Search Results (5,011)

Accurate quality-of-transmission (QoT) estimation prior to lightpath deployment is essential for minimizing design margins in optical networks. Owing to their high precision and strong generalization capabilities, artificial neural networks (ANNs) have emerged as a promising approach for lightpath QoT estimation. However, focusing exclusively on prediction accuracy is inadequate for maximizing global network capacity. Conventional models employing symmetric loss functions apply identical penalties to both overestimation and underestimation errors, thereby precluding controlled bias in predictions and their impact on overall network capacity. This paper investigates the margin configuration for the whole network capacity and proposes a novel QoT estimation method with asymmetric loss functions, which jointly considers the assessment of global network capacity and gives different penalties for overestimation and underestimation. We further present an iterative search algorithm grounded in network capacity considerations to optimize the parameters of these asymmetric loss functions. Simulation results confirm that our ANN-based models facilitate efficient modulation format assignment, leading to corresponding increases in network capacity. Full article

Background/Objectives: Optic neuropathies (ON) are a clinically heterogeneous group of disorders that can cause profound and lasting visual disability, with wide-ranging effects on patients’ quality of life. Although the NEI-VFQ-25 is an instrument for assessing vision-related quality of life (VRQoL), few studies have systematically compared patient-reported outcomes across multiple ON subtypes, especially in underrepresented populations. We aimed to delineate how etiological differences and longitudinal visual acuity trajectories shape VRQoL in a diverse Portuguese cohort with ON. Methods: This retrospective, cross-sectional study included 152 patients diagnosed with ON and followed at São João University Hospital, Portugal. All participants completed the validated NEI-VFQ-25. Diagnosis-specific differences in VRQoL were interrogated using ANCOVA and linear mixed-effects models, controlling for age and sex. Visual acuity changes over time were analyzed in relation to patient-reported outcomes. Results: Substantial heterogeneity in VRQoL was observed across ON subtypes. Patients with MS-related ON (MS-RON) and idiopathic ON reported significantly higher NEI-VFQ-25 scores in domains such as general vision, mental health, and dependency (F = 3.30, p = 0.013; ηp² = 0.08), while those with ischemic or other inflammatory etiologies showed persistently lower scores. Notably, both final visual acuity and diagnosis were independently associated with NEI-VFQ-25 composite scores, highlighting the correlation between objective and subjective measures of visual function. Age and diagnosis independently predicted poorer VRQoL. Conclusions: This study provides the first comprehensive evaluation of vision-related quality of life (VRQoL) across a diverse cohort of optic neuropathy patients in a Portuguese tertiary center, using the NEI-VFQ-25. Our results underscore the heterogeneity of functional impact across ON subtypes, emphasizing the value of integrating sensitive, multidimensional assessment tools into neuro-ophthalmic clinical care, especially in populations historically underrepresented in research. Full article

Photoacoustic tomography is an innovative non-ionizing imaging technique that combines optical contrast with ultrasound resolution for 3D object characterization. While promising, its broader adoption is limited by challenges such as shallow penetration depth and strong optical scattering. To address these issues, this study introduces a dual illumination and detection photoacoustic tomography method, specifically designed for symmetrical objects like hollow metallic cylinders. The illumination system plays a critical role in determining the quality of photoacoustic signals and, thus, the final image. This approach enhances spatial resolution and contrast by using complementary light delivery and signal detection. In industrial settings, where accurate and efficient non-destructive testing is essential, traditional techniques often lack the precision required. The dual illumination and detection strategy offers significant improvements in effective resolution, contrast, defect detection, and artifact reduction, surpassing the limitations of unidirectional approaches. This technique not only strengthens the characterization of metal structures but also contributes to a deeper understanding of their physical behavior. Applications extend across various fields, including aerospace and biomedical engineering. This paper explores the underlying principles and potential of this advanced imaging modality, highlighting its value in modern diagnostic and inspection technologies. Full article

Age-related macular degeneration (AMD) is a leading cause of irreversible blindness, yet current tools for forecasting treatment outcomes remain limited by either the opacity of deep learning or the rigidity of rule-based systems. To address this gap, we propose a hybrid neuro-symbolic and large language model (LLM) framework that combines mechanistic disease knowledge with multimodal ophthalmic data for explainable AMD treatment prognosis. In a pilot cohort of ten surgically managed AMD patients (six men, four women; mean age 67.8 ± 6.3 years), we collected 30 structured clinical documents and 100 paired imaging series (optical coherence tomography, fundus fluorescein angiography, scanning laser ophthalmoscopy, and ocular/superficial B-scan ultrasonography). Texts were semantically annotated and mapped to standardized ontologies, while images underwent rigorous DICOM-based quality control, lesion segmentation, and quantitative biomarker extraction. A domain-specific ophthalmic knowledge graph encoded causal disease and treatment relationships, enabling neuro-symbolic reasoning to constrain and guide neural feature learning. An LLM fine-tuned on ophthalmology literature and electronic health records ingested structured biomarkers and longitudinal clinical narratives through multimodal clinical-profile prompts, producing natural-language risk explanations with explicit evidence citations. On an independent test set, the hybrid model achieved AUROC 0.94 ± 0.03, AUPRC 0.92 ± 0.04, and a Brier score of 0.07, significantly outperforming purely neural and classical Cox regression baselines (p ≤ 0.01). Explainability metrics showed that >85% of predictions were supported by high-confidence knowledge-graph rules, and >90% of generated narratives accurately cited key biomarkers. A detailed case study demonstrated real-time, individualized risk stratification—for example, predicting an >70% probability of requiring three or more anti-VEGF injections within 12 months and a ~45% risk of chronic macular edema if therapy lapsed—with predictions matching the observed clinical course. These results highlight the framework’s ability to integrate multimodal evidence, provide transparent causal reasoning, and support personalized treatment planning. While limited by single-center scope and short-term follow-up, this work establishes a scalable, privacy-aware, and regulator-ready template for explainable, next-generation decision support in AMD management, with potential for expansion to larger, device-diverse cohorts and other complex retinal diseases. Full article

This study developed and characterized zein-based edible films enriched with curcumin as natural pH-sensitive indicators for monitoring fish freshness. Colorimetric films were prepared with different curcumin concentrations (1–7% wt) and evaluated for physicochemical, mechanical, optical, and antioxidant properties. Increasing curcumin content reduced water vapor permeability (0.085–0.110 g·mm/m²·h·kPa), lowered water contact angles (<90°), and enhanced hydrophilicity. Films exhibited high brightness, with decreased a* and increased b* values, while light transmission decreased, improving UV barrier properties. Colorimetric response (ΔE*) across pH 3–10 was more pronounced at higher curcumin levels, confirming pH-sensitivity. Antioxidant activity significantly increased with curcumin loading (up to 24.18 µmol Trolox/g). Mechanical analysis revealed decreased tensile strength but improved elongation at break, bursting strength, and deformation, supported by SEM images showing more homogeneous, micro-porous structures at 7% curcumin. Zein films containing 7% (wt) curcumin (Z/CR7) were applied to gilthead sea bream (Sparus aurata) fillets stored at 4 °C for 13 days. Results showed lower TBARS and TVB-N values in Z/CR7 compared to the control, indicating delayed lipid oxidation and spoilage. Colorimetric changes in the films corresponded with fish freshness deterioration, providing a clear visual indicator. Microbiological results supported chemical findings, though antimicrobial effects were limited. Curcumin-enriched zein films demonstrated strong potential as intelligent, biodegradable packaging for real-time monitoring of seafood quality. Full article

Pluriannual time series of fine particle concentrations suspended in the atmosphere are often lacking. Such data is necessary in evaluating the efficiency of policies aiming to improve air quality in megacities. In this work, a recently developed empirical method is applied over the megacities of Paris, Cairo, and New Delhi. The method utilizes observations of the aerosol optical depth, Angström Exponent, and atmospheric precipitable water as inputs to estimate the PM10. The modeled values validated against their respective reference measurements exhibited the best performance at daily, weekly, and monthly averages when using inputs of the AERONET. When exploiting inputs of the CAMS and MERRA-2 reanalyses, the results were found to be satisfactory with MERRA-2 on the monthly scale. This allows the reconstruction of the variability of the PM10 for the last 45 years. Analysis shows that average annual PM10 concentration has decreased from 40 to 20 µg·m⁻³ in Paris, increased from 70 to 250 µg·m⁻³ in New Delhi, and stayed relatively stable (around 100 µg·m⁻³) in Cairo. Provided that at least one year of PM10 measurements are available to calibrate the empirical method, the method herein is replicable over other megacities around the world. Full article

This research contrasts model-centric (MCAI) and data-centric (DCAI) strategies in artificial intelligence, focusing specifically on optical quality control. It addresses the necessity for a thorough empirical study to evaluate both approaches under identical conditions. By examining casting and leather datasets, the study highlights that the quality and diversity of data play a more vital role in the success of models than merely fine-tuning hyperparameters. While MCAI delivers dependable results with superior datasets, DCAI methods—such as label correction, data augmentation, and generating synthetic data through diffusion models—significantly enhance recognition performance. For the casting dataset, accuracy increased from 83% to 93%, and for the leather dataset, from 53% to 62%. These results indicate that robust AI systems are built on high-quality, balanced data. Full article

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article

We demonstrate an integrated core-pumped 4-core erbium-doped fiber amplifier (4C-EDFA) that achieves a record-low differential core gain of 0.5 dB across the whole C-band. This is enabled by utilizing a 4C-EDF with a minimal core-dependent absorption coefficient and passive devices with low core-dependent loss. The 4C-EDFA also exhibits an average gain of 15.50 dB, an average output power of 22.5 dBm, and a maximum noise figure of 4.91 dB. Furthermore, simulations on a 4-core fiber (4CF) transmission link confirm that the proposed 4C-EDFA can support transmission exceeding 10,000 km with a minimal inter-core Q² difference of only 0.5 dB. Here, Q² is defined as the ratio of the mean received signal levels to the corresponding noise variances. It is a critical metric in optical systems to quantify the signal quality, which highlights its potential for high-capacity and long-haul uncoupled 4CF systems. Full article

Optimizing fertilizer management is essential for reducing salinity-related risks and improving nutrient efficiency in ornamental plant production. Fertilization enhances plant performance; however, excessive nutrient inputs can disrupt substrate chemistry, elevate salinity, and promote nitrogen leaching—particularly in containerized systems with limited rooting volume. This study evaluated the growth, physiological performance, and soil–plant nutrient dynamics of Sabal palmetto (cabbage palm) cultivated under six fertilization regimes over 180 days in a subtropical shade-house environment. Treatments ranged from a single baseline application of 15 g per plant (T0) to a cumulative 75 g (T5) using granular slow-release fertilizer. Morphological traits (plant height: 26–70 cm; leaf number: 4–18) and physiological indices (atLEAF⁺: 34.3–66.4; NDVI: 0.26–0.77) were monitored every 30 days. Substrate nitrogen and carbon concentrations increased from 0.57% and 41.78% at baseline to 1.24% and 42.94% at 180 days, while foliar nitrogen ranged from 1.46% to 2.57%. Fertilization significantly influenced all parameters (p < 0.05). Higher fertilization levels elevated electrical conductivity, salinity, and nitrogen leaching, with principal component analysis revealing strong positive associations among total nitrogen, electrical conductivity, and salinity. Moderate fertilization (T2 = 45 g) maintained favorable substrate chemistry, high foliar nitrogen, and balanced canopy growth with minimal nutrient losses. Sensor-based chlorophyll indices (atLEAF⁺ and NDVI) correlated strongly (r = 0.71, p < 0.001), confirming their reliability as non-destructive diagnostics for nitrogen management. These findings demonstrate that integrating optical monitoring with adaptive fertilization mitigates substrate salinization, sustains ornamental quality, and promotes the sustainable cultivation of Sabal palmetto in urban horticultural systems. Full article

Aluminum nitride (AlN) is a wide-bandgap semiconductor (6.2 eV) with a broad transparency window spanning from the ultraviolet (UV) to the mid-infrared (MIR) wavelength region, making it a promising material for integrated photonics. In this work, AlN thin films using reactive RF sputtering are deposited, followed by annealing at 600 °C in a nitrogen atmosphere to reduce slab waveguide propagation losses. After annealing, the measured loss is 0.84 dB/cm at 978 nm, determined using the prism coupling method. A complete microfabrication process flow is then developed for the realization of optical channel waveguides. A key challenge in the processing of AlN is its susceptibility to oxidation when exposed to water or oxygen plasma, which significantly impacts device performance. The process is validated through the fabrication of microring resonators (MRRs), used to characterize the propagation losses of the AlN channel waveguides. The fabricated MRRs exhibit a quality factor of 12,000, corresponding to a propagation loss of 4.4 dB/cm at 1510–1515 nm. The dominant loss mechanisms are identified, and strategies for further process optimization are proposed. Full article

In this paper, synchronous mechanical and optical measurements are proposed using the dual cantilever mode of dynamic mechanical analysis (DMA). It was demonstrated that this mode enables the detection of phase transitions in both the core and cladding materials of polymer optical fibers (POFs), with corresponding changes in optical signal intensity observed across different light wavelengths. In dual cantilever mode DMA, an increase in optical transmission was recorded between the two detected glass transition temperatures. The initial increase in transmission is attributed to cladding softening and the consequent reduction in internal stresses in the POF, while the maximum in optical transmission coincides with the beginning of the phase transition in the core material. To compare and interpret the optical and thermo-mechanical results, Differential scanning calorimetry (DSC) and Fourier transform infrared (FTIR) measurements were carried out on POF pieces, as well as separately on the core and cladding materials. This integrated technique yields quantitative data on a material’s viscoelasticity and light-transmission changes, making it valuable for quality control and for predicting the long-term behavior of advanced POFs in various applications. Full article

Within the framework of an extensive conservation project involving multiple stained-glass windows of the Basilica of San Petronio in Bologna, Italy, this study reports the results of the diagnostic campaign on the rose window depicting Sant’Ambrogio between two angels holding the coats of arms of the Marsili family. The rose window is located in the homonymous chapel and, based on recent studies attributing the cartoon to the Bolognese painter Biagio Pupini, who was active in San Petronio from 1519, is dated to the early sixteenth century. No evidence was found regarding the workshop responsible for the production of the stained-glass window. The window showed no significant conservation issues, either in the glass elements or in the lead cames. However, the extensive degradation of the grisaille—likely caused by a low-quality mixture, improper firing, or aggressive cleaning—resulted in the loss of the original drawing. This study presents the results of non-invasive investigations on the glass tiles of the rose windows and the analyses of deposits present on their surfaces. Fiber Optic Spectroscopy (FOS) in transmittance, X Ray Fluorescence (XRF), and Hyper Spectral Imaging (HIS) in transmittance were used to investigate the glass composing the rose window. Fourier Transform Infrared Spectroscopy (FT-IR) was applied to study deposit samples collected from the external surface of the window. Additionally, only four glass samples, obtained from hidden areas or already detached fragments, were analyzed using Scanning Electron Microscope with Energy-Dispersive Spectroscopy (SEM-EDS). In addition, a photographic processing method is described, which enabled the recovery of the ghost image, the faint trace or imprint left by the grisaille on the glass during firing, allowing the conservators to faithfully reintegrate the original drawing. Full article

Greenhouse horticulture is an energy-intensive production system that requires innovative solutions to reduce energy demand without compromising crop yield or quality. Functional greenhouse covers are particularly promising, as they regulate solar radiation while integrating energy-harvesting technologies. In this study, six nanostructured glass coatings incorporating semiconductor-based quantum dots (QDs) and quantum wires (QWs) of Ge and TiN are developed using magnetron sputtering—an industrially scalable technique widely applied in smart window and energy-efficient glass manufacturing. The coatings’ optical properties are characterized in the laboratory, and their agronomic performance is evaluated in greenhouse trials with lamb’s lettuce (Valerianella locusta) and radish (Raphanus sativus). Plant growth, yield, and leaf color (CIELAB parameters) are analyzed in relation to spectral transmission and the daily light integral (DLI). Although uncoated horticultural glass achieves the highest yields, several Ge-QD coatings provide favorable compromises by selectively absorbing non-photosynthetically active radiation (non-PAR) while maintaining acceptable crop performance. These results demonstrate that nanostructured coatings can simultaneously sustain crop growth and enable solar energy conversion, offering a practical pathway toward energy-efficient and climate-smart greenhouse systems. Full article

Background/Objectives: Breast cancer is the most prevalent neoplasm in women. Improved screening and systemic therapies have allowed more patients to choose breast-conserving surgery over mastectomy. However, preserving glandular tissue while achieving negative margins remains difficult. Traditional intraoperative margin assessment techniques like frozen section analysis, cavity shave margins, intraoperative ultrasonography, and specimen radiography aim to reduce positive margins and re-excision rates but face several limitations, including time consumption, interpretive challenges, and operator dependency. Our aim was to critically evaluate both conventional and emerging intraoperative margin assessment techniques in breast-conserving surgery, highlighting their clinical utility, limitations, and potential to reduce re-excision rates and improve patient outcomes. Methods: We assessed PubMed and Google Scholar databases using search terms such as specimen radiography, intraoperative ultrasonography, mass spectrometry, optical coherence tomography, artificial intelligence, and others. Studies were selected based on relevance, language, and completeness, and refined through author consensus. Conclusions: Conventional techniques have demonstrated value in reducing re-excisions and preserving cosmetic outcomes. Emerging tools like MarginProbe, fluorescence imaging, mass spectrometry (MasSpec Pen, iKnife), OCT, and AI-enhanced imaging show promise in offering real-time feedback and higher diagnostic accuracy. However, high costs, training needs, and data variability limit their widespread adoption. Investment in standardised protocols and multicentre trials is essential. Integration of imaging, spectroscopy, and AI may offer the most robust framework for improving surgical outcomes and quality of life for breast cancer patients. Full article