Search Results (873)

In complex traffic environments such as low light, strong glare, occlusion and at night, systems that rely solely on visible light single sensors for pedestrian detection have drawbacks such as low detection accuracy and poor robustness. Based on the YOLOv8 convolutional network, this paper adopts a dual-branch structure to process visible light and infrared images simultaneously, fully utilizing feature information at different scales to effectively detect pedestrian targets in complex and changeable environments. To address the issues of insufficient interaction of modal feature information and fixed fusion weights, a cross-modal feature interaction and enhancement mechanism was introduced. A modal-channel interaction block (MCI-Block) was designed, in which residual connection structures and weight interaction were added within the module to achieve feature enhancement and filter out noise information. Introduce a dynamic weighted feature fusion strategy, adaptively adjusting the contribution ratio of different modal features in the fusion process, aiming to enhance the discrimination ability of the key pedestrian area. The training and testing of the network designed in this paper were completed on the visible light and infrared pedestrian detection dataset LLVIP and Kaist. At the same time, the test results of the dual-branch model and the model designed in this paper were further verified in actual traffic scenarios. The results show that the dual-branch YOLOv8 network for visible light and infrared images, which was constructed in this paper, can reliably enhance the detection performance of pedestrian targets in complex traffic environments, including accuracy, recall rate, and mAP@0.5, etc., thereby improving the robustness of pedestrian detection. Full article

The development of efficient, stable, and sustainably fabricated photocatalysts for solar-driven hydrogen evolution remains a critical challenge in the field. Herein, we report a novel green coprecipitation strategy to synthesize calcium-doped zinc oxide (Ca-ZnO) nanosheets, utilizing cactus juice as a natural, multifunctional medium for the coprecipitation process. This method enables the in situ, tunable incorporation of 3–7% Ca²⁺ ions into the wurtzite ZnO lattice without the use of harsh chemical reagents. Comprehensive characterization confirms that Ca²⁺ substitutionally replaces Zn²⁺, which preserves the intrinsic crystal structure of ZnO well while inducing the formation of uniform nanosheet morphology. This doping strategy effectively modulates the electronic band structure, progressively narrowing the bandgap from 3.19 eV to 2.90 eV and significantly enhancing visible-light absorption. Crucially, the incorporation of Ca²⁺ also generates oxygen vacancies, which serve as efficient electron traps to suppress photogenerated charge carrier recombination. The optimized 5%Ca-ZnO photocatalyst demonstrates a favorable hydrogen evolution rate of 889 μmol·g⁻¹·h⁻¹ under full-spectrum irradiation, with stability, retaining 94.8% of its activity after four cycles. This work not only provides a high-performance material but also establishes a generalizable, sustainable paradigm for the design of advanced semiconductor photocatalysts. Full article

Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light cameras leads to frequent overexposure or underexposure under extreme space lighting. Compounded by sparse textures and strong specular reflections, these factors significantly constrain reconstruction accuracy. While existing general-purpose feed-forward models such as MapAnything offer efficient inference, their geometric recovery capabilities degrade sharply when facing significant domain shifts. To address these issues, this paper proposes an enhanced 3D reconstruction framework tailored for the space environment named In-Orbit MapAnything. First, to mitigate data scarcity, we construct a high-quality space target dataset incorporating extreme illumination characteristics, which provides comprehensive auxiliary modalities including accurate camera poses and dense point clouds. Second, we propose the SatMap-Adapter module to mitigate feature degradation caused by severe specular reflections. This architecture employs a hierarchical cascade sampling strategy to align multi-level backbone features and utilizes a lightweight adaptive fusion module to dynamically integrate shallow photometric cues, intermediate structural information, and deep semantic features. Finally, we employ a weight-decomposed low-rank adaptation strategy to achieve parameter-efficient fine-tuning while strictly freezing the pre-trained backbone. Experimental results demonstrate that the proposed method decreases the absolute relative error and Chamfer distance by 15.23% and 20.02% respectively compared to the baseline MapAnything model, while maintaining a rapid inference speed. The proposed approach effectively suppresses reconstruction noise on metallic surfaces and recovers fine geometric structures, validating the effectiveness of our feature-enhanced framework in extreme space environments. Full article

Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article

In this study, the in situ solvothermal synthesis of a copper-based metal–organic framework (Cu-BTC MOF) into two porous ceramic substrates with a 10 cm diameter and 2 cm thickness was reported. X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, diffuse reflectance spectroscopy (DRS), Tauc plot analysis, optical microscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) were the techniques that were utilized to verify the formation and incorporation of the MOF into ceramics (two samples, with different SiO₂ particles; 500 µm (Ceramic 1), and 150 µm (Ceramic 2)). The synthesized Cu-MOF exhibited a crystalline structure. Both the composites and the Cu-MOF exhibited visible-light absorption, with optical band gaps of 2.5 eV and 2.4 eV, respectively, as determined by DRS. TEM images demonstrated that crystalline MOF domains were successfully included inside the ceramics. Methyl orange (MO), Congo red (CR), and methylene blue (MB) were used to assess the composites’ ability to remove dyes. Catalytic hydrogenation, powered by in situ hydrogen production from NaBH₄ hydrolysis, demonstrated high removal efficiencies of 91–97% after 60 min. Adsorption, on the other hand, was ineffective. Despite undergoing four consecutive cycles without performance degradation, the materials demonstrated remarkable recyclability. Cu-MOF@ceramic composites are effective, durable, and practically applicable for improved wastewater treatment. Full article

Optical attenuation caused by absorption and scattering in turbid water significantly degrades underwater image quality, making reliable underwater imaging a challenging problem. Underwater polarization imaging has attracted increasing attention because of its ability to suppress scattered light and provide additional polarization cues. However, existing polarization-based enhancement approaches often adapt conventional underwater image enhancement strategies, and the multi-dimensional characteristics of polarization information are not always fully utilized, which may limit detail restoration in complex underwater environments. To address this issue, this paper proposes a bio-inspired underwater polarization image enhancement framework motivated by the polarization vision mechanism of marine organisms. Specifically, a two-stage architecture consisting of a Polarization Adversarial Network (PAN) and a Polarization Enhancement Network (PEN) is designed. The PAN incorporates a Bionic Antagonistic Module (BAM) to exploit complementary information among polarization channels, while Salient Feature Extraction (SFE) is introduced to reduce redundant feature interference. The subsequent PEN integrates a frequency-aware Mamba-based structure to enhance feature representation and improve detail reconstruction. Experiments on simulated underwater polarization datasets indicate that the proposed framework can effectively suppress backscattering and improve structural detail visibility in challenging underwater scenes, demonstrating competitive performance compared with representative traditional and learning-based methods. Full article

A novel catalyst, Tm₂FeSbO₇, was synthesized by employing the solid-phase high-temperature sintering method, and, for the first time, it was utilized to create a Z-type heterojunction with BiYO₃. A direct Z-scheme Tm₂FeSbO₇/BiYO₃ heterojunction photocatalyst (TBHP) was successfully produced by employing the ball-milling technique. X-ray diffraction analysis results indicated that Tm₂FeSbO₇ crystallized in a cubic pyrochlorestructure which owned the Fd-3m space group, with a unit cell parameter of 10.1769 Å, whereas BiYO₃ displayed a fluorite structure in the Fm-3m space group, with a unit cell parameter of 5.4222 Å. The Mossbauer spectrum of Tm₂FeSbO₇ showed that Fe³⁺ ions might locate at octahedral sites. The measured bandgap widths for the TBHP, Tm₂FeSbO_7, and BiYO₃ were 2.14 eV, 2.21 eV, and 2.30 eV, respectively. Multiple experimental results demonstrated that the TBHP exhibited a higher valence band ionization potential, a narrower band gap width, and a higher removal efficiency of the sulfamethoxazole (SMX) compared with the Dy₂TmSbO₇/BiHoO₃ heterojunction photocatalyst. Under visible-light irradiation (VISLI) of 115 min, the TBHP showcased exceptional photocatalytic elimination performance; therefore, the elimination rate of the SMX and the total organic carbon (TOC) mineralization rate reached 99.51% and 98.10%, respectively. In contrast to single-component Tm₂FeSbO₇, BiYO_3, or conventional nitrogen-doped titanium dioxide (N-TiO₂) catalyst, the TBHP exhibited removal efficiency enhancement for degrading the SMX by 1.17 times, 1.31 times, or 4.06 times. Simultaneously, the matching mineralization rate for removing the TOC density by employing the TBHP was 1.20 times, 1.34 times, or 4.73 times higher than that by employing Tm₂FeSbO₇, BiYO₃, or conventional N-TiO₂. Above experimental results indicated that the mineralization efficiency for removing TOC density by employing the TBHP was higher than that by employing Tm₂FeSbO₇, BiYO₃, or N-TiO₂. Radicals trapping experiments and the electron paramagnetic resonance spectroscopy results revealed that hydroxyl radicals, superoxide anions, and photoinduced holes were the primary active species during the catalytic elimination course of the SMX by employing the TBHP under VISLI. The results demonstrated that the direct Z-scheme TBHP, which was developed in this study, exhibited the maximal removal efficiency for degrading the SMX in contrast to Tm₂FeSbO₇, BiYO₃, or N-TiO₂. Additionally, the possible elimination routes and elimination mechanisms of the SMX were proposed. Therefore, an important scientific foundation for developing high-performance heterojunction catalysts was established. Full article

The photocatalytic efficacy of metallic silver nanoparticle/zinc oxide (Ag-NPs/ZnO) composite thin films, COMP-Ag_x, with varying silver concentrations (0 mol% ≤ x ≤ 100 mol%), is investigated for the degradation of methyl orange (MO). The films were spin-coated on a silica glass surface at 600 °C utilizing the molecular precursor method (MPM). The XRD spectra of these composite thin films revealed three significant peaks corresponding to the diffraction planes of (0 0 2), (1 0 0), and (1 0 1), indicative of the formation of ZnO crystallites in diverse orientations, in conjunction with an additional signal for cubic Ag crystals. The magnitude of the ZnO peaks diminishes as the mol% of silver increases. The images from the SEM confirm the integration of Ag-NPs into the ZnO matrix. The UV/Vis absorption spectra exhibit a 410 nm surface plasmon resonance (SPR) peak for composite Ag-NP/ZnO thin films. The absorption spectra of ZnO and Ag-NP/ZnO composite thin films demonstrate the band gap of ZnO to be 3.4 eV, while the band gaps of the composite thin films nearly approximate that of ZnO. The decomposition rates of the MO solution indicate that composite thin films function effectively under visible irradiation compared to pure ZnO. The optical properties indicated that the SPR of Ag-NPs contributed to the visible responsiveness of the composite thin films. The SPR demonstrate significant visible light responsiveness and essential characteristics during photoexcited electron transfer from the Ag-NPs to the ZnO conduction band. Full article

CO₂ cycloaddition with epoxides offers a sustainable route for CO₂ utilization, yet the simultaneous activation of both substrates remains challenging. Herein, using UiO-66(Zr)-NH₂ (denoted as UZN) as a model system, we illustrate that dual-active sites consisting of unsaturated Zr⁴⁺ centers and amine groups can efficiently accelerate CO₂ fixation with epoxides under visible light. The unique ensemble in UZN optimizes light harvesting, promotes charge carrier separation, and enriches bifunctional active sites for efficient adsorption and activation of epoxides and CO₂. Consequently, UZN exhibits significantly improved CO₂-epoxide cycloaddition performance compared to UiO-66(Zr)-H (denoted as UZH), achieving a PC yield of 99.5%, with a production rate of 9.97 mmol·g⁻¹·h⁻¹. This work establishes a clear coordination–photocatalytic synergy in MOF-based systems and provides fundamental insights and a generalizable strategy for designing advanced catalysts for CO₂ transformation. Full article

This study presents an efficient, environmentally benign approach for synthesizing chitosan-entrapped titanium dioxide (TiO₂) nanocomposites utilizing aqueous orange peel extract playing its role in reduction and stabilization of the nanoparticles and exploring its anticancer activity in vitro. TiO₂ nanoparticles were initially synthesized via a modified sol-gel method incorporating the orange peel extract. Subsequently, these nanoparticles were entrapped within a chitosan matrix. The orange peel extract was thoroughly characterized using analysis of phytochemicals present, and Gas Chromatography–Mass Spectrometry (GC–MS) analysis of a reconstructed methanolic extract to identify potential biomolecules responsible for the reduction and capping processes. The synthesized chitosan-entrapped TiO₂ nanoparticles were subjected to comprehensive characterization using various analytical techniques, like UV–visible spectroscopy, Dynamic Light Scattering (DLS) and Zeta Potential analysis, X-ray Diffraction (XRD), FTIR, High-Resolution Scanning Electron Microscopy (HR-SEM) and Energy-Dispersive X-ray Spectroscopy (EDAX). An absorption peak was observed at 296 nm, a hydrodynamic diameter of 400 nm, a+ 35.88 mV zeta potential, and an SEM image showing a diameter in the range of 300–645 nm, indicating polymer entrapment with enhanced size. Brine shrimp assay, MTT assay using normal fibroblasts, 3T3-L1, and zebrafish embryo assay were done to observe the biocompatibility of the synthesized nanostructure. The concentration of 50 μg/mL was found to be inert in both in vitro and in vivo. Furthermore, cervical cancer cells, SiHa, were treated with the nanoparticles to exhibit their cancer-killing capability with an IC₅₀ value of 30.74 μg/mL. The results demonstrate the effectiveness of orange peel extract as a sustainable agent for TiO₂ nanoparticle synthesis and the successful formation of a stable chitosan-entrapped nanocomposite. This approach offers a promising pathway for producing functional metal oxide nanomaterials with reduced environmental impact and enhanced properties for diverse biomedical applications. Future studies using other types of cancer cells and animal models for cancerous tumors need to be explored. Full article

In this paper, we propose a robust multi-input single-output (MISO) underwater visible light communication (UVLC) system. By integrating NLTCP and NW-Viterbi decoding, the system effectively alleviates nonlinear distortions and stochastic power fluctuations. NLTCP is employed to achieve probabilistic shaping by generating a non-uniformly distributed constellation, which effectively suppresses the occurrence of high-amplitude symbols to mitigate device nonlinearity. To further optimize power allocation, a MISO architecture is utilized to distribute the signal load and reduce the power burden on individual devices. Moreover, the NW-Viterbi decoder incorporates a noise-aware weighting mechanism to optimize the decision metric, thereby enhancing decoding reliability in response to signal-dependent power fluctuations and noise variations in the underwater channel. Experimental results confirm that at an aggregate data rate of 5.8 Gbps, the proposed scheme achieves a significant Q-factor gain of 0.92 dB compared to the traditional PAM4 scheme, alongside a 90.76% enlargement in the effective operating dynamic range. This approach offers a computationally efficient yet effective solution to nonlinearity and power jitter, demonstrating significant potential for practical underwater deployments. Full article

As a next-generation immersive service, holographic video enables users to move freely within a virtual world. This imposes stringent requirements on wireless networks. Given the massive bandwidth capacity inherent to visible light, visible light communication (VLC) can effectively meet the transmission requirements of holographic video and is an ideal wireless technology for next-generation indoor immersive services. However, VLC channels are highly dependent on Line-of-Sight (LoS) links. Due to user mobility, traditional VLC systems relying on fixed-orientation Photodetectors (PDs) often suffer from severe channel fading, which significantly degrades the transmission performance. In this paper, we propose an indoor VLC holographic video transmission architecture supporting rotatable PDs, utilizing rotatable PDs mounted on Head-Mounted Displays (HMDs) to assist in holographic video transmission. To minimize the total transmission delay of all users, we address the holographic video transmission problem by jointly optimizing the transmit power allocation of VLC Access Points (APs) and the pitch and roll angles of the users’ PDs. By formulating the problem as a Markov Decision Process (MDP), we address it using a novel Deep Reinforcement Learning (DRL) strategy leveraging the Soft Actor–Critic (SAC) architecture. Simulation results demonstrate that the proposed scheme reduces the overall latency by up to 29.6% compared to the benchmark schemes. Furthermore, the convergence speed of the algorithm is improved by 35% compared to traditional deep reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG). Full article

Solid-state lighting, especially light-emitting diodes (LEDs), is revolutionizing indoor lighting due to its energy efficiency, long lifespan, low heat output, and enhanced color rendering. LEDs can quickly adjust light intensity, enabling the development of visible light communication (VLC) technology. However, the modulation bandwidth of phosphor-converted white LEDs commonly used for illumination is limited, potentially affecting the speed of the VLC links. This paper presents a receiver-side equalization technique to overcome bandwidth limitations in VLC links due to LEDs. The proposed approach utilizes a novel transimpedance amplifier with an embedded T-network shunt-feedback equalizer (TIA-TE) to introduce adjustable high-frequency peaking in the TIA’s frequency response. By incorporating this peaking, the system’s bandwidth is extended without sacrificing important performance parameters like gain, noise, or power dissipation. The TIA-TE is followed by a main amplifier and a standalone continuous-time linear equalizer (CTLE) for further signal conditioning, while a 50 Ω buffer interfaces the receiver with measurement equipment. Post-layout simulations in a 0.35 µm CMOS process validate the approach. Using a 4 pF photodiode, the system bandwidth was initially limited by the LED’s 3 MHz modulation bandwidth. The proposed TIA-TE extends the bandwidth to 8.4 GHz without sacrificing the gain or power dissipation. The subsequent CTLE further extends the bandwidth to 14 MHz. The receiver front end achieves a mid-band transimpedance of 110 dBΩ and an input-referred noise current of 7.2 nA_rms, while dissipating 2.48 mW (excluding the 50 Ω buffer). Simulated 28 Mb/s NRZ eye diagrams demonstrate the feasibility of the proposed TIA-TE architecture for LED-limited VLC links. Full article

Novel porous geopolymer (IGP&SS), possessing mesoporous structure and a compressive strength of 9.40 MPa, was synthesized through alkali activation of double solid wastes such as iron tailings and steel slag. To overcome the high activation energy barrier of oxidants for refractory pollutant treatment, the IGP&SS was designed to efficiently activate peroxymonosulfate (PMS) under visible-light irradiation, generating reactive radicals for the rapid degradation of methylene blue (MB). The system achieved nearly complete removal within 30 min. To enhance MB removal, the effects of key factors including IGP&SS dosage, PMS dosage, initial MB concentration, temperature, and pH on the degradation process were systematically investigated. Quenching experiments revealed that several reactive oxygen species contributed to MB degradation, with the order of contribution being •OH > ¹O₂ > SO₄•⁻ > •O₂⁻. Mechanistic studies indicated that the efficient MB degradation was primarily attributed to the flexible Fe(II)/Fe(III) redox cycling in IGP&SS, which accelerated PMS activation and radical generation. X-ray photoelectron spectroscopy (XPS) analysis of the post-reaction catalyst confirmed its structural robustness, revealing a characteristic binding energy shift in the O 1s peak to 530.8 eV and a quantitative redistribution of iron species (Fe(III) content increasing from 40.4% to 57.0%). Given its outstanding performance, demonstrated stability, and eco-friendly preparation, IGP&SS holds great promise for PMS-based advanced oxidation processes in dye wastewater treatment, offering a sustainable approach for high-value utilization of iron tailings and steel slag while alleviating resource scarcity. Full article

Infrared thermography is a significant and extensively utilized method for assessing the operational condition of power equipment. Nonetheless, the constrained spatial resolution of infrared imaging systems, imaging noise, and the inadequate representational capacity of single-modality data render the precise segmentation of power equipment targets difficult, particularly in intricate backdrops and settings with weak structures. Simultaneously, obtaining high-quality pixel-level annotations for power equipment is expensive and laborious, leading to a scarcity of training samples and thus diminishing the efficacy of conventional supervised segmentation techniques. This research offers a super-resolution guided cross-modal segmentation strategy to tackle these issues in data-scarce circumstances and examines the applicability of the general-purpose segmentation model Segment Anything Model 3 (SAM3) for infrared image segmentation of power equipment. A super-resolution reconstruction framework based on a high-order degradation model is built to enhance low-resolution infrared images collected in real-world contexts. An Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) -based network incorporating residual-in-residual dense blocks (RRDB) is utilized to reconstruct infrared thermograms, hence improving structural features and boundary representations. Secondly, the concurrently obtained visible-light images are improved by low-light enhancement methods, and an anchor-free object detection framework is employed to ensure accurate localization of power equipment targets. The identified areas in visible images are aligned with the coordinate system of infrared super-resolution images via cross-modal geometric transformation, establishing a cross-modal spatial prior that efficiently limits the search space for infrared segmentation and mitigates background interference. The general-purpose segmentation model SAM3 is introduced, utilizing cross-modal detection boxes as prompts to facilitate precise segmentation of power equipment targets in infrared super-resolution images, achieving high-accuracy segmentation without the necessity for extensive task-specific annotated data. The experimental results demonstrate that our proposed approach significantly improves both the accuracy and robustness of infrared image segmentation for power equipment under complex conditions, attaining a Jaccard index of 89.86% and a Dice coefficient of 91.12%, thereby validating its efficacy and practical applicability in data-scarce environments. Full article