Search Results (284)

This essay proposes operative creativity as a conceptual and artistic response to the shifting roles of images in the age of algorithmic perception. Departing from Harun Farocki’s seminal artwork Eye/Machine, which first introduced the operative image as functioning not to represent but to activate within machinic processes, it traces the transformation of images from representational devices to machinic agents embedded in systems of simulation and realization. Although operative images were initially engineered for strictly technological functions, they have, from their inception, been subject to repurposing for human perception and interpretation. Drawing on literature theorizing the redirection of operative images within military, computational, and epistemic domains, the essay does not attempt a comprehensive survey. Instead, it opens a conceptual aperture within the framework, expanding it to illuminate the secondary redeployment of operative images in contemporary visual culture. Concluding with the artwork Terms and Conditions, co-created by Ruti Sela and the author, it examines how artistic gestures might neutralize the weaponized gaze, offering a mode of operative creativity that troubles machinic vision and reclaims a space for human opacity. Full article

This study proposes an image processing framework based on Bionic principles to optimize 3D visual perception in virtual reality (VR) systems. By simulating the physiological mechanisms of the human visual system, the framework significantly enhances depth perception and visual fidelity in VR content. The research focuses on three core algorithms: Gabor texture feature extraction algorithm based on directional selectivity of neurons in the V1 region of the visual cortex, which enhances edge detection capability through fourth-order Gaussian kernel; improved Retinex model based on adaptive mechanism of retinal illumination, achieving brightness balance under complex illumination through horizontal–vertical dual-channel decomposition; the RGB adaptive adjustment algorithm, based on the three color response characteristics of cone cells, integrates color temperature compensation with depth cue optimization, enhances color naturalness and stereoscopic depth. Build a modular processing system on the Unity platform, integrate the above algorithms to form a collaborative optimization process, and ensure per-frame processing time meets VR real-time constraints. The experiment uses RMSE, AbsRel, and SSIM metrics, combined with subjective evaluation to verify the effectiveness of the algorithm. The results show that compared with traditional methods (SSAO, SSR, SH), our algorithm demonstrates significant advantages in simple scenes and marginal superiority in composite metrics for complex scenes. Collaborative processing of three algorithms can significantly improve depth map noise and enhance the user’s subjective experience. The research results provide a solution that combines biological rationality and engineering practicality for visual optimization in fields such as implantable metaverse, VR healthcare, and education. Full article

The endangered mangrove Scyphiphora hydrophyllacea is found in China only in Hainan’s intertidal zones. Its populations are declining severely due to anthropogenic disturbances and regeneration failure. To clarify its environmental adaptation mechanisms, we investigated the effects of temperature, light intensity, photoperiod, salinity, soil, and flooding cycle on seed germination, seedling growth, and physiological traits, revealing that (1) the optimal germination conditions for seeds were 30–35 °C, 24 h continuous illumination at 25,000 lux, and 0‰ salinity, with soil type showing no significant effect (p > 0.05); (2) seedlings at 1–2 months post-germination achieve maximal growth at 30 °C in non-saline conditions, with salinity suppressing growth and light intensity affecting only crown expansion; and (3) flooding responses are age-dependent: seedlings at 1–2 months post-germination show optimal growth at 8 h per day (100% survival), while 12 h (h) per day reduces survival by 13.3%. One-year-old seedlings exhibit distinct strategies: 4 h per day flooding induces escape responses (peak growth, chlorophyll, sugars), 8 h per day shows photosynthetic compensation despite metabolic trade-offs, and 12 h per day triggers tolerance mechanisms (biomass maximization via structural reinforcement). These findings demonstrate S. hydrophyllacea’s multifactorial adaptation to intertidal conditions, providing critical physiological benchmarks for conservation strategies targeting this threatened ecosystem engineer. Full article

As an important tangible carrier of historical and cultural heritage, ancient city walls embody the historical memory of urban development and serve as evidence of engineering evolution. However, due to prolonged exposure to complex natural environments and human activities, they are highly susceptible to various types of defects, such as cracks, missing bricks, salt crystallization, and vegetation erosion. To enhance the capability of cultural heritage conservation, this paper focuses on the ancient city wall of Jingzhou and proposes a multi-stage defect-detection framework based on computer vision technology. The proposed system establishes a processing pipeline that includes image processing, 2D defect detection, depth estimation, and 3D reconstruction. On the processing end, the Restormer and SG-LLIE models are introduced for image deblurring and illumination enhancement, respectively, improving the quality of wall images. The system incorporates the LFS-GAN model to augment defect samples. On the detection end, YOLOv12 is used as the 2D recognition network to detect common defects based on the generated samples. A depth estimation module is employed to assist in the verification of ancient wall defects. Finally, a Gaussian Splatting point-cloud reconstruction method is used to achieve a 3D visual representation of the defects. Experimental results show that the proposed system effectively detects multiple types of defects in ancient city walls, providing both a theoretical foundation and technical support for the intelligent monitoring of cultural heritage. Full article

The synergistic advancement of “Digital China” and “Beautiful China” represents a pivotal national strategy for achieving high-quality economic development and a low-carbon transition. To illuminate the intrinsic mechanisms linking the digital economy (DE) to urban carbon emission performance (CEP), this study develops a novel two-sector theoretical framework. Leveraging panel data from 278 Chinese prefecture-level cities (2011–2023), we employ a comprehensive evaluation method to gauge DE development and utilize calibrated nighttime light data with downscaling inversion techniques to estimate city-level CEP. Our empirical analysis integrates static panel fixed effects, panel threshold, and moderating effects models. Key findings reveal that the digital economy demonstrably enhances urban carbon emission performance, although this positive effect exhibits a threshold characteristic linked to the maturity of digital infrastructure; beyond a specific developmental stage, the marginal benefits diminish. Crucially, this enhancement operates primarily through the twin engines of fostering technological innovation and driving industrial structure upgrading, with the former playing a dominant role. The impact of DE on CEP displays significant heterogeneity, proving stronger in northern cities, resource-dependent cities, and those characterized by higher levels of inclusive finance or lower fiscal expenditure intensities. Furthermore, the effectiveness of DE in reducing carbon emissions is dynamically moderated by policy environments: flexible economic growth targets amplify its carbon reduction efficacy, while environmental target constraints, particularly direct binding mandates, exert a more pronounced moderating influence. This research provides crucial theoretical insights and actionable policy pathways for harmonizing the “Dual Carbon” goals with the overarching Digital China strategy. Full article

Daylighting plays a pivotal role in mosques, shaping their sacred atmosphere and enhancing the spiritual experience for worshippers. Beyond a mere architectural consideration, the integration of natural light into mosque design fundamentally influences the ambiance and functionality of these religious spaces. This study investigates the key factors that enhance daylight levels and visual comfort within prayer halls. It specifically evaluates illuminance levels, light distribution, and glare in four domed mosques located in Saudi Arabia. Field measurements were conducted beneath the domes of these prayer spaces, each featuring clerestory windows of varying forms and dimensions. Based on architectural specifications and material properties, daylight simulations and modeling were performed using the RADIANCE engine integrated with Grasshopper. The simulation results were validated against on-site illuminance measurements to ensure model accuracy and reliability. The primary objective was to assess whether the existing daylighting conditions comply with the recommended illuminance standards for reading and prayer, typically ranging from 150 to 500 lux. This study revealed that the illuminance levels in the central dome area exceeded the recommended values, reaching over 3000 lux. To improve daylight distribution, shading systems such as flat and curved shelves were added to the drum’s windows. This research concludes that the light shelves and vacuum double glazing significantly improved indoor daylight performance by preventing direct sunlight entry into the prayer hall and redirecting it towards the dome. This intervention successfully reduced excessive illuminance levels to a more optimal level of around 447–774 lux during the noon prayer period, ensuring a balanced and comfortable environment for worshippers. Full article

The photon spin Hall effect (PSHE) arises from the spin–orbit interaction of light. Metasurfaces enable precise control over the PSHE through their influence. Using electromagnetic simulations as its foundation, this work engineers a metal–insulator–metal (MIM) metasurface for generating vortex beams in the near-infrared band, targeting enhanced modulation of the PSHE. Electromagnetic simulations embed vanadium dioxide (VO₂)—a thermally responsive phase-change material—within the MIM metasurface architecture. Numerical evidence confirms that harnessing VO₂’s insulator–metal-transition-mediated optical switching dynamically tailors spin-dependent splitting in the illuminated MIM-VO₂ hybrid, thereby achieving a significant amplification of the PSHE displacement. Electromagnetic simulations determine the reflection coefficients for both VO₂ phase states in the MIM-VO₂ structure. Computed spin displacements under vortex beam incidence reveal that VO₂’s phase transition couples to the MIM’s top metal and dielectric layers, modifying reflection coefficients and producing phase-dependent PSHE displacements. The simulation results show that the displacement change of the PSHE before and after the phase transition of VO₂ reaches 954.7 µm, achieving a significant improvement compared with the traditional layered structure. The dynamic modulation mechanism of the PSHE based on the thermal–optical effect has been successfully verified. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

Plant diseases resulting from pathogens and pests constitute a persistent threat to global food security. Pathogenic infections of plants are influenced by environmental factors; a concept encapsulated in the “disease triangle” model. It is important to elucidate the complex molecular mechanisms underlying the interactions among plants, their pathogens and various environmental factors in the disease triangle. This review aims to highlight recent advancements in the application of systems biology to enhance understanding of the plant disease triangle within the context of microbiome rising to become the 4th dimension. Recent progress in microbiome research utilizing model plant species has begun to illuminate the roles of specific microorganisms and the mechanisms of plant–microbial interactions. We will examine (1) microbiome-mediated functions related to plant growth and protection, (2) advancements in systems biology, (3) current -omics methodologies and new approaches, and (4) challenges and future perspectives regarding the exploitation of plant defense mechanisms via microbiomes. It is posited that systems biology approaches such as single-cell RNA sequencing and mass spectrometry-based multi-omics can decode plant defense mechanisms. Progress in this significant area of plant biology has the potential to inform rational crop engineering and breeding strategies aimed at enhancing disease resistance without compromising other pathways that affect crop yield. Full article

In response to the demand for precise measurement of illuminance distribution in the quality control of LED monitoring fill light products and the iterative direction of secondary optical design, distributed photometric sensors have shown advantages, but their measurement uncertainty assessment faces challenges. This paper addresses the problem of uncertainty evaluation in photometric parameter measurement with a two-dimensional horizontal distributed photometric sensor and proposes an uncertainty evaluation framework for this task. We have established an uncertainty analysis model for the measurement system and provided two uncertainty synthesis methods, The Guide to the Expression of Uncertainty in Measurement and the Monte Carlo method. This study designed illuminance measurement experiments to validate the feasibility of the proposed uncertainty evaluation method. The results demonstrate that the actual probability distribution of the measurement data follows a trapezoidal distribution. Furthermore, the expanded uncertainty calculated using the GUM method was 21.1% higher than that obtained by the MCM. This work effectively addresses the uncertainty evaluation challenge for illuminance measurement tasks using a two-dimensional horizontal distributed photometric sensor. The findings offer valuable reference for the uncertainty assessment of other high-precision optical instruments and possess significant engineering value in enhancing the reliability of optical metrology systems. Full article

This paper presents an interpretable and modular framework for energy management in smart buildings based on fuzzy logic and the IEEE Std 1855-2016. The proposed system builds upon the JFML-IoT library, enabling the integration and execution of fuzzy rule-based systems on resource-constrained IoT devices using a lightweight and extensible architecture. Unlike conventional data-driven controllers, this approach emphasizes semantic transparency, expert-driven control logic, and compliance with fuzzy markup standards. The system is designed to enhance both operational efficiency and user comfort through transparent and explainable decision-making. A four-layer architecture structures the system into Perception, Communication, Processing, and Application layers, supporting real-time decisions based on environmental data. The fuzzy logic rules are defined collaboratively with domain experts and encoded in Fuzzy Markup Language to ensure interoperability and formalization of expert knowledge. While adherence to IEEE Std 1855-2016 facilitates system integration and standardization, the scientific contribution lies in the deployment of an interpretable, IoT-based control system validated in real conditions. A case study is conducted in a realistic indoor environment, using temperature, humidity, illuminance, occupancy, and CO₂ sensors, along with HVAC and lighting actuators. The results demonstrate that the fuzzy inference engine generates context-aware control actions aligned with expert expectations. The proposed framework also opens possibilities for incorporating user-specific preferences and adaptive comfort strategies in future developments. Full article

Lead sulfide colloidal quantum dots (PbS QDs) have demonstrated great potential in short-wave infrared (SWIR) photodetectors due to their tunable bandgap, low cost, and broad spectral response. While significant progress has been made in surface ligand modification and defect state passivation, studies focusing on the interface between QDs and electrodes remain limited, which hinders further improvement in device performance. In this work, we propose an interface engineering strategy based on 3-aminopropyltriethoxysilane (APTES) to enhance the interfacial contact between PbS QD films and ITO interdigitated electrodes, thereby significantly boosting the overall performance of SWIR photodetectors. Experimental results demonstrate that the optimal 0.5 h APTES treatment duration significantly enhances responsivity by achieving balanced interface passivation and charge carrier transport. Moreover, The APTES-modified device exhibits a controllable dark current and faster photo-response under 1310 nm illumination. This interface engineering approach provides an effective pathway for the development of high-performance PbS QD-based SWIR photodetectors, with promising applications in infrared imaging, spectroscopy, and optical communication. Full article

Despite recent advances in deep learning for fire detection, much of the current research prioritizes model-centric metrics over dataset fidelity, particularly from a fire safety engineering perspective. Commonly used datasets are often dominated by fully developed flames, mislabel smoke-only frames as non-fire, or lack intra-video diversity due to redundant frames from limited sources. Some works treat smoke detection alone as early-stage detection, even though many fires (e.g., electrical or chemical) begin with visible flames and no smoke. Additionally, attempts to improve model applicability through mixed-context datasets—combining indoor, outdoor, and wildland scenes—often overlook the unique false alarm sources and detection challenges specific to each environment. To address these limitations, we curated a new video dataset comprising 1108 annotated fire and non-fire clips captured via indoor surveillance cameras. Unlike existing datasets, ours emphasizes early-stage fire dynamics (pre-flashover) and includes varied fire sources (e.g., sofa, cupboard, and attic fires), realistic false alarm triggers (e.g., flame-colored objects, artificial lighting), and a wide range of spatial layouts and illumination conditions. This collection enables robust training and benchmarking for early indoor fire detection. Using this dataset, we developed a spatiotemporal fire detection model based on the mixed convolutions ResNets (MC3_18) architecture, augmented with Convolutional Block Attention Modules (CBAM). The proposed model achieved 86.11% accuracy, 88.76% precision, and 84.04% recall, along with low false positive (11.63%) and false negative (15.96%) rates. Compared to its CBAM-free baseline, the model exhibits notable improvements in F1-score and interpretability, as confirmed by Grad-CAM++ visualizations highlighting attention to semantically meaningful fire features. These results demonstrate that effective early fire detection is inseparable from high-quality, context-specific datasets. Our work introduces a scalable, safety-driven approach that advances the development of reliable, interpretable, and deployment-ready fire detection systems for residential environments. Full article

Laser energy is widely used as a selective photothermal heating agent in cancer treatment, standing out for not relying on ionizing radiation. However, in vivo tests have highlighted the need to develop irradiation techniques that allow precise control over the illuminated area, adapting it to the tumor size to further minimize damage to surrounding healthy tissue. To address this challenge, a proof of concept based on a laser irradiation system has been designed, enabling control over energy, exposure time, and irradiated area, using galvanometric mirrors. The control software, implemented in Python, employs a set of cameras (visible and infrared) to detect and monitor real-time thermal distributions in the region of interest, transmitting this information to a microcontroller responsible for adjusting the laser power and controlling the scanning process. Image alignment procedures, tunning of the controller’s gain parameters and the impact of the different engineering parameters are illustrated on a dedicated setup. As proof of concept, this approach has demonstrated the ability to irradiate a phantom of black modeling clay within an area of up to 5 cm × 5 cm, from 15 cm away, as well as to monitor and regulate the temperature over time (5 min). Full article

This study investigates how lighting conditions influence emotional and neural responses in a standardized, simulated museum environment. A multimodal evaluation framework combining subjective and objective measures was used. Thirty-two international students assessed their viewing experiences using 14 semantic differential descriptors, while real-time EEG signals were recorded via the EMOTIV EPOC X device. Spectral energy analyses of the α, β, and θ frequency bands were conducted, and a θα energy ratio combined with γ coefficients was used to model attention and comfort levels. The results indicated that high illuminance (300 lx) and high correlated color temperature (4000 K) significantly enhanced both attention and comfort. Art majors showed higher attention levels than engineering majors during short-term viewing. Among four regression models, the backpropagation (BP) neural network achieved the highest predictive accuracy (R² = 88.65%). These findings provide empirical support for designing culturally inclusive museum lighting and offer neuroscience-informed strategies for promoting the global dissemination of traditional Chinese culture, further supported by retrospective interview insights. Full article