Search Results (8,822)

We propose a field-transformation-based framework for generating phase-only light-field holograms from a single RGB image. The method establishes an explicit pipeline from monocular scene inference to holographic wavefront synthesis, without requiring multi-view capture or task-specific hologram-network training. First, we construct a layered occlusion RGB-D model from the input image using monocular depth estimation, connectivity-based layer decomposition, and occlusion-aware inpainting, which provides a lightweight 3D prior for sparse-view rendering in the small-parallax regime. Second, we transform the rendered sparse RGB-D light field into a target complex wavefront on the recording plane through local frequency mapping, thereby bridging explicit scene geometry and wave-optical field construction. Third, we optimize the phase-only hologram under multi-plane amplitude constraints using a geometrically consistent initial phase and an error-driven adaptive depth-sampling strategy, which improves convergence stability and reconstruction quality under a limited computational budget. Numerical experiments show that the proposed method achieves better depth continuity, occlusion fidelity, and lower speckle noise than representative layer-based and point-based methods, and improves the average PSNR and SSIM by approximately 3 dB and 0.15, respectively, over Hogel-Free Holography. Optical experiments further confirm the physical feasibility and robustness of the proposed framework. Full article

Background/Objectives: To develop and assess the feasibility of a natural language processing (NLP) framework for automated assessment of radiology–pathology concordance in breast biopsy using machine learning-based analysis of unstructured reports. Methods: This retrospective study included 766 paired radiology and pathology reports from ultrasound- or mammography-guided breast biopsies (August 2020–May 2024). Reports underwent translation, normalization, tokenization, lemmatization, and synonym expansion, followed by structured encoding of BI-RADS and pathology categories. Three models were trained: a Decision Tree, a LightGBM classifier, and a fine-tuned BioBERT model. Concordance labels were defined by multidisciplinary consensus. Performance metrics included accuracy, sensitivity, specificity, F1-score, area under the curve (AUC), and Cohen’s kappa. SHapley Additive exPlanations (SHAP) analysis was used to identify influential features. Results: Among 766 cases, 707 (92.3%) were concordant and 59 (7.7%) were initially discordant. After excluding B3 lesions (n = 46), 13 true discordant cases remained (1.7%). Including B3 lesions increased clinically non-concordant or indeterminate cases from 1.7% to 7.7%, indicating that the apparent performance of the models is likely sensitive to case definition and dataset composition. BI-RADS 4a was the most common category (31.3%), and benign pathology (B2) accounted for 64.4% of biopsies. Within this dataset, LightGBM yielded the highest apparent AUC (0.999) (however, given the extremely small number of true discordant cases, this estimate is likely unstable and should be interpreted with caution), while BioBERT showed the strongest agreement with expert consensus (κ = 0.89). SHAP analysis identified clinically meaningful terms such as calcification, hypoechoic, ductal, and carcinoma as key contributors to model predictions. Given the very limited number of true discordant cases, these performance estimates are likely unstable and should be regarded as preliminary, requiring validation in larger, multi-center cohorts. Conclusions: This study presents a proof-of-concept NLP-based framework for radiology–pathology concordance assessment. The models showed promising performance in identifying potentially discordant cases; however, given the limited number of true discordant samples, these findings should be considered preliminary and require further validation in larger, multi-center datasets before clinical implementation. Full article

Background: Cataract is a progressive lens opacity. According to the World Health Organization, about 45 million people in the world are blind, with about half of these cases attributable to cataracts. Due to the complexity of cataract disease, current research on cataracts is far from sufficient, so it is especially important to understand the development process and the pathogenic factors of cataracts. Myodes rufocanus (M. rufocanus) is an animal of the M. rufocanus of the hamster family Volinae. In developing M. rufocanus, we found an individual of M. rufocanus with a congenital cataract phenotype. We confirmed the symptoms of cataract under natural light and using a slit lamp. Methods: Therefore, we analyzed the mechanism of congenital cataract in M. rufocanus from the aspects of pathological histology, physiology and biochemistry, and gene level, aiming to explore the feasibility of its development into an animal model of cataract. Cataract is a progressive lens opacity and a leading cause of visual impairment. Understanding its pathogenesis requires appropriate animal models. In a laboratory-bred colony of M. rufocanus, we identified individuals with a spontaneous congenital cataract phenotype, confirmed by gross observation and slit lamp examination. To characterize this phenotype and explore its potential as an animal model, we performed pathological, physiological, biochemical, and transcriptomic analyses using three cataract-affected and three normal age-matched male individuals (8 weeks old per group). Results: Blood tests revealed significantly lower white blood cell and lymphocyte counts in the cataract group, while blood glucose and other biochemical parameters showed no significant differences. Histologically, cataractous lenses exhibited eosinophilic aggregation in the nuclear region with disorganized fiber cells. Transcriptome analysis identified 6544 differentially expressed genes, including downregulation of crystallin genes (CRYBB2, CRYBA4, CRYGS) known to be associated with congenital cataract. KEGG pathway enrichment analysis highlighted retinol metabolism, tyrosine metabolism, and cytochrome P450-related pathways. RT-qPCR confirmed reduced CRYBB2 expression in cataractous eyes. Conclusions: This study provides the first transcriptome dataset for M. rufocanus ocular tissues and offers preliminary evidence that this naturally occurring cataract phenotype may serve as a potential model for congenital cataract research. Full article

Extreme low-light image enhancement (ELLIE) targets the restoration of visual quality under ultra-dim environments (<0.1 lux). Conventional image signal processing (ISP) pipelines often fail in such scenarios due to the limitations of heuristic, hand-crafted algorithms. While deep learning has advanced the field via end-to-end mapping, existing models suffer from constrained generalization and suboptimal perceptual fidelity, primarily stemming from the scarcity of large-scale, high-diversity datasets. To bridge this gap, we present the Day and Night (DaN) dataset, a semi-synthetic benchmark synthesized through a rigorous physics-based noise model. This approach effectively captures authentic noise characteristics while enabling the scalable generation of paired samples across multifaceted illumination conditions and scenes. Furthermore, we propose No Longer Vigil (NLV), a fully differentiable AI-ISP framework. By replacing traditional rigid blocks with adaptive non-linear networks, NLV facilitates scene-dependent transformations without requiring manual priors. Comprehensive evaluations demonstrate that our method significantly outshines state-of-the-art approaches, yielding a 4.15 dB gain in PSNR and a 0.026 improvement in SSIM. Full article

Among the most critical structural deficiencies observed in existing reinforced concrete (RC) buildings worldwide are inadequately detailed beam–column joint regions, often constructed without reinforcement. Despite extensive research, the numerical modeling of these critical components still remains a major challenge, as a robust and universally accepted modeling framework has yet to be established, especially when extensive nonlinear analyses have to be performed. This study specifically addresses how joint reinforcement detailing governs the transition between flexure-dominated and shear-dominated joint behavior in non-ductile exterior sub-assemblages, and evaluates whether and how a simplified macro-model can reliably reproduce these mechanisms at full scale. The seismic behavior of exterior RC beam–column joints without adequate transverse reinforcement was first investigated herein through a full-scale experimental program. Five sub-assemblages were tested under quasi-static cyclic loading with increasing displacement history. They mainly differ for beam and column longitudinal reinforcement amount and joint panel (light or null) reinforcement layout, with equal geometric and material properties. The experimental results are first investigated in terms of global response, damage evolution, and energy dissipation capacity, comparing their seismic performance with varying beam or joint reinforcement. Then, nonlinear analyses were carried out by using a computationally efficient macro-modeling strategy in the OpenSees platform to numerically reproduce the observed response. The joint panel behavior was idealized through an empirical quadrilinear rotational spring, whereas flexural and fixed-end-rotation contributions are mechanically defined. The simulations reproduced the global load–drift envelopes, stiffness deterioration, and post-peak softening branch with satisfactory accuracy, although some discrepancies can be observed in the pinching effect. Nevertheless, the comparison between experimental and full-scale numerical results confirms that the adopted model provides reliable predictions of the cyclic response of non-ductile RC joints, also resulting in suitable solutions for extensive analyses as required, for example, for large-scale studies. Full article

The world’s growing need for energy, fueled by industrial expansion and a rising population, continues to be a challenge for the scientific community. The heavy reliance on fossil fuels that contribute to environmental degradation and public health concerns, is shifting toward sustainable alternatives, with hydrogen production via advanced catalysts as an energy source emerging as a promising solution. This transition addresses the challenges posed by harmful combustion emissions. In this study, we developed an innovative PANI@Cu-NA-MOF nanocomposite catalyst through a sol–gel synthesis approach that strategically integrates conducting polymers with metal–organic frameworks. The catalyst was characterized using different sets of techniques. Surface morphology and elemental composition were investigated using SEM-EDX, while structural analysis was carried out with FTIR that helped to identify the chemical bonds and functional groups, and UV-Vis spectroscopy provided information on its light absorption properties. In addition, TGA was used to evaluate thermal behavior, and XPS offered detailed surface chemical analysis. It was observed by morphology that PANI@Cu-NA-MOF is a noncapsular-like structure. It is thermally highly stable; a TGA study showed that up to 550 °C, almost 2.5% of weight was lost. The single peak in UV-Vis is the preparation of a successful composite. XPS and FTIR reveal the required peaks of functional groups and elements. The PANI@Cu-NA-MOF composite turned out to be quite effective for water electrolysis, requiring an overpotential of just 0.47 V to drive the reaction. When tested against the reversible hydrogen electrode, we observed onset potentials of 1.6 V/RHE for the oxygen evolution reaction and 0.2 V/RHE for the hydrogen evolution reaction. What makes this particularly interesting is that such performance significantly cuts down on the energy needed for electrolysis, which could make hydrogen production much more practical. Since hydrogen burns cleanly and offers a real alternative to fossil fuels, having an efficient catalyst like this brings us one step closer to sustainable energy. Full article

A discrete Hartley transform (DHT)-based orthogonal frequency division multiplexing (OFDM) scheme is investigated for intensity modulation/direct detection (IM/DD) visible light communication (VLC) systems, where transmitted signals are required to be real-valued and non-negative. To address this constraint, a practical unipolar transmission framework with corresponding bipolar reconstruction is developed. By exploiting the real-valued and self-inverse properties of the DHT, the proposed scheme removes the need for Hermitian symmetry and enables full utilization of available subcarriers. Under equal-bandwidth conditions, this results in an approximately 50% reduction in computational complexity compared with conventional DCO-OFDM and ACO-OFDM schemes. Theoretical analysis and numerical results further show that the proposed approach achieves comparable bit error rate (BER) performance while exhibiting improved spectral confinement, as reflected by reduced out-of-band sidelobes under identical filtering conditions. In addition, it maintains spectral efficiency equivalent to DCO-OFDM under the same bandwidth constraint. These advantages are achieved at the cost of restricting subcarrier modulation to real-valued constellations, which may reduce flexibility in frequency-selective channels. Overall, these findings support DHT-OFDM as a low-complexity, spectrally confined multicarrier waveform for IM/DD VLC systems, particularly in scenarios where efficient spectrum utilization and reduced adjacent-channel interference are required. Full article

Cancer remains a primary global health challenge, accounting for millions of new cases and significant mortality annually. Although doxorubicin (DOX) is a fundamental anthracycline used for various malignancies, its therapeutic index is severely limited by poor selectivity, systemic toxicity, and dose-dependent cardiotoxicity. To address these issues, Janus nanoparticles (JNPs) have emerged as a promising bifunctional platform characterized by a structural asymmetry that allows for the independent functionalization of each hemisphere. This review examines primary fabrication routes—such as masking, microfluidics, self-assembly, and phase separation—and their specific applications in DOX delivery. The anisotropic architecture of JNPs enables a “separate rooms” concept, allowing for the co-delivery of incompatible drugs while facilitating multi-stimuli-responsive release mechanisms triggered by pH, enzymes, or NIR light. Furthermore, JNPs have demonstrated enhanced tumor accumulation and reduced systemic toxicity compared to conventional isotropic carriers. Recent developments even highlight the use of autonomous nanomotors to improve therapeutic delivery while minimizing premature leakage. However, clinical translation is currently hindered by manufacturing complexity, high equipment costs, scalability issues, and a lack of standardized reporting in the literature. Ultimately, JNPs represent a sophisticated frontier in precision oncology, though robust manufacturing processes and characterization protocols are required for future medical adoption. Full article

Deep-sea mining operations demand continuous, drift-free positioning over multi-day missions—a requirement that traditional acoustic dead-reckoning systems struggle to meet due to cumulative error accumulation and frequent DVL bottom-lock loss in sediment plume environments. Inspired by Google Cartographer’s 2D grid mapping paradigm, we present a prior map-based visual localization framework that decouples offline mapping from real-time localization, fundamentally eliminating drift through absolute image registration against pre-built seabed mosaics. By integrating adaptive keyframe selection, Multi-Scale Retinex (MSR) enhancement, and the AD-LG deep feature matching architecture, our system constructs globally consistent seabed maps for absolute positioning. The framework leverages deformable convolutions and LightGlue to effectively mitigate challenges such as low texture and non-rigid distortion. Quantitative validation on tank simulation datasets demonstrates significant superiority over IMU-only and standard fusion schemes; qualitative deployment on real Pacific CCZ imagery confirms near-real-time operational feasibility on an embedded Jetson Orin NX platform. This system establishes visual navigation as a viable backup to acoustic systems, addressing a critical gap in deep-sea mining vehicle autonomy. Full article

De-S-acylation enzymes mediate the reversible S-acylation cycle and play critical roles in plant development and stress responses. However, the precise origin and evolutionary dynamics of this gene family in plants remain poorly understood. In this study, a total of 718 ABAPT genes were identified across 73 plant genomes, including 622 ABHD17 and 96 ABHD13 homologs, which share only a 20–30% conserved sequence identity between them. We further performed comprehensive analyses of gene duplication and structure, protein properties, synteny networks, and expression profiles to establish a systematic framework by classifying ABAPT genes in land plants. Our results revealed that ABHD13 genes have been retained as a single copy in most angiosperm genomes, whereas ABHD17 genes have undergone extensive expansion. ABAPT genes formed three major evolutionary clades: Clade 1 contained ABHD13 homologs, while Clades 2 and 3 harbored ABHD17 homologs. The three clades showed distinct disparities in intron–exon structural patterns and IDR properties. Phylogenomic synteny network analyses revealed the deeply conserved genomic syntenies within each of the six ABAPT subclades among the three clades, while Cluster4-Monocot was more dynamic and showed distinct lineage-specific duplication patterns restricted to Poaceae. ABHD13s exhibited constitutive expression patterns, while the tissue-specific expression genes were predominantly found within the ABHD17s subfamily. Notably, the ABAPT8/9 subgroups were specifically expressed in reproductive organs, and the weighted gene co-expression network identified specific groups to find ABAPT-specific regulatory features, implying the presence of potential modules for the protein S-acylation cycle during pollen development. Additionally, our results suggested that C-terminal Cys-rich region was required for ABAPT10 localization. Altogether, this study sheds light on the evolutionary divergence of the ABAPT subclades across major green plant lineages and emphasizes the need for future functional characterizations. Full article

Excessive nitrate accumulation in leafy vegetables presents significant health risks, requiring sustainable strategies to optimize yield while minimizing nitrogen-related anti-nutritional factors in controlled environments. This study investigated the effects of varying LED light intensities 236.9 µmol·m⁻²·s⁻¹ (high), 189.8 µmol·m⁻²·s⁻¹ (medium), and 117.6 µmol·m⁻²·s⁻¹ (low) on nitrates (NO₃⁻) dynamics, growth, and biochemical composition in two Lollo Rossa lettuce cultivars, Carmesi and Carnelian, grown in NFT hydroponic systems. Conducted under constant temperature (20/18 °C day/night) and CO₂ (625 µmol·mol⁻¹) to isolate light’s influence, the experiment used a replicated design with three replicates per treatment, each including two cultivars. Morphological traits (plant height, rosette diameter, leaf number, biomass, root development) and biochemical parameters (nitrate and sugar contents) were assessed via mean comparisons, trends, and correlations. Results demonstrated that higher light intensity significantly suppressed nitrate accumulation in lettuce through enhanced assimilation and dilution effects linked to increased growth. Nitrate levels dropped to 2091.67 mg kg⁻¹ from 2443.33 mg kg⁻¹ in Carmesi and 2013.33 mg kg⁻¹ from 2515.00 mg kg⁻¹ in Carnelian. Negative correlations were observed between nitrate content and growth parameters: nitrates vs. fresh biomass (r = −0.89); nitrates vs. plant height (r = −0.79). Concurrently, it boosted carbohydrate content (Carmesi: 3.03 °Brix; Carnelian: 3.08 °Brix) and promoted vigorous growth, with Carmesi achieving superior metrics under high light (height: 22.12 cm, rosette diameter: 29.87 cm, fresh biomass: 206.88 g, root biomass: 19.58 g) compared to low light (17.45 cm height, 183.42 g biomass). Carnelian exhibited similar trends but prioritized root elongation. These findings underscore light’s role in regulating nitrate transporters and assimilation enzymes (e.g., nitrate reductase), offering a low-cost approach to reduce nitrate risks, enhance nutritional quality, and improve yield in controlled horticultural systems (CHS). Full article

Poor visibility contributes to nighttime accidents at highway intersections, especially in developing countries where vehicles mix with vulnerable road users (VRUs) such as pedestrians and cyclists. Unlike downtown intersections with traffic signals and ambient lighting, rural intersections have no signals and minimal ambient light, forcing drivers to rely on roadway lighting for hazard recognition. Improving illumination arrangements can significantly reduce the likelihood of crashes. However, there are significant differences in the effects of illumination on drivers’ visual search ability at different vehicle speeds. Therefore, the collaborative matching of illumination and speed limits can effectively improve traffic efficiency and reduce the probability of nighttime accidents. In this paper, we establish a collaborative optimization model of illumination and speed limits at rural highway intersections that considers drivers’ visual recognition of VRUs. We then design an experiment with illuminance, vehicle speed, and VRU type/location as control variables to collect recognition distances, and finally analyze their effects to calculate speed limits under different illuminances. Results indicate that pedestrians and cyclists appearing from the left side are recognized 24.73% and 15.79% earlier than those from the right, suggesting that VRUs from the right side are more vulnerable. Additionally, the safety benefit of improving illumination on increasing speed limits gradually diminishes as illuminance rises. Therefore, determining the most suitable illumination and speed limit configuration requires a comprehensive evaluation of the cost–benefit relationship between lighting investments and the gains resulting from higher speed limits. Full article

We studied experimentally and computationally the structures and optical properties of sulfur (S), selenium (Se) and tellurium (Te) ring clusters. We encapsulated S, Se and Te into AFI, MOR, CHA and LTA zeolites via vapor adsorption or high-pressure injection from melt and studied Raman and optical absorption spectra (RS and OAS, respectively) of zeolite single crystals with incorporated S, Se and Te ring clusters. Importantly, strict orientation of the rings in zeolite crystals allowed us to study the polarization/orientation dependency of ring RS and OAS. The obtained experimental spectra are found to be in agreement with density functional theory results (DFT using the PBE0 functional and def2-TZVP basis sets) for S₈, Se₆, Se₈, Se₁₂, Te₆ and Te₈ ring molecules. The agreement is especially good for Te rings, while for S and Se rings harmonic frequency scaling factors are required. The S and Se rings display light-induced effects, which we attribute to the presence of conical intersections between their ground and excited electronic states, resulting in isomerization and subsequent fragmentation. We consider this effect using the Se₆ ring example. This phenomenon is important for understanding photostructural changes not only in chalcogen clusters but also in bulk materials such as amorphous selenium. Full article

Existing Environmental, Social, and Governance (ESG) monitoring approaches rely primarily on static reports and dashboard-based interfaces, limiting real-time analysis and interactive exploration of sustainability data in complex built environments. In addition, current digital twin systems often lack integration with IoT-based sensing or depend on cloud-based rendering infrastructures, increasing deployment complexity and restricting accessibility. This study proposes a lightweight Web3D-based digital twin framework for real-time ESG monitoring in smart buildings. The system integrates an independently developed IoT sensor network with a browser-native 3D visualization platform, enabling real-time monitoring of ESG indicators—including electricity consumption—without requiring proprietary software or dedicated rendering hardware. ESG indicators are derived using a rule-based classification aligned with the WELL Building Standard v1. The framework was validated through a 12-month real-world deployment involving 60 IoT sensors. Results demonstrate stable performance, achieving 66 FPS rendering, 78 ms system latency, and 98% sensor data consistency based on cross-sensor agreement. The system also enabled timely detection of environmental anomalies, leading to measurable improvements in air quality and lighting conditions. Unlike prior digital twin systems, the proposed framework delivers a fully browser-native, lightweight architecture that integrates real-time IoT sensing, adaptive Web3D visualization, and structured ESG monitoring within a single deployable system. This approach provides a practical solution with potential for broader deployment in real-time sustainability monitoring for smart buildings. Full article

Optogenetic modulation employs light-sensitive proteins known as opsins to regulate cellular activity. A unique therapeutic application of this technique involves modulating pain perception by selectively targeting neural pathways within the spinal cord. Multi-Characteristic Opsin (MCO) represents an innovative optogenetic actuator capable of activation across a broad spectrum of light wavelengths, exhibiting a slow depolarizing phase that resembles natural photoreceptors. This study examines the current advancements in spinal optogenetic modulation utilizing MCO for pain management. Due to its high sensitivity, MCO facilitates minimally invasive, remotely controlled optogenetic modulation of spinal neurons. This approach enables the regulation of extensive spatial regions, provided the MCO channel receives sufficient light intensity to surpass the activation threshold. Nano-enhanced optical delivery (NOD) successfully transfected spinal neurons with the GAD67-MCO2-mCherry construct, as confirmed by membrane-localized mCherry fluorescence with DAPI-labeled nuclei. Using this platform, 5 Hz spinal optogenetic stimulation produced a significant reduction in formalin-evoked pain behaviors, demonstrating frequency-specific modulation of spinal pain circuits. Neither 2 Hz nor 10 Hz stimulation yielded comparable analgesic effects, underscoring the importance of precise stimulation parameters. The therapeutic impact also depended on transfection efficiency: reducing the fGNR–plasmid concentration diminished MCO expression and weakened the analgesic response. Together, these results show that effective spinal optogenetic pain modulation requires both optimal stimulation frequency and robust gene delivery. Full article