Search Results (1,895)

The development and implementation of microbial consortium-based biofertilizers represent a promising direction in sustainable agriculture, particularly in the context of the ongoing global ecological and agricultural crisis. This article examines the agroecological and economic impacts of applying microbial consortiums and explores the mechanisms of technology transfer using the example of two countries with differing levels of scientific and technological advancement–China and Kazakhstan. The analysis of the Chinese experience reveals that the successful integration of microbial biofertilizers into agricultural practice is made possible by a well-established institutional framework that includes strong governmental support for R&D, a robust scientific infrastructure, and effective coordination with the private sector. In contrast, Kazakhstan, despite its favorable agroecological conditions and growing interest among farmers in environmentally friendly technologies, faces several challenges from limited funding to a fragmented technology transfer system. The comparative study demonstrates that adapting Chinese models requires consideration of local specificities and the strengthening of intergovernmental cooperation. The article concludes by emphasizing the need to establish a multi-level innovation ecosystem encompassing the entire cycle of development and deployment of microbial biofertilizers, as a prerequisite for improving agricultural productivity and ensuring food security in countries at different stages of economic development. Full article

This study aims to provide a comprehensive evaluation of the bioactive compounds present in honeysuckle flower (Lonicera japonica Thunb.) extract (HSF) and their remarkable antioxidant activity. A docking simulation was performed to clarify the binding affinities of the identified phytochemicals to enzymes associated with anti-aging and anti-inflammatory activities. In addition, the low-energy nanoemulsions based on optimally formulated polyglycerol fatty acid esters (PGFEs), developed through D-optimality, were designed for the incorporation of HSF extract. The result revealed that HSF is a rich source of diverse phenolic and flavonoid compounds that contribute to its remarkable antioxidant capacity. Molecular docking analysis indicates that its compounds exhibit anti-aging and anti-inflammatory activities, particularly through collagenase, hyaluronidase, and TNF-α inhibition. Furthermore, D-optimality revealed that HSF-loaded nanoemulsions can be fabricated by a surfactant to oil ratio (SOR) of 2:1 with a ratio of low hydrophilic-lipophilic balance (HLB) surfactant to high HLB surfactant (LHR) of 1:2. Polyglyceryl-6 laurate as a high HLB surfactant produced the optimal nanoemulsion with small particle size and possessed an encapsulation efficiency (EE) of 74.32 ± 0.19%. This is the first report to combine D-optimal design-based nanoemulsion development with a multi-level analysis of HSF, including phytochemical profiling, antioxidant evaluation, and in silico molecular docking. These findings highlight that HSF-loaded polyglycerol fatty acid ester-based nanoemulsions could be a skin health-promoting ingredient and effective alternative for a variety of skincare applications. Full article

Cross-line operation and express–local train coordination are both crucial for enhancing the efficiency of multi-level urban rail transit systems. Most studies address suburban railway operations in isolation, overlooking coordination and inducing supply–demand mismatches that weaken system efficiency. This study addresses the joint optimization of cross-line operation and express–local scheduling by proposing a novel train timetable model. The model determines train service plans and departure times to minimize total system cost, including train operating and passenger travel costs. A space–time network represents integrated train–passenger interactions, and an extended adaptive large neighborhood search (E-ALNS) algorithm is developed to solve the model efficiently. Numerical experiments verify the effectiveness of the proposed approach. The E-ALNS achieves near-optimal solutions with less than 4% deviation from Gurobi. Comparative analysis shows that the proposed hybrid operation mode reduces total passenger travel cost by 6% and improves the cost efficiency ratio by 13% compared to independent operations. Sensitivity analyses further confirm the model’s robustness to variations in transfer walking time, passenger penalties, and waiting thresholds. This study provides a practical and scalable framework for optimizing train timetables in complex cross-line transit systems, offering insights for enhancing system coordination and passenger service quality. Full article

This article presents a comparison of two qualitative case studies. The first case study is a partnership group involving two urban secondary school teachers working with one engineer and one education faculty member where they implemented several science, technology, engineering, and mathematics (STEM) lessons over the course of an academic year. The second case study is a partnership group involving undergraduate college students working together to build a data collection device attached to a high-altitude balloon to answer a scientific question or solve an engineering problem and translate the project into engaging lessons for a K-12/secondary student audience. The studies employed a socio-cultural theoretical framework as the lens to examine the individuals’ perspectives, experiences, and engineering meaning-making processes, and to consider what these meant to the partnership itself. The methods included interviews, focus groups, field notes, and artifacts. The analysis involved multi-level coding. The findings indicated that the strength of the partnership (pre, little p, or big P) among participants influenced the strength of the secondary engineering lessons. The partnership growth implications in terms of K-12/secondary and collegiate engineering education included the engineering lesson strength, partnership, and engineering project sustainability The participant partnership meanings revolved around lesson creation, incorporating engineering ideas into the classroom, increasing communication, and increasing secondary students’ learning, while tensions arose from navigating (not quite negotiating) roles as a team. A call for attention to school–university partnerships and the voices heard in engineering partnership building are included since professional skills are becoming even more important due to advances in artificial intelligence (AI) and other technologies. Full article

The scientific measurement of corporate resilience is a prerequisite for identifying risk vulnerabilities, formulating targeted support policies, and enhancing the stability of the economic system. This paper utilizes data from 2054 listed companies on China’s A-share market from 2007 to 2023 to construct a corporate resilience evaluation system integrating three dimensions: risk resistance, adaptive adjustment, and recovery growth. Using a multi-level dynamic factor analysis, it depicts the multi-dimensional structure of resilience while introducing time series dynamic changes. This study found that corporate resilience has shown a steady upward trend overall, with phased fluctuations before and after major crisis events, which is highly consistent with macro- and microeconomic indicators. And fluctuations are primarily concentrated among low-resilience enterprises. The further analysis of low-resilience enterprises revealed the following: At the industrial level, compared with the primary industry, the secondary and tertiary industries have a higher proportion of low-resilience enterprises. At the regional level, the proportion of low-resilience enterprises in eastern and central regions decreased during shocks, while western regions showed a significant divergence, and northeastern regions consistently underperformed. This study offers empirical evidence and management insights for strengthening corporate resilience and enhancing the resilience of China’s economy. It also offers valuable insights for other countries in addressing external uncertainties and building economic resilience. Full article

Monitoring the structural health of bridges in road infrastructure is crucial for ensuring public safety and efficient maintenance. This paper presents a multi-level semi-automatic methodology for bridge monitoring, using Multi-Temporal Differential SAR Interferometry (MT-DInSAR) data. The proposed approach requires a dataset of satellite-derived MT-DInSAR measurements for the Area of Interest. The methodology involves creating a georeferenced database of bridges which allows the filtering of measurement points (generally named Persistent Scatterers—PSs) using spatial queries. Since existing datasets often provide only point geometries for bridge locations, additional data sources such as OpenStreetMaps-derived repositories have been utilized to obtain linear representations of bridges. These linear features are segmented into 20 m sections, which are then converted into polygonal geometries by applying a uniform buffer. Spatial joining between the bridge polygons and PS datasets allows the extraction of key statistics, such as mean displacement velocity, PS density and coherence levels. Based on predefined velocity thresholds, warning flags are triggered, indicating the need for further in-depth analysis. Finally, an upscaling step is performed to provide a practical tool for infrastructure managers, visually categorizing bridges based on the presence of flagged pixels. The proposed approach facilitates large-scale bridge monitoring, supporting the early detection of potential structural issues. Full article

High-quality growth of the construction industry and an improved human settlements environment are essential to sustainable urbanization. Existing studies have paid limited systematic attention to the spatial and temporal dynamics of the coordinated development between the construction industry and human settlements, as well as the underlying factors driving regional disparities. This gap restricts the formulation of precise, differentiated sustainable policies tailored to regions at different development stages and with varying resource endowments. Southern China, characterized by pronounced spatial heterogeneity and unique development trends, offers a natural laboratory for examining the spatio-temporal interaction between these two dimensions. Using panel data for 15 southern provinces (2013–2022), we applied the entropy method, coupling coordination model, Dagum Gini coefficient, spatial trend surface analysis, gravity model, and grey forecasting to evaluate current conditions and predict future trends. The main findings are as follows. (1) The coupling coordination degree rose steadily, forming a stepped spatial pattern from the southwest through the center to the southeast. (2) The coupling coordination degree appears obvious polarization effect, presenting a spatial linkage pattern with Jiangsu-Shanghai-Zhejiang, Hubei-Hunan-Jiangxi, and Sichuan-Chongqing as the core of the three major clusters. (3) The overall Dagum Gini coefficient declined, but intra-regional disparities persisted: values were highest in the southeast, moderate in the center, and lowest in the southwest; inter-regional differences dominated the total inequality. (4) Forecasts for 2023–2027 suggest further improvement in the coupling coordination degree, yet spatial divergence will widen, creating a configuration of “eastern leadership, central catch-up acceleration, and differentiated southwestern development.” This study provides an evidence base for policies that foster high-quality construction sector growth and enhance the living environment. The findings of this study indicate that policymaking should prioritize promoting synergistic regional development, enhancing the radiating and driving role of core regions, and establishing a multi-level coordinated governance mechanism to bridge regional disparities and foster more balanced and sustainable development. Full article

Land subsidence significantly threatens vulnerable coastal environments. This study aims to explore how Semarang’s government, local communities, and researchers address land subsidence and its role in exacerbating flood risk, against the backdrop of ongoing efforts within flood risk governance. Employing an integrated mixed-methods approach, the research combined quantitative geospatial analysis (InSAR and land cover change detection) with qualitative socio-political and governance analysis (interviews, FGDs, field observations). Findings show high subsidence rates in Semarang. Line of sight displacement measurements revealed a continuous downward trend from late 2014 to mid-2023, with rates varying from −8.8 to −10.1 cm/year in Karangroto and Sembungharjo. Built-up areas concurrently expanded from 21,512 hectares in 2017 to 23,755 hectares in 2023, largely displacing cropland and tree cover. Groundwater extraction was identified as the dominant driver, alongside urbanization and geological factors. A critical disconnect emerged: community views focused on flooding, often overlooking subsidence’s fundamental role as an exacerbating factor. The study concluded that multi-level collaboration, improved risk communication, and sustainable land management are critical for enhancing urban coastal resilience against dual threats of subsidence and flooding. These insights offer guidance for similar rapidly developing coastal cities. Full article

This paper introduces an efficient 3D point cloud downsampling algorithm (DFPS) based on adaptive multi-level grid partitioning. By leveraging an adaptive hierarchical grid partitioning mechanism, the algorithm dynamically adjusts computational intensity in accordance with terrain complexity. This approach effectively balances the global feature retention of point cloud data with computational efficiency, making it highly adaptable to the growing trend of large-scale 3D point cloud datasets. DFPS is designed with a multithreaded parallel acceleration architecture, which significantly enhances processing speed. Experimental results demonstrate that, for a point cloud dataset containing millions of points, DFPS reduces processing time from approximately 161,665 s using the original FPS method to approximately 71.64 s at a 12.5% sampling rate, achieving an efficiency improvement of over 2200 times. As the sampling rate decreases, the performance advantage becomes more pronounced: at a 3.125% sampling rate, the efficiency improves by nearly 10,000 times. By employing visual observation and quantitative analysis (with the chamfer distance as the measurement index), it is evident that DFPS can effectively preserve global feature information. Notably, DFPS does not depend on GPU-based heterogeneous computing, enabling seamless deployment in resource-constrained environments such as airborne and mobile devices, which makes DFPS an effective and lightweighting tool for providing high-quality input data for subsequent algorithms, including point cloud registration and semantic segmentation. Full article

The deep learning-based analysis of liver CT images is expected to provide assistance for clinicians in the diagnostic decision-making process. However, the accuracy of existing methods still falls short of clinical requirements and needs to be further improved. Therefore, in this work, we propose a novel multi-level hierarchical framework for liver tumor segmentation. In the first level, we integrate inter-slice spatial information by a 2.5D network to resolve the accuracy–efficiency trade-off inherent in conventional 2D/3D segmentation strategies for liver tumor segmentation. Then, the second level extracts the inner-slice global and local features for enhancing feature representation. We propose the Res-Inception-SE Block, which combines residual connections, multi-scale Inception modules, and squeeze-excitation attention to capture comprehensive global and local features. Furthermore, we design a hybrid loss function combining Binary Cross Entropy (BCE) and Dice loss to solve the category imbalance problem and accelerate convergence. Extensive experiments on the LiTS17 dataset demonstrate the effectiveness of our method on accuracy, efficiency, and visual results for liver tumor segmentation. Full article

Background/objectives: Racial and ethnic minority patients are underrepresented in cancer clinical trials (CCTs) and multilevel strategies are required to increase participation. This study describes barriers and facilitators to minority CCT participation alongside feedback on a multilevel intervention (MLI) designed to reduce participation barriers, as posited by the socioecological model (SEM). Methods: Interviews with Moffitt Cancer Center (MCC) physicians, community physicians, patients with cancer, community residents, and clinical research coordinators (CRCs) were conducted from June 2023–February 2024. Verbal responses were analyzed using thematic analysis and categorized into SEM levels. Mean helpfulness scores rating interventions (from 1 (not helpful) to 5 (very helpful)) were summarized. Results: Approximately 50 interviews were completed. Thematic findings confirmed CCT referral and enrollment barriers across all SEM levels. At the community level, MCC patients and community residents felt that community health educators can improve patient experiences and suggested they connect patients to social/financial resources, assist with patient registration, and provide CCT education. While physicians and CRCs reacted positively to all institutional-level tools, the highest scored tool simultaneously addressed CCT referral and enrollment at the institution (e.g., trial identification/referrals) and interpersonal level (communication platform for community and MCC physicians) (mean = 4.27). At the intrapersonal level, patients were enthusiastic about a digital CCT decision aid (mean = 4.53) and suggested its integration into MCC’s patient portal. Conclusions: Results underscore the value of conducting formative research to tailor interventions to target population needs. Our approach can be leveraged by future researchers seeking to evaluate MLIs addressing additional CCT challenges or broader health topics. Full article

This article examines the relevance of Smart Specialisation Strategies (RIS3) in structurally weak but non-peripheral regions, using Poland’s Lubusz Voivodeship as a case study. Based on employment data from 2009 and 2021, this study uses Location Quotient (LQ) analysis to evaluate the alignment between the region’s economic structure and its RIS3 domains: Innovative Industry, Health and Quality of Life, and Green Economy. The findings show that while Innovative Industry and Health and Quality of Life strengthened their relative specialisation, the Green Economy domain made only limited progress. Notably, sectors such as metal fabrication and social care services emerged as new specialisations, while several traditional industries declined. These results support the hypothesis that RIS3 priorities only partially reflect endogenous economic strengths, and they highlight the challenges of implementing innovation strategies in territorially fragmented and capacity-constrained regions. This article calls for dynamic priority reviews, improved multi-level coordination, and targeted instruments to better align RIS3 frameworks with the structural realities of “in-between” regions in the EU. Full article

Rising water temperatures due to climate change pose a significant threat to Phoxinus lagowskii, a cold-water fish that is ecologically vital to the high-latitude regions of China. This study assessed heat stress effects on behavioral, hematological, histological, physiological, and molecular responses in P. lagowskii. The critical maximum temperature (CT_max) was determined using the loss of equilibrium (LOE) method, with the CT_max reaching 29 °C. Elevated temperatures lead to an increase in the OBR. Fish were subjected to acute heat stress at 28 °C (below CT_max) for 48 h, with samples collected during the 48 h period. RBC, WBC, HGB, and HCT significantly increased during heat stress but decreased 12 h after heat stress. The levels of serum cortisol and blood glucose after heat stress were significantly higher than those in the control group. After heat stress, the height of the ILCM in the gills increased significantly, and the liver exhibited vacuolar degeneration and hypopigmentation. The activities of Na⁺-K⁺-ATPase and Ca²⁺-Mg²⁺-ATPase in the gills initially increased and then decreased over the duration of heat stress. Most enzyme activities (PK, LDH, PFK, and HK) decreased during heat stress, while LPL and HL levels increased, indicating that lipid metabolism was the primary utilization process under heat stress. There was an increase in SOD activity at 12 h, followed by a decrease at 24 h, and an increase in CAT activity under heat stress. Integrated biomarker response (IBR) and principal component analysis (PCA) were employed to synthesize multi-level responses. The IBR values reached their peak at 3 h and 48 h of heat stress. We observed an upregulation of heat shock proteins (Hsp70, Hsp90, and Hsc70) as well as interleukin-10 (IL-10) in response to heat stress. Our findings offer novel insights into the mechanisms underlying the heat stress response in P. lagowskii, thereby enhancing our understanding of the effects of heat stress on cold-water fish. Full article

Lithium-ion batteries play a vital role in human society. Therefore, it is of critical significance to reliably predict the evolution of State of Health (SOH) degradation patterns in order to improve the high accuracy and stability of lithium-ion battery SOH prediction. This paper proposes a novel SOH predication method by combing the four-vector intelligent metaheuristic (FVIM) with the CNN-LSTM-Attention basic model. The model adopts the collaborative architecture of a convolutional neural network and time series module, strengthens the cross-level feature interaction by introducing a multi-level attention mechanism, then uses the FVIM optimization algorithm to optimize the key parameters to realize the overall model architecture. By analyzing the charging voltage curve of lithium-ion batteries, the health factors with high correlation are extracted, and the correlation between the health factors and battery capacity is verified using two correlation coefficients. After the model is verified on a single NASA battery aging dataset, the model is compared with other models under the same relevant parameters and environmental settings to verify the high-precision prediction of the model. During the analysis and comparison process, CNN-LSTM-Attention-FVIM achieved a high fitting ability for battery SOH prediction estimation, with the mean absolute error (MAE) and root mean square error (RMSE) within 0.99% and 1.33%, respectively, reflecting the model’s high generalization ability and high prediction performance. Full article

Multilevel thresholding image segmentation plays a crucial role in various image processing applications. However, achieving optimal segmentation results often poses challenges due to the intricate nature of images. In this study, a novel metaheuristic search algorithm named Weighted Chimp Optimization Algorithm with Fitness–Distance Balance (WChOA-FDB) is developed. The algorithm integrates the concept of Fitness–Distance Balance (FDB) to ensure balanced exploration and exploitation of the solution space, thus enhancing convergence speed and solution quality. Moreover, WChOA-FDB incorporates weighted Chimp Optimization Algorithm techniques to further improve its performance in handling multilevel thresholding challenges. Experimental studies were conducted to test and verify the developed method. The algorithm’s performance was evaluated using 10 benchmark functions (IEEE_CEC_2020) of different types and complexity levels. The search performance of the algorithm was analyzed using the Friedman and Wilcoxon statistical test methods. According to the analysis results, the WChOA-FDB variants consistently outperform the base algorithm across all tested dimensions, with Friedman score improvements ranging from 17.3% (Case-6) to 25.2% (Case-4), indicating that the FDB methodology provides significant optimization enhancement regardless of problem complexity. Additionally, experimental evaluations conducted on color image segmentation tasks demonstrate the effectiveness of the proposed algorithm in achieving accurate and efficient segmentation results. The WChOA-FDB method demonstrates significant improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) metrics with average enhancements of 0.121348 dB, 0.012688, and 0.003676, respectively, across different threshold levels (m = 2 to 12), objective functions, and termination criteria. Full article