Search Results (7,350)

Smart city initiatives increasingly rely on interoperable and high-quality urban data, yet many cities lack systematic methods for prioritizing which datasets should be developed first. This study proposes an evidence-based framework for smart city data prioritization that integrates data need, data availability, and policy urgency into a unified decision-support model. Using standardized data elements across seven nationally defined smart city domains, the framework was applied to thirty-six certified smart cities in Thailand. Data were collected from municipal authorities and national platforms and structured using ISO-based data element and metadata principles. For each data element, a Need Priority Index, Coverage score, and Policy Readiness indicator were computed to assess governance-relevant data readiness. The results reveal a persistent imbalance between high data demand and low data availability across all domains, with Smart Mobility, Smart Living, Smart Energy, and Smart Economy showing the highest urgency. A Core Common Data Set representing 6.7% of assessed properties was identified, centered on population data, geospatial infrastructure, and plans and performance indicators. The framework provides a scalable approach for guiding investments in interoperable smart city data systems. Full article

Uncertainty in optimization models often causes awkward properties in their deterministic equivalent formulations (DEFs), even for simple linear models. Chance-constrained programming is a reasonable tool for handling optimization problems with random parameters in objective functions and constraints, but it assumes that the distribution of these random parameters is known, and its DEF is often associated with the complicated computation of multiple integrals, hence impeding its extensive applications. In this paper, for optimization models with chance constraints, the historical data of random model parameters are first exploited to construct an adaptive approximate density function by incorporating piecewise linear interpolation into the well-known histogram method, so as to remove the assumption of a known distribution. Then, in view of this estimation, a novel confidence set only involving finitely many variables is constructed to depict all the potential distributions for the random parameters, and a computable reformulation of data-driven distributionally robust chance constraints is proposed. By virtue of such a confidence set, it is proven that the deterministic equivalent constraints are reformulated as several ordinary constraints in line with the principles of the distributionally robust optimization approach, without the need to solve complicated semi-definite programming problems, compute multiple integrals, or solve additional auxiliary optimization problems, as done in existing works. The proposed method is further validated by the solution of the stochastic multiperiod capacitated lot-sizing problem, and the numerical results demonstrate that: (1) The proposed method can significantly reduce the computational time needed to find a robust optimal production strategy compared with similar ones in the literature; (2) The optimal production strategy provided by our method can maintain moderate conservatism, i.e., it has the ability to achieve a better trade-off between cost-effectiveness and robustness than existing methods. Full article

This review critically examines the implementation of Universal Design for Learning (UDL) in science education, providing an integrative overview of research, methodologies, and disciplinary applications. The first section explores UDL across educational stages—from early childhood to higher education—highlighting how age-specific adaptations, such as play-based and outdoor learning in early years or language- and problem-focused strategies in secondary education, enhance engagement and equity. The second section analyses science-specific pedagogies, including inquiry-based science education, the 5E model (Engage, Explore, Explain, Elaborate, Evaluate), STEM/STEAM approaches, and gamification, demonstrating how their alignment with UDL principles fosters motivation, creativity, and metacognitive development. The third section addresses the application of UDL across scientific disciplines—biology, physics, chemistry, geosciences, environmental education, and the Nature of Science—illustrating discipline-oriented adaptations and inclusive practices. Finally, a section on multiple scenarios of diversity synthesizes UDL responses to physical, sensory, and learning difficulties, neurodivergence, giftedness, and socio-emotional barriers. The review concludes by calling for enhanced teacher preparation and providing key ideas for professionals who want to implement UDL in science contexts. Full article

This study evaluated two formulations of L-carnitine, which were developed and impregnated in an oil-based self-emulsifying system (SEDDS), the first with tyrphostin AG17 and the second without the addition of tyrphostin AG17. The formulation with tyrphostin AG17 showed the presence of stable microvesicles up to 498 h after its preparation. To establish a robust safety profile in compliance with modern regulatory frameworks and the 3Rs principle (replacement, reduction, and refinement), a toxicological evaluation was conducted integrating an in silico quantitative structure–activity relationship (QSAR) analysis with confirmatory in vivo subchronic toxicity studies. The QSAR analysis, performed using the OECD QSAR Toolbox and strictly adhering to Organization for Economic Co-operation and Development (OECD) validation principles, predicted an acute oral LD50 of 91.5 mg/kg in rats, a value showing high concordance with the historical experimental data (87 mg/kg). Furthermore, computational modeling for repeated-dose toxicity yielded a no-observed-adverse-effect level (NOAEL) of 80.0 mg/kg bw/day, a no-observed-effect level (NOEL) of 60.4 mg/kg bw/day, and an ADI = 56 mg/day. These computational findings were substantiated by a 90-day subchronic toxicity study in male Wistar rats, where daily intragastric administration of tyrphostin AG17 at doses up to 1.75 mg/kg resulted in not statistically significant hematotoxic activity (p < 0.05), with a maximum cumulative dose over 90 days of 157.5 mg/kg. Collectively, these data indicate that tyrphostin AG17 combines high stabilizing efficacy with a manageable safety profile, supporting its proposed regulatory status as a functional food additive. Based on these results, it is concluded that tyrphostin AG17 shows promising characteristics for use as a stabilizer in food and other substances. Full article

Green apples exhibit a coloration that closely matches their surrounding environment, leading to low recognition accuracy for existing artificial intelligence models. This paper presents a green apple recognition algorithm that integrates an improved U-shaped network (U-Net) and you only look once network (YOLO) v8 to address this challenge. First, the U-Net is enhanced via Dilated Convolution, Attention Gates, and Residual Connections to blur the background, thereby emphasizing the green apple target. Second, convolutional transformations and an attention mechanism are incorporated into YOLO v8, enabling it to focus more effectively on green apple targets within similarly colored backgrounds. Finally, the improved YOLO v8 is employed to recognize green apple targets segmented by the U-Net, with its performance compared against existing models. Research results show that the proposed algorithm achieves a precision of 92.5% and a Recall of 96.8% in green apple recognition, representing a significant improvement over classical models. To mitigate omission issues and further enhance overall performance, an improved YOLO v8 module is connected in parallel with the primary model. Based on its underlying principles, this approach is also applicable to other green fruits with colors and textures highly similar to their backgrounds, demonstrating strong robustness and generalization capabilities. Full article

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes. Furthermore, we formalize the biological governance mechanisms, demonstrating that, unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency, energy budgets, and safety-case hooks that support certification and adoption across autonomous domains. Full article

Taking climate funds (e.g., the Green Climate Fund) as the main financial mechanism for providing funding to developing countries, this paper examines a long-term climate funding relationship between two parties—the rich country and the poor country. Conflicts between the rich and poor countries arise when determining (1) the size of climate funding that the rich country contributes to the poor country and (2) the funding allocation between climate adaptation and mitigation projects in the poor country. In addition, the rich country cannot be forced to commit contractual contributions to the poor country, and in each period, there is a probability that the countries can renegotiate the contract. This paper derives two main dynamic comparative–static results: (1) climate funds converge to the first-best in the long run, both in the size of climate funding in adaptation and mitigation projects, if and only if climate damage becomes sufficiently severe; (2) fewer renegotiations between the rich and poor countries make climate funding contracts more efficient, remedying inequality between the poor and rich countries. These results highlight how increasing climate damages and reducing the frequency of renegotiation can push climate funds closer to a first-best allocation, suggesting design principles for climate funding mechanisms like the Green Climate Fund. Full article

Conventional quantum mechanics treats the electron as a point-like particle endowed with intrinsic properties—mass, charge, and spin—that are inserted as axioms rather than derived from first principles. Here, we propose a thermodynamic reformulation of the electron grounded in entropy field dynamics, based on S-Theory. In this framework, the electron is composed of three distinct entropic components: S_core (a collapsed entropy core from configurational mass), S_EM (a structured electromagnetic entropy field from charge), and S_thermal (a diffuse entropy component from ambient interactions). We show that spin emerges as a rotating S_EM shell around S_core, and that electron collapse—as in quantum measurement—can be modeled as a Recursive Amplification of Sfield (RAS) process driven by entropic feedback. Through mathematical formulation and high-resolution simulations, we demonstrate how the S-field components evolve under entropic excitation, culminating in a collapse threshold defined by local entropy density matching. This model not only explains the emergence of quantum properties but also offers a thermodynamic mechanism for electron–photon interaction, wavefunction collapse, and spin generation, revealing the inner structure and dynamics of one of nature’s most fundamental particles. Full article

Artificial intelligence (AI) is now embedded across many aspects of healthcare, yet most implementations remain fragmented, task-specific, and layered onto legacy workflows. This paper does not review AI applications in healthcare per se; instead, it examines what an AI-first healthcare system would look like, one in which AI functions as a foundational organizing principle of care delivery rather than an adjunct technology. We synthesize evidence across ambulatory, inpatient, diagnostic, post-acute, and population health settings to assess where AI capabilities are sufficiently mature to support system-level integration and where critical gaps remain. Across domains, the literature demonstrates strong performance for narrowly defined tasks such as imaging interpretation, documentation support, predictive surveillance, and remote monitoring. However, evidence for longitudinal orchestration, cross-setting integration, and sustained impact on outcomes, costs, and equity remains limited. Key barriers include data fragmentation, workflow misalignment, algorithmic bias, insufficient governance, and lack of prospective, multi-site evaluations. We argue that advancing toward AI-first healthcare requires shifting evaluation from accuracy-centric metrics to system-level outcomes, emphasizing human-enabled AI, interoperability, continuous learning, and equity-aware design. Using hypertension management and patient journey exemplars, we illustrate how AI-first systems can enable proactive risk stratification, coordinated intervention, and continuous support across the care continuum. We further outline architectural and governance requirements, including cloud-enabled infrastructure, interoperability, operational machine learning practices, and accountability frameworks—necessary to operationalize AI-first care safely and at scale, subject to prospective validation, regulatory oversight, and post-deployment surveillance. This review contributes a system-level framework for understanding AI-first healthcare, identifies priority research and implementation gaps, and offers practical considerations for clinicians, health systems, researchers, and policymakers. By reframing AI as infrastructure rather than isolated tools, the AI-first approach provides a pathway toward more proactive, coordinated, and equitable healthcare delivery while preserving the central role of human judgment and trust. Full article

During batch soldering, assembly of micro image sensor modules, initial random pose, and feature partially occlude target micro-component image, leading to issues of missed and erroneous detection, and low 3D spatial positioning accuracy due to cross-depth-of-field detection errors in microscopic vision. This paper proposes Small object-YOLO11-Cross-Depth-of-field Positioning (SO-YOLO11-CDP), an instance segmentation-based approach for precision cross-depth-of-field positioning micro-component. First, an improved Small object-YOLO11 (SO-YOLO11) image segmentation algorithm is designed. By incorporating a coordinate attention mechanism (CA) into segmentation head to enhance localization of micro-targets, the backbone uses non-stride convolution to preserve fine-grained feature, while target regression performance is boosted via Efficient-IoU (EIoU) loss combined with normalized Wasserstein distance (NWD). Subsequently, to further improve spatial position detection accuracy in cross-depth-of-field detection, a calibration error compensation model for image Jacobian matrix is established based on pinhole imaging principles. Experimental results indicate that SO-YOLO11 achieves 16.1% increase in precision, 4.0% increase in recall, and 9.9% increase in mean average precision (mAP0.5) over baseline YOLO11. Furthermore, it accomplishes spatial detection accuracy superior to 6.5 μm for target micro-components. The method presented in this paper holds significant engineering application value for high-precision spatial position detection of micro image sensor components. Full article

Reliability of medium-voltage (MV) cable systems, for distribution to industrial and renewable plants, is becoming an issue for various reasons, among which are increased global aging, unconventional voltage waveforms, and insufficient commissioning tests. The major component undergoing premature failures is splices, and most of those failures can be associated with flaws in installation, commissioning and, in general, workmanship. One of the topics of an ongoing Department of Energy (DOE) Advanced Research Projects Agency-Energy (ARPA-E) project, GOPHURRS, is, indeed, to increase splice reliability through simpler design and installation procedures, which can minimize assembly and aging risks. This paper deals with design and testing techniques, which can allow scaling up to MV, a type of splice design and assembly that has been successful in low-voltage (LV) applications. A new design paradigm, the three-leg approach, is applied for the first time to LV splices to evaluate their operation likelihood and reliability up to 30 kV nominal voltage, allowing intrinsic life to reach the specified target (e.g., 30 years at a failure probability of 5%) and preventing extrinsic aging, namely, partial discharge occurrence. Design principles and validation, including accelerated aging and forensic observations, are presented and discussed. Full article

This study investigates the sustainability, resilience, and institutional performance of urban governance systems by operationalizing key thermodynamic principles energy, exergy, entropy, equilibrium, open systems, and irreversibility within a political and behavioral systems framework. Urban political systems are conceptualized as open, non-equilibrium systems, characterized by continuous flows of resources, information, and institutional feedback across metropolitan governance structures. Within this model, energy represents systemic inputs to urban governance, exergy denotes usable governing capacity at the city and metropolitan scale, and entropy reflects levels of institutional disorder, inefficiency, and systemic degradation affecting urban sustainability. The study first formulates a conceptual analytical model defining urban political entropy and systemic exergy as measurable variables associated with institutional stability, crisis-management capability, adaptability, and reform potential in urban and metropolitan governance. It then conducts a comparative empirical analysis of Germany, Türkiye, China, and South Africa using normalized indicators derived from international datasets for 2023, with particular attention to their implications for urban governance capacity and city-level institutional performance. These indicators are employed to construct proxy measures for the Exergy Efficiency Ratio, Societal and Institutional Entropy, and overall urban governance capacity. The comparative results reveal that open and decentralized governance systems tend to maintain higher exergy efficiency and lower entropy levels at the urban scale, whereas highly centralized systems, although effective in resource mobilization, tend to accumulate greater systemic entropy over time. Transitional governance systems exhibit hybrid and fluctuating thermodynamic characteristics in their urban institutional structures. The findings empirically support the Thermodynamic Model of Political Systems and demonstrate its utility as a predictive and diagnostic framework for evaluating urban institutional efficiency, resilience, and sustainability. By quantifying political energy flows and entropy dynamics within urban governance systems, this study contributes to the development of integrated systems thermodynamics of cities and provides a robust analytical foundation for sustainable urban governance, institutional reform, and long-term strategic policy design Full article

This article presents an integrated framework for robust control and cybersecurity of an industrial robot, combining Quantitative Feedback Theory (QFT), digital twin (DT) technology, and a programmable logic controller–based architecture aligned with the requirements of the NIS2 Directive. The study considers a five-axis industrial manipulator modeled as a set of decoupled linear single-input single-output systems subject to parametric uncertainty and external disturbances. For position control of each axis, closed-loop robust systems with QFT-based controllers and prefilters are designed, and the dynamic behavior of the system is evaluated using predefined key performance indicators (KPIs), including tracking errors in joint space and tool space, maximum error, root-mean-square error, and three-dimensional positional deviation. The proposed architecture executes robust control algorithms in the MATLAB/Simulink environment, while a programmable logic controller provides deterministic communication, time synchronization, and secure data exchange. The synchronized digital twin, implemented in the FANUC ROBOGUIDE environment, reproduces the robot’s kinematics and dynamics in real time, enabling realistic hardware-in-the-loop validation with a real programmable logic controller. This work represents one of the first architectures that simultaneously integrates robust control, real programmable logic controller-based execution, a synchronized digital twin, and NIS2-oriented mechanisms for observability and traceability. The conducted simulation and digital twin-based experimental studies under nominal and worst-case dynamic models, as well as scenarios with externally applied single-axis disturbances, demonstrate that the system maintains robustness and tracking accuracy within the prescribed performance criteria. In addition, the study analyzes how the proposed architecture supports the implementation of key NIS2 principles, including command traceability, disturbance resilience, access control, and capabilities for incident analysis and event traceability in robotic manufacturing systems. Full article

Magnesium matrix composites formed by incorporating ceramic particles into a magnesium alloy matrix can effectively leverage the complementary properties of the matrix and reinforcement. This approach significantly enhances the mechanical properties of the material at both room and elevated temperatures, offering a viable solution to the inherent limitations of Mg alloys, such as insufficient absolute strength, stiffness, and poor heat resistance. This article reviews the latest research progress in the field of ceramic particle-reinforced magnesium matrix composites in recent years. First, the current research status of magnesium matrix composites reinforced with different types of ceramic particles is comprehensively summarized. Subsequently, it provides a summary and in-depth analysis of the principles, key technologies, and microstructural characteristics of both mainstream and emerging preparation processes, and discusses their advantages and disadvantages. Finally, the challenges in current research are analyzed, and future cutting-edge directions for developing high-performance ceramic particle-reinforced magnesium matrix composites are discussed. Full article

The scholastic tradition is often criticized for starting from abstract principles or philosophical definitions. In this way, scholastic theology is frequently contrasted with post-conciliar theology, which is developed on the basis of the Paschal event, understood as the hermeneutical criterion for the whole of theology. Karl Rahner accuses Thomas Aquinas of “isolating” the Trinity from other areas of theology, such as soteriology, anthropology, moral theology, and spirituality. He also criticizes Aquinas for separating Christology from the Trinity, arguing that in the Thomistic account of the Incarnation it is not essential that the Son becomes incarnate, since any divine Person could, in principle, have done so. Rahner contends that this doctrine weakens the connection between God’s inner Trinitarian life and the missions. Consequently, our adoption as children of God would no longer be grounded in the Son’s own sonship, and what God reveals of himself in history would not truly express who he is as the Triune God. The purpose of this article is to show that such criticisms do not accurately represent Thomistic teaching and to present the doctrine of our divine filiation within a Trinitarian Christological framework. It will first examine the relationship between the immanent Trinity and the economic Trinity, then present the Thomistic doctrine of the divine missions and processions. Finally, it will address the role of the Holy Spirit and our adoptive divine filiation, all considered from a Thomistic perspective. Alongside the Summa theologiae, particular attention will be paid to Aquinas’s biblical commentaries, where he focuses on the Trinitarian economy and its implications for salvation, drawn directly from his reading of Scripture. Full article