Cherenkov light detectors in LHAASO and scientific results

ABSTRACT Joint clustering of hyperspectral and Light Detection and Ranging (LiDAR) data is challenging due to their heterogeneity and differing spatial-spectral characteristics. To address this, we propose an adaptive multi-view graph convolutional network (MVGCN) that integrates visual Bidirectional Encoder Representations from Transformers (VisualBERT), referred to as MVGCN-VisualBERT, to extract high-level semantic features from both modalities. These features form a superpixel-level graph that preserves spatial structure while reducing redundancy. A multi-view graph convolutional network then propagates and aggregates information to enhance cluster cohesion. Evaluated on the MUUFL and UH2013 datasets, MVGCN-VisualBERT outperforms state-of-the-art methods, achieving improvements of 2.8% in overall accuracy, 2.6% in the Kappa coefficient, 2.9% in normalized mutual information and 5.8% in the adjusted Rand index on MUUFL. These results highlight the potential of the proposed approach for improving unsupervised multimodal land-cover analysis in remote sensing applications.

Operational evaluation of unmanned aerial vehicle-applied granular larvicides in an integrated mosquito management program.

Learning invariant representation for light field adversarial salient object detection

Monolayer molybdenum disulfide (MoS2) is a highly prominent material in optoelectronic devices, yet its intrinsic band structure limits its performance in the near-infrared regime. Here, we demonstrate a high-performance broadband photodetector based on monolayer MoS2 hybridized with eco-friendly CuInS2 quantum dots (CIS-QDs). The Type-II band alignment at the heterojunction interface enables efficient spatial separation of photogenerated carriers, while the CIS-QDs simultaneously passivate surface defects and induce n-type doping in the MoS2 channel. Consequently, the device exhibits a dramatically extended photoresponse spanning ultraviolet (395 nm) to NIR (980 nm), with the photocurrent enhanced by more than 2 orders of magnitude at 980 nm relative to pristine MoS2. The hybrid photodetector achieves a specific detectivity (D*) exceeding 1013 Jones at 395 and 625 nm, and a responsivity improvement of over 1 order of magnitude at 780 nm. These results validate the MoS2/CIS-QD heterostructure as a promising eco-friendly platform for high-performance broadband optoelectronic applications.

Abstract Determining long‐term soil erosion and deposition rates and understanding landform evolution are important for managing both natural and human‐modified landscapes, including postmining rehabilitation sites. Although various landform evolution models (LEMs) have been developed to simulate erosion processes and landscape change, relatively few studies have directly compared modelled outputs with field‐based erosion estimates. This study evaluates and compares two LEMs, (i) SIBERIA, which is widely used in the Australian mining sector, and (ii) SSSPAM, a newer coupled soilscape–LEM, together with the well‐established soil erosion model (the revised universal soil loss equation). A formerly grazed hillslope in the Upper Hunter region of New South Wales was used as the case study, and a high‐resolution light detection and ranging (LiDAR)‐derived digital elevation model was used as the landscape input. Field‐based erosion rates were quantified using sediment yield data from a catchment dam and 137 Cs isotopic analysis. Sediment trap measurements indicated erosion rates ranging from 0.43 to 0.61 t/ha/year, while 137 Cs results showed erosion and deposition rates of up to 1.5 and 1.1 t/ha/year, respectively. SIBERIA predicted erosion rates of 1.07 t/ha/year under dense vegetation cover and 3.74 t/ha/year under moderate cover, while SSSPAM estimated 0.35 and 2.43 t/ha/year for the same conditions. The RUSLE model predicted an average erosion rate of 2.23 t/ha/year, with values ranging from 0.58 to 4.65 t/ha/year. Overall, the modelled erosion estimates were broadly consistent with the field observations, demonstrating the ability of both SIBERIA and SSSPAM to reproduce realistic hillslope erosion rates. These findings support the use of LEMs as valuable tools for guiding sustainable land management and rehabilitation practices in both natural and constructed landscapes.

Three-dimensional (3D) imaging underpins applications ranging from autonomous navigation to defense and biomedicine, with single-photon avalanche diode (SPAD) light detection and ranging (LiDAR) enabling fast, long-range, and photon-efficient depth sensing. In practice, reconstruction quality is constrained by limited sensor resolution, particularly in the short- and medium-wave infrared, as well as sensor dark counts, background illumination, atmospheric effects, and motion. We introduce a unified framework for continuous-surface 3D scene representation that integrates multimodal sensing with score-based priors on latent variables. The proposed approach models scenes using a parametric continuous surface, enabling robust rendering at arbitrary spatial resolutions, even in the presence of multiple depth layers allowing imaging through camouflage. We demonstrate capabilities including data compression, targeted high-resolution rendering, and guided super-resolution of dynamic 3D videos. Validated across multiple sensing scenarios using diverse off-the-shelf priors, this framework enables compressed, high-fidelity 3D imaging in real-world environments.

Precise, real time and non-destructive assessment of plant water status is important for advancing plant physiological understanding, optimizing water usage, improving crop resilience and supporting precision agriculture in the face of increasingly variable climatic conditions. Classical methods for measuring plant water status reviewed in Part 1 of this two-part review have significant limitations for field level applications, providing only discrete, single-point measurements and potentially altering plant physiology through destructive sampling. This second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations. We review techniques such as ZIM-probe, terahertz spectroscopic techniques, microwave remote sensing, infrared transmission sensor, microtensiometers, dendrometers and leaf thickness sensors, light detection and ranging (i.e. LiDAR), imaging spectroscopy, NMR relaxation, spectroscopy based on equivalent water thickness, spectral indices, derivative spectra, post-continuum removal indicators, visible and near-infrared spectroscopy, and infrared thermography. These emerging techniques facilitate high-resolution, real-time monitoring of water status across leaf, canopy and ecosystem scales. This comprehensive comparison provides guidance for selecting most appropriate technique based on experimental objectives, guiding applications ranging from single leaf to canopy scale ecosystem assessment. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Photodetectors that simultaneously integrate high responsivity, fast response, self-powered operation, and polarization sensitivity are highly needed for advanced imaging and optical information processing, yet remain challenging to realize in a simple device architecture. Here, the in-plane anisotropic ReSe2 and narrow-bandgap Ta2NiSe5 are utilized to construct a ReSe2/Ta2NiSe5 van der Waals heterojunction photodetector with self-powered broadband operation from 375 to 850 nm. Under 532 nm illumination in the self-powered mode, the device exhibits a responsivity of 0.381 A W-1 and a specific detectivity of 5.71 × 109 Jones. The device also presents a fast temporal response with rise and fall times of 44 and 70 μs, respectively, as well as a -3 dB bandwidth of approximately 5 kHz, demonstrating excellent weak light and high-speed detection capability. Moreover, under zero bias, the device exhibits photocurrent polarization ratios of 1.56 and 1.83 for the vertically stacked and laterally stacked configurations, respectively, indicating a clear polarization-sensitive photoresponse. Benefiting from these features, the device further enables real-time polarization-resolved imaging of specific patterns and reliable optoelectronic signal recognition. This work establishes a simple and effective strategy for the development of miniaturized, low-power, broadband photodetectors with intrinsic polarization sensitivity based on 2D van der Waals heterostructures.

This article presents a prototype design and experimental validation of intelligent control of a street light luminaire containing CW (cool white) and WW (warm white) LED based on human or object detection, motion, and weather conditions. The system is mainly controlled by a Raspberry Pi 5(RPi 5) integrated with a wireless CCTV camera for obtaining real-time object presence and speed estimation, along with a DHT11 sensor and LDR for temperature, humidity, and light level detection, and sensing of fog and rain from a weather application programming interface (API). The designed system uses vision-based object detection, determines the type and speed of object movement, and then dynamically adjusts the LED light output, consists of alternate array of CW and WW LED arrays, based on the required illuminance on road surface corresponding to the object's speed. On the other hand, it senses weather behaviour from weather API data to generate control signal and transfer via IoT network to ESP8266 to switch between CW and WW LEDs in a single LED module. In this system, sensor fusion techniques are used to correlate environmental parameters to actuate the situation demanded light level. A hardware prototype is designed and experimentally tested in a controlled environmental scenario to evaluate system response time, light output adjustment variation with speed, and lighting performance under dynamic weather conditions. This control setup helps to develop a smart street lighting solution as well as to provide suitable visual conditions and safety to support the development of a smart city.

Vector vortex beams, structured light fields characterized by polarization and phase singularities, offer substantial promise for next-generation photonic technologies. While the detection of scalar vortex beams has been investigated, the direct, all-electrical, and filter-free detection of vector vortex beams has remained a challenge. Here, we demonstrate an on-chip, all-electrical detection platform capable of simultaneously resolving the orbital angular momentum (OAM) order and polarization state of incident vector vortex beams. This functionality is achieved by coupling van der Waals layered thermoelectric materials with a phase-gradient metagrating, which spatially maps OAM and polarization information onto surface plasmon polariton intensity distributions, thereby inducing directionally modulated photothermoelectric responses. Leveraging these distinctive photoresponses, we demonstrate a proof-of-concept two-dimensional encrypted optical communication protocol. Our platform establishes a scalable approach for high-dimensional light field detection and lays the groundwork for advanced optoelectronic systems spanning secure communications, quantum information, optical manipulation, and high-resolution imaging.

Effect of surfactants on agitation-induced submicron and subvisible particles of therapeutic proteins.

Phase noise in Frequency Modulated Continuous Wave (FMCW) Light Detection and Ranging (LiDAR) systems degrades measurement accuracy by distorting the received signal during frequency sweeping. This leads to errors in distance estimation and reduces system performance, particularly in complex and dynamic environments. To address this challenge, this manuscript proposes a Binarized Simplicial Convolutional Neural Network (Bi-SCNN)-based framework for effective phase noise mitigation and accurate distance estimation in FMCW LiDAR systems. The objective is to enhance Frequency Modulated Continuous Wave (FMCW) Light Detection and Ranging (LiDAR) performance by compensating phase noise and improving distance accuracy, where signals are preprocessed using an Adaptive Two-Stage Unscented Kalman Filter (ATSUKF), followed by Bi-SCNN for phase distortion prediction, and optimized using Electric Eel Foraging Optimization (EEFO) for accurate compensation and distance estimation. The proposed method is implemented in Python, and its performance is evaluated utilizing Accuracy, Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and Signal-to-Noise Ratio (SNR). The Bi-SCNN achieves 97.5% accuracy, 0.02 RMSE, 0.023 MSE, 44 dB SNR, and 86 seconds computational time, outperforming existing methods such as Phase Noise Compensation (PNC), Iterative Learning Control (ILC), and Coherently Coupled Orbital Angular Momentum (CCOAM).

Diagnosis and surveillance of bladder cancer rely on white-light cystoscopy (WLC). However, this modality is operator-dependent and associated with a risk of missed lesions, contributing to high recurrence rates, especially in non-muscle invasive bladder cancer. Recent advances in artificial intelligence (AI) enable software-based decision support for bladder lesion detection, with potential for vendor-independent deployment and broad integration into routine clinical workflows. To develop and externally validate an AI-based clinical decision support system for real-time bladder lesion detection during cystoscopy. CystoAID, a convolutional neural network-based object detection system, was trained on prospectively collected video recordings from flexible cystoscopies and transurethral resections of bladder tumors. Diagnostic accuracy was evaluated using a retrospective external validation dataset representative of routine clinical practice, in accordance with STARD-AI recommendations. In the external validation cohort, CystoAID achieved a sensitivity of 1.00 (95% CI 0.95-1.00). Precision was 88.1% (95% CI 81.3-92.7), exceeding published estimates for WLC. Precision-recall analysis showed consistently high precision (>0.8) across clinically relevant recall levels, with declining precision at higher recall, reflecting the expected trade-off between sensitivity and false-positive detections. The system operated with low processing latency, supporting feasibility for real-time clinical use. Sensitivity was prioritized to mitigate the clinical risk associated with false-negative findings. CystoAID is a real-time, AI-based decision support tool for cystoscopy that demonstrated high sensitivity and favorable precision in external validation. These findings support its potential role as an assistive technology in routine urologic practice. Prospective studies are warranted to evaluate clinical impact, workflow integration, and performance in detecting challenging lesion subtypes, including flat lesions and carcinoma in situ.

Dual-mode photodetectors with vertically stacked photoactive layers enable bias-controlled, band-selective extraction from their constituent photoactive layers. While their applications ranging from non-invasive diagnostics to optical communication demand high-fidelity detection of faint light, performance is often limited by interlayer-derived structural complexity and noise. Here, we present an interlayer-free monolithic organic/PbS photodetector that achieves visible and short-wave infrared dual-mode operation with low noise and crosstalk. Inducing vertical and lateral phase-separation in the organic layer facilitates charge carrier dynamics that yield high specific detectivity without auxiliary interlayers, enabling simple device structures that detect small signals with high precision. This strategy can be utilized in a variety of photoactive layer combinations spectrally targeted for application-specific devices. Its practicality is demonstrated through single-pixel imaging, which reconstructs high-fidelity images across visible and SWIR bands even under light attenuation. Furthermore, its dual-mode capability enables silicon alignment through registration of front- and back-side features.

Mechanisms of photoperiodic polyphenism in vertebrates.

Lee County Mosquito Control District (LCMCD) implemented helicopter-mounted Light Detection and Ranging (LiDAR) technology to create high-resolution elevation models for refining larval mosquito habitat treatment boundaries in coastal and inland environments. Using a Ranger ULTRA laser scanning system equipped with an IMU-30 inertial measurement unit mounted on an Airbus H125 helicopter, the district mapped approximately 184,567 ha across Lee County during the 2026 dry season (January 13 to May 1), achieving vertical accuracy of 2.0 cm and horizontal point densities exceeding 30 points per square m. The LiDAR-derived elevation data enabled precise delineation of tidal and rainfall-driven flooding zones that may reduce treatment areas and associated larvicide costs while maintaining operational effectiveness. This operational note describes the equipment specifications, flight parameters, data processing methodology, cost-benefit outcomes, and lessons learned from large-scale implementation of LiDAR for integrated mosquito management and represents the first large-scale operational implementation of LiDAR within a mosquito control program.

The development of chiral organic semiconductors with efficient circularly polarized light (CPL) detection capability is crucial for advanced optoelectronic applications, such as secure communication and optical information processing. However, translating their promising molecular-level chirality into solid-state devices remains a challenge. The core issue lies in the difficulty of controlling the quality of the active film during film formation, including ordered molecular packing and suppressed surface defects, which governs both charge transport and chiral expression. Herein, we demonstrate high-quality thin films of a novel n-type chiral π-conjugated polymer, (S)-P(NDI2MH-T), fabricated via a kinetically controlled dip-coating method. Systematic investigation of the dip-coating parameters, particularly tailoring the surface wettability, identifies that the substrate with a contact angle of ∼36° and a temperature of 25 °C at a polymer concentration of 5 mg mL-1 yields continuous, pinhole-free films with enhanced molecular ordering. Based on these films, organic n-type phototransistors demonstrate a high electron mobility of 0.82 cm2 V-1 s-1 and a high photoresponsivity of 38 A W-1. Furthermore, the devices show pronounced chiral selectivity towards CPL, with a photocurrent dissymmetry factor (gph) of up to 0.28. Leveraging this selectivity, we successfully demonstrate the application of the device in binary logic information encoding and decoding, mimicking Morse code communication. This work provides a viable pathway from material processing to device integration for constructing sensitive and integrable chiral optoelectronic systems.

Pixel-programmable miniaturized optical arrays with large pixel count are essential for cutting-edge fields such as micro-displays, photonic chips, and light detection modules. In recent advances, a universal strategy with ultrahigh pixel count and highly flexible programmability remains lacking. Here we report a programmable optical nano-kirigami matrix with pixelated electromechanical reconfigurations. Deformable pixel arrays with high duty cycle and optical contrast are conceptually designed and experimentally realized based on a suspended turn-shaped nano-kirigami configuration. By employing the central plate to induce electrostatic force and the deformed arms to scatter incident light, switchable optical encryption and reconfigurable information display are demonstrated by programing the nano-kirigami matrices with a pitch size of only a few micrometers. Furthermore, line-level modulation based programmable information transmission and light projection are achieved by using a stripe-shaped addressable nano-kirigami matrix with 3.87 megapixels, showcasing an optical micro-array with large pixel count and flexible programmability. Our work enables the high visibility and precise addressability of freely controllable electromechanical arrays with massive pixels, which could greatly improve the practical applicability for miniaturized optical arrays and brings potential applications in micro-displays, photoelectronic chips, intelligent machine visions, hyperspectral image sensors, etc.

Shortwave infrared (SWIR) imaging is widely employed in light detection and ranging, biomedical imaging, industrial inspection, and night vision. However, current InGaAs-based SWIR cameras remain expensive due to their complex fabrication and cooling requirements. Here, we report a cost-effective alternative using a standard silicon camera, augmented by upconversion from NaYF4:Er@NaYF4 core-shell nanoparticles in a tunable, dual-resonance Fabry-Pérot cavity. A spatially varying cavity length allows spectral tunability, and the dual-resonance design enhances infrared absorption and visible emission simultaneously, resulting in up to 104-fold increase in upconversion intensity over a broad range of excitation wavelengths (1530-1570 nm). This enhancement enables imaging at 1550 nm with sub-10 μm spatial resolution, comparable to InGaAs-based systems, but at a significantly lower cost. We further demonstrate the potential of this platform for silicon wafer alignment and low-visibility imaging. This work introduces a scalable, cost-effective approach for SWIR imaging by leveraging mature silicon technologies and cavity-enhanced photon upconversion.