Search Results (14,189)

Accurate diagnosis of PM and precise histologic subtyping are critical for optimal therapeutic decision-making, as treatment strategies—including chemotherapy, immunotherapy, or multimodality approaches—are largely subtype-dependent. Because of the several-decade latency between fiber inhalation and symptom onset, many cases are diagnosed at an advanced stage, when patients are already in poor clinical condition. As observed across multiple solid malignancies, earlier-stage diagnosis is associated with improved prognosis and expanded therapeutic options. However, the rarity of PM, the absence of validated screening strategies, and its nonspecific clinical and radiologic presentation—often mimicking both benign and metastatic pleural conditions, frequently result in diagnostic delay. Furthermore, the lack of pathognomonic histopathologic markers further complicates timely and definitive diagnosis. This review aims to delineate the epidemiologic, clinical, radiologic, and pathologic barriers that hinder accurate and early detection of PM. Current clinical evidence points to an urgent need to develop novel, validated biomarkers in PM, which will require a multidisciplinary approach. Full article

Construction projects contributing to smart city (SC) development largely consist of megaprojects due to their complex and multidisciplinary nature and their high costs. Effective project management (PM) is essential for the implementation of these projects in the Construction 5.0 era. This study aims to systematically analyze the research trends and identify FRDs in construction PM for megaprojects, which are essential for the development of SCs in the Construction 5.0 era. With this aim, a systematic literature review based on the PRISMA 2020 checklist was performed through a bibliometric analysis using VOSviewer version 1.6 Studies are gathered under five main clusters (i.e., the PM cluster, the smart construction and data security cluster, the SC and technology cluster, the spatial data integration cluster, and the lifecycle cluster). It has been determined that two main nodes (i.e., SC and digital twin) are located at the center of all these clusters. As a result of the analysis, two future research directions are determined (i.e., the relationship between megaprojects and SCs and the relationship between construction project management and SCs). As the identified clusters, nodes, and future research directions are interrelated and comply with the PMBOK 7th edition performance domains, focusing on them to support construction PM performance complies with efforts to facilitate the successful implementation of megaprojects integrated with SCs. The findings demonstrate the lack of a PM model within the SC ecosystem that synchronizes all phases of megaproject construction with SCs. Thus, this study can contribute to the development of smart, sustainable, and resilient cities. Full article

The transformation of university libraries into learning commons has highlighted the importance of informal learning spaces (ILSs). However, the mechanisms through which spatial elements influence learning experiences remain underexplored, particularly in western China. Drawing on person-environment fit theory and a multi-coupling framework, this study develops a four-dimensional analytical model comprising spatial layout, facility configuration, environmental quality, and cultural perception. A mixed-methods approach was employed, including 532 valid questionnaires, behavioral observations, and comprehensive environmental measurements (illuminance, noise, CO₂, PM2.5, TVOC, thermal conditions) across three university libraries in Lanzhou, China. Structural equation modeling (SEM) and coupling coordination degree modeling were used for analysis. Spatial layout (β = 0.324, p < 0.001), facility configuration (β = 0.287, p < 0.001), environmental quality (β = 0.196, p < 0.01), and cultural perception (β = 0.158, p < 0.05) all significantly predicted learning satisfaction, jointly explaining 67.3% of the variance. Learning satisfaction partially mediated the relationship between spatial elements and learning outcomes (indirect effect 31.2%). Coupling coordination degrees ranged from 0.578 to 0.634, revealing a “high coupling, low coordination” pattern, with cultural perception as the common shortfall. Environmental measurements showed CO₂ concentrations ranging from 823 to 946 ppm in quiet zones and up to 1085 ppm in lounge areas, correlating negatively with satisfaction (r = –0.41, p < 0.05). Spatial elements influence learning outcomes primarily through satisfaction enhancement. An integrated optimization framework is proposed, offering actionable strategies for ILS design in similar contexts. Full article

Virtual coupling control of trains is a promising technology for improving railway capacity and operational efficiency. However, existing multi-agent reinforcement learning (MARL) approaches struggle to capture long-sequence temporal dependencies among train states in complex multi-train interaction scenarios, resulting in limited robustness and coordination stability. To address this issue, this paper proposes a Predictive Mamba-based Multi-Agent Soft Actor–Critic (PM-MASAC) framework. A Mamba-based state prediction module is embedded into the centralized Critic network to model historical state sequences and generate predictive state representations, thereby enhancing value estimation accuracy. In addition, a multi-agent aggregated prioritized experience replay (PER) mechanism is introduced to improve the utilization of critical cooperative samples and stabilize training. A hierarchical local–global reward structure is further designed to ensure individual tracking performance while promoting overall formation coordination. Experimental results under realistic railway operating conditions demonstrate that PM-MASAC achieves superior robustness compared with baseline MARL methods. Velocity and spacing tracking errors are maintained within 3% and 1%, respectively, and the steady-state formation success rate exceeds 95.7% in the training environment. Full article

The synthesis of nitrogen-containing heterocycles remains a subject of significant interest due to their applications in medicinal chemistry and materials science. This paper describes the preparation of imidazo[1,2-a]pyridine using a catalytic system consisting of the deep eutectic solvent (DES) choline chloride (ChCl)–tartaric acid (1:2) and I₂ by reaction between 2-aminopyridines and chalcones (1,3-diphenylprop-2-en-1-ones). The proposed mechanism suggests the activation of the chalcone carbonyl by the DES, enhancing the polarization of the conjugated system which suffers electrophilic addition by I₂ to the C=C bond. The resulting intermediate undergoes a nucleophilic attack by 2-aminopyridine followed by cyclization and iodine-promoted oxidation and aromatization to yield the corresponding imidazo[1,2-a]pyridine products. The role of the DES is crucial, as it facilitates carbonyl activation through hydrogen bond interactions, stabilizes reactive intermediates, and promotes protonation–deprotonation steps, thereby eliminating the need for metal catalysts or toxic organic solvents. Theoretical calculations at the PM6 level of theory suggest that the DES acts as a catalyst in this reaction, due to the nature of its components enabling the development of more sustainable synthetic strategies. Full article

This work investigates the behavior of carbon dioxide (CO₂) near the surface of different single-atom alloys to evaluate their potential as catalysts for decarbonization processes. Specifically, 26 transition metals from the first three transition series, alloyed with three low Miller index copper supports, were considered. Adsorption energies and distances of linear CO₂, trigonal CO₂, and CO* + O* on the surfaces were calculated using the semiempirical computational method MOPAC-PM7. Additionally, activation energies were determined from previously published research. The proposed methodology is less computationally demanding than DFT studies, and results show good agreement with both experimental and simulated data. This approach provides a computationally efficient methodology for screening promising materials that convert CO₂ into valuable products, such as methane and methanol. Full article

As a core decarbonization technology, the scaling up of Near-Zero Energy Building (NZEB) technologies under the “dual carbon” goal necessitates collaboration among governments, technology suppliers, and construction enterprises. However, high research and development (R&D) costs coupled with low market acceptance impede widespread adoption. This study develops a tripartite evolutionary game model to analyze strategic interactions among stakeholders. Using MATLAB 2022B simulations, we simulate the strategy sets for the government (subsidize/no subsidy), suppliers (R&D/no R&D), and enterprises (procure/no purchase). The results identify two Evolutionary Stable Strategies (ESS): a market-driven ESS (0, 1, 1) emerges when the green premium (Pm) exceeds the incremental cost (Cb); while a policy-driven ESS (1, 1, 1) requires government subsidies (S) to offset R&D gaps, specifically when $S>Cr/\alpha -Pmz$. These findings provide a theoretical basis for understanding the synergistic mechanisms underlying NZEB adoption and highlight the dynamic interplay between policy incentives and market forces. Full article

Accurate forecasting of near-surface PM_2.5 concentrations remains challenging due to the complex coupling between atmospheric vertical structure, thermodynamic stability, and pollutant accumulation processes. Most existing surface-based statistical and deep learning approaches struggle to represent the three-dimensional state of the atmosphere, which limits their robustness under complex meteorological conditions. In this study, we propose a multi-source spatiotemporal learning framework(MST-Net) to enhance PM_2.5 forecasting accuracy by integrating vertically resolved atmospheric information from lidar and radiosonde observations. The proposed approach incorporates vertical profile features together with surface measurements to provide complementary information on atmospheric vertical structure and its temporal evolution. Experimental results demonstrate that MST-Net consistently outperforms conventional time-series models across multiple forecast horizons. Notably, at extended lead times (12–24 h), the proposed framework exhibits enhanced stability and slower error growth. For 24 h forecasts, MST-Net reduces RMSE by approximately 13% and MAE by about 19%. These results indicate that leveraging multi-source vertical atmospheric information can effectively improve the reliability of urban air quality forecasting. Full article

To address water-heat constraints and environmental risks associated with plastic film mulching in winter wheat production in the semi-arid region of Northwest China, a two-year field experiment (2021–2023) was conducted in Tongwei County, Gansu Province. A single-factor randomized block design was applied, with full plastic film mulching (PM) and bare land (CK) as controls, to evaluate the effects of 3-row (S3), 4-row (S4), and 5-row (S5) corn stalk strip mulching on soil hydrothermal conditions, active carbon fractions, and yield under rainfed conditions. Results showed that straw mulching significantly enhanced soil water retention, particularly in the 0–40 cm layer, where moisture content increased by 7.70–19.28% compared with CK (p < 0.05), with S3 performing best. Treatment S5 achieved the highest accumulated temperature and reduced the soil diurnal temperature range by 20.73–35.62% (p < 0.05). Active carbon fractions were also significantly improved, especially during the jointing–grain-filling stage (BBCH 31–87). In terms of yield, S5 exhibited the greatest increase, with a 15.88% higher two-year average grain yield than CK (p < 0.05), reaching over 90% of PM. Overall, S5 demonstrated optimal synergistic regulation of water, heat, and carbon, indicating strong potential as a sustainable alternative to plastic film mulching. Full article

Silicon photomultipliers (SiPMs) are vital for calorimetric applications in high-energy physics and medical imaging due to their high gain, compactness, and insensitivity to magnetic fields. However, their finite pixel count induces non-linear response behaviour at high photon fluxes, affecting energy resolution and systematic accuracy. This work presents a comprehensive methodology to characterise SiPM response functions and derive correction curves using a single-step laser-based measurement approach. Three SiPMs with varying pixel sizes ([list-final-separator= and , list-units = single]15;25;50$\mathrm{m}$) are studied under controlled temperature conditions, with response functions extracted across different overvoltages and integration windows. The correction method, independent of precise light source calibration, effectively linearises the response up to saturation levels exceeding $100$ of the pixel count, achieving deviations of the order of $3$ across a broad operational parameter space, and outperforming the traditional calibration model. The analysis demonstrates minimal dependence of the correction on temperature, overvoltage, and pixel size, indicating universal applicability. These findings enhance SiPM performance in high-energy calorimetry and offer a practical framework for improving detector linearity and dynamic range extensions in large-scale applications. Full article

Urban green spaces provide essential ecosystem services, yet mismatches between subjective perceptions and objective assessments may constrain effective planning. This study examines the correspondence between perceived and measured ES across two contrasting urban green spaces in Shanghai: Century Park, a managed urban park, and Sanlin Green Space, a naturalistic urban forest. Objective ecosystem services (regulating, supporting, and cultural) were quantified using UAV-based biotope mapping and indicators including biophysical metrics (Net Primary Production, Water Retention, PM10 removal, and Land Surface Temperature), structural diversity indices (Shannon Diversity of land cover, vegetation, and tree structure), and visual–spatial proxies (Green View Index, Sky View Index, Water View Index, color metrics, and spatial openness). Subjective perceptions were derived from panoramic image-based questionnaires, with perception scores predicted using XGBoost and aggregated via SHapley Additive exPlanations (SHAP). Correlation analyses, spatial regression models, and partial least squares structural equation modeling were applied to explore relationships and pathways. Results show weak but significant positive associations in the urban park, whereas no overall correspondence was observed in the urban forest. Spatial mismatches were concentrated in biotopes with distinctive visual–ecological features and in fragmented areas. Green View Index is associated with higher perceptions in both sites, while the Sky View Index reduced perception in the forest context. These findings highlight strong context dependence in perceived–measured ecosystem service relationships and underscore the importance of integrating ecological structure and visual legibility in the design and management of the studied urban green spaces in Shanghai. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Sea buckthorn leaves are a relatively underutilised component of sea buckthorn processed products; however, various studies have indicated that they possess hypoglycaemic potential. Under alkaline solubilisation and acid-precipitation conditions, the extraction yield of sea buckthorn leaf protein (SLP) reached 19.33%. Trypsin was selected as the hydrolysing enzyme to extract SLPPs-T, with half-maximal inhibitory concentrations (IC₅₀) against α-glucosidase and DPP-IV of 0.1361 ± 0.017 mg/mL and 0.1286 ± 0.012 mg/mL, respectively. UV spectroscopy, Fourier transform infrared spectroscopy, circular dichroism spectroscopy and particle size analysis indicated that the secondary and microstructures of SLP underwent changes following its hydrolysis to SLPPs-T; following separation, purification, sequence identification and computer screening, two novel peptides, PM-8 and VG-11, were obtained; molecular docking, solid-phase synthesis and in vitro experiments confirmed that VG-11 exhibited superior inhibitory activity, with half-maximal inhibitory concentrations (IC₅₀) against α-glucosidase and DPP-IV of 0.3885 ± 0.015 mM and 0.2611 ± 0.021 mM, respectively. In summary, this study explored the potential of sea buckthorn leaf protein as a natural hypoglycaemic product through a combination of computational modelling and experimental methods, thereby significantly enhancing the value of sea buckthorn resources. Full article

Spectral interrogation of fiber Bragg gratings (FBGs) in the ~850 nm band remains relatively uncommon, largely due to the limited availability of commercial instruments and the restricted applicability of conventional interrogation schemes in this wavelength range. This work presents a practical and high-precision wavelength demodulation method for 850 nm FBG sensing based on an imaging Charge-Coupled Device (CCD) spectrometer. A Czerny–Turner (C–T) optical configuration is employed for spatial spectral dispersion, and the optical system is theoretically analyzed and optimized using ZEMAX to balance spectral resolution, optical throughput, and compactness. A polynomial wavelength–pixel calibration model is established, and Gaussian fitting is adopted for robust peak-position extraction under multimode fiber conditions. Experimental validation is carried out using four serially cascaded FBGs distributed over 830–880 nm. The wavelength–pixel calibration yields an RMS residual of 0.46 nm. Within a strain range of 0–2000 με, the average wavelength demodulation bias of a single FBG is 6.8 pm, with a wavelength demodulation RMS error of 86.9 pm and a measured strain sensitivity of 0.72 pm/με. The results demonstrate that the proposed CCD-based imaging interrogation scheme is feasible for 850 nm FBG sensing and enables accurate wavelength demodulation in this relatively underexplored band. Since the system is implemented using standard off-the-shelf components, it also provides a practical technical route for the deployment of FBG sensing systems in engineering applications. Full article

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis. Full article