Search Results (396)

The effective focal length is a critical determinant of optical performance and imaging quality, serving as a fundamental parameter for components ranging from ophthalmic lenses to precision microlens arrays. With the rapid advancement of complex optical systems in microscopy and smart manufacturing, there is an increasing demand for high-precision measurement techniques that can characterize these parameters with low uncertainty. In this paper, a quadri-wave lateral shearing interferometry (QWLSI) measurement system was developed. A novel precision focal length measurement method of optical lenses based on the principle of QWLSI is presented. A theoretical model for solving the focal length of the measured lens from the curvature radius of the wavefront was derived. We also proposed a novel algorithm and subsequently developed a dedicated hardware platform and a corresponding software package for its real-time implementation. Different sets of repeated measurement experiments were carried out for two convex lenses with symmetrical and asymmetrical structures, a large-scale concave lens, and a microlens array, to verify the measurement uncertainty and robustness of the QWLSI measurement system. The expanded uncertainty was also analyzed and determined as 0.31 mm (k = 1.96, normal distribution). The results show that the proposed QWLSI measuring system possesses good performance in measuring the focal lengths of different kinds of lenses and can be widely used in fields such as advanced optics manufacturing. Full article

Semantic communication frameworks aim to convey the underlying significance of data rather than reproducing it exactly, a perspective that enables substantial efficiency gains in settings constrained by latency or bandwidth. Motivated by this shift, we study the rate–distortion–perception (RDP) trade-off for image compression, a setting in which reconstructions must be not only accurate but also perceptually faithful. Our analysis is carried out through the lens of randomized distributed function computation (RDFC) framework, which provides a principled means of synthesizing randomness and shaping output distributions. Leveraging this framework, we establish finite-blocklength characterizations of the RDP region, quantifying how communication rate, distortion, and perceptual fidelity interact in non-asymptotic regimes. We further broaden this characterization by incorporating two practically relevant extensions: (i) scenarios in which encoder and decoder share side information, and (ii) settings that require strong secrecy guarantees against adversaries, which might include those with quantum capabilities. Moreover, we identify the corresponding asymptotic region under a perfect realism constraint and examine how side information, finite blocklength effects, and secrecy demands influence achievable performance. The resulting insights provide actionable guidance for the development of low-latency, secure, and realism-aware image compression and generative modeling systems. Full article

This study presents an integrated, low-cost system for measuring extremely small displacements in Ionic Polymer–Metal Composite (IPMC) actuators operating in aqueous environments. A custom optical setup was developed, combining a glass tank, a tubular microscope with a 10× achromatic objective, a digital USB camera and uniform LED backlighting, enabling side-view imaging of the actuator with high contrast. The microscopy system achieves a spatial sampling of 0.536 μm/pixel on the horizontal axis and 0.518 μm/pixel on the vertical axis, while lens distortion is limited to a maximum edge deviation of +0.015 μm/pixel (≈+2.8%), ensuring consistent geometric magnification across the field of view. On the image-processing side, a predictive grid-based tracking algorithm is introduced to localize the free tip of the IPMC. The method combines edge detection, Harris corners and a constant-length geometric constraint with an adaptive search over selected grid cells. On 1920 × 1080-pixel frames, the proposed algorithm achieves a mean processing time of about 10 ms per frame and a frame-level detection accuracy of approximately 99% (98.3–99.4% depending on the allowed search radius) for actuation frequencies below 2 Hz, enabling real-time monitoring at 30 fps. In parallel, dedicated electronic circuitry for supply and load monitoring provides overvoltage, undervoltage, open-circuit and short-circuit detection in 100 injected fault events, all faults were detected and no spurious triggers over 3 h of nominal operation. The proposed microscopy and tracking framework offer a compact, reproducible and high-resolution alternative to laser-based or Digital Image Correlation techniques for IPMC displacement characterization and can be extended to other micro-displacement sensing applications in submerged or challenging environments. Full article

Seed phenomics is a critical research field for understanding seed germination mechanisms. Metasurfaces, composed of subwavelength nanostructures, offer a promising pathway to achieve both dispersion control and imaging functionalities within an ultra-compact form factor. Recent advances in micro–nano-optics and computational imaging have opened new avenues for high-dimensional, multimodal imaging. However, conventional hyperspectral and light-field systems still face limitations in compactness, depth resolution, and spectral–spatial integration. This review summarizes recent progress in metalens and metasurface lens array-based light-field systems for hyperspectral imaging and 3D reconstruction, with a focus on the underlying principles, design strategies, and reconstruction algorithms that enable single-shot 3D hyperspectral acquisition. We further present a forward-looking roadmap toward the realization of a revolutionized imaging paradigm: a metasurface-based light-field platform that fully integrates 3D and hyperspectral imaging capabilities. In particular, we examine how dispersive metasurfaces serve as core optical elements for precise dispersion control in hyperspectral imaging systems, while metalens arrays enable accurate modulation of spatial–angular distributions in light-field configurations. We systematically review both 3D and spectral reconstruction algorithms, highlighting their roles in decoding complex optical encodings. The application of these integrated systems in seed phenotyping is emphasized, demonstrating their capability to capture 3D spatial–spectral distributions in a single exposure. This approach facilitates high-throughput analysis of morphological traits, germination potential, and internal biochemical composition, offering a comprehensive solution for advanced seed characterization. Finally, we outline a practical roadmap for implementing a metasurface-based light-field platform that integrates hyperspectral imaging and computational 3D reconstruction. This review offers a comprehensive overview of the state of the art in compact 3D light-field systems and multimodal hyperspectral imaging platforms, while providing forward-looking insights aimed at advancing smart seed phenotyping, precision agriculture, and next-generation optical imaging technologies. Full article

This study examines global econo-environmental capability for 118 countries over 1995 to 2024 using a five-lens framework covering productive capacity (PC), developmental momentum (DM), resource efficiency (RE), degradation and depletion ratio (DDR), and remaining development potential (RDP). Using pooled k-means, a stable four archetype typology is identified and shown to persist over time. The analysis assesses how archetypes characterize country–year outcomes (RQ1), whether cross-sectional fairness is changing and relates to frontier slowdown (RQ2), and how archetypes, distance, and regional context shape transition probabilities and club convergence (RQ3). Inequality in five-dimensional capability declines slightly over the period (Gini from 0.109 to 0.092 and Palma from 1.563 to 1.464), implying modest convergence rather than increasing polarization. Average capability also improves, with larger gains for initially distant countries and smaller gains near the frontier, which is consistent with mild club convergence. Regionally, high capability cases are concentrated in Western Europe and North America, while sustained upgrading is observed in parts of Eastern Europe, mixed stability is observed in East and Central Asia, and selective advances are observed in ASEAN. Policy implications should be based on a country’s archetype and its distance to the capability ideal. Lagging countries should prioritize diffusion of proven high efficiency options and basic capability building, while frontier countries should priorities innovation, structural change, and deeper decarbonization. Policy emphasis should be updated as countries move within the capability space over time. Full article

Fractal geometry offers a mathematical framework to quantify the complexity of brain structure and function. The fractal dimension (FD) captures self-similarity and irregularity across spatial and temporal scales, surpassing the limits of traditional Euclidean metrics. In neuroscience, FD serves as a key descriptor of the brain’s hierarchical organization—from dendritic arborization and cortical folding to neural dynamics measured by diverse neuroimaging techniques. This review summarizes theoretical foundations and methodological advances in FD estimation, including the box-counting approach for imaging, and Higuchi’s and Katz’s algorithms for electrophysiological data, addressing reliability and reproducibility issues. In addition, we illustrate how fractal analysis characterizes brain complexity in health and disease. Clinical applications include detecting white matter alterations in multiple sclerosis, atypical maturation in intrauterine growth restriction, reduced cortical complexity in Alzheimer’s disease, and altered neuroimaging patterns in schizophrenia. Emerging evidence highlights FD’s potential for distinguishing consciousness states and quantifying neural integration and differentiation. Bridging mathematics, physics, and neuroscience, fractal analysis provides a quantitative lens on the brain’s multiscale organization and pathological deviations. FD thus stands as both a theoretical descriptor and a translational biomarker whose standardization could advance precision diagnostics and understanding of neural dynamics. Full article

Within the context of China’s green economy aimed at sustainable development, research on the linkage between water resources and industry has garnered considerable attention in the academic community. However, the impact of total body water (TBW) transfer and allocation embodied in the labor force—the primary economic actors—has not been addressed in the economic sector. On methodology, the “EEIO-SDA-SPD-II” (ISSI) model employed in this study encompasses measurements methods, such as an environmentally extended input–output model (EEIO), structural decomposition analysis (SDA), structural path decomposition (SPD), and the imbalance index (II), to explore the crucial paths, driving factors, and distribution of water transfer in TWB spanning 15 Chinese industries between 2007 and 2022. The findings indicate that the shifts in TBW in the manufacturing sector are more discernible when viewed through the lens of social driving factors. The construction business exhibits the most significant increase in male total body water (MTBW), whereas the education sector reflects the rapid growth in female total body water (FTBW). Pertaining to final demand, domestic consumption constitutes the primary contributor category to the increase in TWB, followed by fixed capital formation and exports. According to the SPD results, the construction sector exerts the greatest influence on the transfer of MTBW, while the education sector is characterized by the highest path coefficient value for FTBW. In contrast, the manufacturing sector shows the most pronounced initial path. Based on the imbalance index analysis, agriculture derives the greatest economic gains from TBW input, whereas the education sector yields the lowest. Full article

Knowledge Management (KM) has evolved over the last five decades as a complex socio-technical system shaped by interactions between organizational processes, technologies and social actors. This study maps the systemic evolution of KM research between 1975 and 2025 by examining its intellectual, conceptual and social subsystems. Using a large-scale bibliometric science-mapping approach that combines performance indicators and network-based techniques, we analyze 33,153 documents indexed in the Web of Science Core Collection. The results reveal a pronounced post-1995 expansion of the field, marked by a consolidated core of specialized journals and influential scholars, alongside an increasingly global research network. The analysis shows that KM research is structured around four interdependent dimensions: organizational processes, strategic orientations, technological infrastructures and socio-cultural factors. More recent developments indicate the emergence of new system trajectories associated with digital transformation, knowledge governance and sustainability. Viewed through a system-level lens, these findings position KM as an evolving research system characterized by adaptive and cross-domain dynamics. Full article

Educational chatbots are increasingly deployed to scaffold student learning, yet educators lack scalable ways to assess the cognitive depth of these dialogues in situ. Bloom’s taxonomy provides a principled lens for characterizing reasoning, but manual tagging of conversational turns is costly and difficult to scale for learning analytics. We present a reproducible high-confidence pseudo-labeling pipeline for multi-label Bloom classification of Socratic student–chatbot exchanges. The dataset comprises 6716 utterances collected from conversations between a Socratic chatbot and 34 undergraduate statistics students at Nanyang Technological University. From three chronologically selected workbooks with expert Bloom annotations, we trained and compared two labeling tracks: (i) a calibrated classical approach using SentenceTransformer (all-MiniLM-L6-v2) embeddings with one-vs-rest Logistic Regression, Linear SVM, XGBoost, and MLP, followed by per-class precision–recall threshold tuning; and (ii) a lightweight LLM track using GPT-4o-mini after supervised fine-tuning. Class-specific thresholds tuned on 5-fold cross-validation were then applied in a single pass to assign high-confidence pseudo-labels to the remaining unlabeled exchanges, avoiding feedback-loop confirmation bias. Fine-tuned GPT-4o-mini achieved the highest prevalence-weighted performance (micro-F1 $=0.814$), whereas calibrated classical models yielded stronger balance across Bloom levels (best macro-F1 $=0.630$ with Linear SVM; best classical micro-F1 $=0.759$ with Logistic Regression). Both model families reflect the corpus skew toward lower-order cognition, with LLMs excelling on common patterns and linear models better preserving rarer higher-order labels, while results should be interpreted as a proof-of-concept given limited gold labeling, the approach substantially reduces annotation burden and provides a practical pathway for Bloom-aware learning analytics and future real-time adaptive chatbot support. Full article

Background: Fontan-associated liver disease (FALD) is a progressive condition resulting from chronic hepatic venous congestion following the Fontan procedure for univentricular heart defects. As survival improves in these patients, recognition and management of FALD have become increasingly important. Objective: To describe the pathophysiological mechanisms, imaging findings, and diagnostic approach to FALD, with a focus on the role of ultrasonography, including contrast-enhanced ultrasound (CEUS). Methods: This narrative review explores the evolution of FALD through a multidisciplinary lens, integrating cardiovascular and hepatic imaging data. Particular attention is paid to Doppler ultrasound and CEUS, both in early parenchymal changes and in the differential diagnosis of potential complications such as hepatic nodules. Results: FALD is characterized by progressive fibrosis due to long-standing passive congestion, resulting in a wide spectrum of imaging findings. B-mode ultrasound reveals hepatomegaly, heterogeneous parenchyma, and gallbladder wall thickening. Doppler studies show altered hepatic venous flow patterns, while CEUS provides dynamic vascular evaluation, highlighting areas of altered perfusion. In advanced stages, hypo-vascular areas in the late phase may simulate malignant lesions, emphasizing the need for careful interpretation. The role of liver biopsy, though limited by invasiveness, remains crucial in selected cases. Surveillance strategies are not standardized but require close multidisciplinary follow-up. Conclusions: FALD presents complex diagnostic challenges requiring integrated imaging and clinical assessment. CEUS emerges as a valuable, non-invasive tool in characterizing hepatic congestion and guiding management. Increased awareness and standardized protocols are essential for early detection and tailored care in this growing patient population. Full article

Despite the recognition of Blockchain Technology’s disruptive potential, there is ongoing debate about its ontological and axiomatic foundations. This study develops a theoretical framework to explain the underline structural principles of blockchain technology through the lens of Arthur’s theory of technology, and the framework is developed through adopting Narrative Literature Review. By integrating conceptual analysis with a structural examination of Ethereum, this study reveals that blockchain technology is not a single invention but a composite technological system developed through recursive interactions among sub-technologies. The proposed framework identifies three interrelated structural patterns—the Combinatorial Pattern of Components elucidating blockchain technology’s structural ontology, the Capturing Pattern of Algorithms revealing the operational source of its innovation, and the Recursive Pattern of Technologies characterizing its inner logical structure of components—that together explain blockchain technology’s generative and evolving nature. The study extends Arthur’s theory by clarifying the “technology within technology” dynamic that underlies blockchain technology innovation. The Ethereum case confirms the framework’s applicability and generalizability, showing that blockchain systems, despite their diversity, share a consistent structural logic. Beyond its theoretical contribution, the framework offers practical guidance for sustainable technological innovation. It provides analytical support for designing blockchain-based applications’ architectures that enhance transparency, efficiency, and adaptability, contributing to the sustainable evolution of digital technologies. Full article

Saltwater intrusion increasingly jeopardizes groundwater in low-lying coastal plains worldwide, where the combined effects of sea-level rise, land subsidence, and hydraulic regulation further exacerbate aquifer vulnerability and threaten the long-term sustainability of freshwater supplies. To move beyond sparse and fragmented piezometric observations, we propose “integrated coastal supersites”: wells equipped with multiparametric sensors and multilevel piezometers that couple high-resolution vertical conductivity–temperature–depth (CTD) profiling with continuous hydro-meteorological time series to monitor the hydrodynamic behavior of coastal aquifers and saltwater intrusion. This study describes the installation of two supersites and presents early insights from the first monitoring period, which, despite a short observation window limited to the summer season (July–September 2025), demonstrate the effectiveness of this approach. Two contrasting supersites were deployed in the coastal plain between the Brenta and Adige Rivers (Italy): Gorzone, characterized by a thick, laterally persistent aquitard, and Buoro, where the aquitard is thinner and discontinuous. Profiles and fixed sensors at both sites reveal a consistent fresh-to-saline transition in the phreatic aquifers and a secondary freshwater lens capping the confined systems. At Gorzone, the confining layer hydraulically isolates the deeper aquifer, preserving low salinity beneath a saline, tidally constrained phreatic zone. Groundwater heads oscillate by about 0.2 m, and rainfall events do not dilute salinity; instead, pressure transients—amplified by drainage regulation and inland-propagating tides—induce short-lived EC increases via upconing. Buoro shows smaller water-level variations, not always linked to rainfall, and, in contrast, exhibits partial vertical connectivity and faster dynamics: phreatic heads respond chiefly to internal drainage and local recharge, with rises rapidly damped by pumping, while salinity remains steady without episodic peaks. The confined aquifer shows buffered, delayed responses to surface forcings. Although the monitoring window is currently limited to 2025 through the summer season, these results offer compelling evidence that coastal supersites are reliable, scalable, and management-critical relevance platforms for groundwater calibration, forecasting, and long-term assessment. Full article

In coastal regions, the interaction between freshwater and seawater creates a dynamic system in which the spatial distribution of salinity critically constrains the use of freshwater for human consumption. Although saline intrusion is a globally widespread phenomenon, its inland extent varies significantly with hydrological conditions, posing a persistent threat to groundwater quality and sustainability. This study aimed to characterize salinity distribution using an integrated karst-watershed approach, thereby enabling the identification of both lateral and vertical salinity gradients. The study area is in the northwestern Yucatan Peninsula. Available hydrogeological data were analyzed to determine aquifer type, soil texture, evidence of saline intrusion, seawater fraction, vadose zone thickness, and field measurements. These included sampling from 42 groundwater sites (open sinkholes and dug wells), which indicated a fringe zone approximately 5 km in size influenced by seawater interaction, in mangrove areas and in three key zones of salinity patterns: west of Mérida (Celestun and Chunchumil), and northern Yucatan (Sierra Papacal, Motul, San Felipe). Vertical Electrical Sounding (VES) and conductivity profiling in two piezometers indicated an apparent seawater influence. The interface was detected at a depth of 28 m in Celestun and 18 m in Chunchumil. These depths may serve as hydrogeological thresholds for freshwater abstraction. Results indicate that saltwater can extend several kilometers inland, a factor to consider when evaluating freshwater availability. This issue is particularly critical within the first 20 km from the coastline, where increasing tourism exerts substantial pressure on groundwater reserves. A coastal-to-inland salinity was identified, and an empirical equation was proposed to estimate the seawater fraction (fsea%) as a function of distance from the shoreline in the Cenote Ring trajectory. Vertically, a four-layer model was identified in this study through VES in the western watershed: an unsaturated zone approximately 2.6 m thick, a confined layer in the coastal Celestun profile about 9 m thick, a freshwater lens floating above a brackish layer between 8 and 25 m, and a saline interface at 37 m depth. The novelty of this study, in analyzing all karstic water surfaces together as a system, including the vadose zone and the aquifer, and considering the interactions with the surface, is highlighted by the strength of this approach. This analysis provides a better understanding and more precise insight into the integrated system than analyzing each component separately. These findings have significant implications for water resource management in karst regions such as Yucatan, underscoring the urgent need for sustainable groundwater management practices to address seawater intrusion. Full article

This study examines Apostle Paul’s missionary work through the lens of modern behavioral and organizational scholarship. Paul was highly intentional in both his words and actions. What would we learn if we used the conceptual frameworks from the social sciences to analyze his actions and decisions? In this study, we use entrepreneurial and identity theories to re-examine Paul’s life. Paul was an extraordinary entrepreneur who possessed all the core features of an entrepreneur, but with a distinct motivation and mindset grounded in his stewardship identity. We introduce the term “entrepreneurial steward” to characterize his unique identity, which combines the two paradoxical qualities of entrepreneurship and stewardship. Full article

Background: Diabetes mellitus is a multifactorial disease characterized by complex metabolic dysfunctions and chronic complications induced by hyperglycaemia. The design of multitarget ligands, capable of simultaneously controlling different pathogenic processes, was proposed as a promising approach to identify novel antidiabetic drugs endowed with improved efficacy. Methods: (5-Arylidene-4-oxothiazolidin-3-yl)alkanoic acid derivatives 1a–g and 2a–g were synthesized as potential multitarget antidiabetic agents. They were tested in vitro as inhibitors of both human recombinant AKR1B1 and PTP1B, and kinetic studies and molecular docking simulations for both enzymes were performed. Their effects on cellular glucose uptake, insulin signalling, and mitochondrial potential were assayed in cultures of murine C2C12 myocytes. A lipid accumulation assay was performed in HepG2 liver cells. The effects on high glucose-induced sorbitol accumulation were evaluated in lens HLE and retinal MIO-M1 cells. Results: All compounds displayed excellent AKR1B1 inhibitory activity (IC₅₀ 0.03–0.46 μM 1a–g; IC₅₀ 0.48–6.30 μM 2a–g); 1g and 2e–g also appreciably inhibited PTP1B at micromolar concentrations. Propanoic derivatives 2e–g significantly stimulated glucose uptake in C2C12 myocytes, in an insulin-independent way, reduced lipid accumulation in HepG2 liver cells, and caused hyperpolarization of C2C12 mitochondria at 10 μM concentration. Derivative 2e significantly reduced sorbitol accumulation in both HLE and MIO-M1 cells at a 5 μM concentration. Conclusions: The results reported here provided new insights into the mechanisms of action and structure/activity relationships of 4-thiazolidinone derivatives, underscoring the capability of compounds 2e–g of eliciting insulin-mimetic effects independent of hormone signalling. Among them, compound 2e also proved to inhibit AKR1B1-dependent sorbitol accumulation and, thus, emerged as a promising multitarget agent that can be considered for further investigations. Full article