Search Results (23,798)

Urban air quality is a key component of environmental sustainability and public health in large metropolitan areas. Following the substantial but temporary improvements in air quality observed during the COVID-19 lockdowns, it remains unclear whether structural changes in urban air pollution have persisted in the post-pandemic period. This study analyzes the temporal dynamics of major atmospheric pollutants in Mexico City between 2021 and 2024, including CO, NO₂, NO_x, O₃, PM₁₀, PM_2.5, and SO₂, using hourly data from the Mexico City Atmospheric Monitoring System (SIMAT). Annual and monthly median concentrations were computed to reduce the influence of extreme values and short-term pollution episodes. Station-level monotonic trends were evaluated using the non-parametric Mann–Kendall test, complemented by the use of Sen’s slope estimator to quantify the magnitude and direction of change. Absolute and relative changes between 2021 and 2024 were also analyzed to capture incremental variations not reflected by trend significance tests and performed together with hourly monthly analyses to characterize diurnal and seasonal patterns. Results indicate that no statistically significant monotonic trends were detected for any pollutant across the analyzed stations (p > 0.05), suggesting an overall stabilization of air quality levels during the post-pandemic period. Nevertheless, moderate increases in annual median concentrations were observed at specific locations, particularly for PM₁₀, PM_2.5, NO₂, and NO_x, with relative changes ranging from approximately 5% to 35%. Persistent diurnal and seasonal patterns were identified, closely associated with traffic activity, photochemical processes, and meteorological conditions. These findings suggest that, although no robust long-term trends are evident, incremental increases and stable temporal structures remain relevant from a sustainability perspective. Continued monitoring and targeted air quality management strategies are therefore necessary to support long-term urban environmental sustainability. Full article

Physics-based simulations are increasingly used to improve understanding of electrosurgical processes and to enable model-based estimation of tissue state when direct sensing is limited. The performance of such simulation-based virtual sensing approaches strongly depends on an accurate representation of the electrode–tissue interface. Despite its central role in electrical and thermal coupling, the influence of the electrode–tissue contact area has received limited attention in existing simulation studies. In this work, the influence of the electrode–tissue contact area on the sensitivity of key temperature-dependent tissue parameters was investigated for electrosurgical monopolar soft coagulation. Using a multiphysics finite element model under controlled boundary conditions, the sensitivity of maximum temperature development and necrotic tissue volume formation was analyzed with respect to varying contact areas and initial values of electrical conductivity, thermal conductivity, and effective heat capacity. The results demonstrate that parameter sensitivities are strongly contact-area-dependent. Electrical conductivity exhibits the most pronounced influence, particularly at larger contact areas, while thermal conductivity remains of minor relevance. In contrast, effective heat capacity significantly affects necrotic tissue volume formation, with increasing sensitivity for larger contact areas. These findings emphasize the importance of accurately accounting for electrode–tissue contact conditions in simulation-based analyses and clarify how contact-area-dependent sensitivities influence model-based tissue state estimation in electrosurgical coagulation. Full article

China is one of the world’s leading producers of celery. In recent years, the market price of celery has often experienced rollercoaster-like fluctuations. Such volatility has become a significant factor affecting the income of vegetable farmers, market stability, and household consumption. The key to addressing this issue lies in understanding the threshold of the celery planting area at which supply and demand are balanced. However, relevant research has been rarely conducted on this topic to date. Shandong Province is a major vegetable-producing region in China, and its celery output and pricing have a crucial impact on the national market. Therefore, this study takes Yucheng City, Shandong Province, as a case study. By leveraging the land vacancy characteristics before the celery planting period, the NDVI data was calculated, and the object-based supervised classification was used to extract the celery planting area from remote sensing imagery. Based on a comprehensive statistical analysis of collected annual celery wholesale prices and break-even prices over the past decade, it was found that when the autumn celery planting area in the study region exceeds 12,000 hectares, oversupply occurs, leading to losses for celery farmers. Moreover, this situation recurs approximately every four years. To prevent celery oversupply, the government should estimate the prospective celery planting area using remote sensing imagery during the one-month land vacancy period before celery transplantation. Once the estimated data reach or exceed the supply–demand balance threshold, proactive guidance should be provided to encourage celery farmers to switch to other vegetables, thereby reducing potential losses for farmers. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices. This study could also maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices and could enable farmers to achieve sustained profitability. The sustainable profit could maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. Full article

This work presents a comprehensive literature review on Continuous Oscillatory Baffled Reactors (COBRs), surveying advancements from 2014 to 2025. Although widespread industrial adoption of COBRs remains a future goal, the analysis reveals the significant maturation of COBR technology, marked by a growing exploration of novel applications—particularly in enabling the transition from batch to continuous manufacturing. The review synthesizes both theoretical and experimental studies, categorizing them into key thematic areas to provide a clear and accessible overview of the field. The study concludes by identifying critical research gaps and offering a perspective on future directions, thereby aiming to guide and inspire subsequent research endeavors in overcoming the barriers to commercialization. Full article

Scaevola taccada, as a key dominant plant in coastal ecosystems, plays an irreplaceable role in sand fixation, shoreline protection, and maintaining the ecological stability of coastal zones. To investigate the effects of drought stress on the Binghai plant Scaevola taccada seedlings, a natural drought treatment was applied. Physiological indicators were measured at 0, 10, 25, and 40 days of stress, and 5 days after rewatering. Transcriptome sequencing and long non-coding RNA (lncRNA) analysis were also conducted to reveal the drought response mechanisms and molecular regulatory networks. The results showed that: (1) Prolonged drought significantly inhibited growth, with relative height increase, leaf number, and relative water content declining by 46.8%, 37.2%, and 63.4%, respectively, at T40 compared to the control. (2) In terms of photosynthetic physiology, Rubisco activity, RCA activity, SPAD value, Fv/Fm, and qP all continuously declined with increasing stress, while NPQ increased, suggesting damage to the photosynthetic system but also the activation of energy dissipation mechanisms to alleviate photooxidative stress. (3) The antioxidant system played a crucial role in the drought response. Under drought stress, the activities of SOD, POD, and CAT, and MDA content, underwent significant changes, with antioxidant enzyme activities rebounding notably after rewatering. (4) Transcriptome analysis revealed that differentially expressed mRNAs and lncRNA-targeted genes were significantly enriched in the ‘photosynthesis’ and ‘carbon metabolism’ pathways. Key genes involved, including PSAD-1, PSAL, NPQ4, six LHCs, BAM3, BAM1, SSII-A, and FRK1, were identified as core components of the regulatory network. In summary, Scaevola taccada effectively responds to drought stress through multi-level mechanisms, including photosynthetic regulation, carbon metabolism regulation, antioxidant defense, and transcriptional reprogramming, demonstrating strong drought resistance and post-rewatering recovery potential. These findings provide scientific evidence for plant selection and application in ecological restoration projects in coastal areas in the context of global climate extremes. Full article

Type B trichothecenes (B-TCTs), predominant mycotoxins in wheat, threaten human health. However, their contamination profile in China, a major wheat producer, remains unclear. This study analyzed 1337 wheat samples (2022–2024) from main production areas using liquid chromatography-mass spectrometry and deterministic assessment to investigate B-TCTs’ watershed-scale distribution, spatiotemporal variations, associated health risks, and key climatic drivers. Results indicate that deoxynivalenol (DON) and its transformation product DON-3-glucoside (DON-3G) were the predominant contaminants, while nivalenol (NIV) was detected in specific river basins. Although overall exposure was low, elevated risks were identified in certain basins during particular years, especially for young children. DON-3G contributed 23.5% to total DON exposure. Relative humidity (rs = 0.34, p < 0.01), precipitation (rs = 0.37, p < 0.01), and its duration (rs = 0.38, p < 0.01) during the flowering-to-harvest period were identified as critical climatic drivers. The findings highlight the need to include DON-3G in food safety regulations and to develop climate-adapted control strategies. Full article

Generative artificial intelligence and foundation models have changed machine learning by allowing systems to produce readable text, realistic images, and other multimodal content with little direct input from a user. Foundation models are large neural networks trained on very large and varied datasets, and they form the core of many current generative AI (GenAI) systems. Their rapid development has led to major advances in areas like natural language processing, computer vision, multimodal learning, and robotics. Examples include GPT, LLaMA, and diffusion-based architectures, such as models often used for image generation. Systems such as Stable Diffusion show this shift by illustrating how AI can interpret information, draw basic inferences, and produce new outputs using more than one type of data. This review surveys common foundation model architectures and examines what they can do in generative tasks. It reviews Transformer, diffusion, and multimodal architectures, focusing on methods that support scaling and transfer across domains. The paper also reviews key approaches to pretraining and fine-tuning, including self-supervised learning, instruction tuning, and parameter-efficient adaptation, which support these systems’ ability to generalize across tasks. In addition to the technical details, this review discusses how GenAI is being used for text generation, image synthesis, robotics, and biomedical research. The study also notes continuing challenges, such as the high computing and energy demands of large models, ethical concerns about data bias and misinformation, and worries about privacy, reliability, and responsible use of AI in real settings. This review brings together ideas about model design, training methods, and social implications to point future research toward GenAI systems that are efficient, easy to interpret, and reliable, while supporting scientific progress and ethical responsibility. Full article

Hydrogen-mediated microbial electrosynthesis (MES) of chemicals from CO₂ relies on effective gas–liquid transfer at the cathode interface, yet the extent to which cathode surface area regulates acetate productivity remains insufficiently quantified. In this study, three identical MES reactors equipped with stainless-steel cathodes of different geometric areas (8 × 1, 8 × 4, and 8 × 16 cm²) were operated at a constant electric current of 0.3 A. The largest cathode significantly accelerated hydrogen mass transfer (k_La = 0.592 h⁻¹), reaching dissolution equilibrium within 3 min, which was nearly twice as fast as the smallest electrode. Upon inoculation with enriched acetate-producing microbial consortia, the 8 × 16 cm²_cathode reactor fed with CO₂ achieved the highest steady-state acetate concentration of 32 g·L⁻¹ produced at a rate of 2.12 g·L⁻¹·d⁻¹, with 94% hydrogen utilization, and 59% coulombic efficiency. In contrast, smaller electrodes exhibited rapid bubble detachment and reduced residence time, thereby limiting microbial gas uptake, and resulting in low acetate productivity. These findings demonstrate that cathode surface area is a key engineering lever controlling both hydrogen availability and electron recovery efficiency in H₂-driven MES. The results provide practical guidance for electrode design and scale-up of CO₂-to-acetate bioconversion via the MES process. Full article

The Black Sea harbour porpoise (Phocoena phocoena relicta Abel, 1905) is an endemic cetacean with poorly understood spatial ecology in Bulgarian waters. This study aimed to update knowledge on its distribution, abundance, and habitat use throughout the Bulgarian Exclusive Economic Zone (EEZ). We conducted systematic aerial line-transect surveys in all four seasons between October 2022 and October 2023, combined with distance sampling and MaxEnt habitat modelling. Porpoises were present year-round across the EEZ, with marked seasonal shifts in distribution and habitat preferences. Highest densities were observed in spring, while winter distributions were concentrated offshore. Habitat suitability was dynamic, with key high-use areas identified near Cape Emine and in southern offshore waters near the Turkish border. Overall, about 40% of the EEZ represented high-suitability habitat. These findings provide the first comprehensive, year-round baseline for P. p. relicta in Bulgarian waters, highlighting the species’ flexible habitat use and seasonality. The study was conducted under extraordinary conditions due to regional military activity, which may have influenced porpoise behaviour and spatial patterns. The provided results are critical for designing effective conservation and management measures in the face of both natural and anthropogenic pressures and threats. Full article

This study investigates how key planning variables—the number of wood assortments, the geometric shape of clear-cut areas, and the extraction (forwarding) distance—influence greenhouse gas (GHG) emissions. Twelve plots formed a heterogeneous sample with similar site type and soil moisture conditions. A Komatsu 931 harvester and a 855 forwarder, driven by the experienced operators, were used to ensure consistency in operator skill. For each plot, the isoperimetric quotient was computed to quantify how plot shape correlated with labor hours, fuel consumption, and the resulting volume of GHG emitted. The number of assortments extracted per plot ranged from three to fourteen product groups. The results show that plots with more complex shapes require significantly more operator time and fuel. Increasing the number of assortments amplifies handling time and fuel use. Longer extraction distances further exacerbate the emissions. These findings underscore the importance of integrating spatial geometry and wood assortment planning into harvest scheduling to enhance productivity and reduce the carbon footprint of forest operations. Recommendations for practitioners include prioritizing more compact treatment units, optimizing assortment grouping, and minimizing extraction distances as key strategies for precision forestry. Full article

Geochemical anomaly detection is a critical stage in mineral exploration, playing a key role in predicting potential mineral targets. Traditional methodologies often struggle to integrate the spatial structure of geochemical data with underlying geological constraints effectively. To address this limitation, we propose GeoCLA, a geochemical anomaly detection framework that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, and an Attention Mechanism (AM). This integrated spatial-attention architecture captures complex correlations among multiple features to improve anomaly identification. The method constructs spatial sequential samples from geochemical data. The CNNs extract local spatial patterns, the BiLSTM models sequential dependencies, and the AM enhances the representation of critical features. Anomaly scores are computed using the reconstruction error between the model output and the original data. In addition, a fault-distance weighting factor is incorporated to build a comprehensive anomaly evaluation index. The proposed model was applied to the Hatu gold district in Xinjiang, China. Both visual analysis and quantitative evaluation demonstrate effectiveness, achieving a ROC-AUC of 0.86 and a mineral occurrence coverage rate of 97% within moderate-to-high anomaly prospective areas, significantly outperforming baseline methods. Full article

This study evaluates how different alert conditions influence driver attention transitions under conditions of visual distraction using sequence analysis. Employing a within-subject experimental design, 13 participants underwent trials in a driving simulator, experiencing three distinct alert conditions: face-tracking auditory alerts, steering wheel auditory torque alerts, and a control scenario without alerts. An eye-tracking system was used to capture drivers’ gaze durations and sequences across three key areas of interest: road, dashboard, and tablet-based infotainment system. Analysis involved computation of transition probabilities, Markov chain modeling for long-term attentional distributions, and entropy analyses to quantify the randomness of gaze transitions. Results showed that face-tracking alerts significantly increased the likelihood of gaze redirection to the road compared to the other conditions, enhancing both immediate and sustained attention. Steering wheel torque alerts demonstrated minimal effectiveness, sometimes performing worse than the no-alert condition due to their passive nature, allowing drivers to bypass attention redirection. Steady-state analyses confirmed that face alerts notably improved sustained driver focus on the road by approximately 3.6%, reinforcing their utility for prolonged attentional control. Entropy analyses further revealed that face alerts provided an optimal balance between structured attention shifts and behavioral flexibility, enhancing attentional predictability. Findings are consistent with previous literature, emphasizing the superior effectiveness of active, gaze-based interventions over passive mechanisms. This research underscores the importance of designing proactive alert systems in vehicle safety technology to effectively mitigate visual distraction-related risks. Full article

With the acceleration of global climate change and urbanization, air pollution, particularly ozone pollution, has become a critical environmental issue, especially in the Beijing–Tianjin–Hebei region of China. This study investigates the spatiotemporal distribution of ozone pollution and its precursors, focusing on formaldehyde as a key indicator of volatile organic compounds. Utilizing high-resolution remote sensing data from the China High-Resolution Air Pollutants dataset and TROPOMI HCHO observations from 2013 to 2022, we employed advanced techniques such as the Kolmogorov–Zurbenko filter and high-value area identification to analyze ozone pollution trends, meteorological influences, and the spatial distribution of HCHO concentrations. Our findings reveal a significant increase in ozone concentrations across BTH, with an annual growth rate of 2.51 μg/m³, peaking during the summer months. The KZ filter decomposition highlighted that short-term and seasonal variations dominate ozone fluctuations, driven by meteorological factors such as solar radiation and temperature. Furthermore, the identification of HCHO HVAs demonstrated that urban agglomeration and expansion zones exhibit higher HCHO concentrations, with VOCs-limited zones showing the most pronounced HCHO levels. The study also introduced the PHV (Percentage Higher than Vicinity) index to quantify anomalous HCHO emissions, providing a robust tool for pinpointing pollution hotspots. Based on these insights, we propose targeted emission control strategies for key regions, including urban expansion zones in Zhangjiakou and non-urban zones in Qinhuangdao, to mitigate ozone pollution effectively. This research offers valuable scientific support for regional air quality management and the formulation of precise pollution control measures in the Beijing–Tianjin–Hebei region. Full article

Modern computing platforms increasingly rely on random number generators (RNGs) for modeling probabilistic processes in simulation, probabilistic computing, and system validation. They are also essential for cryptographic operations such as key generation, authenticated encryption, and digital signatures. Deterministic Random Bit Generators (DRBGs), as specified in the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90A, provides a standardized method for expanding entropy into cryptographically strong pseudorandom sequences. This work presents the design and Field Programmable Gate Array (FPGA) implementation of a hash-based DRBG using Ascon-Hash256, a lightweight, quantum-resistant hash function from the NIST-standardized Ascon cryptographic suite. It implements hash-based derivation, instantiation, generation, and reseeding of the generator via iterative hash invocations and state updates. Leveraging Ascon’s sponge-based structure, the design achieves efficient entropy absorption and diffusion while maintaining an area-efficient FPGA architecture, making it well suited for resource-constrained platforms. The diffusion properties of the proposed DRBG are evaluated through avalanche and reproducibility analyses, confirming strong sensitivity to input variations and secure, repeatable operation. Moreover, Monte Carlo and stochastic-diffusion evaluation of the generated bitstreams demonstrates correct convergence and statistically consistent behavior. These results confirm that the proposed hash-based DRBG provides reproducible, hardware-efficient, and cryptographically secure random numbers suitable for next-generation neuromorphic, probabilistic computing systems, and Internet of Things (IoT) devices. Full article

As a key spatial platform for implementing China’s Northeast Revitalization Strategy, coordinated development of production–living–ecological (PLE) functions in the Changchun Metropolitan Area is crucial for high-quality regional development. This study uses 24 counties (districts) in the metropolitan area as analytical units and develops a quantitative indicator system to evaluate PLE functions. We integrate the entropy-weighted TOPSIS method, social network analysis (SNA), and geographically and temporally weighted regression (GTWR) to examine the spatiotemporal dynamics, spatial correlation networks, and driving mechanisms of the three functions from 2013 to 2023. Temporally, the production function follows a growth–decline–recovery trajectory, the living function increases overall despite fluctuations, and the ecological function strengthens continuously. Overall, the three functions increasingly exhibit coupling and synergy. Spatially, the production function concentrates in core areas and diffuses along major axes. The living function is led by the core and followed by county-level catch-up. The ecological function is higher in the east, relatively stable in the west, and connected by corridors, together forming a multi-center, axis-based synergistic pattern. In the spatial correlation networks, densities of the production and ecological networks remain largely stable, whereas the living network becomes markedly denser. The three networks display distinct topologies and continue to evolve structurally. For driving mechanisms, the GTWR model provides the best fit. Geographic proximity positively contributes to the formation of all three functional networks, while the eight explanatory factors show pronounced spatiotemporal heterogeneity. These findings provide an evidence base for optimizing functional coordination and implementing differentiated spatial governance in metropolitan areas. Full article