Search Results (2,831)

With the advancement of digital technology, serious games have become an essential tool for disseminating and educating individuals about cultural heritage. However, systematic empirical research remains limited with respect to how visual elements influence users’ cognitive and emotional engagement through interactive behaviors. Using the “Macau Historic Centre Science Popularization System” as a case study, this mixed-methods study investigates the mechanisms by which visual elements affect user experience and learning outcomes in digital interactive environments. Eye-tracking data, behavioral logs, questionnaires, and semi-structured interviews from 30 participants were collected to examine the impact of visual elements on cognitive resource allocation and emotional engagement. The results indicate that the game intervention significantly enhanced participants’ retention and comprehension of cultural knowledge. Eye-tracking data showed that props, text boxes, historic buildings, and the architectural light and shadow shows (as incentive feedback elements) had the highest total fixation duration (TFD) and fixation count (FC). Active-interaction visual elements showed a stronger association with emotional arousal and were more likely to elicit high-arousal experiences than passive-interaction elements. The FC of architectural light and shadow shows a positive correlation with positive emotions, immersion, and a sense of accomplishment. Interview findings revealed users’ subjective experiences regarding visual design and narrative immersion. This study proposes an integrated analytical framework linking “visual elements–interaction behaviors–cognition–emotion.” By combining eye-tracking and information dynamics analysis, it enables multidimensional measurement of users’ cognitive processes and emotional responses, providing empirical evidence to inform visual design, interaction mechanisms, and incentive strategies in serious games for cultural heritage. Full article

Effective disaster management depends on rapidly understanding earthquake damage, yet traditional methods struggle to operate at scale and rely on expert inspections that become difficult when access is limited or time is critical. Satellite-based damage detection also faces limitations, particularly under adverse weather conditions and delays associated with satellite overpass schedules. This study introduces a machine learning-based approach to assess post-earthquake building damage using real observations collected after the event. The aim is to develop fast and reliable estimation techniques that can be deployed immediately after the mainshock by integrating structural, seismic, and geographic data. Three machine learning models—Random Forest, Histogram Gradient Boosting, and Bagging Classifier—are evaluated across both reinforced concrete and masonry buildings and across multiple spatial levels, including building, district, and city scales. Damage is categorized using practical three-class (traffic light) and detailed four-class systems. The models generally perform better in simpler classifications, with the Bagging Classifier offering the most consistent results across different scales. Although detecting severely damaged buildings remains challenging in some cases, the three-class system proves especially effective for supporting rapid decision-making during emergency response. Overall, this study demonstrates how machine learning can provide faster, scalable, and practical earthquake damage assessments that benefit emergency teams and urban planners. Full article

With the rapid development of the power Internet of Things (IoT), the traditional manual inspection mode can no longer meet the growing demand for power equipment inspection. Unmanned aerial vehicle (UAV) intelligent inspection technology, with its efficient and flexible features, has become the mainstream solution. The rapid development of computer vision and deep learning (DL) has significantly improved the accuracy and efficiency of UAV intelligent inspection systems for power equipment. However, mainstream deep learning models have complex structures, and manual design is time-consuming and labor-intensive. In addition, the images collected during the power inspection process by UAVs have problems such as complex backgrounds, uneven lighting, and significant differences in object sizes, which require expert DL domain knowledge and many trial-and-error experiments to design models suitable for application scenarios involving power inspection with UAVs. In response to these difficult problems, evolutionary computation (EC) technology has demonstrated unique advantages in simulating the natural evolutionary process. This technology can independently design lightweight and high-precision deep learning models by automatically optimizing the network structure and hyperparameters. Therefore, this review summarizes the development of evolutionary deep learning (EDL) technology and provides a reference for applying EDL in object detection models used in UAV intelligent power inspection systems. First, the application status of DL-based object detection models in power inspection is reviewed. Then, how EDL technology improves the performance of the models in challenging scenarios such as complex terrain and extreme weather is analyzed by optimizing the network architecture. Finally, the challenges and future research directions of EDL technology in the field of UAV power inspection are discussed, including key issues such as improving the environmental adaptability of the model and reducing computing energy consumption, providing theoretical references for promoting the development of UAV power inspection technology to a higher level. Full article

Based on the pressing need to develop efficient desulfurization technologies for fuel oils, this study presents a novel photoresponsive adsorbent for the removal of refractory thiophenic sulfides. Conventional hydrodesulfurization exhibits limited efficiency for such compounds, while adsorption–desorption processes often suffer from high energy consumption during regeneration. Inspired by natural stimuli-responsive systems, we designed a photothermal adsorbent by incorporating silver nanoparticles (Ag NPs) into a porous aromatic framework (PAF) via a green photoreduction method. The resulting materials, denoted as Ag(0)PBPAF-n (n = 1, 2, 3), were thoroughly characterized to confirm successful synthesis and structural integrity. The introduced Ag NPs serve as adsorption sites, enhancing uptake capacity through weak interactions with sulfur atoms in thiophenic molecules. More significantly, under light irradiation, the localized surface plasmon resonance (LSPR) of Ag NPs enables efficient photothermal conversion, triggering rapid desorption without conventional heating. Adsorption–desorption tests demonstrated that up to 48% of adsorbed thiophenic sulfur could be released upon illumination. Fixed-bed experiments further verified that light can effectively stimulate regeneration and improve energy efficiency. This work offers a promising strategy for designing recyclable adsorbents with low-energy regeneration driven by clean solar energy. Full article

Accurate measurement of luminous flux from solid-state light sources typically requires spectroradiometric equipment or integrating spheres. This work investigates a compact alternative based on a fiber-coupled RGB photodiode system and develops the optical, spectral, and geometric foundations required to obtain traceable flux estimates from reduced-channel measurements. The system under study comprises an LED with known spectral power distribution (SPD), optical head, optical fiber, a protective sensor window, and a photodiode matrix type sensor. A complete end-to-end analysis of the optical path is presented, including geometric coupling efficiency, fiber transmission and angular redistribution, Fresnel losses in the sensor window, and the mosaic structure of the sensor. Additional effects such as fiber–sensor alignment, fiber-facet tilt, air gaps, and LED placement tolerances are quantified and incorporated into a formal uncertainty budget. Using the manufacturer-supplied SPD of the reference LED together with the measured R, G, and B channel responsivity functions of the sensor, a calibration-based mapping is established to reconstruct photopic luminous flux from the three-channel outputs. These results demonstrate that, with appropriate modeling and calibration of all optical stages, a fiber-coupled RGB photodiode mosaic can provide practical and scientifically meaningful luminous-flux estimation for white LEDs, offering a portable and cost-effective alternative to conventional photometric instrumentation in mid-accuracy applications. Further optimization of computation speed can enable fully integrated measurement systems in resource-constrained environments. Full article

Predicting internal quality parameters, such as Brix and water content, of apples, is essential for quality control. Existing near-infrared (NIR) and hyperspectral imaging (HSI)-based techniques have limited applicability due to their dependence on equipment and environmental sensitivity. In this study, a transportable quality assessment system was proposed using spatiotemporal domain analysis with long-wave infrared (LWIR)-based thermal diffusion phenomics, enabling non-destructive prediction of the internal Brix of apples during transport. After cooling, the thermal gradient of the apple surface during the cooling-to-equilibrium interval was extracted. This gradient was used as an input variable for multiple linear regression, Ridge, and Lasso models, and the prediction performance was assessed. Overall, 492 specimens of 5 cultivars of apple (Hongro, Arisoo, Sinano Gold, Stored Fuji, and Fuji) were included in the experiment. The thermal diffusion response of each specimen was imaged at a sampling frequency of 8.9 Hz using LWIR-based thermal imaging, and the temperature changes over time were compared. In cross-validation of the integrated model for all cultivars, the coefficient of determination (R²_cv) was 0.80, and the RMSEcv was 0.86 °Brix, demonstrating stable prediction accuracy within ±1 °Brix. In terms of cultivar, Arisoo (Cultivar 2) and Fuji (Cultivar 5) showed high prediction reliability (R²_cv = 0.74–0.77), while Hongro (Cultivar 1) and Stored Fuji (Cultivar 4) showed relatively weak correlations. This is thought to be due to differences in thermal diffusion characteristics between cultivars, depending on their tissue density and water content. The LWIR-based thermal diffusion analysis presented in this study is less sensitive to changes in reflectance and illuminance compared to conventional NIR and visible light spectrophotometry, as it enables real-time measurements during transport without requiring a separate light source. Surface heat distribution phenomics due to external heat sources serves as an index that proximally reflects changes in the internal Brix of apples. Later, this could be developed into a reliable commercial screening system to obtain extensive data accounting for diversity between cultivars and to elucidate the effects of interference using external environmental factors. Full article

Low-illumination conditions significantly degrade the performance of vision-based garbage recognition systems in practical smart city applications. To address this issue, this paper presents a garbage recognition framework that combines low-light image enhancement with attention-guided feature learning. A multi-branch low-light enhancement network (MBLLEN) is employed as the enhancement backbone, and a Convolutional Block Attention Module (CBAM) is integrated to alleviate local over-enhancement and guide feature responses under uneven illumination. The enhanced images are then used as inputs for a deep learning-based garbage classification model. In addition, a lightweight encryption mechanism is considered at the system level to support secure data transmission in practical deployment scenarios. Experiments conducted on a self-collected low-light garbage dataset show that the proposed framework achieves improved image quality and recognition performance compared with baseline approaches. These results suggest that integrating low-light enhancement with attention-guided feature learning can be beneficial for garbage recognition tasks under challenging illumination conditions. Full article

Ginseng is widely regarded as the “King of Herbs” in traditional Chinese medicine. In recent years, escalating global warming and intensified human activities have led to a continuous rise in environmental temperatures, posing a significant threat to ginseng cultivation in China. Therefore, understanding how high-temperature stress affects the photosynthetic performance of ginseng is essential for developing efficient and sustainable cultivation practices. In this study, four temperature regimes were established to systematically investigate the impact of elevated temperatures on the photosynthetic system of ginseng leaves: 25/16 °C (CK), 30/20 °C, 35/24 °C, and 40/28 °C (day/night). The results demonstrated that high-temperature stress significantly inhibited photosynthesis. Specifically, the activities of key chlorophyll biosynthesis enzymes—porphobilinogen deaminase and delta-aminolevulinate dehydratase—were markedly reduced, resulting in the accumulation of critical intermediates in the chlorophyll pathway, including protoporphyrinIX, Mg-protoporphyrinIX, and protochlorophyll. Chlorophyll synthesis was severely impaired as a result. Consequently, the contents of chlorophyll a, chlorophyll b, and carotenoids declined by 25.38%, 12.52%, and 54.63%, respectively, indicating substantial disruption of the photosynthetic pigment system. Anatomical observations revealed that high-temperatures induced stomatal closure, impairing stomata exchange and further reducing photosynthetic efficiency. Moreover, chloroplast ultrastructure was severely compromised, characterized by excessive accumulation of osmiophilic granules, disorganized and loosely stacked thylakoid membranes, and impaired capacity for light energy capture and conversion. This study provides theoretical insights into the response mechanisms of ginseng leaf photosynthesis under heat stress and establishes a scientific basis for enhancing thermotolerance through breeding programs and improved cultivation management strategies. Full article

Mumps virus (MuV) is a single-stranded, negative-sense RNA virus of the Family Paramyxoviridae. MuV is a highly contagious human pathogen that causes primarily mild symptoms, including hallmark swelling of the parotid glands. Severe cases can occur, leading to neurological complications, including deafness, meningitis, and encephalitis. The mumps vaccine, now included in combination with measles and rubella vaccines (MMR), was first made available in the 1960s. After its introduction, mumps incidence dropped dramatically to less than 500 cases annually in the US. However, even with long-standing vaccination programs, MuV continues to challenge the landscape of public health due to a resurgence of cases in the past several decades and a still present lack of approved antiviral drugs and treatments available for the disease. This review will explore the biology of MuV, focusing on how MuV replicates and interacts with the host immune system. Recent studies have also shed light on the role of protein phosphorylation in regulating viral RNA synthesis—particularly the dynamic interactions between the nucleoprotein (NP) and phosphoprotein (P)—offering new insights into how the virus controls its replication machinery both mechanistically and through utilizing host cell advantages. We also examine how the immune system responds to mumps infection and vaccination, and how those responses may vary across viral genotypes. Although the Jeryl Lynn vaccine strain has played a key role in controlling mumps for decades, outbreaks among vaccinated individuals have raised questions about the present vaccine’s efficacy against circulating and emerging genotypes and if novel strategies will be required to prevent future outbreaks. We review current epidemiological data, highlighting shifts in MuV transmission and genotype distribution, and discuss the need for updated or genotype-matched vaccines. By connecting molecular virology with real-world trends in disease spread and vaccine performance, this review aims to support ongoing efforts to strengthen mumps control strategies and inform the development of next-generation vaccines. Full article

Background: Zoonotic spillover events with pandemic potential are increasingly associated with environmental change, ecosystem disruption, and intensified human–animal interactions. Although the specific origin and timing of future pandemics remain uncertain, there is a clear need to complement traditional preparedness strategies with approaches that support earlier anticipation and prevention. Objectives: This study aims to propose a conceptual approach to reframe pandemic preparedness toward proactive surveillance and spillover prevention. Methods: We introduce a tachyon-inspired conceptual approach, using a thought experiment based on hypothetical faster-than-light particles to illustrate anticipatory observation of pandemic emergence. The framework is informed by interdisciplinary literature on emerging infectious diseases, One Health surveillance, predictive epidemiology, and public-health preparedness. Results: The proposed approach highlights the importance of proactive, integrated surveillance systems that combine human, animal, and environmental data. Key elements include the use of advanced analytical tools such as neural networks, early characterization of population risk profiles, strengthened public-health infrastructure, coordinated governance, adaptable financial resources, and a resilient healthcare workforce. The integration of animal welfare considerations, translational research, and planetary health principles is emphasized as central to reducing spillover risk. Conclusions: Tachyon-inspired thinking offers a conceptual tool to support a shift from reactive pandemic response toward proactive anticipation and prevention. Embedding integrated surveillance and One Health principles into public-health systems may enhance early detection capacity and contribute to mitigating the impact of future pandemics. Full article

Agroforestry is defined as a multifunctional approach to land management that enhances biodiversity and soil health while mitigating environmental impacts compared to intensive agriculture. The efficacy of maize cultivation in agroforestry systems is significantly influenced by nutrient competition. The factors that influence this phenomenon include the dimensions and configuration of the tree rows, as well as the availability of nutrients. This study examined the effect of nitrogen fertilization, tree line spacing, and seasonal changes on the productivity and the leaf spectral characteristics of the intercropped maize (Zea mays L.) within a willow-based agroforestry system in eastern Hungary. The experiment involved the cultivation of maize with two spacings (narrow and wide field strips) and four nitrogen levels (0, 50, 100, and 150 kg N ha⁻¹) across two growing seasons (2023–2024). The results demonstrated that yield-related parameters, including biomass, cob size and weight, and grain weight, exhibited a strong response to nitrogen level and tree line spacing. The reduction in spacing resulted in a decline in maize productivity. However, a high nitrogen input (150 kg N ha⁻¹) partially mitigated this effect in the first growing season. Vegetation indices demonstrated a high degree of sensitivity to annual variations, particularly with regard to tree competition and weather conditions. Multispectral vegetation indices exhibited a heightened responsiveness to environmental and management factors when compared to indices based on visible light (RGB). The findings of this study demonstrate that a combination of optimized tree spacing and optimized nitrogen management fosters productivity while maintaining agroecological sustainability in temperate agroforestry systems. Full article

Street livability is widely recognized as a fundamental indicator of urban livability. Despite growing global agendas advocating human-centered, sustainable, and smart cities, the microscale implementation of streetscape interventions remains limited and non-integrated. This gap is particularly evident in developing cities’ contexts where policy-level frameworks fail to translate into tangible street-level transformations. Responding to this challenge, this paper investigates how streetscape components can enhance everyday street livability. The study aims to explore opportunities for improving street livability through the utilization of three core streetscape components: vegetation, street furniture, and lighting. The discourse on street livability identifies vegetation, street furniture, and lighting as the primary drivers of high-quality urban spaces. Scholarly research suggests that these micro-interventions are most effective when viewed through the combined lenses of human-centered design, environmental sustainability, and smart city technology. While the literature indicates that integrating climate-responsive greenery and renewable energy systems can enhance social interaction and safety, it also highlights significant implementation hurdles. Specifically, researchers point to policy limitations, technical feasibility in developing nations, and the socio-economic threat of green gentrification. Despite these complexities, microscale streetscape improvements remain a vital strategy for fostering inclusive and resilient cities. Full article

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between their variations and disaster events have challenged traditional studies that rely on instantaneous correlations to uncover the underlying dynamic mechanisms. This study focuses on the Ulva prolifera disaster in the South Yellow Sea, systematically analyzing its spatiotemporal distribution patterns, the temporal accumulation and lag effects of environmental factors, and the coupled driving mechanisms using the Floating Algae Index (FAI). The results indicate that: (1) The disaster shows significant interannual variability, with 2019 experiencing the most severe outbreak. Monthly, the disaster begins offshore of Jiangsu in May, moves northward and peaks in June, expands northward with reduced scale in July, and largely dissipates in August. Years with large-scale outbreaks exhibit higher distribution frequency and broader spatial extent. (2) Environmental factors demonstrate significant accumulation and lag effects on Ulva prolifera disasters, with a mixed temporal mode of both accumulation and lag effects being dominant. Temporal parameters vary across different factors—nutrients generally have longer lag times, while light and temperature factors show longer accumulation times. These parameters change dynamically across disaster stages and display a clear inshore–offshore gradient, with shorter effects in coastal areas and longer durations in offshore waters, revealing significant spatiotemporal heterogeneity in temporal response patterns. (3) The driving mechanism of Ulva prolifera disasters follows a “nutrient-dominated, temporally relayed” pattern. Nutrient accumulation (PO₄, NO₃, SI) from the previous autumn and winter serves as the decisive factor, explaining 86.8% of interannual variation in disaster scale and 56.1% of the variation in first outbreak timing. Light and heat conditions play a secondary modulating role. A clear temporal relay occurs through three distinct stages: the initial outbreak triggered by nutrients, the peak outbreak governed by light–temperature–nutrient synergy, and the system decline characterized by the dissipation of all driving forces. These findings provide a mechanistic basis for developing predictive models and targeted control strategies. Full article

Photopharmacology is an emerging field in medicinal chemistry that seeks to control the pharmacological effects of target compounds using light. This approach addresses challenges such as limited receptor selectivity by enabling precise spatiotemporal control of therapeutic effects. The light-responsiveness is a central molecular feature used in photopharmacology to modulate the activity of various biological systems, including the endocannabinoid system (ECS). Although the ECS plays a well-established role in the treatment of neurodegeneration, inflammation, and pain, targeting its receptors is challenging due to side effects resulting from receptor activation or inactivation and the incomplete selectivity of available ligands. In this review, we present a comprehensive analysis of the most important ECS photoagonists and photoantagonists, highlighting how this photopharmacological approach overcomes traditional limitations of therapeutic targeting and reduces off-target effects. Full article

Plant roots interact dynamically with complex environments, and their capacity to adapt is crucial for growth, development, survival, and productivity. Basic leucine zipper (bZIP) transcription factors have emerged as key regulators in managing the root’s response to various environmental signals. The shift from bulk tissue analysis to single-cell RNA sequencing (scRNA-seq) has enabled the creation of a highly detailed expression atlas for root bZIPs, significantly enhancing our understanding of their functions. This review first summarizes the classification and structural features of bZIPs in Arabidopsis, and compares representative members with their orthologs in cereal crops. Next, we integrate the expression patterns of various bZIP members in root cells and clarify their roles through single-cell expression profiling. Furthermore, we delineate characterized bZIP regulatory modules that respond to signals spanning light, hormones, nutrients, and stresses, thereby orchestrating transcriptional reprogramming to facilitate plant adaptation. By combining single-cell omics with functional genetics, we reveal how bZIPs control critical processes, including responses to light signals, hormonal interactions, nutrient uptake and balance, and reactions to abiotic stresses. Ultimately, this integrated perspective highlights the potential for targeting bZIP transcription factors in the development of climate-resilient crops with optimized root systems, thereby enabling them to adapt to changing environmental conditions. Full article