Search Results (631)

The suppression of Rayleigh backscattering noise in a resonant fiber optic gyro (RFOG) is accompanied by the emergence of residual amplitude modulation (RAM) effects, which impact the bias performance of the RFOG output. In this paper, we propose a double demodulation technique that integrates reciprocal modulation and RAM feedback. By utilizing reciprocal modulation–demodulation along with a RAM feedback control method, we effectively suppress both RAM and laser frequency noise. Furthermore, the inherent suppression characteristics of the double modulation–demodulation scheme facilitate effective backscatter noise reduction. As a result, the gyro angular random walk of the RFOG has improved to $3^{°}/\sqrt{\mathrm{h}}$, and the long-term bias instability has been enhanced to 0.1°/h over a test duration of 10 h. Full article

The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) techniques for several sub-tropical locations. The initial method used for the prediction of visibility in this study was the SVRA, and the results were enhanced using the ANN and ANFIS techniques. Throughout the study, neural networks with various algorithms and functions were trained with different atmospheric parameters to establish a relationship function between inputs and visibility for all locations. The trained neural models were tested and validated by comparing actual and predicted data to enhance visibility prediction accuracy. Results were compared to assess the efficiency of the proposed systems, measuring the root mean square error (RMSE), coefficient of determination (R²), and mean bias error (MBE) to validate the models. The standard statistical technique, particularly SVRA, revealed that the strongest functional relationship was between visibility and RH, followed by WS, T, and P, in that order. However, to improve accuracy, this study utilized back propagation and hybrid learning algorithms for visibility prediction. Error analysis from the ANN technique showed increased prediction accuracy when all the atmospheric variables were considered together. After testing various neural network models, it was found that the ANFIS model provided the most accurate predicted results, with improvements of 31.59%, 32.70%, 30.53%, 28.95%, 31.82%, and 22.34% over the ANN for Durban, Cape Town, Mthatha, Bloemfontein, Johannesburg, and Mahikeng, respectively. The neuro-fuzzy model demonstrated better accuracy and efficiency by yielding the finest results with the lowest RMSE and highest R² for all cities involved compared to the ANN model and standard statistical techniques. However, the statistical performance analysis between measured and estimated visibility indicated that the ANN produced satisfactory results. The results will find applications in Optical Wireless Communication (OWC), flight operations, and climate change analysis. Full article

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

This study addresses the challenges of manual measurement bias and low robustness in detecting small, occluded targets in complex marine environments during real-time onboard sorting of Portunus trituberculatus. We propose YOLO-DFAM, an enhanced YOLOv11n-based model that replaces the global average pooling in the Focal Modulation module with a spatial–channel dual-attention mechanism and incorporates the ASF-YOLO cross-scale fusion strategy to improve feature representation across varying target sizes. These enhancements significantly boost detection, achieving an mAP@50 of 98.0% and precision of 94.6%, outperforming RetinaNet-CSL and Rotated Faster R-CNN by up to 6.3% while maintaining real-time inference at 180.3 FPS with only 7.2 GFLOPs. Unlike prior static-scene approaches, our unified framework integrates attention-guided detection, scale-adaptive tracking, and lightweight weight estimation for dynamic marine conditions. A ByteTrack-based tracking module with dynamic scale calibration, EMA filtering, and optical flow compensation ensures stable multi-frame tracking. Additionally, a region-specific allometric weight estimation model (R² = 0.9856) reduces dimensional errors by 85.7% and maintains prediction errors below 4.7% using only 12 spline-interpolated calibration sets. YOLO-DFAM provides an accurate, efficient solution for intelligent onboard fishery monitoring. Full article

The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions. Full article

Aluminum nitride (AlN) thin films were deposited on SiO₂ substrates by RF-magnetron sputtering at varying powers (110–140 W) and subsequently subjected to thermal annealing at 450 °C under nitrogen atmosphere. A comprehensive multi-technique investigation—including X-ray reflectometry (XRR), X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), optical profilometry, spectroscopic ellipsometry (SE), and electrical measurements—was performed to explore the physical structure, morphology, and optical and electrical properties of the films. The analysis of the film structure by XRR revealed that increasing sputtering power resulted in thicker, denser AlN layers, while thermal treatment promoted densification by reducing density gradients but also induced surface roughening and the formation of island-like morphologies. Optical studies confirmed excellent transparency (>80% transmittance in the near-infrared region) and demonstrated the tunability of the refractive index with sputtering power, critical for optoelectronic applications. The electrical characterization of Au/AlN/Al sandwich structures revealed a transition from Ohmic to trap-controlled space charge limited current (SCLC) behavior under forward bias—a transport mechanism frequently present in a material with very low mobility, such as AlN—while Schottky conduction dominated under reverse bias. The systematic correlation between deposition parameters, thermal treatment, and the resulting physical properties offers valuable pathways to engineer AlN thin films for next-generation optoelectronic and high-frequency device applications. Full article

Topological insulators (TIs) hold considerable promise for the advancement of optoelectronic technologies, including spectroscopy, imaging, and communication, owing to their remarkable optical and electrical characteristics. This study proposes a novel combination of Bi${}_2$Se${}_3$ TIs and ReSe${}_2$ for self-powered broadband photodetectors with high sensitivity and fast response time. The Bi${}_2$Se${}_3$/ReSe${}_2$ heterojunction photodetector achieves broadband response spectra ranging for 375 nm to 1 $\mathsf{\mu }$m. It demonstrates a significant responsivity of 64 mA/W at a wavelength of 600 nm (1 mW/cm${}^2$), exhibits a rapid response speed of 345 $\mathsf{\mu }$s rise/336 $\mathsf{\mu }$s fall time, and has a 3 dB bandwidth of 1.4 kHz under zero-bias conditions. The high performance can be attributed to the suitable energy band structure of Bi${}_2$Se${}_3$/ReSe${}_2$ and high carrier mobility in surface states of Bi${}_2$Se${}_3$. Excitingly, self-powered TIs photodetectors allow for high-quality signal transmission. The TIs employed in photodetectors can stimulate the production of new optoelectronic features, but they could also be used for highly integrated photonic circuits in the future. Full article

By measuring the transfer function of the single-mode multi-aperture vertical-cavity surface-emitting laser (SM MA VCSEL) transmitting over a long single-mode fiber at 850 nm, we confirm that the chirp of the SM MA VCSEL under study is dominated by transient chirp with an alpha value of −3.81 enabling a 19 GHz bandwidth over 10 km of single-mode fiber. The detailed measurement of the VCSEL with different bias currents also allows us to recover other key characteristics of the VCSEL, thereby enabling us to practically construct the optical eye diagrams that closely match the experimentally measured ones. The link-level transfer function can be obtained using an analytical equation including effects of modal dispersion and laser chirp–chromatic dispersion (CD) interaction for an MMF of a given length and bandwidth grade. The narrow linewidth and chirp characteristics of the SM MA VCSEL enable transmission performance that surpasses that of conventional MM VCSELs, achieving comparable transmission distances at moderate modal bandwidths for OM3 and OM4 fibers and significantly longer reaches when the modal bandwidth is higher. The transmission performance was also confirmed with the modeled eye diagrams using extracted VCSEL parameters. The chirp properties also provide sufficient bandwidth for SM MA VCSEL transmission over kilometer-scale lengths of single-mode fibers at a high data rate of 100G or above with sufficient optical power coupled into the fibers. Advanced transmission distances are possible over multimode and single-mode fibers versus chirp-free devices. Full article

A timely and accurate distribution of crop types and cropping patterns provides a crucial reference for the management of agriculture and food security. However, accurately mapping crop types and cropping patterns in subtropical hilly and mountainous areas often face challenges such as mixed pixels resulted from fragmented patches and difficulty in obtaining optical satellites due to a frequently cloudy and rainy climate. Here we propose a crop type and cropping pattern mapping framework in subtropical hilly and mountainous areas, considering multiple sources of satellites (i.e., Landsat 8/9, Sentinel-2, and Sentinel-1 images and GF 1/2/7). To develop this framework, six types of variables from multi-sources data were applied in a random forest classifier to map major summer crop types (singe-cropped rice and double-cropped rice) and winter crop types (rapeseed). Multi-scale segmentation methods were applied to improve the boundaries of the classified results. The results show the following: (1) Each type of satellite data has at least one variable selected as an important feature for both winter and summer crop type classification. Apart from the endmember variables, the other five extracted variable types are selected by the RF classifier for both winter and summer crop classifications. (2) SAR data can capture the key information of summer crops when optical data is limited, and the addition of SAR data can significantly improve the accuracy as to summer crop types. (3) The overall accuracy (OA) of both summer and winter crop type mapping exceeded 95%, with clear and relatively accurate cropland boundaries. Area evaluation showed a small bias in terms of the classified area of rapeseed, single-cropped rice, and double-cropped rice from statistical records. (4) Further visual examination of the spatial distribution showed a better performance of the classified crop types compared to three existing products. The results suggest that the proposed method has great potential in accurately mapping crop types in a complex subtropical planting environment. Full article

Photodetectors are critical components in modern optoelectronic systems due to their extensive applications in information conversion and image storage. Selenium (Se), an element with a low melting point, a broad spectral response, and rapid response speed, exhibits a disadvantage of high optical reflectivity, which leads to a reduction in response. Spiro-MeOTAD, featuring controllable energy bands and facile processing, has its practical application limited by inadequate thermal and environmental stability. In this study, Spiro-MeOTAD-1 with enhanced stability was prepared through the optimization of dopants (Zn(TFSI)₂ and CNT:TiO₂) within Spiro-MeOTAD, to create a Se-F/Spiro-MeOTAD-1 heterojunction photodetector by subsequently compositing with selenium foam (Se-F). The self-powered device demonstrates exceptional photovoltaic performance within the wavelength range of 350–800 nm at 0 V bias, exhibiting a maximum responsivity of 108 mA W⁻¹, a switching ratio of 5 × 10³, a specific detectivity of 2.96 × 10¹² Jones, and a response time of 20 ms/50 ms. The device also demonstrates elevated environmental stability pretreatment at 140 °C following a one-month period. The photodetection stability of the Se-F/Spiro-MeOTAD-1 flexible PD was demonstrated by its capacity to retain 76.3% of its initial light current when subjected to 70 bending cycles at 30°. This finding further substantiates the photodetection stability of the material under various bending conditions. Further verification of the applicability of Spiro-MeOTAD-1 in Se-based devices establishes a novel paradigm for designing photodetectors with enhanced performance and stability. Full article

This study reports a gate-tunable three-terminal optoelectronic synaptic device based on an Al/ZnO nanoparticles (NPs)/SiO₂/Si structure for neuromorphic in-sensor memory applications. The ZnO NP film, fabricated via spin coating, exhibited strong UV-induced excitatory post-synaptic current (EPSC) responses that were modulated by gate voltage through charge injection across the SiO₂ dielectric rather than by conventional field effect. Optical stimulation enabled short-term synaptic plasticity, with paired-pulse facilitation (PPF) values reaching 185% at a gate voltage of −5.0 V and decreasing to 180% at +5.0 V, confirming gate-dependent modulation of synaptic weight. Repeated stimulation enhanced learning efficiency and memory retention, as demonstrated by reduced pulse numbers for relearning and slower EPSC decay. Wickelgren’s power law analysis further revealed a decrease in the forgetting rate under negative gate bias, indicating improved long-term memory characteristics. A 3 × 3 synaptic device array visualized visual memory formation through EPSC-based color mapping, with darker intensities and slower fading observed under −5.0 V bias. These results highlight the critical role of gate-voltage-induced charge injection through the SiO₂ dielectric in controlling optical potentiation and electrical depression, establishing ZnO NP-based optoelectronic synaptic devices as promising platforms for energy-efficient, light-driven neuromorphic computing. Full article

Lithium disilicate ceramics (LDSs) are widely used in restorative dentistry for their excellent aesthetic and mechanical properties. Variants like zirconia-reinforced lithium silicate (ZLS) and advanced lithium disilicate (ALD) were developed to enhance these characteristics. However, differences in their physical and optical properties, as well as the influence of processing techniques (heat pressing vs. CAD-CAM), remain unclear. This study aimed to evaluate the physical and aesthetic properties of LDS, ZLS, and ALD ceramics. A systematic review and meta-analysis following PRISMA guidelines were conducted. Studies published in the last ten years were retrieved from PubMed, Web of Science, Scopus, Cochrane, and Scielo. The inclusion criteria encompassed in vitro studies analyzing LDS, ZLS, and ALD ceramics with quantitative data on mechanical and aesthetic properties. Meta-analyses were performed using a random-effects model, with subgroup analyses based on ceramic type and processing technique. Twenty-two studies met the inclusion criteria. Meta-analyses showed significant differences in flexural strength, hardness, surface roughness, wear, and translucency. The processing technique influenced these properties, with CAD-CAM materials exhibiting distinct performance compared to heat-pressed ceramics. Publication bias was assessed using Egger’s test and the Trim and Fill method, and heterogeneity via meta-regression. LDS showed the highest fracture resistance and least wear, while ALD had greater roughness depth. Heat pressing enhanced hardness and reduced roughness, whereas CAD-CAM improved flexural strength. Considering these findings and study limitations, LDS appears the most suitable option for clinical use due to its superior mechanical performance. Full article

This study develops a comprehensive, data-driven framework for predicting satellite imagery prices using four state-of-the-art ensemble learning algorithms: XGBoost, LightGBM, AdaBoost, and CatBoost. Two distinct datasets—optical and Synthetic Aperture Radar (SAR) imagery—were assembled, each characterized by nine technical and economic features (e.g., imaging mode, spatial resolution, satellite manufacturing cost, and acquisition timeliness). Bayesian optimization is employed to systematically tune hyperparameters, thereby minimizing overfitting and maximizing generalization. Models are evaluated on held-out test sets (20% of data) using Pearson’s correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), unbiased RMSE (ubRMSE), Nash–Sutcliffe Efficiency (NSE), and Kling–Gupta Efficiency (KGE). For optical imagery, the Bayesian-optimized XGBoost model achieves the best performance ($R=0.9870$, $\mathrm{RMSE}=\$3.44/{\mathrm{km}}^2$, $\mathrm{NSE}=0.9651$, $\mathrm{KGE}=0.8950$), followed closely by CatBoost ($R=0.9826$, $\mathrm{RMSE}=\$3.83/{\mathrm{km}}^2$). For SAR imagery, CatBoost outperforms all others after optimization ($R=0.9278$, $\mathrm{RMSE}=\$9.94/{\mathrm{km}}^2$, $\mathrm{NSE}=0.8575$, $\mathrm{KGE}=0.8443$), reflecting its robustness to heavy-tailed price distributions. AdaBoost also demonstrates competitive accuracy, while LightGBM and XGBoost exhibit larger errors in high-value regimes. SHapley Additive exPlanations (SHAP) analysis reveals that imaging mode and spatial resolution are the primary drivers of price variance across both domains, followed by satellite manufacturing cost and acquisition recency. These insights demonstrate how ensemble models capture nonlinear, high-dimensional interactions that traditional rule-based pricing schemes overlook. Compared to static, experience-driven price brackets, our machine learning approach provides a scalable, transparent, and economically rational pricing engine—adaptable to rapidly changing market conditions and capable of supporting fine-grained, application-specific pricing strategies. Full article

Ambient fine particulate matter of size less than 2.5 μm in aerodynamic diameter (PM_2.5) is a key ambient air pollutant that has been linked to numerous adverse health outcomes. Reliable estimates of PM_2.5 are important for supporting epidemiological and health impact assessment studies. Precise measurements of PM_2.5 are available through networks of monitors; however, these are spatially sparse and temporally incomplete. Chemical transport model (CTM) simulations and satellite-retrieved aerosol optical depth (AOD) measurements are two data sources that have been used to develop prediction models for PM_2.5 at fine spatial resolutions with increased spatial coverage. As part of the Multi-Angle Imager for Aerosols (MAIA) project, a geostatistical regression model has been developed to bias-correct AOD, followed by Bayesian ensemble averaging to gap-fill missing AOD values with CTM simulations. Here, we present a suite of statistical software (available in the R package ensembleDownscaleR) to facilitate the adaptation of this modeling approach to other settings and air quality modeling applications. We describe the Bayesian ensemble averaging approach, model specifications, estimation methods, and evaluation via cross-validation that is implemented in the software. We also provide a case study of estimating PM_2.5 using 2018 data from the Los Angeles metropolitan area with an accompanying tutorial. All code is fully reproducible and available on GitHub, data are made on Zenodo, and the ensembleDownscaleR package is available for download on GitHub. Full article

Diamond, known for its exceptional physical and chemical properties, shows great potential in advanced fields such as medicine, semiconductors, and optics. However, reducing surface roughness is critical for enhancing its performance. This study employs inductively coupled plasma (ICP) polishing to etch single-crystal diamond and analyzes the impact of different etching parameters on surface roughness using atomic force microscopy (AFM). Using the change in surface roughness before and after etching as the main evaluation metric, the optimal etching parameters were determined: Ar/O₂/SF₆ gas flow ratio of 40/50/10 sccm, ICP power of 200 W, RF bias power of 40 W, chamber pressure of 20 mTorr, and etching time of 10 min. Results show that increased etching time and SF₆ flow rate raise surface roughness; although higher ICP and RF power reduce roughness, they also cause nanostructure formation, affecting surface quality. Lower chamber pressure results in smaller roughness increases, while higher pressure significantly worsens it. Based on the optimized process parameters, the pristine single-crystal diamond was further etched in this study, resulting in a significant reduction of the surface roughness from 2.22 nm to 0.562 nm, representing a 74.7% decrease. These improvements in surface roughness demonstrate the effectiveness of the optimized process, enhancing the diamond’s suitability for high-precision optical applications. Full article