Search Results (10,019)

This paper introduces a Symbiotic Digital Environment Framework (SDEF) that integrates Human Digital Twins (HDTs) and Machine Digital Twins (MDTs) to advance lifecycle circularity across all stages of the CADMID model (i.e., Concept, Assessment, Design, Manufacture, In-Service, and Disposal). Unlike existing frameworks that address either digital twins or sustainability in isolation, SDEF establishes a bidirectional adaptive system where human, machine, and environmental digital entities continuously interact to co-optimize performance, resource efficiency, and well-being. The framework’s novelty lies in unifying human-centric adaptability (via HDTs) with circular economy principles to enable real-time symbiosis between industrial processes and their operators. Predictive analytics, immersive simulation, and continuous feedback loops dynamically adjust production parameters based on operator states and environmental conditions, extending asset lifespan while minimizing waste. Two simulation-based scenarios in VR using synthetic data demonstrate the framework’s capacity to integrate circularity metrics (material throughput, energy efficiency, remanufacturability index) with human-machine interaction variables in virtual manufacturing environments. SDEF bridges Industry 4.0’s automation capabilities and Industry 5.0’s human-centric vision, offering a scalable pathway toward sustainable and resilient industrial ecosystems by closing the loop between physical and digital realms. Full article

Second-order derivative information, including mixed curvature, is central to Newton and trust-region optimization, uncertainty quantification, and simulation-based design. Classical finite differences (FD) remain popular but require delicate step-size tuning and can suffer from cancelation and noise amplification. Complex-step differentiation offers machine-precision gradients without subtractive cancelation, yet many second-derivative complex-step formulas reintroduce differencing. Hyper-dual numbers provide an algebraically principled alternative: by lifting real code to a four-component commutative nilpotent algebra, one obtains exact first and mixed second derivatives from a single evaluation, without finite differencing. This article gives a consolidated theoretical and experimental foundation for hyper-dual numbers. We formalize the algebra, prove exact Taylor truncation at second order, derive coefficient–extraction formulas for gradients and Hessians, and state assumptions for exactness, including limitations at non-smooth points and the need to branch on real parts. We present implementation patterns and language skeletons (C++, Python 3.11.5, MATLAB R2023b), and we provide fair numerical comparisons with FD, complex-step, and AD baselines. Stability tests under additive noise and ill-conditioning, together with runtime and memory profiling, demonstrate that hyper-dual coefficients are robust and reproducible in floating-point arithmetic, particularly for black-box codes where Hessian information is needed but differencing is fragile. Full article

Self-powered microfluidic systems represent a promising direction toward autonomous and portable lab-on-chip technologies, yet conventional electrowetting platforms remain constrained by bulky high-voltage supplies and intricate control circuitry. In this work, we design a triboelectric nanogenerator (TENG)-based microfluidic system that harvests mechanical energy for droplet manipulation without any external electronics. The TENG integrates two triboelectric units with a 25° phase offset, enabling periodic high-voltage generation. Finite element simulations elucidate the electric field distributions of the TENG and microfluidic chip, validating the operating principle of the integrated microfluidic system. Experimental studies further quantify the effects of electrode geometry and rotational speed on the critical drivable droplet volume, demonstrating stable transport over linear, S-shaped, and circular trajectories. Remarkably, the droplet motion direction can be instantaneously reversed by reversing the TENG rotation direction, achieving bidirectional control without auxiliary circuitry. This work establishes a voltage-optimized, structurally tunable, and fully self-powered platform, offering a new paradigm for portable digital microfluidics. Full article

Pavement cracks are a critical indicator for assessing structural health and forecasting deterioration trends. Accurate and automated crack classification is of paramount importance for the intelligent maintenance of road structures. Inspired by the principles of symmetry—which often lead to robust and efficient structures in both nature and engineering—this paper proposes ASPCCNet, a lightweight network that embeds these principles into its core design. The network centers on a novel building block, AugShuffleBlock, which embodies a symmetry-informed design through the integration of Partial Convolution (PConv), a tunable channel splitting mechanism (AugShuffle), and the Channel Prior Convolutional Attention (CPCA). This design achieves efficient feature extraction and fusion with minimal computational overhead. Experimental results on the public RCCD dataset demonstrate that ASPCCNet significantly outperforms mainstream lightweight models, achieving an F1-score of 0.816, which is 6.4% to 10.9% higher than other mainstream models, with only 0.294 M parameters and 48.68 MFLOPs. This work showcases how a symmetry-guided design philosophy can be leveraged to achieve a superior balance between accuracy and efficiency for real-time edge deployment. Full article

The increasing availability of free-text components in electronic medical records (EMRs) offers unprecedented opportunities for machine learning research, enabling improved disease phenotyping, risk prediction, and patient stratification. However, the use of narrative clinical data raises distinct ethical challenges that are not fully addressed by conventional frameworks for structured data. We conducted a narrative review synthesizing conceptual and empirical literature on ethical issues in free-text EMR research, focusing on privacy, fairness, autonomy, interpretability, and governance. We examined technical methods, including de-identification, differential privacy, bias mitigation, and explainable AI, alongside normative approaches, such as participatory design, dynamic consent models, and multi-stakeholder governance. Our analysis highlights persistent risks, including re-identification, algorithmic bias, and inequitable access, as well as limitations in current regulatory guidance across jurisdictions. We propose ethics-by-design principles that integrate ethical reflection into all stages of machine learning research, emphasize relational accountability to patients and stakeholders, and support global harmonization in governance and stewardship. Implementing these principles can enhance transparency, trust, and social value while maintaining scientific rigor. Ethical integration is therefore not optional but essential to ensure that machine learning research using free-text EMRs aligns with both clinical relevance and societal expectations. Full article

Experience shows that knowledge transfer and understanding of fundamental FPGA principles are greatly improved by exercising laboratory practices and manual hands-on operations. Hence, a case study was performed on two didactic platforms for students of intelligent control techniques that were upgraded with FPGAs to be involved in laboratory practices. Among others, platforms allow implementation of traditional linear control algorithms, such as PID, or modern non-linear control algorithms, such as fuzzy logic or artificial neural networks. Initially, the underlying physics can be carefully studied, and the mathematical model can be derived. Then, such a model can be discretized into its digital form, an appropriate controller can be designed, and its performance can be compared to the known benchmark. Controllers and control parameters can be practiced by students themselves, offering underlying potential for improving students’ understanding of the fundamentals of FPGA. Full article

The integration of Artificial Intelligence into educational environments is reshaping the way digital resources support teaching and learning, which reinforces the need to understand how prompting strategies can enhance engagement, autonomy, and personalisation. This study examines the pedagogical role of prompt engineering in the transformation of static digital materials into adaptive and interactive learning experiences aligned with the principles of Education 4.0. A systematic literature review was conducted between 2023 and 2025 following the PRISMA protocol, comprising a sample of 166 studies retrieved from the ACM Digital Library and Scopus databases. The search strategy employed the keywords “artificial intelligence” OR “intelligent tutoring systems” AND “e-learning” OR “digital education” AND “personalised learning” OR “academic performance” OR “student engagement” OR “motivation” OR “ethical issues” OR “student autonomy” OR “limitations of AI”. The analysis identified consistent improvements in academic performance, motivation, and student engagement, although persistent limitations remain related to technical integration, ethical risks, and limited pedagogical alignment. Building on these findings, the article proposes a structured prompt engineering methodology that integrates interdependent components including role definition, audience specification, feedback style, contextual framing, guided reasoning, operational rules, and output format. A practical illustration shows that embedding prompts into digital learning resources, exemplified through PDF-based exercises, enables AI agents to support personalised and adaptive study sessions. The study concludes that systematic prompt design can reposition educational resources as intelligent, transparent, and pedagogically rigorous systems for knowledge construction. Full article

Natural Product Synthesis (NPS) is a cornerstone of organic chemistry, historically rooted in the dual goals of structure elucidation and synthetic strategy development for bioactive compounds. Initially focused on identifying the structures of medicinally relevant natural products, NPS has evolved into a dynamic field with applications in drug discovery, immunotherapy, and smart materials. This evolution has been propelled by advances in reaction design, mechanistic insight, and the integration of green chemistry principles. A particularly promising development in NPS is the use of photochemistry, which harnesses light—a renewable energy source—to drive chemical transformations. Photochemical reactions offer unique excited-state reactivity, enabling synthetic pathways that are often inaccessible through thermal methods. Their precision and sustainability make them ideal for modern synthetic challenges. This review explores a wide range of photochemical reactions, from classical to contemporary, emphasizing their role in total synthesis. By showcasing their potential, the review aims to encourage broader adoption of photochemical strategies in the synthesis of complex natural products, promoting innovation at the intersection of molecular complexity, sustainability, and synthetic efficiency. Full article

Photoacoustic spectroscopy (PAS) is a powerful technique for selective gas detection; however, its performance in non-resonant configurations is fundamentally constrained by the poor low-frequency response of conventional acoustic detectors. Commercial MEMS microphones, although compact and cost effective, exhibit limited infrasound sensitivity, which restricts the development of truly miniaturised and broadband PAS systems. To address this limitation, we present a novel MEMS fluidic microphone (f-mic) that operates on a thermal sensing principle and is explicitly optimised for the infrasound regime. The sensor demonstrates a constant sensitivity of 32 μV/Pa for frequencies below 20 Hz. A detailed analytical model incorporating frequency-dependent effects is developed to identify and investigate the critical design parameters that influence system performance. The overall system exhibits a band-pass frequency response, enabling broadband operation. Based on these insights, a miniaturised photoacoustic cell is fabricated, ensuring efficient optical coupling and f-mic integration. Experimental validation using a CO₂-targeted laser system demonstrates a linear response up to 5000 ppm, a sensitivity of 6 nV/ppm, and a theoretical detection limit of 300 ppb over 100 s, resulting in an NNEA of $6\times {10}^{-6}$ W cm⁻¹ Hz^−0.5. These results establish the f-mic as a robust, scalable solution for non-resonant PAS, effectively overcoming a significant bottleneck in compact gas sensing technologies. Full article

The manual spraying of sealant on train side doors is associated with high costs and significant safety risks. To address this challenge, this study proposes an automated crack localization method for sealant-spraying devices by enhancing the YOLOv5s network, with a specific focus on leveraging principles of symmetry. First, an automated sealant-spraying device is designed for operation and data acquisition. Geometric symmetry is then exploited through Zhang’s camera calibration method to accurately establish the two-dimensional mapping between spatial coordinates and the image plane, a process fundamental to spatial reasoning. The core of our approach lies in introducing structural and computational symmetry into the deep learning model. The original YOLOv5s network is improved by integrating the Selective Context Convolutional module and the Skew Intersection over Union (IoU) Loss function, which streamline computation and boost detection accuracy. Furthermore, we replace the standard C3 module with an improved version that incorporates a Reparameterization Visual Transfer block, enhancing feature representation through structural re-parameterization symmetry between training and inference phases. Validation using data from a coal handling facility demonstrates that the improved YOLOv5s model achieves superior performance in precision, mAP@0.5, and recall compared to the original. The results underscore the critical role of geometric and architectural symmetry in developing robust and efficient vision systems for industrial automation. Full article

Steel surface defect detection is critical for ensuring industrial product quality and safety. Although deep learning-based detectors like the YOLO series have demonstrated considerable promise, they often struggle with three key challenges under computational constraints: the anisotropic morphology (i.e., direction-variant shapes) of defects, insufficient modeling of long-range dependencies, and the confusion between signal and noise in feature representation. To address these issues, this paper proposes PSC-YOLO, an enhanced model based on YOLOv11n. Our core design philosophy leverages symmetry principles to guide feature representation and fusion. First, we introduce Pinwheel-shaped Convolution (PConv), whose set of rotationally symmetric kernels explicitly captures multi-directional features to effectively represent anisotropic defects. Second, a Pyramid Sparse Transformer (PST) module is integrated to capture global context via its efficient cross-scale sparse attention, which reduces computational complexity by dynamically focusing on the most relevant features across different scales, leveraging a symmetrical pyramid architecture for balanced multi-scale fusion, thereby overcoming the bottleneck in long-range dependency modeling. Finally, a Channel-Prior Convolutional Attention (CPCA) mechanism is embedded to perform dynamic feature recalibration, which leverages internal structural symmetry—through symmetric pooling pathways and parallel multi-scale convolutions—to suppress background noise and highlight salient defects. Comprehensive experiments on the public NEU-DET dataset show that PSC-YOLO achieves superior performance, obtaining a mAP@0.5 of 78.3% and a mAP@0.5:0.95 of 48.3%, while maintaining a real-time inference speed of 2.8 ms per image. This demonstrates the model’s strong potential for deployment on industrial production lines, enabling high-precision, real-time quality inspection. Full article

Smart cities rely on digital infrastructures and utilize data-driven frameworks to enhance quality of life, optimizing public services by promoting transparency in urban and heritage management. Based on the ArchTerr project for archeological heritage protection, this study introduces an integrated framework uniting two components: GIS-based land mapping and blockchain-enabled document management. The system supports urban planning, land administration, and governance by combining spatial intelligence with secure data handling. The GIS module enables precise land mapping using geographic coordinates, facilitating spatial analysis, land use monitoring, and infrastructure planning. The document management system employs blockchain storage functionalities to ensure the immutability, transparency, and traceability of records such as land ownership documents, permits, and regulatory filings. Developed using the Design Science Research methodology, the framework translates abstract principles of data immutability and interoperability into a functional architecture that addresses persistent issues of fragmented datasets, insecure records, and limited institutional accountability and improves scalability, efficiency, and transparency in a variety of urban situations. We explored its implications for policy and governance, illustrating how interdisciplinary technology serves as a basis for transparent, accountable, and resilient urban management. This study advances theoretical understanding of how the convergence of spatial and trust-based technologies can foster geo-trusted governance and contribute to more transparent and resilient heritage management. Full article

The Bloom filter remains one of the most influential constructs in probabilistic computation, a structure that achieves a mathematically elegant balance between accuracy, space efficiency, and computational speed. Since the original formulation of Dr. Burton H. Bloom in 1970, its design principles have expanded into a family of approximate membership query (AMQ) structures that now underpin a wide spectrum of modern computational systems. This review synthesizes the theoretical, algorithmic, and applied dimensions of Bloom filters, tracing their evolution from classical bit-vector models to contemporary learned and cryptographically reinforced variants. It further underscores their relevance in artificial intelligence and blockchain environments, where they act as relevance filters. Core developments, which include counting, scalable, stable, and spectral filters, are outlined alongside information-theoretic bounds that formalize their optimality. The analysis extends to adversarial environments, where cryptographic hashing and privacy-oriented adaptations enhance resilience under active attack, and to data-intensive domains such as network systems, databases, cybersecurity, and bioinformatics. Through the integration of historical insight and contemporary advances in learning, security, and system design, the Bloom filter emerges not merely as a data structure but as a unified paradigm for computation under uncertainty. The results presented in this review support practical advances in network traffic control, cybersecurity analysis, distributed storage systems, and large-scale data platforms that depend on compact and fast probabilistic structures. Full article

Automated pollen recognition is a foundational tool for diverse scientific domains, including paleoclimatology, biodiversity monitoring, and agricultural science. However, conventional methods create a critical data bottleneck, limiting the temporal and spatial resolution of ecological analysis. Existing deep learning models often fail to achieve the requisite localization accuracy for microscopic pollen grains, which are characterized by their minute size, indistinct edges, and complex backgrounds. To overcome this, we introduce HieraEdgeNet, a novel object detection framework. The core principle of our architecture is to explicitly extract and hierarchically fuse multi-scale edge information with deep semantic features. This synergistic approach, combined with a computationally efficient large-kernel operator for fine-grained feature refinement, significantly enhances the model’s ability to perceive and precisely delineate object boundaries. On a large-scale dataset comprising 44,471 annotated microscopic images containing 342,706 pollen grains from 120 classes, HieraEdgeNet achieves a mean Average Precision of 0.9501 (mAP@0.5) and 0.8444 (mAP@0.5:0.95), substantially outperforming state-of-the-art models such as YOLOv12n and the Transformer-based RT-DETR family in terms of the accuracy–efficiency trade-off. This work provides a powerful computational tool for generating the high-throughput, high-fidelity data essential for modern ecological research, including tracking phenological shifts, assessing plant biodiversity, and reconstructing paleoenvironments. At the same time, we acknowledge that the current two-dimensional design cannot directly exploit volumetric Z-stack microscopy and that strong domain shifts between training data and real-world deployments may still degrade performance, which we identify as key directions for future work. By also enabling applications in precision agriculture, HieraEdgeNet contributes broadly to advancing ecosystem monitoring and sustainable food security. Full article

High-entropy alloys (HEAs), as a novel class of materials, have attracted widespread attention in the field of materials science due to their unique multi-element high-concentration mixing design. Recent research has found that this alloy mixing strategy not only exhibits excellent performance in structural properties but also shows potential in functional materials. This review summarizes the progress of research on HEAs in the magnetocaloric effect (MCE) area, first introducing the basic principles of MCE and the related concepts of HEAs. It then summarizes the research progress of rare-earth HEAs, non-rare-earth HEAs, and rare-earth-transition metal composite HEAs in MCE. Finally, this review outlines future research directions for HEAs in the MCE field, laying the groundwork for further applications of HEAs in the magnetocaloric field. Full article