Search Results (10,961)

High-accuracy spatiotemporal monitoring of surface nitrogen dioxide (NO₂) concentrations is essential for air quality management. This study evaluates machine learning-based estimates of near-surface NO₂ concentrations using data from the geostationary GEMS instrument and the polar-orbiting TROPOMI over China in 2022. Four tree-based models—Random Forest, XGBoost, CatBoost, and LightGBM—were trained by integrating satellite vertical-column densities with multi-source meteorological and ancillary data. Results show that CatBoost achieved the highest accuracy, with an R² of 0.842 for GEMS and 0.765 for TROPOMI, alongside the lowest RMSE and MAE. Models trained on GEMS data consistently outperformed TROPOMI-based models across all metrics. This advantage is primarily attributed to the substantially larger training sample size enabled by GEMS’s high temporal resolution, as confirmed through a controlled experiment with consistent sample sizes which isolated the effect of data volume. Spatially, GEMS estimates captured sharper concentration gradients and localized emission hotspots, while TROPOMI produced smoother fields. Temporally, only GEMS allowed the reconstruction of detailed diurnal patterns and near-real-time pollution episode tracking. This study confirms the significant added value of geostationary satellite data for high-frequency air quality monitoring and analysis when combined with machine learning. Full article

Building-integrated photovoltaics (BIPVs) can substantially increase renewable electricity utilization in buildings under China’s “dual-carbon” targets. Yet, fixed photovoltaic (FPV) windows cannot respond to seasonal and diurnal variations in solar altitude and azimuth, limiting their ability to jointly optimize power generation, shading, and solar heat gains. This study proposes a shading-type sun-tracking photovoltaic (STPV) window for south-facing residential glazing and evaluates its annual performance for a detached house in Hangzhou (hot-summer and cold-winter climate). Representative clear-sky field measurements were combined with annual EnergyPlus simulations to quantify PV yield, radiation regulation, and impacts on air-conditioning (HVAC) and lighting electricity use. STPV windows deliver an additional annual PV gain of ~336 kWh relative to FPV windows, mainly during transition seasons and around summer noon. Using the no-shading case as the baseline (4967 kWh/year), FPV windows reduce total electricity use to 4010 kWh (−957 kWh), while STPV windows further reduce it to 3281 kWh (−1686 kWh), providing an extra −729 kWh versus FPV. Accounting for PV generation, the annual net electricity demand decreases from 2929 kWh (FPV) to 1864 kWh (STPV), i.e., −1065 kWh (36.4%). These results highlight the synergy of tracking-enabled generation enhancement and cooling-load reduction for façade PV in Hangzhou-like climates. Full article

Downy mildew of hops represents a serious disease affecting hops production in all growing regions. Disease management is primarily based on the application of fungicides at regular intervals based on a short-term forecasting methodology that is essential for evaluating the occurrence of theoretical infections. To enable a more reliable assessment of the pathogen’s presence in a given area, spore traps capturing airborne Pseudoperonospora humuli sporangia can be utilized. The use of quantitative real-time PCR (qRT-PCR) for the detection of sporangia collected by these traps allows for the elimination of laborious and time-consuming microscopic counting. Among four tested P. humuli-specific nuclear DNA sequences, an effective qRT-PCR detection method was developed based on the c127233.5e3 sequence. This detection approach was used for the quantification of sporangia from volumetric spore trap samples collected in situ under field conditions at three selected localities in Bohemia and Moravia during the 2021–2022 period. The obtained results were compared with the short-term forecasting method of the downy mildew (HDM) weather index (I) based on meteorological data. The overall course of the HDM weather index (I) closely correlated with the occurrence of sporangia: after reaching the maximum HDM weather index (I) value, the highest sporangium detection was observed with a time delay of 1–2 weeks at all the monitored sites. The results corresponded well with data obtained from volumetric spore traps in Germany, and the qRT-PCR method proved to be fully comparable to light microscopy. The combination of volumetric spore traps and qRT-PCR can significantly improve the precision of short-term forecasting systems for P. humuli infection, thereby enabling more efficient fungicide application programs in hops protection and contributing to a better understanding of the pathogen’s dispersal dynamics. Full article

Agrivoltaics (AVs), the co-location of photovoltaic panels and agricultural production, is increasingly promoted as a strategy to enhance land-use efficiency and support renewable energy transitions. While numerous studies emphasize potential synergies, growing evidence indicates that AV systems also entail significant biophysical, ecological and socio-economic trade-offs. This review synthesizes published literature on the negative impacts and management challenges associated with agrivoltaics across diverse crops, climates and institutional contexts. A structured literature analysis was conducted, integrating findings from experimental field studies, ecological assessments, economic evaluations and policy analyses. The reviewed evidence demonstrates that panel-induced shading and altered microclimatic conditions frequently reduce photosynthetically active radiation, modify soil temperature and moisture regimes, and impair photosynthetic efficiency, yield stability, and quality in light-demanding crops. Open-field AV installations further alter understory vegetation, pollinator activity and soil arthropod communities, leading to functional biodiversity losses beneath panel-covered areas. Economic and institutional analyses reveal high investment costs, regulatory ambiguity and land-tenure constraints that disproportionately transfer agronomic and financial risks to farmers, while land-use conflicts may reduce food production and contribute to indirect land-use change. Overall, open-field AV outcomes are strongly context- and design-dependent. The review highlights the need for long-term, integrative assessments and governance frameworks that explicitly address trade-offs to ensure that AVs contribute to sustainable land-use transitions rather than undermining agricultural and ecological functions. Full article

Energy obtained by green ways with releasing environmental pollution is still a challenge for sustainable development for model society. Among energy technologies, photothermal conversion by using solar energy has become a new field and a hot topic in recent years. Based on the exploration of nanomaterials in the past decades, photothermal nanomaterials by using nanomaterials bring new chances for expending the utilization of green energy with high efficiency, mainly including metal semiconductors and carbon nanomaterials. Their modulated structure for enhancing light absorption, accelerating transformation of photon into heat, and located heat management were also considered important for promoting the utilization of solar energy and therefore, the strategies for designed and controllable preparing of photothermal nanomaterials were also summarized. The applications of photothermal nanomaterials were also reviewed to reveal the new chances for energy conversion engineering or promoting the solar energy utilization of solar energy in some cold regions or somewhere with low solar irradiation. Full article

The attosecond (10⁻¹⁸ s) light pulse represents the fastest time scale currently mastered by the scientific community, which enables the observation of electron dynamics within atoms and molecules, offering powerful tools to probe chemical reaction mechanisms and advance research in photovoltaic materials and biological processes. In this work, we investigate the generation of visible attosecond optical pulses via spectral broadening in Hollow-Core Fiber (HCF), followed by coherent recombination using a Three-Channel Light Field Synthesizer (TCLFS). The influence of the input pulse duration on Group Delay Dispersion (GDD), Third-Order Dispersion (TOD), and spectral broadening is systematically analyzed. Furthermore, the effects of GDD, TOD, and the carrier–envelope phase (CEP) on waveform synthesis are quantitatively examined for the first time. These findings provide valuable insights into dispersion management strategies essential for developing high-quality visible attosecond light sources, paving the way for future applications in ultrafast spectroscopy and light field-driven electron dynamics. Full article

Background/Objectives: Accurate three-dimensional (3D) facial scanning is increasingly important in digital dentistry for diagnosis, treatment planning, and virtual patient creation. Multiple facial scanning technologies are available; however, their metric reliability varies depending on acquisition principles and anatomical orientation. This study aimed to evaluate the trueness, orientation-dependent performance (vertical midline versus horizontal facial measurements), and scanning time of four facial scanning technologies using calibrated manual anthropometry as the reference standard. Methods: Thirty dentate adult participants received adhesive fiducial markers on five predefined facial landmarks. Four linear facial distances were measured clinically using a digital caliper and compared with corresponding measurements obtained from standardized 3D facial scans. Digital measurements were extracted following uniform metric normalization. Inter-examiner reliability, measurement trueness, orientation-related differences, and scanning time were analyzed. Results: Inter-examiner reliability was excellent for both clinical and digital measurements (ICC > 0.93). All facial scanning technologies significantly overestimated manual distances (p < 0.001). The structured-light scanning system showed the smallest deviations (typically <1 mm) and the highest overall accuracy, followed by the depth-fusion system, while photogrammetry-based and NeRF-based approaches demonstrated larger errors, frequently exceeding 2–3 mm. Horizontal facial distances consistently showed greater deviations than vertical midline measurements across all systems. Scanning time differed significantly between technologies, with passive image-based approaches being the fastest and NeRF-based acquisition requiring the longest capture time. Conclusions: Active structured-light facial scanning demonstrated the highest trueness for linear facial anthropometry, whereas passive photogrammetry and NeRF-based approaches showed lower metric trueness and are currently more suitable for educational applications. Full article

Accurate identification of building structural types (BSTs) is essential for seismic vulnerability assessment and disaster risk management. Traditional field survey methods are constrained by high costs, low efficiency, and limited scalability. Although remote sensing-based approaches offer strong potential for large area applications, they are often hindered by limited spatial resolution, spectral confusion, and difficulties in capturing information related to internal building structures. To address these limitations, this study proposes a BST classification approach that integrates remote sensing image features with multisource prior knowledge. In addition to conventional remote sensing features derived from building shape, spectral, and texture, multiple types of prior information are incorporated to compensate for the insufficient structural discriminative capability of remote sensing imagery alone. These include distance to roads, terrain conditions, building height, population, gross domestic product (GDP), and nighttime light intensity. Considering the limited number of labeled samples and the high dimensionality of features, fourteen mainstream machine learning algorithms are systematically evaluated. Through feature selection and model optimization, XGBoost is identified as the most effective classifier, achieving the highest weighted F1 score of 78.62%. The results demonstrate that, under the same machine learning model settings, models trained solely on remote sensing features consistently underperform those integrating multisource features combined with feature selection, confirming the effectiveness of synergistically fusing remote sensing features with prior knowledge for improving overall BST classification performance. Further analyses demonstrate that different groups of remote sensing features and prior knowledge are associated with reductions in misclassification between specific BSTs. Compared with approaches based exclusively on remote sensing imagery, the proposed method exhibits higher and more balanced classification performance across different BSTs, with particularly notable advantages for structure categories that are difficult to distinguish using single-source remote sensing features. This study provides the foundation for subsequent seismic vulnerability analysis and related risk studies. Full article

Autonomous drone navigation in confined tubular environments remains a major challenge due to the constraining geometry of the conduits, the proximity of the walls, and the perceptual limitations inherent to such scenarios. We propose a reinforcement learning (RL) approach enabling a drone to navigate unknown three-dimensional tubes without any prior knowledge of their geometry, relying solely on local observations from a Light Detection and Ranging (LiDAR) sensor and a conditional visual detection of the tube center. In contrast, the Pure Pursuit algorithm, used as a deterministic baseline, benefits from explicit access to the centerline, creating an information asymmetry designed to assess the ability of RL to compensate for the absence of a geometric model. The agent is trained through a progressive curriculum learning strategy that gradually exposes it to increasingly curved geometries, where the tube center frequently disappears from the visual field. A turning-negotiation mechanism, based on the combination of direct visibility, directional memory, and LiDAR symmetry cues, proves essential for ensuring stable navigation under such partial observability conditions. Experiments show that the Proximal Policy Optimization (PPO) policy acquires robust and generalizable behavior, consistently outperforming the deterministic controller despite its limited access to geometric information. Validation in a high-fidelity three-dimensional environment further confirms the transferability of the learned behavior to continuous physical dynamics. In particular, this work introduces an explicit formulation of the turn negotiation problem in tubular navigation, coupled with a reward design and evaluation metrics that make turn-handling behavior measurable and analyzable. This explicit focus on turn negotiation distinguishes our approach from prior learning-based and heuristic methods. The proposed approach thus provides a complete framework for autonomous navigation in unknown tubular environments and opens perspectives for industrial, underground, or medical applications where progressing through narrow and weakly perceptive conduits represents a central challenge. Full article

Phaeodactylum tricornutum is one of the most well-characterized microalgae and serves as a pivotal model diatom in global carbon fixation and the mediation of biogeochemical cycling of essential nutrients. Over the past few decades, the availability of a complete genome assembly, coupled with the development of robust DNA manipulation tools and efficient DNA delivery methodologies, has established P. tricornutum as a promising photosynthetic chassis for the sustainable bioproduction of high-value compounds, including fucoxanthin and eicosapentaenoic acid (EPA). This review systematically summarizes the research progress in the strain improvement toolkit of P. tricornutum, encompassing both genetic and non-genetic engineering strategies. It elaborates on the types and applications of its representative bioactive products, as well as the molecular mechanisms underlying key synthetic pathways. Additionally, this work synthesizes the research findings on the optimization of critical cultivation conditions (e.g., light, temperature, and nutrient composition) that modulate the growth and product synthesis of P. tricornutum. On this basis, the challenges encountered by P. tricornutum in industrial applications are proposed for further discussion, aiming to provide a reference for in-depth exploration of related research directions and facilitate the expansion of its application scope in the field of biomanufacturing. Full article

Bismuth-based semiconductors have emerged as a promising class of visible-light-responsive photo(electro)catalysts for environmental remediation owing to their tunable electronic structures, moderate band gaps, and relatively low toxicity. The stereochemically active Bi³⁺ 6s² lone pair and strong Bi–O orbital hybridization tailor valence-band states, enabling enhanced utilization of the solar spectrum and favorable charge-carrier dynamics. In addition, layered, perovskite-like, and aurivillius-type crystal frameworks generate internal electric fields that are advantageous for photoelectrochemical (PEC) operation. This review critically examines advances from 2015 to 2025 in the design, synthesis, modification, and environmental applications of bismuth-based photo(electro)catalysts, with particular emphasis on PEC systems for pollutant degradation. Major material families, including bismuth oxides, oxyhalides, oxychalcogenides, chalcogenides, perovskite-like oxides, and complex metal oxides, are discussed in relation to their structure–property–performance relationships. Key synthesis strategies, such as solid-state, sol–gel, hydro/solvothermal, microwave-assisted, spray pyrolysis, and electrodeposition methods, are compared with respect to morphology control, defect chemistry, and electrode integration. Performance-enhancing approaches, including elemental doping, oxygen-vacancy engineering, and the rational design of type-II, p–n, Z-scheme, and S-scheme heterojunctions, are critically assessed. Practical considerations related to stability, scalability, and techno-economic constraints are highlighted. Finally, current challenges and future directions toward durable and application-ready bismuth-based PEC technologies are outlined. Full article

As a class of self-organized soft matter systems merging fluidic mobility with long-range molecular order, cholesteric liquid crystals (CLCs) possess immense potential for the development of high-sensitivity, visually tractable flexible sensors. Leveraging their unique helical superstructures and stimuli-responsive photonic bandgaps, CLCs can transduce subtle physical or chemical perturbations into discernible optical signatures, such as Bragg reflection shifts or mesomorphic textural transitions. Nonetheless, the intrinsic fluidity of CLCs often compromises their structural integrity, while conventional one-dimensional (1D) or two-dimensional (2D) confinement geometries exhibit pronounced angular dependence, significantly constraining their detection precision in complex environments. Recently, microfluidic technology has emerged as a pivotal paradigm for achieving sophisticated three-dimensional (3D) spatial confinement of CLCs through the precise manipulation of microscale fluid volumes. This review systematically delineates recent advancements in microfluidics-enabled CLC sensors. Initially, the fundamental self-assembly principles and optical properties of CLCs are introduced, emphasizing the unique advantages of 3D spherical confinement in mitigating angular sensitivity and intensifying interfacial interactions. Subsequently, the primary sensing mechanisms are bifurcated into bulk-driven sensing via pitch modulation and interface-driven sensing via topological configuration transitions. We then detail the microfluidic-based fabrication strategies and engineering protocols for diverse 3D architectures, including monodisperse/multiphase droplets, microcapsules, shells, and Janus structures. Building upon these structural frameworks, current sensing applications in physical (temperature, strain/stress), chemical (volatile organic compounds, ions, pH), and biological (biomarkers, pathogens) detection are evaluated. Lastly, in light of persistent challenges, such as intricate signal interpretation and limited robustness in complex matrices, we propose future research trajectories, encompassing the co-optimization of geometric parameters (size and curvature), artificial intelligence-enhanced automated diagnostics, and multi-field-coupled intelligent integration. This work seeks to provide a comprehensive roadmap for the design of next-generation, high-performance, and portable liquid-state photonic sensing platforms. Full article

Pepper (Capsicum annuum) is a globally significant horticultural crop cultivated for its culinary, medicinal, and economic value. Traditional approaches for boosting the agricultural production of pepper, notably, expanding farmland, have become increasingly unsustainable. Recent advancements in artificial intelligence and 3D computer vision have started to transform crop cultivation and phenotyping, which has shed new light on increasing production by advanced breeding. However, currently, the field still lacks 3D pepper data that contains enough detail for organ-level analysis. Therefore, we propose Pepper-4D, a new, high-precision 4D point cloud dataset that records both the spatial structure and temporal development of pepper plants across various continuous growth stages. Our dataset is divided into three subsets, including a total of 916 individual point clouds from 29 indoor-cultivated pepper plant samples. Our dataset provides manual annotations at both the plant-level and organ-level, supporting phenotyping tasks such as pepper growth status classification, organ semantic segmentation, organ instance segmentation, organ growth tracking, new organ detection, and even the generation of synthetic 3D pepper plants. Full article

We investigate a double-probe-field-driven cavity optomechanical system with a degenerate optical parametric amplifier (OPA). When the system is in a mechanical PT-symmetric case, we study the generation mechanism of the sum sideband and how to enhance the generation efficiency of the sum sideband by controlling parametric interactions. Our model consists of two directly coupled PT-symmetric mechanical resonators, which are coupled to a Fabry–Pérot cavity equipped with an optical parametric amplifier. Research indicates that in a PT-symmetric mechanical resonator, there exist special exceptional points (EPs). Near EPs, the generation efficiency of the sum sideband is significantly enhanced. Notably, the introduction of an OPA can remarkably boost the efficiency of sum sideband generation (SSG) and establish a new sideband matching condition for the upper sum sideband. We conduct a detailed analysis of the dependence of SSG on system parameters, such as mechanical coupling strength, OPA nonlinear gain, OPA pump light field phase, and probe field frequency detuning. The research reveals that even with a weak driving field, a significantly enhanced efficiency of SSG can be achieved by adjusting the OPA gain coefficient and phase. This research offers new insights into enhancing or regulating light propagation in nonlinear optomechanical devices and holds potential for applications in high-precision measurement and optical communication. Full article

The integrated III-V self-injection locked (SIL) laser exhibits excellent linewidth compression, noise reduction, and frequency stability. However, the laser’s low efficiency and fluctuating output power severely limit its applications in optical coherent transmission, light detection and ranging (LiDAR), spectroscopy, and so on. Based on the rate equations for a semiconductor laser coupled to counter-propagating fields in a micro-ring resonator (MRR), we systematically investigate the laser power and linewidth compression under self-locking conditions. We improve the slope efficiency by adjusting the injection phase, diode–MRR coupling efficiency, the normalized mode-coupling rate between clockwise (CW) and counter-clockwise (CCW) modes, and the MRR Q-factor. The results show that the enhanced diode–MRR coupling efficiency effectively increases the laser slope efficiency and improves the stability of the injection phase and feedback intensity. The injection phase significantly influences the range of the self-injection locked state. The normalized mode-coupling rate effectively affects the locking bandwidth and maintains stable power transfer. The MRR intrinsic Q-factor has a positive correlation with improving the laser slope efficiency and compressing the linewidth. Full article