Search Results (282)

The Middle-Wave Infrared Imaging Spectrometer for Target Asteroids (MIST-A) will be launched in 2028 aboard the Emirates Mission to the Asteroid belt (EMA) and will operate in the 2–5 μm spectral range to study the asteroids’ surface composition and thermo-physical properties. MIST-A’s Optical Head (OH) design is inherited from the Jovian IR Auroral Mapper (JIRAM), from which the instrument also received two spare Hybrid-Thinned Mercury-Cadmium-Telluride (MCT) photodetectors: the Engineering Model EM2 and the Flight Spare FS1. These are tested to assess their performance after a long period of storage. The laboratory setup for testing both detectors consists of a blackbody and a cryostat which houses the focal plane, maintained at temperatures of 85 K, its nominal operative temperature, and 90 K. Two sets of measurements are performed: (1) characterization of the dark current at different integration times (0 ms, 224 ms, 448 ms, 672 ms, 869 ms, 1120 ms); (2) verification of the detectors’ response linearity, measuring a blackbody at different temperatures (from 50 °C to 100 °C), including ambient temperature (25 °C, with the blackbody turned off). The results of these tests confirm that both models are fully operational and allow us to evaluate the consequences of the years of inactivity on their performance. Through a detailed analysis of the detectors’ properties and a comparison study with the results of the sensors’ first characterization performed by their producer in 2009, we come to the conclusion that both instruments are able to fulfill MIST-A’s scientific requirements. The FS1 displays a better performance with respect to the EM2 and for this has been selected as MIST-A’s Flight Model. Full article

Underwater micro-bubble detection entails multiple challenges, including diminutive target sizes, sparse pixel information, pronounced specular highlights and water scattering, indistinct bubble boundaries, and adhesion or overlap between instances. To address these issues, we propose HSMD-YOLO, an improved detector tailored for high-resolution micro-bubble detection and built upon YOLOv11. The model incorporates three novel components: the Scale Switch Block (SSB), a scale-transformation module that suppresses artifacts and background noise, thereby stabilizing edges in thin-walled bubble regions and enhancing sensitivity to geometric contours; the Global Local Refine Block (GLRB), which achieves efficient global relationship modeling with an asymptotic linear complexity ($O\left(N\right)$) in spatial dimensions while further refining local features, thereby strengthening boundary perception and improving bubble–background separability; and the Bidirectional Exponential Moving Attention Fusion (BEMAF), which accommodates the multi-scale nature of bubbles by employing a parallel multi-kernel architecture to extract spatial features across scales, coupled with a multi-stage EMA based attention mechanism to enhance detection robustness under weak boundaries and complex backgrounds. Experiments conducted on an Side-Illuminated Light Field Bubble Database (SILB-DB) and a public gas–liquid two-phase flow dataset (GTFD) demonstrate that HSMD-YOLO achieves mAP@50 scores of 0.911 and 0.854, respectively, surpassing mainstream detection methods. Ablation studies indicate that SSB, GLRB, and BEMAF contribute performance gains of 1.3%, 2.0%, and 0.4%, respectively, thereby corroborating the effectiveness of each module for micro-scale object detection. Full article

China is one of the world’s major lychee producers, and the fruit’s soft texture, small size, and thin peel make non-destructive robotic harvesting particularly challenging. Accurate fruit detection, branch segmentation, and precise picking-point localization are critical for enabling automated harvesting in complex natural orchard environments. This study proposes an integrated perception framework for lychee harvesting that combines object detection, density-based clustering, and semantic segmentation. An improved YOLO11s-based detection network incorporating SimAM attention, CMUNeXt feature enhancement, and MPDIoU loss is developed to enhance robustness under illumination variation, occlusion, and scale changes. The proposed detector achieves a precision of 84.3%, recall of 73.2%, and mAP of 81.6%, outperforming baseline models. Density-based clustering is employed to group individual detections into fruit clusters. Comparative experiments demonstrate that MeanShift achieves the highest clustering consistency, with an average Adjusted Rand Index (ARI) of 0.768, outperforming k-means and other baselines. An improved DeepLab v3+ semantic segmentation network with a ResDenseFocal backbone and Focal Loss is designed for accurate branch extraction under complex backgrounds. Finally, a rule-based geometric picking-point localization algorithm is formulated in the image coordinate system by integrating detection, clustering, and branch segmentation results. Experimental validation demonstrates that the proposed framework can reliably localize picking points in two-dimensional images under natural orchard conditions. The proposed method provides a practical perception solution for intelligent lychee harvesting and establishes a foundation for future 3D robotic manipulation and field deployment. Full article

Silicon carbide (SiC) detectors continue to emerge as a promising technology for applications requiring radiation hardness, fast response times, and stable operation in harsh environments. In this work, the charge-collection dynamics of ultra-thin membrane SiC detectors are investigated through time-dependent TCAD simulations, consistent with previously reported measurements. The study analyzes the transient response following the localized generation of electron–hole pairs induced by ions, comparing bulk and membrane detector geometries with identical active-layer thicknesses. Two-dimensional simulations provide a time-resolved characterization of the electron and hole current-density distributions within the active region of the device. The results show that both device architectures present a transient current signal featuring two main components. Despite similarities in the prompt drift-driven signal component, the SiC membrane response is characterized by a short secondary component returning to zero within 3.5 × 10^–10 s at zero external bias, making it well-suited for reliable single-ion detection. In contrast, bulk devices exhibit a markedly different response, characterized by a significantly more intense and prolonged secondary component followed by a long tail that does not return to zero within the simulation time window for all investigated reverse biases. This tail is the result of the collection of carriers generated in the substrate that reach the depletion region through diffusion-driven processes. These findings contribute to the optimization of SiC-based solid-state detectors for quantum-technology device fabrication, demonstrating that the removal of the substrate drastically reduces the diffusion-dominated current component, thereby ensuring precise timing and minimal charge loss. Full article

With the conclusion of proton–proton collision data-taking in 2025, the ATLAS experiment has now integrated a luminosity exceeding 300 ${\mathrm{fb}}^{-1}$ during the Run 3 period, which began in July 2022 following Long Shutdown 2 (LS2). During LS2, a series of detector upgrades were implemented, including the installation of the New Small Wheel (NSW) in the innermost stations of the Muon Spectrometer end-caps. The ATLAS Muon Spectrometer, the largest muon system ever built at a collider, now comprises both established gaseous detectors—Monitored Drift Tubes, Thin Gap Chambers, and Resistive Plate Chambers—and newer detectors like Micromegas and small-strip TGCs in the NSW. These new systems are now in stable operation following an extensive phase of construction and commissioning, providing enhanced muon tracking and trigger capabilities. This presentation covers the performance of the muon system, focusing on the stability of the established detectors over time, their ability to handle increasing luminosity and associated irradiation levels, and studies on detector aging. Emphasis will be placed on the NSW upgrade, including the strategies adopted for alignment, track reconstruction, and trigger. The performance results presented in this contribution are based on Run 3 data collected up to 2024. Full article

A conceptual design study is presented for a hemispherical, two-chamber water Cherenkov detector instrumented with bladder-embedded light traps. The detector consists of a rigid aluminium vessel enclosing a water volume that is divided into an outer, optically black chamber and a inner, reflective chamber lined by a flexible bladder. Arrays of light-trap modules, based on plastic scintillators with wavelength-shifting elements and thin silicon photomultipliers, are integrated into the bladder and selected inner surfaces. This geometry is intended to enhance muon tagging, increase acceptance for inclined air showers, and enable improved discrimination between electromagnetic and hadronic components. The study describes the mechanical and optical layout of the detector, the baseline aluminium housing, and the use of 3D-printed hexagonal prototypes to validate integration of the bladder and readout electronics. A first-order structural assessment based on thin-shell and plate theory is presented, indicating large safety margins for the hemispherical shells and identifying the flat base as the mechanically most loaded component. While GEANT4 simulations for detector response to extensive air showers in the atmosphere and performance measurements are left to future work, the present study establishes a mechanically validated, costed baseline design and outlines the steps needed to assess its impact in air-shower arrays. Full article

Early detection of small-scale fires is crucial for minimizing damage and enabling rapid emergency response. While recent deep learning-based fire detection systems have achieved high accuracy, they still face three key challenges: (1) limited deployability in resource-constrained edge environments due to high computational costs, (2) performance degradation caused by feature interference when jointly learning flame and smoke features in a single backbone, and (3) low sensitivity to small flames and thin smoke in the initial stages. To address these issues, we propose a lightweight dual-stream fire detection architecture based on YOLOv5n, which learns flame and smoke features separately to improve both accuracy and efficiency under strict edge constraints. The proposed method integrates two specialized attention modules: ESCFM++, which enhances spatial and channel discrimination for sharp boundaries and local flame structures (flame), and ESCFM-RS, which captures low-contrast, diffuse smoke patterns through depthwise convolutions and residual scaling (smoke). On the D-Fire dataset, the flame detector achieved 74.5% mAP@50 with only 1.89 M parameters, while the smoke detector achieved 89.2% mAP@50. When deployed on an NVIDIA Jetson Xavier NX (NVIDIA Corporation, Santa Clara, CA, USA)., the system achieved 59.7 FPS (single-stream) and 28.3 FPS (dual-tream) with GPU utilization below 90% and power consumption under 17 W. Under identical on-device conditions, it outperforms YOLOv9t and YOLOv12n by 36–62% in FPS and 0.7–2.0% in detection accuracy. We further validate deployment via outdoor day/night long-range live-stream tests on Jetson using our flame detector, showing reliable capture of small, distant flames that appear as tiny cues on the screen, particularly in challenging daytime scenes. These results demonstrate overall that modality-specific stream specialization and ESCFM attention reduce feature interference while improving detection accuracy and computational efficiency for real-time edge-device fire monitoring. Full article

Applying coatings that suppress the radiance changes related to temperature-dependent blackbody emission enables temperature-independent optical and sensing systems. Phase-change materials can significantly modify their optical properties within their transition window, but compensating for the large mid-wave infrared (MWIR, 3–5 µm) variation is demanding: blackbody radiance at 3 µm increases nearly 10-fold as the temperature rises from 30 °C to 80 °C. Vanadium dioxide VO₂, whose insulator–metal transition offers a sharp contrast and a low-loss insulating state, is attractive for applications in thermal management, but simple thin-film designs cannot provide full compensation. We demonstrate metasurface coatings that provide this compensation by constructing an array of metal–VO₂–metal antennas tuned to maintain constant thermal emission at a target wavelength over a temperature range of 30 °C to 80 °C. Antennas of several lateral sizes are combined, so their individual resonances collectively track the Planck change. This design provides both optical contrast and the correct temperature derivative, which are unattainable with homogeneous layers. Our approach results in a negligible apparent temperature change of the metasurface across the 30–80 °C range, effectively masking thermal signatures from MWIR detectors stemming from the low losses of VO₂. Full article

Accurate real-time dosimetry is key in megavoltage radiotherapy; however, many detectors require external biasing or complex instrumentation. This study evaluated thin-film CdTe solar cells operating in photovoltaic (zero-bias) mode as medical dosimeters. Superstrate ITO/CdS/CdTe/Cu/Au devices were fabricated and irradiated with 6-MV photons from a clinical linear accelerator to 20 kGy cumulative dose. Electrical and dosimetric properties were assessed based on AM 1.5 current–voltage measurements, external quantum efficiency (EQE), dose linearity, dose-rate dependence, field-size dependence, percentage depth dose (PDD), and one-month reproducibility. With increasing dose (5–20 kGy), the open-circuit voltage and fill factor decreased by ~2–3%, the short-circuit current density by ~10%, retaining ~87% initial efficiency. Series and shunt resistances were stable, while EQE decreased uniformly (~5%), indicating degradation mainly from increased nonradiative recombination. Dose–signal linearity remained intact, and post-irradiation sensitivity loss was corrected with a single calibration factor. Dose-rate dependence was minor; low reverse bias (~3–7 V) enhanced response without nonlinearity. Field-size and PDD responses agreed with ionization chamber data within ~1%, and weekly stability was within ~1%. Parallel stacking of two cells increased signal nearly linearly. CdTe solar-cell detectors thus enable zero-bias, real-time, stable, and scalable dosimetry and strongly agree with reference standards. Full article

Coating Brownian thermal noise is a major limitation to the sensitivity of gravitational-wave detectors. To reduce it, future detectors are planned to operate at cryogenic temperatures. This implies a change of their mirror coating materials and the use of a longer laser wavelength, such as 1550 nm. A stack of amorphous silicon and silicon nitride layers has previously been proposed as a promising combination of low- and high-refractive index materials to realize low-noise highly-reflective coatings. An essential step towards such coatings is the production of both materials via ion-beam sputtering. In this paper, for the first time, we present a study of the optical properties at 1550 nm of silicon nitride thin films deposited via ion beam sputtering. The refractive index and optical absorption as a function of post-deposition heat treatment temperature are investigated using a spectrophotometer and a photo-thermal common-path interferometer. Finally, we discuss the prospect of combining this material with amorphous silicon. Full article

Lead halide perovskites, exemplified by methylammonium (MA) lead iodide (MAPbI₃), combine strong optical absorption, long carrier diffusion lengths, and defect-tolerant electronic structure with facile processing, making them attractive for photovoltaics and radiation detection. Yet, their behavior under electron irradiation remains insufficiently understood, limiting deployment in space and dosimetry contexts. Here, we employ Monte Carlo simulations (Geant4) to model electron interactions with MAPbI₃ across energies from 0.1 to 100 MeV and absorber thicknesses from 10 μm to 1 cm. We quantify deposited energy, event statistics, energy per interaction, non-ionizing energy loss, and dominant radiation effects. The results reveal strong thickness-dependent regimes: thin photovoltaic-type layers (~hundreds of nanometers) are largely transparent to MeV electrons, minimizing bulk damage but allowing localized ionization, exciton self-trapping, and photoexcitation-driven ion migration. Although localized excitations can temporarily improve carrier collection under short-term exposure, their cumulative effect drives ionic rearrangement and defect growth, ultimately reducing device stability. In contrast, thicker detector-type films (10–100 μm) sustain multiple scattering and ionization cascades, enhancing sensitivity but accelerating defect accumulation. At centimeter scales, energy deposition saturates, enabling bulk-like absorption for high-flux dosimetry. Overall, electron irradiation in MAPbI₃ is dominated by electronic excitation rather than ballistic displacements, underscoring the need to optimize thickness and composition to balance efficiency, sensitivity, and durability. Full article

Neuroplasticity refers to the nervous system’s ability to modify its structure and function in response to intrinsic and extrinsic stimuli. Impairments in this capacity are associated with various neurological disorders, underscoring the need for therapies that preserve or enhance neuronal plasticity. Medicinal plants offer a promising source of bioactive compounds with neuroplastic properties and neuroprotective potential. In this work, we report the chemical and neuroplastic properties of Tillandsia usneoides, a medicinal native plant from America. Ethanolic extracts (EtOH) of leaves and stems, along with subfractionated ethyl acetate (EtOAc) and hydroethanolic (H₂O:EtOH) extracts, were analyzed using High-Performance Thin-Layer Chromatography (HPTLC) and Ultra-Performance Liquid Chromatography coupled with a Diode Array Detector (UPLC-DAD), revealing the presence of 14 phenolic acids, 6 flavonoids, and triterpene. Additionally, functional analysis using Sholl analysis showed that the EtOAc fraction of Tillandsia usneoides significantly enhanced structural plasticity in vitro, increasing both dendritic branching and dendrite length at concentrations between 0.03 and 1 μg mL⁻¹, likely through the activation PI3K/Akt and ERK1/2 signaling pathways. Together, our results suggest that Tillandsia usneoides contains bioactive polar metabolites capable of inducing neuronal structural plasticity. Full article

In this work, we present a novel compact particle identification (PID) detector concept based on Silicon Photomultipliers (SiPMs) optimized to perform combined Ring-Imaging Cherenkov (RICH) and Time-of-Flight (TOF) measurements using a common photodetector layer. The system consists of a Cherenkov radiator layer separated from a photosensitive surface equipped with SiPMs by an expansion gap. A thin glass slab, acting as a second Cherenkov radiator, is coupled to the SiPMs to perform Cherenkov-based charged particle timing measurements. We assembled a small-scale prototype instrumented with various Hamamatsu SiPM array sensors with pixel pitches ranging from 2 to 3 mm and coupled with 1 mm thick fused silica window. The RICH radiator consisted of a 2 cm thick aerogel tile with a refractive index of 1.03 at 400 nm. The prototype was successfully tested in beam test campaigns at the CERN PS T10 beam line with pions and protons. We measured a single-hit angular resolution of about 4 mrad at the Cherenkov angle saturation value and a time resolution better than 50 ps RMS for charged particles with Z = 1. The present technology makes the proposed SiPM-based PID system particularly attractive for space applications due to the limited detector volumes available. In this work, we present beam test results obtained with the detector prototype and we discuss possible configurations optimized for the identification of ions in space applications. Full article

NUSES is a pathfinder satellite that will be deployed in a low Earth orbit, designed with new technologies for space-based detectors. Ziré is one of the payloads of NUSES and aims to measure the spectra of electrons, protons, and light nuclei in a kinetic energy range spanning from a few MeVs to several hundred MeVs, as well as photons in the energy range from 0.1 MeV to 30 MeV. Ziré consists of a Fiber TracKer (FTK), a Plastic Scintillator Tower (PST), a calorimeter (CALOg), an AntiCoincidence System (ACS) and a Low Energy Module (LEM). The FTK is based on thin scintillating fibers read out by Silicon Photomultiplier (SiPM) arrays. We assembled a prototype of Ziré (Zirettino) equipped with a single FTK layer, a reduced number of PST layers and a partially instrumented CALOg. A preliminary version of the Ziré custom Front-End Board (FEB) featuring the on-the-shelf ASIC CITIROC by OMEGA/Weeroc was used for the readout. We carried out several beam test campaigns at CERN’s PS facility and a dynamic qualification test. The performance of FTK will be presented and discussed. Full article

Rocky desertification severely restricts socio-economic development in the karst regions. However, assessments linking karst rocky desertification and NPP changes over the long term and at high resolution are limited. This study aims to reveal the spatiotemporal patterns and driving mechanisms of NPP changes in Wenshan Prefecture, addressing the scientific gap in quantitative process research and mechanism identification in karst desertification areas. We estimated vegetation NPP from 2000 to 2020 using remote sensing data and the CASA model. The Theil–Sen trend analysis and Mann–Kendall test were applied to assess temporal variation, while a Geographical Detector identified the dominant natural and human factors and their interactions shaping NPP spatial patterns. Our results showed that NPP increased overall by 4.07 gC m⁻² a⁻¹, alongside a general decline in rocky desertification. The most significant improvement occurred between 2010 and 2015, when rocky desertification shrank by 2224 km² and the dynamic rate reached 1.42%. Mean NPP reached 1057 gC m⁻² a⁻¹, with a “northwest high–southeast low” spatial pattern, and 77% of the region showed significant increases. Rocky desertification was most severe at elevations between 1000 and 2000 m. In the karst region, NPP is mainly controlled by natural factors, with soil depth and slope being the strongest influences. Human activity had the largest negative impact, and most factors interacted synergistically, where hydrothermal gradients and human disturbances more strongly suppressed NPP on steep, thin slopes than individually expected. These findings provide robust scientific evidence and practical decision-making support for ecological restoration, rocky desertification control and long-term sustainable development in Wenshan and other karst regions, highlighting the importance of continuous monitoring and adaptive management strategies to consolidate restoration achievements and guide future land-use planning and regional ecological policy. Full article