Search Results (666)

With the rapid development of the global urbanization process, the resource utilization of construction waste has become one of the core issues of the development of a circular economy and has been widely concerned by the international community. However, China’s resource utilization efficiency in this field is still in the development stage, and cthere is still a gap with developed countries. It is urgent to systematically solve scientific problems such as low resource utilization efficiency, prominent technical bottlenecks, and imperfect whole process management mechanisms, so as to realize the coordinated high-quality development of the economy, society, and the environment. In order to scientifically predict the generation trend of construction waste and assess the resource potential, this study takes Beijing as the research object. Based on the historical data samples of construction waste in Beijing from 2001 to 2024, the analysis framework of “output estimation—trend prediction—value evaluation” is constructed. The ARIMA model is selected as the core tool of prediction, because it can match the phased change characteristics of construction waste output with the development of the city in time series data processing. Combined with the cost–benefit analysis method, it makes a quantitative analysis of the future production scale of construction waste and the economic benefits of resource utilization in Beijing. The research results show that from 2025 to 2034, the production of construction waste in Beijing will show a trend of first decreasing and then increasing, and it will reach 13.599 million tons by 2034. The resource utilization of construction waste in the next 10 years is expected to bring about USD 2.998 billion of economic benefits. This prediction result may be related to the policy guidance of Beijing’s urban renewal, changes in construction activities, and industrial technology upgrading. Accordingly, this study puts forward countermeasures and suggestions to help the development of industrialization, providing theoretical support and practical references for the sustainable development of the resource utilization of construction waste. Full article

Continuous global monitoring of volcanic activity from space requires balancing spatial and temporal resolution, a long-standing trade-off between polar-orbiting and geostationary satellites. Polar sensors such as MODIS, VIIRS, and SLSTR provide high spatial resolution (375 m–1 km) but with limited temporal coverage. In contrast, geostationary sensors like SEVIRI offer high temporal resolution (5–15 min) but with coarser spatial detail (~3 km), often missing lower-intensity thermal events. The recently launched Flexible Combined Imager (FCI) aboard the geostationary Meteosat Third Generation (MTG-I) satellite represents a major improvement, providing images every 10 min with a spatial resolution of 1–2 km, comparable to that of polar orbiters. Here, we adapted the established Remote Sensing Data Fusion (RSDF) algorithm to exploit the enhanced capabilities of FCI for detecting volcanic thermal anomalies and estimating Volcanic Radiative Power (VRP). The algorithm was applied to Mount Etna during three different eruptive phases that occurred in 2025. The VRP derived from FCI data was compared with that obtained from the geostationary SEVIRI and the polar-orbiting MODIS, SLSTR, and VIIRS sensors. The results show that FCI provides a more detailed and continuous characterization of volcanic thermal output than SEVIRI, while maintaining close agreement with polar sensors. These findings confirm the capability of FCI to deliver high-frequency, high-resolution thermal monitoring, representing a major step toward operational, near-real-time volcanic surveillance from space. Full article

This paper introduces a novel framework for sensory representation of brain imaging data, combining deep learning-based segmentation with multimodal visual and auditory outputs. Structural magnetic resonance imaging (MRI) predictions are converted into color-coded maps and stereophonic/MIDI sonifications, enabling intuitive interpretation of cortical activation patterns. High-precision U-Net models efficiently generate these outputs, supporting clinical decision-making, cognitive research, and creative applications. Spatial, intensity, and anomalous features are encoded into perceivable visual and auditory cues, facilitating early detection and introducing the concept of “auditory biomarkers” for potential pathological identification. Despite current limitations, including dataset size, absence of clinical validation, and heuristic-based sonification, the pipeline demonstrates technical feasibility and robustness. Future work will focus on clinical user studies, the application of functional MRI (fMRI) time-series for dynamic sonification, and the integration of real-time emotional feedback in cinematic contexts. This multisensory approach offers a promising avenue for enhancing the interpretability of complex neuroimaging data across medical, research, and artistic domains. Full article

Generative artificial intelligence (GenAI) is rapidly permeating research practices, yet knowledge about its use and topical profile remains fragmented across tools and disciplines. In this study, we present a cross-disciplinary map of GenAI research based on the Web of Science Core Collection (as of 4 November 2025) for the ten tool lines with the largest number of publications. We employed a transparent query protocol in the Title (TI) and Topic (TS) fields, using Boolean and proximity operators together with brand-specific exclusion lists. Thematic similarity was estimated with the Jaccard index for the Top–50, Top–100, and Top–200 sets. In parallel, we computed volume and citation metrics using Python and reconstructed a country-level co-authorship network. The corpus comprises 14,418 deduplicated publications. A strong concentration is evident around ChatGPT, which accounts for approximately 80.6% of the total. The year 2025 shows a marked increase in output across all lines. The Jaccard matrices reveal two stable clusters: general-purpose tools (ChatGPT, Gemini, Claude, Copilot) and open-source/developer-led lines (LLaMA, Mistral, Qwen, DeepSeek). Perplexity serves as a bridge between the clusters, while Grok remains the most distinct. The co-authorship network exhibits a dual-core structure anchored in the United States and China. The study contributes to bibliometric research on GenAI by presenting a perspective that combines publication dynamics, citation structures, thematic profiles, and similarity matrices based on the Jaccard algorithm for different tool lines. In practice, it proposes a comparative framework that can help researchers and institutions match GenAI tools to disciplinary contexts and develop transparent, repeatable assessments of their use in scientific activities. Full article

Analyzing the evolution of trajectory topics is fundamental to understanding urban mobility and human activity. Existing methods, however, often struggle to capture complex spatio-temporal semantics and are constrained by fixed time windows, which limits multi-scale temporal analysis. This paper presents a novel method to model the dynamic topics of trajectories to address these limitations. The proposed method combines a domain-specific trajectory embedding strategy, a flexible dynamic topic modeling pipeline, and an interactive visualization system to address these limitations. Firstly, the method introduces a novel embedding method that uses a retrained RoBERTa model with a word-level tokenizer on Morton-coded trajectories to effectively learn spatial context and sequential patterns. Secondly, a BERTopic-based approach is employed for topic modeling, featuring an adjustable time window that allows for flexible analysis of topic dynamics across different temporal scales without model retraining. Furthermore, an interactive visualization system with coordinated spatio-temporal views translates abstract model outputs into an intuitive format, enabling direct exploration of evolving trajectory topics. Experiments on a large-scale taxi trajectory dataset demonstrate the proposed method’s effectiveness in identifying coherent and meaningful patterns of dynamic trajectory topics. Full article

Accurate estimation of regional sewage generation is essential for designing reliable and resource-efficient treatment facilities. This study developed an ensemble machine-learning framework to estimate annual sewage generation (SG) as the primary output variable, using a combination of demographic, socioeconomic, and environmental indicators across multiple regions in Korea. The proposed Voting Regressor model, trained using data from four highly urbanized regions (Regions A–D), effectively captured nonlinear interactions among variables such as population, business establishments, economically active population, rainfall, and gross regional domestic product (GRDP). A generalization test on an unseen region (Region E) confirmed the model’s robustness and transferability, demonstrating that the framework can reliably adapt to regions with different demographic and industrial characteristics. Comparative analyses showed that the model outperformed both the Random Forest and the conventional per capita unit-load (GU) method in terms of the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). SHAP (Shapley Additive Explanations) analysis further identified business establishments and GRDP as the dominant contributors to sewage generation. Moreover, model-based capacity estimations incorporating a 20% safety factor are closely aligned with actual facility capacities, revealing that conventional design standards often apply excessively conservative margins. The findings demonstrate that the proposed machine learning framework can quantitatively assess design adequacy and prevent structural overestimation while maintaining sufficient operational reserves. This data-driven approach provides an interpretable and adaptable foundation for future sewage infrastructure planning and rational capacity design under evolving socioeconomic and environmental conditions. Full article

As a typical representative of soft capping, primary vegetation capping has both protective and destructive effects on earthen city walls. Addressing its detrimental aspects constitutes the central challenge of this project. Because the integration of MICP technology with plants offered advantages, including soil solidification, erosion resistance, and resilience to dry–wet cycles and freeze–thaw cycles, the application of MICP technology to root–soil composites was proposed as a potential solution. Employing a combined approach of RF-RFE-CV modeling and microscopic imaging on laboratory samples from the Western City Wall of the Jinyang Ancient City in Taiyuan, Shanxi Province, China, key factors and characteristics in the mineralization process of Sporosarcina pasteurii were quantified and observed systematically to define the optimal pathway for enhancing urease activity and calcite yield. The conclusions were as follows. The urease activity of Sporosarcina pasteurii was primarily regulated by three key parameters with bacterial concentration, pH value, and the intensity of urease activity, which required stage-specific dynamic control throughout the growth cycle. Bacterial concentration consistently emerged as a high-importance feature across multiple time points, with peak effectiveness observed at 24 h (1.127). pH value remained a highly influential parameter across several time points, exhibiting maximum impact at around 8 h (1.566). With the intensity of urease activity, pH exerted a pronounced influence during the early cultivation stage, whereas inoculation volume gained increasing importance after 12 h. To achieve maximum urease activity, the use of CASO AGAR Medium 220 and the following optimized culture conditions was recommended: an activation culture time of 27 h, an inoculation age of 16 h, an inoculation volume of 1%, a culture temperature of 32 °C, an initial pH of 8, and an oscillation speed of 170 r/min. Furthermore, to maximize the yield of CaCO₃ in output and the yield of calcite in CaCO₃, the following conditions and procedures were recommended: a ratio of urea concentration to Ca²⁺ concentration of 1 M:1.3 M, using the premix method of Sporosarcina pasteurii, quiescent reaction, undisturbed filtration, and drying at room-temperature in the shade environment. Full article

Children with autism spectrum disorder (ASD) often display atypical emotional expressions and physiological responses, making emotion recognition challenging. This study proposes a multimodal recognition model employing a late fusion framework combining facial expression with physiological measures: electrodermal activity (EDA), temperature (TEMP), and heart rate (HR). Emotional states are annotated using two complementary schemes derived from a shared set of labels. Three annotators provide one categorical Ekman emotion for each timestamp. From these annotations, a majority-vote label identifies the dominant emotion, while a proportional distribution reflects the likelihood of each emotion based on the relative frequency of the annotators’ selections. Separate machine learning models are trained for each modality and for each annotation scheme, and their outputs are integrated through decision-level fusion. A distinct decision-level fusion model is constructed for each annotation scheme, ensuring that both the categorical and likelihood-based representations are optimally combined. The experiments on the EMBOA dataset, collected within the project “Affective loop in Socially Assistive Robotics as an intervention tool for children with autism”, show that the late fusion model achieves higher accuracy and robustness than unimodal baselines. The system attains an accuracy of 68% for categorical emotion classification and 78% under the likelihood-estimation scheme. The results obtained, although lower than those reported in other studies, suggest that further research into emotion recognition in autistic children using other fusions is warranted, even in the case of datasets with a significant number of missing values and low sample representation for certain emotions. Full article

Gynogenesis offers a promising route for doubled haploid (DH) production in Cucurbita, yet efficient protocols remain scarce. This study established a reproducible ovary culture system for Cucurbita pepo and evaluated zeatin riboside (ZR) as an alternative cytokinin. Ovaries collected at anthesis and one day before were cultured to screen nine media with different cytokinin–auxin combinations. Subsequently, four optimized ZR-based formulations were evaluated. Both floral stages showed morphogenic activity, but embryo formation occurred almost exclusively in pre-anthesis ovaries. Among ZR treatments, E6.1 (1 mg·L⁻¹ ZR + 3 mg·L⁻¹ NAA, 30 g·L⁻¹ sucrose) achieved the highest embryogenic output (approximately 97 embryos per 100 explants), while high-sucrose media (120 g·L⁻¹) induced abundant swollen ovules but poor conversion, suggesting that excessive osmotic pressure promotes morphogenesis but hampers embryogenic transition. In total, 415 embryos were obtained, and 52 regenerants were analyzed by flow cytometry, confirming haploid, diploid, and mixoploid plants and evidencing spontaneous chromosome doubling during in vitro development. A categorical A–D scoring system enabled early prediction of embryogenic potential. This represents the first successful application of ZR in cucurbit gynogenesis and highlights its value as a biologically compatible cytokinin for DH production. The findings open new avenues for testing ZR-based formulations in other Cucurbita species under different auxin and sucrose regimes. Full article

Chemical recycling is one of the most prominent techniques that enables monomer recovery for plastics like polyethylene terephthalate (PET), which ultimately reduces the dependency on virgin material inputs. In this study, 40 deep eutectic solvents (DESs) were pre-screened using COSMO-RS to identify the best solvent for chemical recycling of PET. Quantitative evaluation was performed based on activity coefficients (γ) to assess solute–solvent interactions. Qualitatively, the sigma profile and sigma potential were analyzed to understand the polarity and affinity of each DES component. This study experimentally validated the two top-performing DESs based on COSMO-RS output. The DES formed by combining thymol with phenol (Thy/Phe (1:2)) achieved 100% PET degradation and 94.5% terephthalic acid (TPA) recovery from post-consumer PET in just 25 min. The rapid dissolution of PET into molten state accelerated the hydrolysis reaction, leading to efficient monomer recovery. The second DES, tetrabutylammonium bromide/sulfolane (TBABr/Sulf (1:7)), attained 93.7% PET degradation and 94% TPA recovery. The PET-to-solvent ratio used in this study was 0.75, while the PET-to-DES ratio in the mixture was only 0.15, the lowest reported for DES-assisted hydrolysis to date. Characterization of the recycled TPA confirmed a purity level comparable to its virgin grade, as verified by FT−IR analysis. This study presents two important outcomes. First, the use of COSMO-RS for DES selection provides a strong rationale for solvent choice in targeted reactions and processes. Second, the use of appropriate DES in this study helps reduce key parameters associated with depolymerisation process, including reaction time, temperature, and catalyst consumption. Full article

The growing demand for precision diagnostics in cystic fibrosis and other genetic disorders, such as cancers, is driving the need for sequencing platforms that combine analytical robustness, scalability, and cost-efficiency. In this study, we performed a direct comparison between two leading Next-Generation Sequencing (NGS) platforms, MiSeq (Illumina, CA, USA) and DNBSEQ-G99RS (MGI Tech Co., Shenzhen, China), using a CE-IVD-certified CFTR panel (Devyser AB), selected for its complexity and variant spectrum, including SNVs, CNVs, and intronic polymorphisms. A total of 47 genomic DNA samples from routine clinical activity were analyzed on both platforms. Illumina sequencing covered all CFTR variants using standard workflows, while MGI data were generated from residual diagnostic DNA, with informed consent. Sequencing data were processed using Amplicon Suite v3.7.0 for variant calling, annotation, and ACMG classification. Quality control metrics and platform-specific parameters were also evaluated. Both platforms demonstrated complete concordance in variant detection, including SNVs, CNVs, and complex alleles (e.g., Poly-T/TG). Illumina exhibited slightly superior basecalling quality and allelic frequency uniformity, while MGI achieved higher sequencing depth (mean ~2793×) and demultiplexing efficiency. No false positives, false negatives, or discordant HGVS annotations were observed. The use of full-gene CFTR sequencing enabled granular and technically rigorous cross-platform validation. These findings confirm the analytical equivalence of Illumina and MGI for diagnostic genotyping. Moreover, MGI’s greater data output and flow cell capacity may offer tangible advantages in high-throughput settings, including somatic applications such as liquid biopsy and molecular oncology workflows. Full article

Background: Shape memory alloy spring actuators offer significant potential for advanced actuation systems in exoskeletons, medical devices, and robotics, but adoption has been limited by slow activation speeds and insufficient design guidelines for achieving rapid response times while maintaining structural integrity. Objective: This study aimed to establish comprehensive design parameters for nickel–titanium spring actuators capable of achieving sub-second activation times through systematic experimental characterization and performance optimization. Methods: Nine different nickel–titanium spring configurations with wire diameters ranging from 0.5 to 0.8 mm and spring indices from 6 to 8 were systematically evaluated using differential scanning calorimetry for thermal characterization, mechanical testing for material properties, high-current electrical activation studies spanning 5–11 A, infrared thermal distribution analysis, and laser displacement sensing for dynamic response measurement. Results: Dynamic testing achieved activation times below 1 s for currents exceeding 5 A, with maximum displacement recoveries reaching 600–800% strain recovery, while springs with intermediate spring index values of 6.5–7.5 provided optimal balance between force output and displacement range, and optimal activation involved moderate current levels of 5–7 A for thin wires and 8–11 A for thick wires. Conclusions: Systematic geometric optimization combined with controlled high-current density activation protocols enables rapid actuation response while maintaining structural integrity, providing essential design parameters for engineering applications requiring fast, reliable actuation cycles. Full article

Stability analysis of landslide areas represents a critical issue in many countries, as landslides can cause large material damage and are a threat to the health and life of inhabitants. This article is aimed at the stability analysis of a built-up locality using a combination of traditional inclinometry with observations carried out using TDR technology (Time Domain Reflectometry) for displacement and groundwater level monitoring. Considering the geological conditions of the site and the occurrence of an old stabilized landslide, groundwater is the main trigger for possible slope deformations. The evaluation of the stability, based on the survey and monitoring outputs, was made using the Finite Element Method. The loss of stability was predicted for a certain uplift of groundwater level and seismic loading, which was lower than normative requirements. The presented case study demonstrates the need for an exhaustive and coordinated survey, as well as the importance of monitoring results and integrated analysis. This careful combination of activities enables us to understand the behavior of the landslide, to evaluate the stability potential of the slope, and to design effective protective measures. Full article

The rapid adoption of electric vehicles (EVs) has increased the need for sustainable charging infrastructure supported by renewable energy. This study presents a comprehensive techno-economic and environmental analysis of private EV charging stations in Kuwait powered by grid-connected solar and wind systems using the HOMER Pro 3.18.4 optimization software. Four configurations—grid-only, grid–solar, grid–wind, and grid–solar–wind—were modelled and evaluated in terms of energy output, cost performance, and carbon emission reduction under Kuwait’s climatic conditions. HOMER simulated 484 systems, of which 244 were technically feasible. The optimal configuration, combining grid, 5 kW photovoltaic (PV) (BEIJIAYI 600 W panels), and a 5.1 kW AWS wind turbine, achieved a renewable fraction of 78%, reducing grid dependency by 78.1% and annual CO₂ emissions by approximately 7027 kg. Although the hybrid system required a higher initial investment (USD 7662) than the grid-only setup (USD 1765), it achieved the lowest Levelized Cost of Energy (LCOE = USD 0.017/kWh) and long-term cost competitiveness through reduced operating expenses. Sensitivity analysis confirmed the hybrid system’s robustness against ±15% variations in wind speed and ±10% changes in solar irradiance. The results highlight that hybrid solar–wind systems can effectively mitigate intermittency through diurnal complementarity, where daytime solar generation and nighttime wind activity ensure continuous supply. The findings demonstrate that integrating renewables into Kuwait’s EV charging infrastructure enhances economic viability, energy security, and environmental sustainability. The study provides practical insights to guide renewable policy development, pilot deployment, and smart grid integration under Kuwait Vision 2030’s clean-energy framework. Full article

Passive microwave remote sensing of soil moisture is crucial for monitoring the Earth’s water cycle and surface dynamics. The penetration depth during this process is significant, as it influences the accuracy of retrieved soil moisture data. Within L-band remote sensing, tools such as the τ-z model interpret microwave emissions to estimate soil moisture, taking into account the complex interactions between soil and radiation. However, in validating these models against high-temporal-resolution, ground-based measurements, especially from extensive networks like the Terrestrial National Ecological Observatory Network (NEON), further research and validation efforts are needed. This study comprehensively validates the τ-z model’s ability to estimate the soil temperature sensing depth (z_Teff) using data from the NEON and Soil Moisture Active Passive (SMAP) satellite missions. A harmonization process was conducted to align the spatial and temporal scales of the two datasets, enabling rigorous validation. We compared soil optical depth (τ)—a parameter capable of theoretically unifying sensing depth representations across wet soil (~0.05 m) to extreme dry/frozen conditions (e.g., up to ~1500 m in ice-equivalent scenarios)—and geometric depth (z) frameworks against outputs from the τ-z model and NEON’s in situ profiles. The results show that: (1) for the profiles that satisfy the monotonic assumption by the τ-z model, z_Teff fits the prediction well at about 0.2 τ for the average; (2) Combining SMAP’s soil moisture, the τ-z model achieves high accuracy in estimating z_Teff, with RMSD (0.05 m) and unRMSD (0.03 m), and correlations (0.67) between estimated and observed values. The findings are expected to advance remote sensing techniques in various fields, including agriculture, hydrology, and climate change research. Full article