges of small-property-right housing and coordinating the objectives of urban renewal with housing security. Full article

Existing CNN-based visual malware classification methods are often constrained by inductive bias mismatch: standard isotropic convolution kernels and global pooling operations neglect the inherent structural anisotropy of malware images, and these methods struggle to address the spatial rearrangement of code blocks caused by obfuscation, which we term the “Malware Picasso Problem”. To overcome these limitations, we propose AD-CapsFPN, an end-to-end framework representing a significant step toward spatial reasoning over texture memorization, with a synergistic “Rectification–Fusion–Inference” mechanism. Our approach rectifies anisotropic inductive biases in the feature extraction stage, dynamically aggregates cross-scale discriminative features in intermediate layers, injects row-aware spatial biases, and adopts a global pooling-free spatial routing strategy in the classification stage, effectively reconstructing logical associations between obfuscated and scattered code blocks. Experiments on the large-scale Fusion dataset and the obfuscated Androdex dataset demonstrate significant performance improvements: our method achieves a 16.22% boost in macro F1-score over the MobileNetV4 baseline on the Fusion dataset (reaching 97.98%), and hits 92.45% macro F1-score on the highly challenging Androdex-Set1, outperforming state-of-the-art methods such as MDC-RepNet (88.97%) and TAEfficientNet (88.15%). This work confirms that embedding malware domain priors into architecture design is the key to robust malware classification. Full article

Integer Linear Programming (ILP) is a powerful way to formulate sophisticated optimization models for making geospatial decisions in GIS. One of the general modeling constructs in ILP is the multi-level closest assignment (MLCA) constraint in the reliable facility location models with facility failure considerations. Compared with simpler constructs (such as the single-level closest assignment constraint), it involves assigning customers to backup facilities when the closer facility is unavailable. Part of the art of ILP modeling is to find suitable linear constructs to express such complex logic. The desired linear constructs may or may not exist. Even if a model construct is given, whether it can faithfully enforce the intended meaning is unknown. The correctness of the modeling construct is often shown based on informal reasoning or is not verified at all. Consequently, unverified ILP models may be (occasionally) infeasible or give wrong solutions. With the advancement of computerized theorem proving, it is becoming possible to mechanically prove the correctness of modeling constructs in ILP. In this article, we demonstrate that sophisticated model constructs such as MLCA can be proven using induction. This overcomes the inabilities of prior works to handle multiple levels of recursive definitions. Consequently, we are able to provide a first proof (formal or informal) that the specific MLCA form is mathematically correct. Given the generality of the induction method, we expect that it can be applied to prove the correctness of other types of models. Full article

Direct Torque Control (DTC) for induction motors (IMs) is an advanced method derived from Field-Oriented Control (FOC). In DTC, a voltage source inverter (VSI) is employed to directly regulate the stator flux linkage and electromagnetic torque through space vector modulation (VSM), where the optimal switching vector is selected for the VSI. Similarly to FOC, the stator flux and electromagnetic torque are independently controlled to deliver enhanced dynamic performance. However, DTC still suffers from certain drawbacks, such as slow transient response, limited dynamic performance, and high ripples in torque and flux. In this paper, an improved DTC method is proposed for a three-phase squirrel-cage induction motor. Specifically, a Type-2 fuzzy logic controller is employed to regulate both the stator flux and electromagnetic torque (T2FLC). The proposed method (FLCDTC) combines a three-level VSI with dual-band hysteresis (DBHW) switching to generate the gating signals for the insulated gate bipolar transistors (IGBTs). This approach effectively reduces the total harmonic distortion (THD) in torque and stator current, lowers the common-mode voltage (CMV), and enhances the overall motor performance. Simulation results under random noise distribution demonstrate the robustness of the proposed controller, even at low operating speeds. Finally, the effectiveness of the algorithm is validated in real-time through hardware-in-the-loop (HIL) implementation. Full article

Sports non-fungible tokens (NFTs) have rapidly emerged as tradable digital goods within platform-mediated marketplaces, reshaping how sports organizations, athletes, and brands design fan experiences and monetize digital assets. To consolidate fragmented scholarship and clarify the concept space, this study conducts a systematic quantitative literature review combined with thematic analysis, following PRISMA 2020 and a SPIDER-guided review logic. Searches across six major databases (Web of Science, Scopus, ScienceDirect, PubMed, IEEE Xplore, ProQuest) plus Google Scholar (2017–March 2025) yielded 40 peer-reviewed studies that met predefined inclusion criteria and passed quality appraisal. Results show a sharp growth of sports-NFT research from 2021 to 2024, with strong inter-disciplinary convergence spanning sports marketing, information systems, computer science, and law. Integrating findings through a consumer-value lens, we inductively propose a five-type taxonomy—collectible, empowerment, identity/authentication, physical-asset linked, and virtual-interaction NFTs—each associated with distinct value mechanisms and e-commerce functionalities. The thematic synthesis further identifies four dominant research streams (industry digitalization, consumer psychology/behavior, legal–regulatory issues, and digital marketing), while revealing gaps in theory operationalization, method diversity (e.g., limited experiments/longitudinal designs), cross-context generalizability, and governance/sustainability. The study contributes to marketing and management scholarship by positioning sports NFTs as emerging technologies that reorganize customer engagement, brand-community building, and governance in platform-mediated sport markets, and it offers a research agenda for measuring consumer, brand, and organizational effects. Full article

This study addresses the need to transform enterprise scientific and technological intelligence (STI) services from a discrete resource-supply model toward a more systematic value-creation approach, an important challenge in the digital transformation of knowledge-intensive industries. As an exploratory qualitative inquiry, this work combines large language model-assisted analysis with grounded theory to examine the construction logic and operational mechanisms of an embedded intelligent STI service system. Drawing on in-depth interviews with STI professionals, a qualitative corpus was analyzed using human–machine collaborative coding to systematically derive and organize key constructs. The findings yield a preliminary three-layer conceptual framework: “supply-demand interactive matching, organizational embedded services, and digital-intelligent platform support.” Specifically, the supply–demand matching layer facilitates targeted alignment through demand insight, dynamic response, and quality closed-loop management; the organizational embedded service layer delivers intelligence through scenario integration, process integration, and responsibility–authority integration; and the digital-intelligent platform support layer enables core capabilities via data element induction, intelligent diffusion, and tacit knowledge conversion. The proposed framework offers an initial, structured perspective on how embedded intelligent STI services may operate, providing a foundational reference for both research and practice in this emerging domain. Full article

The growing deployment of unmanned aerial vehicles (UAVs) in energy-constrained applications has highlighted the need for appropriate energy consumption models. However, selecting between physics-based (white-box) and data-driven (black-box) modeling paradigms remains a largely implicit process. Researchers often navigate undocumented trade-offs among required predictive accuracy, empirical data availability, and access to aerodynamic testing infrastructure without a formalized structure. This study proposes a two-stage decision-making framework to formalize UAV energy model selection. In the first stage, a qualitative decision tree is inductively derived from a corpus of 23 recent studies, explicitly mapping infrastructural and informational constraints to five distinct modeling regimes. In the second stage, the Analytic Hierarchy Process (AHP) is applied to quantitatively evaluate the feasible alternatives based on context-specific criteria: accuracy, interpretability, development cost, and customization adaptability. The structural logic of the framework is evaluated against an independent set of 24 holdout studies, demonstrating a high degree of consistency between the framework’s recommendations and the methodologies employed in the literature. Furthermore, the quantitative AHP scoring introduces “fallback flexibility,” enabling researchers to mathematically identify alternative modeling strategies when primary experimental conditions are compromised. Supported by an open-source Python graphical interface, this framework aims to reduce methodological ambiguity and support more structured, reproducible model selection in UAV energy research. Full article

The article addresses the challenges of modernizing Kazakhstan’s railway infrastructure under conditions of technological dependence on foreign automation systems and obsolete relay-based equipment. These factors pose significant risks to economic and information security and limit the throughput capacity of level crossings. A digital system, KZ-DALCS-AI, is proposed, based on a multi-level safety architecture and the integration of artificial intelligence into monitoring and control processes. A key component is an obstacle detection and classification algorithm that considers object types (vehicles, humans and animals, foreign objects, and environmental factors) and enables intelligent real-time decision making using the KZ-ODC-AI controller with data from video surveillance, microwave sensors, and inductive loops. The system architecture, operational logic, and level crossing control algorithm are developed, including optimization of closing time by minimizing the deviation between calculated and actual values. The results of the performed calculations confirm the effectiveness of the proposed notification algorithm, ensuring the required level of safety while reducing unnecessary delays for road traffic. The implementation of the system improves throughput, reduces operational costs, enhances reliability, and minimizes the impact of the human factor. Full article

With the large-scale integration of wind power, modern power systems are facing reduced equivalent inertia, weakened primary frequency regulation capability, and insufficient coordination between wind turbines and energy storage during joint frequency support. To address these issues, this paper investigates a wind–storage hybrid system composed of doubly fed induction generators (DFIG) and supercapacitor energy storage and proposes a coordinated primary frequency regulation strategy combining fuzzy logic control (FLC) and model predictive control (MPC). Considering the variations in rotor kinetic energy reserve and frequency support capability under different wind speed regions, a coordinated regulation mechanism is developed for multiple operating conditions. In addition, a variable-coefficient synthetic inertia control scheme with rotor speed safety constraints is designed to adaptively adjust the turbine regulation coefficients, while an SOC-feedback-based adaptive virtual droop strategy is introduced to improve the sustained support capability of the energy storage unit. On this basis, a multi-objective model predictive control framework is established to optimize the reference power allocation between the wind turbine and the energy storage unit in a rolling manner. The proposed method is characterized by three coordinated features, namely, multi-region wind–storage frequency regulation, rotor-speed-safe adaptive support of the wind turbine and SOC-aware adaptive support of the storage unit, as well as MPC-based rolling power allocation. Simulation results show that the proposed strategy improves the frequency nadir, reduces the steady-state frequency deviation, and enhances coordinated power sharing, thereby improving the primary frequency regulation performance and overall frequency stability of the wind–storage hybrid system. Full article

Industrial development is commonly described as a sequence of technological stages, from automation to artificial intelligence. This study examines whether successive industrial paradigms—from Industry 3.0 to the emerging Industry 6.0—can be more adequately understood as transformations in technological rationality rather than merely technological upgrades. The analysis adopts a conceptual–philosophical methodology informed by targeted review of peer-reviewed literature indexed in Scopus and Web of Science, integrating Kuhn’s notion of paradigms with Peircean inferential logic. Through systematic comparison of technological configurations, problem-framing practices, and epistemic assumptions, the study maps each paradigm onto a dominant mode of inference. The findings indicate that Industry 3.0 privileges deductive rule-based control, Industry 4.0 relies on inductive data-driven optimization, Industry 5.0 foregrounds hermeneutic interpretation and normative judgment, and prospective Industry 6.0 can be coherently interpreted as oriented toward abductive hypothesis generation within human–AI systems. Industrial change thus emerges as a reconfiguration of epistemic limits rather than a linear trajectory of technical improvement. The analysis concludes that expanding machine intelligence does not eliminate human authority but intensifies epistemic responsibility, understood as the obligation to determine relevance, value, and legitimacy in socio-technical systems. Full article

Disinformation has become one of the main contemporary social and political concerns. However, both public and academic debates continue to exhibit an epistemological confusion between disinformation—characterized by a deliberate intention to deceive—and the errors or deficiencies arising from journalistic practice. The aim of this study is to conceptually define these errors under the phenomenon of poor journalism and to propose a taxonomy that allows it to be examined as distinct from disinformation. To this end, a qualitative integrative systematic review was conducted, based on the inductive analysis of peer-reviewed academic publications in Spanish and English, indexed in Scopus, Web of Science, and EBSCO Host. The analysis identifies two main analytical dimensions: deficient practices and structural causes. The findings show that poor journalism does not stem from a deliberate intention to deceive, but rather from structural factors, commercial logics, and corporate interests within the media ecosystem. This phenomenon is intensified by a circular logic in which the same causes that generate it also reinforce it. This study helps to clarify a relevant conceptual gap by offering a definition and a taxonomy that may be used in future research and media literacy initiatives. Full article

The burgeoning scale of Pre-trained Large Models (PLMs) has intensified the demand for efficient inference without compromising performance, while existing large model collaborative frameworks have shown promise, they often suffer from functional redundancy among experts and limited robustness in complex cross-domain scenarios. In this paper, we propose Tri-gate Routing for Inference via Decoupled Efficient Network Technologies (TRIDENT), a highly efficient and robust heterogeneous collaborative inference framework. TRIDENT leverages the complementary inductive biases of MLP (for statistical patterns) and KAN (for symbolic logic) to maximize reasoning potential with minimal parametric overhead. To address feature homogenization in traditional distillation, we introduce Orthogonal Feature Decoupling Distillation, utilizing an orthogonality loss ${\mathcal{L}}_{orth}$ for functional decoupling and a reconstruction loss ${\mathcal{L}}_{recon}$ to anchor decoupled features to the PLM knowledge manifold. During inference, a Dual-Threshold Arbiter effectively detects expert hallucinations by integrating individual confidence ${\tau }_{con}$ and heterogeneous consistency ${\tau }_{agree}$. Extensive experiments on CIFAR-100-LT, XNLI, and GSM8K demonstrate that TRIDENT significantly reduces the Invocation Rate (IR) of PLMs while maintaining high accuracy. Our findings reveal a distinct Pareto optimal balance and validate the spontaneous division of labor between heterogeneous experts. By transcending the limitations of single-architecture systems, TRIDENT provides a robust and interpretable pathway for efficient collaborative intelligence. Full article

In natural agricultural environments, plant disease monitoring faces significant challenges, including a highly uneven (long-tail) distribution of disease species, tiny scales of early-stage lesions, and complex, variable backgrounds. These factors hinder the ability of existing lightweight models to balance detection accuracy and computational efficiency. To address these issues, this paper proposes a detection scheme driven by the synergy of data distribution reshaping and model architecture optimization. At the data level, we propose the CALM-Aug augmentation strategy. Based on the statistical distribution characteristics of disease categories, this strategy utilizes object-level copy-paste logic to specifically compensate for the feature shortcomings of rare disease samples. It introduces a teacher-guided screening mechanism and employs accept–reject sampling to ensure the pathological consistency of the augmented samples, thereby alleviating the model’s inductive bias toward head categories. At the model architecture level, using YOLOv11 as the baseline, the YOLO11-ARL model adapted to agricultural scenarios is constructed. It enhances sensitivity to early point-like disease spots through Efficient Multi-Scale Convolutional Pyramids and lightweight decoupled detection heads. Furthermore, a Layer-wise Adaptive Feature-guided Distillation Pruning (LAFDP) algorithm is utilized to extract a lightweight version, YOLO11-ARL-PD, achieving a significant reduction in parameters and computational cost. Experimental results on the PlantDoc dataset show that the final model achieves a precision of 89.0% and an mAP@0.5 of 85.3%. Compared to the baseline model YOLOv11n, YOLO11-ARL-PD improves precision and average precision by 7.7 and 2.6 percentage points, respectively, while reducing parameters by 51.93% and weights by 46.15%. Cross-dataset tests prove the good generalization performance of the proposed method. This study indicates that, under lightweight constraints, jointly optimizing the training distribution and model architecture is an effective way to improve plant disease monitoring and to support the edge deployment of smart crop-protection systems. All resources for CALM-Aug are available at wyz-2004/CALM-Aug on GitHub. Full article