Accurate single photoelectron (SPE) characterization of photosensors is essential for controlling systematic uncertainties in low-light neutrino and dark matter detectors. We present a compact laboratory setup for the characterization of photosensors under controlled, low-light conditions. Specifically, we demonstrate its use with photomultiplier tubes (PMTs) operated at the SPE-level, using picosecond laser pulses and waveform digitization to determine key PMT properties. Measurements as a function of supply voltage and temperature ( − 50 ° C to + 20 ° C ) are performed on ET Enterprises 9821(Q)B tubes and a Hamamatsu R9980 assembly, which show exponential gain-voltage behavior and device-to-device variation. Cooling increases the gain by ∼ 0 . 1 % / ° C , while the transit time spread (TTS) and peak-to-valley ratio (P/V) exhibit no clear temperature dependence. TTS decreases with voltage. Late pulses remain at the percent level and prepulses at the sub-percent level. Cable length affects both apparent gain and TTS. A model-independent, data-driven self-convolution method is introduced to quantify double photoelectron contributions from pulse charge spectra. The procedures provide a reproducible, practice-oriented reference for SPE-level PMT characterization and can be extended to other photosensor types.

Low-light object detection frequently suffers from weak contrast, high noise, and poor performance on small objects. Existing methods often adopt image enhancement to improve detection performance, which results in substantial computational resource consumption. To address these challenges, we propose TinyDark-YOLO, an adaptive and efficient low-light object detection algorithm based on YOLO11. First, we design an Adaptive Gamma Enhancement (AGE) module to adaptively adjust the brightness of input images. Second, we introduce an Attention-based Intra-scale Feature Interaction (AIFI) module to construct global context modeling on high-level features and capture long-range spatial dependencies, improving the detection accuracy of small objects. Finally, we propose a Lite Efficient Head (LEHead) to reduce computational overhead while enhancing the model's ability to capture weak features in dark environments. We conduct validation and ablation experiments on the ExDark dataset. The results demonstrate that the proposed model achieves an mAP@0.5 of 67.20% on the ExDark dataset, yielding an improvement of 2.39% over the baseline YOLO11n, while reducing computational complexity from 6.3 to 5.8 GFLOPs. TinyDark-YOLO also achieves competitive performance on the DarkFace dataset.

Inspired by temperature-associated temporal redistribution in biological systems, we demonstrate a thermally tunable photonic synaptic transistor (TT-PST) based on a magnesium-doped zinc oxide (MZO)/indium-gallium-zinc oxide (IGZO) heterostructure. By using temperature as a simple external control parameter, we enable continuous and reversible reshaping of synaptic temporal behavior without structural reconfiguration. As the temperature increases from 20 to 80 °C, the maximum photoresponsivity (Max. PR) rises from 0.43 to 2.25 A/W, while the temporal persistence decreases sharply, resulting in a transition from integration-dominant to response-dominant operation. Leveraging this capability, temperature-dependent temporal encoding is evaluated in two representative visual tasks using experimentally extracted device dynamics in a convolutional neural network (CNN) framework. Weak-signal recognition under low-light conditions achieves higher accuracy at 20 °C (82.86%) compared to 80 °C (69.23%), whereas dynamic motion analysis shows superior performance at 80 °C (88.03%) versus 20 °C (74.37%). These findings establish temperature as a bioinspired, physically accessible knob for on-demand temporal dynamics engineering in a single hardware platform, providing an efficient strategy for task-adaptive neuromorphic vision systems.

Interfacial defects and ion migration critically limit the efficiency and operational stability of perovskite solar cells (PSCs), particularly under low-light conditions where interfacial recombination dominates. Herein, a postsurface treatment using the secondary amine dibutylammonium bromide (DBABr) is introduced to induce controlled 2D/3D interfacial reconstruction through the formation of a thin quasi-two-dimensional perovskite layer on a three-dimensional absorber. This dimensionally reconstructed interface effectively passivates surface defects, suppresses ion migration, and improves energy-level alignment without hindering charge extraction while remaining compatible with both organic and inorganic hole-transport layers (HTL). Under standard one-sun illumination (AM 1.5G), DBABr-treated devices exhibit a significant enhancement in power conversion efficiency (PCE), increasing from 13.82% to 15.52% relative to the control. Furthermore, the reconstructed interface enables efficient indoor energy harvesting, delivering a PCE of 30.57% for 0.09 cm2 active-area devices and improving the PCE from 25.64% to 27.77% for 1 cm2 devices under 1000 lx LED illumination, demonstrating scalability across device areas. Benefiting from the combined effects of the hydrophobic 2D surface layer and the carbon electrode, the devices retain approximately 90% of their initial efficiency after 1000 h of operation under ambient conditions. These results establish interfacial dimensional reconstruction via secondary-amine surface engineering as an effective strategy to simultaneously enhance efficiency, low-light performance, and operational durability of carbon-based PSCs, contributing to sustainable photovoltaic technologies aligned with Sustainable Development Goal 7 (affordable and clean energy).

ABSTRACT Introduction Gene therapy with voretigene neparvovec-rzyl was approved primarily based on improvements observed in the Multi-Luminance Mobility Test (MLMT), while the full-field stimulus threshold (FST) test, a secondary endpoint, also demonstrated efficacy. However, the MLMT is difficult to apply in routine practice, and the FST does not provide spatial information regarding retinal function, which limits direct structural-function correlation. Purpose Therefore, this study investigated whether microperimetry could serve as a simpler, more practical alternative by evaluating its changes after this gene therapy. Methods Mean sensitivity and fixation parameters were evaluated in a cohort of 5 patients (10 eyes) who underwent microperi-metry following gene therapy in São Paulo, Brazil. Results Results demonstrated a significant increase in mean sensitivity (MS, p = 0.0006) and a better perception of dimmer stimuli (p = 0.0004), indicating higher sensitivity. Results from individual eyes showed consistent improvements in retinal sensitivity and fixation stability. These findings were consistent with improvements in daily functioning—particularly in low-light conditions—reported by the patients during medical history assessment during the routine visits. Conclusion Overall, the findings suggest that microperimetry could be an useful tool for monitoring functional outcomes after gene therapy.

Daily nocturnal homing reveals goal-directed navigation in a wild teleost

Advanced detection systems increasingly rely on infrared (IR) imaging to overcome the limitations of visible light cameras in adverse environments such as fog, rain, and low-light conditions. However, the effectiveness of IR detection remains fundamentally constrained by the low emissivity contrast between targets and their backgrounds. Here, we present a plasmonic metal-dielectric-metal nanostructure comprising a porous anodic aluminum oxide (AAO) dielectric layer sandwiched between an aluminum substrate and a surface Au nanoparticle layer that enables near-independent modulation of visible reflectance (400-800 nm) and long-wave infrared emissivity (8-14 μm). The decoupling mechanism exploits the distinct characteristic length scales governing each spectral band: visible reflectance is controlled by Fabry-Pérot cavity interference and plasmonic absorption of the Au nanoparticle layer, while infrared emissivity is governed by the intrinsic phonon absorption of the AAO layer and is insensitive to Au coverage. Using scalable anodic oxidation and screen-printing fabrication, we achieve tunable visible reflectance (R = 0.2-0.9) and infrared emissivity (ε = 0.1-0.87). Applied to infrared-enhanced license plate detection, our patterned plates achieve an average recognition rate of ∼45% under adverse environmental conditions, compared to ∼5% for conventional plates. This work offers a scalable route to multispectral patterned surfaces for infrared imaging, thermal sensing, and anticounterfeiting applications.

Commercial motorcycle riders are among the most vulnerable road users in low- and middle-income countries, with poor visibility contributing significantly to road traffic injuries, particularly under low-light conditions. While visibility materials such as reflective jackets and functional lighting systems are effective, low-cost safety measures, their use remains inconsistent in many settings. In addition to individual behaviors, structural factors such as enforcement, access to safety equipment, and road conditions may influence their uptake. This study assessed the use of visibility materials and factors associated with their uptake among commercial motorcycle riders in the Limbe and Tiko Health Districts of Cameroon. A community-based cross-sectional study was conducted among 499 commercial motorcycle riders aged 18 years and above in the Limbe and Tiko Health Districts. Riders were recruited at selected motorcycle pick-up points using a multistage sampling approach. Data were collected using structured interviewer-administered questionnaires and an observational checklist. Descriptive statistics were used to summarize the uptake of visibility materials. Multivariable logistic regression analysis was performed to identify factors independently associated with reflective jacket use, guided by theoretical and contextual relevance. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. The mean age of riders was 32.2 ± 7.6 years, and all participants were male. The most commonly observed visibility feature was a functional headlamp (85.8%), followed by backlights (57.3%) and brake lights (57.3%). Approximately half of the riders were observed wearing reflective jackets (50.9%), while reflective strips were present on only 19.0% of motorcycles. In adjusted analysis, riders who reported obeying traffic regulations were more likely to wear reflective jackets (AOR = 1.52; 95% CI: 1.10-2.30), as were those willing to allocate a budget for visibility materials (AOR = 2.20; 95% CI: 1.30-3.60). Knowledge and attitudes toward visibility materials were also positively associated with reflective jacket use. Each one-point increase in knowledge score was associated with an 18% increase in the odds of reflective jacket use (AOR = 1.18; 95% CI: 1.10-1.26), while each one-point increase in attitude score increased the odds by 14% (AOR = 1.14; 95% CI: 1.07-1.21). Riders reporting safer visibility-related practices were also more likely to wear reflective jackets (AOR = 2.16; 95% CI: 1.36-3.44). The uptake of visibility materials, particularly reflective jackets and reflective strips, remains suboptimal among commercial motorcycle riders despite widespread availability of basic lighting systems. Both behavioral and contextual factors appear to influence their use. Interventions that combine improved awareness with strategies addressing structural barriers-such as affordability, accessibility, and enforcement-may enhance rider visibility and contribute to reducing road traffic injuries in this population.

Accurate identification of frontal sinus boundaries is a critical step in anterior skull base surgery, particularly in transsinusal approaches, as it allows optimization of the surgical bony window while minimizing unnecessary bone removal. To describe a simple and reproducible technique of external frontal bone transillumination for intraoperative frontal sinus mapping. After bicoronal exposure of the frontal bone, a standard fiber-optic light cable connected to the operating room light source is applied in direct contact with the frontal squama under low ambient light conditions. When properly applied, the frontal sinus appears as a brighter translucent area, allowing accurate delineation of its margins. Postoperative CT imaging was retrospectively reviewed to assess correspondence between the intraoperatively identified margins and the actual anatomical boundaries. Between 2015 and 2024, the technique was applied in 14 consecutive patients undergoing anterior skull base surgery, including olfactory groove meningiomas, post-traumatic anterior skull base cerebrospinal fluid fistulas, and one case of intrasinusal osteoma. In all cases, external transillumination enabled clear identification of frontal sinus boundaries and facilitated creation of an adequate surgical window. Postoperative imaging confirmed correspondence between the planned opening and the actual anatomical extent of the frontal sinus opening. No sinus-related complications or postoperative cerebrospinal fluid leaks were observed. External frontal bone transillumination is a simple, fast, and reliable method for intraoperative frontal sinus mapping during anterior skull base surgery. Its ease of adoption and use of routinely available equipment make it a useful adjunct to preoperative imaging for optimizing surgical exposure.

Structure-guided lensless reconstruction via physics-aware decomposition in low-light conditions

Bioherms are common in the pre-evaporitic Messinian in Neogene basins in SE Spain. Coral bioherms reported to date in the region have been interpreted as shallow-water reefs based on coral morphology (stick-like Porites and Tarbellastraea domes) and surrounding sediments. However, at the western margin of the Almería–Níjar Basin, coral bioherms grew in relatively deep waters with low turbidity on the eastern shelf of the upland precursor to the Sierra de Gádor. Bioherms occur in the lower part of a reef unit, in some cases growing directly on the erosion surface separating this unit from the underlying older Miocene rocks. They are formed mainly by Porites , with minor Tarbellastraea , encrusted by foraminifera, coralline algae and microbialite. The thickness of bioherms ranges from less than 1 m to about 20 m in the largest bioherm. Mesophotic conditions can be inferred from direct palaeodepth measurements at the outcrop, showing that bioherms started to develop at several tens of metres water depth. The dominance of thin, laminar, contorted Porites colonies with few finger-like projections is also indicative of low light levels. Facies laterally equivalent to the bioherms—namely algal/bivalve wackestone to packstone, rhodolith rudstone rich in coralline algae of the order Hapalidiales, and Halimeda floatstone to rudstone—are all characteristic of relatively deep waters in the Mediterranean Neogene. The low siliciclastic content of bioherms and surrounding deposits (<8%) suggests that low light conditions in which bioherms grew were primarily due to water depth. The Almería bioherms are the first record of Upper Miocene blue mesophotic reefs in the world. • Upper Miocene blue mesophotic coral reefs reported for the first time worldwide. • They are bioherms mainly composed of thin laminar contorted Porites colonies. • Outcrop geometry and surrounding facies indicate relatively deep palaeoenvironments. • Low siliciclastic content (< 8%) suggests that they developed in clear waters.

Real-world driving behaviors in individuals with homonymous visual field loss: insights from a naturalistic driving study.

Robust small object detection in adverse environments remains challenging due to physical degradations and unstable feature representations. Existing detectors often struggle to maintain semantic consistency in haze, rain, and low-light conditions, leading to degraded accuracy and poor generalization. To address these issues, we propose a unified neural information bottleneck with physics-guided diffusion (NIB-PGD) framework. The proposed framework couples physical priors with information-theoretic regularization in a dual-diffusion paradigm, enabling robust feature encoding and noise-resilient object localization. A multiscale enhance-fuse-context attention (EFC-A) encoder captures hierarchical semantics and contextual dependencies, while a physics-guided feature-level diffusion injects realistic degradation kernels into the noise schedule to enhance robustness. In parallel, a box-level diffusion reformulates detection as iterative denoising, progressively refining bounding boxes without anchors or queries. A second-order information bottleneck (2O-IB) constraint dynamically regulates mutual information to suppress redundancy and preserve task-relevant semantics. On our composite benchmark (constructed from BDD100K, SODA-D, and an in-house small object set), NIB-PGD achieves 52.3% AP and 35.7% AP ${}_{s}$ , outperforming the strongest AP baseline by + 1.2 AP and the strongest AP ${}_{s}$ baseline by +1.1 AP ${}_{s}$ . Comprehensive experiments on multiple benchmarks demonstrate that NIB-PGD achieves state-of-the-art performance in detection accuracy, robustness, and generalization.

Innovative strategies are essential to enhance crop resilience against drought and heat stress intensified by climate change. Crassulacean acid metabolism (CAM) is a specialized photosynthetic pathway that improves water-use efficiency (WUE) by shifting CO2 fixation to the nighttime. The common ice plant (Mesembryanthemum crystallinum) utilizes NADP-malic enzymes (ME) for malate decarboxylation during its facultative CAM transition. In this study, we characterized the cytosolic McNADP-ME1 enzyme, which is highly expressed in the ice plant under water-deficit stress. Transgenic Arabidopsis thaliana plants overexpressing McNADP-ME1 exhibited reduced stomatal density, size, and conductance, leading to enhanced instantaneous WUE. Although these modifications resulted in reduced biomass and seed yield under low-light conditions, the transgenic lines showed significantly improved survival and growth under both acute and chronic water-deficit stress. Additionally, McNADP-ME1 overexpression conferred improved vegetative growth under high-light stress conditions. Notably, McNADP-ME1 overexpression upregulated many genes within the flavonoid biosynthetic pathway, resulting in a marked increase in total flavonoid content. These flavonoids acted as effective antioxidants that facilitated the scavenging of reactive oxygen species (ROS), thereby reducing oxidative damage and malondialdehyde (MDA) levels under both water-deficit and high-light stress conditions. These results demonstrate that cytosolic McNADP-ME1 is a key enzyme for conferring water-deficit stress tolerance by integrating stomatal-mediated water conservation with flavonoid-driven ROS-scavenging mechanisms.

Unstable illumination conditions such as strong reflections in maritime environments and environmental interference in meter reading scenarios severely degrade optical text recognition performance and intensify the trade-off between accuracy and computational efficiency. Existing methods struggle to simultaneously achieve robust recognition and lightweight deployment under such challenging conditions. To address this issue, we propose the lightweight deformable attention guided single-point spotter (LightDASS), an end-to-end optical character spotting framework designed for intelligent edge devices. The proposed method is built upon three key contributions. First, we reformulate text spotting as a sequence prediction problem, unifying detection and recognition as discrete token generation via an auto-regressive transformer. Second, we introduce a single-point annotation paradigm for training, which replaces conventional bounding-box supervision and significantly reduces labeling cost while maintaining competitive learning capability. Third, we design a lightweight transformer block with multi-head deformable attention and a group-wise feed-forward network, enabling adaptive focus on informative regions with substantially reduced computational overhead. We evaluate LightDASS on two challenging scenarios, including ship license plate recognition (SLPR) under severe maritime reflections and automatic meter reading (AMR) under low-light conditions. To further validate the generalization capability of the proposed method, we additionally conduct experiments on the ICDAR2015 dataset. Experimental results across all benchmarks demonstrate that our method consistently achieves superior performance over existing baselines, particularly in small-scale and degraded text scenarios while maintaining high efficiency. Furthermore, the proposed framework is lightweight and well-suited for real-time deployment on edge devices, enabling practical integration into intelligent systems for low-latency decision-making.

The design of biological carbon capture systems to uptake carbon dioxide by photoautotrophic cultivation of algae has been proposed to mitigate atmospheric carbon emissions. Multiple models to predict algal growth as a function of nutrients have been proposed, but few have delved into the complex dynamic reactions of algal growth as influenced by individual inorganic carbon species. In this work, dynamic algal growth models based on inorganic carbon-limited specific growth rates that considered carbon dioxide (CO2), bicarbonate (HCO3 -) and carbonate (CO3 2-) as potential substrates in Monod model equations were investigated and compared to batch, closed reactor data. The model incorporates dynamic rates of inorganic carbon species conversion rather than equilibrium conditions and algal biomass stoichiometry that accounts for algal plasticity as a function of nutrient concentration. After analysis of 8 models, the model that included CO2 and HCO3 - as substitutable substrates is best supported by literature and provided the best estimates of total inorganic carbon concentrations, biomass, and pH for a set of experimental cultures. These results provide a grounded framework for predicting algal growth and carbon speciation, thereby informing the design and operation of algal cultivation systems for carbon abatement and bioproduct formation under carbon-limited, low-light, and high-pH conditions.

Survival of E. coli TVS353 in Soil Mesocosms at Various Moisture Levels and Light Exposure in Controlled Environments.

Nitrogen regulates starch biosynthesis and multiscale structural properties of Sorghum under low-light stress.

Purpose This study proposes an early-stage, non-invasive assistive framework as a proof-of-concept for American Sign Language recognition (ASLR) using radio-frequency (RF) sensing and time-series deep learning (DL) techniques. The framework aims to explore the technical feasibility of ASLR as a potential future support mechanism for communication between individuals with hearing impairments and the hearing community. Traditional approaches, such as using camera-based systems or wearable gloves, face limitations in privacy, environmental adaptability, and full-body motion capture. Methods We propose software-defined radio (SDR) sensing using Universal Software Radio Peripheral (USRP) kits to transmit RF signals and capture signal-induced variations in wireless channel state information (WCSI). These dynamic distortions generate unique time-series patterns in the RF data, which were processed and classified using a DL architecture optimised for time-series analysis. Data for 20 American signs were acquired via WCSI using an orthogonal frequency division multiplexing transceiver to measure human sign imprints. Results Comparative analysis of time-series DL algorithms achieved 98% recognition accuracy, without compromising privacy. Key strengths of the proposed conceptual framework include robustness in nonintrusive operation and low-light conditions, as well as interpretability of complex full-body movements. Conclusions The findings suggest potential applicability for individuals with hearing impairments in future communication-support technologies, with possible implications for fostering more inclusive environments. Integrating SDR frequency sensing with advanced DL techniques, this work can provide preliminary evidence to advance ASLR research and lay the groundwork for future scalable, privacy-preserving assistive systems.

In-vehicle low-light face image enhancement with physical-semantic constrained diffusion and gated selective state-space scanning.