Search Results (6,115)

Civilization diseases are a growing and global health problem in modern societies. Neurological disorders, cancer, and inflammatory diseases affect a large group of patients around the world. Therefore, it is of utmost importance to search for novel drugs, lifestyle tips, and foods that can help restore balance in the living organism, promote the efficiency of the immune system, and provide satisfactory prophylactic measures. Astragaloside IV (ASIV)—a triterpenoid saponin from Astragalus species, one of the world’s most widely used herbs—has been shown to have a variety of biological properties, including anti-inflammatory, antioxidant, antitumor, and neuroprotective effects. In recent years, the number of in vivo studies on this active ingredient in the scientific literature has increased considerably. The aim of this review was therefore to compile the existing knowledge on the use of this compound in the treatment of selected diseases of civilization—cancer, neurological disorders, and inflammatory diseases—in vivo. Full article

Turfgrass has evolved from a fundamental element of natural landscapes to a key component of ornamental, recreational, and ecological systems. This review examines its historical trajectory, from ancient civilizations and medieval monastic gardens to modern urban green spaces, highlighting cultural, technological, and environmental influences. The study integrates historical analysis with agronomic and ecological research to assess turfgrass’s role in soil conservation, carbon sequestration, water management, and biodiversity enhancement. The methodological approach combines a historical–literary review with an analysis of scientific advancements in turfgrass management, focusing on low-input species, precision irrigation, and climate-adaptive strategies. The findings indicate that while turfgrass contributes to urban resilience and ecosystem services, conventional high-maintenance practices pose sustainability challenges. The review underscores the need for multifunctional turfgrass systems that balance aesthetics with ecological benefits. A shift toward eco-friendly management practices will be essential for the long-term sustainability of turfgrass in modern landscapes. Full article

Terraces have long transformed steep slopes into gradual steps, reducing erosion and enabling agriculture on marginal land. In France’s Roya Valley, these dry stone structures, neglected for decades, demonstrated remarkable resilience during storm Alex in October 2020. This prompted civil society and researchers to identify terraces that could support food security and agri-tourism initiatives. This study aimed to develop a semi-automatic method for detecting and mapping terraced areas using LiDAR and orthophoto data from French repositories, processed with GIS and analyzed through a Support Vector Machine (SVM) classification algorithm. The model identified 18 terraces larger than 1 hectare in Saorge and 35 in La Brigue. Field visits confirmed evidence of abandonment in several areas. Accuracy tests showed a user accuracy (UA) of 97% in Saorge and 72% in La Brigue. This disparity reflects site-specific differences, including terrain steepness, vegetation density, and data resolution. These results highlight the value of machine learning for terrace mapping while emphasizing the need to account for local geomorphological and data-quality factors to improve model performance. Enhanced terrace detection supports sustainable land management, agricultural revitalization, and risk mitigation in mountainous regions, offering practical tools for future landscape restoration and food resilience planning. Full article

Due to the widespread prevalence of respiratory diseases such as COVID-19 and H1N1, the use of disposable masks has increased significantly. Consequently, the environmental issues arising from their accumulation have become increasingly severe. This study, therefore, aims to investigate the potential of using masks as soil reinforcement materials. This study conducted triaxial and seepage tests on mask–calcareous sand mixtures with varying ratios to examine the effects of mask content on the strength, modulus, particle fragmentation, and permeability coefficient of calcareous sand, as well as the influence of different mask sizes on shear strength and shear dilation. The results demonstrate that with an increase in mask content, the peak stress ratio of the mask–calcareous sand mixture increases by 4% per level, and the internal friction angle rises by approximately 1.6% per level. Conversely, water permeability and shear swelling are reduced, and particle loss decreases by over 70%. The reinforcing effect of the mask is attributed to the high friction between the mask and the calcareous sand at the contact interface, which restricts the movement of soil particles during deformation, thereby enhancing the overall strength of the mixture. Among the three mask sizes, the smallest mask–calcareous sand mixture exhibited the greatest improvement in shear strength, and the shear shrinkage effect was more pronounced. This indicates that particle size also significantly influences the mechanical properties of the mixtures. The reinforcing effect of the mask on the soil results from the high friction at the interface between the mask and the calcareous sand. When the soil deforms, the mask enhances the overall strength of the mixture by restricting the movement of soil particles. Considering the impact of masks on the performance of calcareous sand, it can be concluded that the optimal mass content of masks is 0.3%. This study offers a new perspective on the reuse of discarded masks in civil engineering applications. Full article

This study explores the stabilization of dredged sediments classified as lean clay (CL) using hydrated lime, type III Portland cement, and compaction. While quicklime is commonly used in practice, this research explores alternative calcium-based binders with the aim of valorizing sediments for civil engineering applications. The mechanical behavior of the treated materials was evaluated through an Unconfined Compressive Strength (UCS) test campaign, with the results interpreted using the porosity/volumetric cement content ($\eta /C_{iv}$) index. This relationship assesses the influence of apparent dry density and cement content on the strength improvement of sediments, aiming to evaluate the suitability of the dredged sediments for engineering applications. A key feature of this study is the extended curing period of up to 90 days, which goes beyond the typical 28-day evaluations commonly found in the literature. Interestingly, strength degradation occurred at advanced curing ages compared to shorter curing times. To understand the mechanisms underlying this resistance degradation, the mixtures were subjected to X-ray fluorescence spectroscopy (XRF), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). These tests identified the presence of the expansive sulfate-based compound ettringite, which is associated with swelling and failure in soils stabilized with calcium-based stabilizers. This research contributes to the field by demonstrating the limitations of calcium-based binders in stabilizing sulfate-bearing dredged materials and emphasizing the importance of long-term curing in assessing the durability of treated sediments. Full article

Workplace procrastination is widespread in the public sector and has severe negative implications. However, research specifically focusing on workplace procrastination among civil servants remains scarce. Drawing on the person–environment (P–E) fit theory and the public service motivation (PSM) fit perspective, this study examines the relationship between perceived overqualification (POQ) and workplace procrastination through job boredom, and further explores how PSM and perceived prosocial impact moderate this relationship. Based on a survey of 363 Chinese civil servants, the findings reveal a positive correlation between POQ and workplace procrastination through job boredom, and this correlation is weaker for civil servants with high PSM than for those with low PSM. A three-way interaction analysis indicates that perceived prosocial impact enhances the ability of PSM to reduce job boredom caused by POQ, which in turn weakens the association between POQ and workplace procrastination. Moreover, when civil servants with high PSM perceive their work as having a high prosocial impact, the association between POQ and workplace procrastination becomes nonsignificant. This study introduces a person–environment interaction perspective for understanding the antecedents of workplace procrastination, underscores the costs of POQ in public organizations, and offers valuable insights for preventing workplace procrastination among civil servants. Full article

Structural health monitoring (SHM) plays a critical role in ensuring the safety and longevity of civil infrastructure by enabling the early detection of structural changes and supporting preventive maintenance strategies. In recent years, deep learning techniques have emerged as powerful tools for analyzing the complex data generated by SHM systems. This study investigates the use of deep belief networks (DBNs) for classifying structural conditions before and after retrofitting, using both ambient and train-induced acceleration data. Dimensionality reduction techniques such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) enabled a clear separation between structural states, emphasizing the DBN’s ability to capture relevant classification features. The DBN architecture, based on stacked restricted Boltzmann machines (RBMs) and supervised fine-tuning, was optimized via grid search and cross-validation. Compared to traditional unsupervised methods like K-means and PCA, DBNs demonstrated a superior performance in feature representation and classification accuracy. Experimental results showed median cross-validation accuracies of $98.04\%$ for ambient data and $96.96\%$ for train-induced data, with low variability. Although random forests slightly outperformed DBNs in classifying ambient data ($99.19\%$), DBNs achieved better results with more complex train-induced signals ($95.91\%$). Robustness analysis under Gaussian noise further demonstrated the DBN’s resilience, maintaining over $90\%$ accuracy for ambient data at noise levels up to ${\sigma }_{\mathrm{noise}}=0.5$. These findings confirm that DBNs are a reliable and effective approach for data-driven structural condition assessment in SHM systems. Full article

Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports. Full article

The construction of ecological civilization is intrinsically connected to green development. The green development of forestry enterprises serves as a key approach to achieving this goal. The research purpose of this paper is to explore the realization path of green development of forestry enterprises. First, an improved CRITIC (Criteria Importance Through Intercriteria Correlation)–entropy weight method was used to construct a reasonable input-output indicator system. Next, a three-stage data envelopment analysis (DEA) model was employed to evaluate the comprehensive technical efficiency of green development across 33 forestry enterprises in China, using panel data from 2017 to 2022. Finally, the study explored various configurational pathways for achieving green development by integrating the Technology–Organization–Environment (TOE) framework with dynamic qualitative comparative analysis (QCA). The findings reveal that green development in forestry enterprises is shaped by the interplay of multiple factors. Four distinct configurations were identified as instrumental in driving high green development. These configurations could be classified into two categories: the environment–organization synergistic development model and the technology–organization dual-driven model. This study provides empirical insights into the complex configurational relationships underlying green development in forestry enterprises, offering valuable guidance for optimizing development strategies. Full article

Free trade zones are key regions experiencing rapid economic growth, urbanization, and a sharp increase in population density. During the development of free trade zones, these areas undergo drastic transformations in landscape types, large-scale urban construction, heightened resource consumption, and other associated challenges. These factors have led to severe landscape ecological risk (LER). Therefore, conducting comprehensive assessments and implementing effective management strategies for LER is crucial in advancing ecological civilization and ensuring high-quality development. This study takes Hainan Island (HI), China, as a case study and utilizes multi-source data to quantitatively evaluate land use and land cover change (LULCC) and the evolution of the LER in the study area from 2015 to 2023. Additionally, it examines the spatial patterns of LER under three future scenarios projected for 2033: a natural development scenario (NDS), an economic priority scenario (EPS), and an ecological conservation scenario (ECS). Adopting a spatiotemporal dynamic perspective framed by the “historical–present–future” approach, this research constructs a zoning framework for LER management to examine the temporal and spatial processes of risk evolution, its characteristics, future trends, and corresponding management strategies. The results indicate that, over an eight-year period, the area of built-up land expanded by 40.31% (504.85 km²). Specifically, between 2015 and 2018, built-up land increased by 95.85 km², while, from 2018 to 2023, the growth was significantly larger at 409.00 km², highlighting the widespread conversion of cropland into built-up land. From 2015 to 2023, the spatial distribution of LER in the study area exhibited a pattern of high-risk peripheries (central mountainous areas) and low-risk central regions (coastal areas). Compared to 2023, projections for 2033 under different scenarios indicate a decline in cropland (by approximately 17.8–19.45%) and grassland (by approximately 24.06–24.22%), alongside an increase in forestland (by approximately 4.5–5.35%) and built-up land (by approximately 23.5–41.35%). Under all three projected scenarios, high-risk areas expand notably, accounting for 4.52% (NDS), 3.33% (ECS), and 5.75% (EPS) of the total area. The LER maintenance area (65.25%) accounts for the largest proportion, primarily distributed in coastal economic development areas and urban–rural transition areas. In contrast, the LER mitigation area (7.57%) has the smallest proportion. Among the driving factors, the GDP (q = 0.1245) and year-end resident population (q = 0.123) were identified as the dominant factors regarding the spatial differentiation of LER. Furthermore, the interaction between economic factors and energy consumption further amplifies LER. This study proposes a policy-driven dynamic risk assessment framework, providing decision-making support and scientific guidance for LER management in tropical islands and the optimization of regional land spatial planning. Full article

Achieving the United Nations 2030 Sustainable Development Goals (SDGs) is a critical global challenge. Ensuring the sustainable utilization of water resources has long been a key policy priority for the Chinese government, balancing economic growth with ecological conservation and advancing ecological civilization. Taking China’s sustainable development agenda innovation demonstration area Chenzhou as the object, this work focuses on SDG6 and examines the progress in sustainable water resource utilization from 2015 to 2022, evaluating three dimensions—water quantity, water environment, and water ecology. A comprehensive evaluation index system closely related to SDG6 was constructed to assess the sustainable development progress. Furthermore, the interactions between SDG6 and related SDGs were analyzed. The results show that (1) from 2015 to 2022, the SDG6 composite index has significantly increased over time, with the establishment of the demonstration area (2019–2022) more than twice compared to before (2015–2018), particularly in water environment and water quantity; (2) the SDG composite index and individual SDG indexes have shown a fluctuating upward trend, with an increase of about 89.74% after the establishment of the demonstration area (2019–2022) compared to before (2015–2018), with the most significant progress in the society dimension; and (3) there were significant synergy effects between the improvements in SDG6 and related SDGs. For each unit increase in SDG6, the overall level of related SDGs increased by 0.73 units, specifically, with particularly strong synergies between SDG2, SDG7, SDG9, and SDG11. This study not only provides scientific guidance for water resource management and policy optimization in Chenzhou and similar water resource-based cities but also offers valuable localized case studies, methodologies, and data to support the monitoring of urban sustainable development at a global scale. Full article

Rammed earth (RE), despite being an ancient method of construction, has smoothly integrated into contemporary civil engineering due to its compatibility with current sustainability requirements for housing structures. However, typical RE needs some improvements to fully realize its potential as both a structurally effective and environmentally friendly building technique. As a result, multiple bio-inspired enhancement methods have been suggested to substitute traditional cement or lime stabilizers. This review examines the various efforts made in the past decade to biologically stabilize natural soil for the construction of RE. It provides a brief overview of the different bio-based materials utilized in this area but primarily concentrates on their effects on the mechanical strength and water durability of RE structures. The review also addresses current obstacles that prevent the widespread industrial adoption of this valuable earth-building method and identifies potential directions for future research. Overall, the available literature on the mechanical performance and durability of bio-based rammed earth (BRE) shows encouraging outcomes. Nonetheless, various issues, such as the absence of thorough data on the discussed topics, issues related to the inherent properties of soil and biomaterials, and doubts regarding the reliability of durability evaluation methods, have been identified as factors that could lead to a lack of confidence among RE practitioners in adopting bio-based treatments. This study will provide a solid foundation for future researchers aiming to advance BRE technology, thus enhancing sustainability within the construction sector. Full article

Air traffic control (ATC) communication is susceptible to speech noise interference, which undermines the quality of civil aviation speech. To resolve this problem, we propose a speech enhancement model, termed DeCGAN, based on the DeConformer generative adversarial network. The model’s generator, the DeConformer module, combining a time frequency channel attention (TFC-SA) module and a deformable convolution-based feedforward neural network (DeConv-FFN), effectively captures both long-range dependencies and local features of speech signals. For this study, the outputs from two branches—the mask decoder and the complex decoder—were amalgamated to produce an enhanced speech signal. An evaluation metric discriminator was then utilized to derive speech quality evaluation scores, and adversarial training was implemented to generate higher-quality speech. Subsequently, experiments were performed to compare DeCGAN with other speech enhancement models on the ATC dataset. The experimental results demonstrate that the proposed model is highly competitive compared to existing models. Specifically, the DeCGAN model achieved a perceptual evaluation of speech quality (PESQ) score of 3.31 and short-time objective intelligibility (STOI) value of 0.96. Full article

Red-bed mudstone from civil excavation is often treated as waste due to its poor water stability and tendency to disintegrate. This study proposes a sustainable approach for its utilization in controlled low-strength material (CLSM) by blending it with cement and water. Laboratory tests evaluated the fresh properties (i.e., flowability, bleeding rate, setting time, and subsidence rate) and hardened properties (i.e., compressive strength, drying shrinkage, and wet–dry durability) of the CLSM. The analysis focused on two main parameters: cement-to-soil ratio (C/S) and water-to-solid ratio (W/S). The results show that increasing W/S significantly improves flowability, while increasing C/S also contributes positively. Flowability decreased exponentially over time, with an approximately 30% loss recorded after 3 h. Bleeding and subsidence rates rose sharply with higher W/S but were only marginally affected by C/S. To meet performance requirements, W/S should be kept below 52%. In addition, the setting times remained within 24 h for all mixtures tested. Compressive strength showed a negative correlation with W/S and a positive correlation with C/S. When C/S ranged from 8% to 16% and W/S from 44% to 56%, the compressive strengths ranged from 0.3 MPa to 1.22 MPa, meeting typical backfilling needs. Drying shrinkage was correlated positively with water loss, and it decreased with greater C/S. Notably, cement’s addition significantly enhanced water stability. At a C/S of 12%, the specimens remained intact after 13 wet–dry cycles, retaining over 80% of their initial strength. Based on these findings, predictive models for strength and flowability were developed, and a mix design procedure was proposed. This resulted in two optimized proportions suitable for confined backfilling. This study provides a scientific basis for the resource-oriented reuse of red-bed mudstone in civil engineering projects. Full article