Search Results (285)

This research examines the effects of vertical jacking construction on the mechanical behavior of shield tunnels. Model tests simulating vertical jacking were performed utilizing a purpose-built apparatus to quantify the reaction forces generated by the diffusion block during the jacking operation. A systematic analysis was conducted on the mechanical responses of shield tunnel lining segments and their interconnecting joints. Utilizing Particle Flow Code (PFC) methodology, a deformation prediction model specifically tailored for vertical jacking conditions was formulated. Correlating simulation results with experimental measurements quantified the sensitivity of tunnel deformation to grouting reinforcement, enabling the identification of an optimal reinforcement zone. Key findings reveal that the jacking reaction force distribution exhibits pronounced nonlinearity: a substantial increase precedes failure, followed by rapid post-failure reduction and eventual stabilization in advanced jacking stages. Tunnel convergence deformation evolves through four distinct phases: significant growth, rapid attenuation, gradual diminution, and final stabilization. The primary zone of influence encompasses the opening ring and its two adjacent rings. Jacking induces longitudinal bending deformation, with maximum joint opening occurring at the opening ring. Abrupt longitudinal load fluctuations cause dislocation between the opening ring and neighboring rings. Internal segment stresses exhibit initial tensile and compressive increases followed by subsequent relaxation. Externally applied grouting reinforcement effectively attenuates jacking-induced tunnel deformation. An optimal reinforcement range was determined at the 60° position relative to the segment springline, substantially lowering resource consumption and construction risks compared to conventional reinforcement strategies. These outcomes furnish theoretical underpinnings and technical benchmarks for optimizing engineering design and facilitating the implementation of vertical jacking technology. Full article

MIL-STD-1530D and the United States Air Force (USAF) Structures Bulletin EZ-SB-19-01 require an ability to predict the growth of naturally occurring three-dimensional cracks with crack depths equal to what they term an equivalent initial damage size (EIDS) of 0.254 mm. This requirement holds for both additively manufactured and conventionally built parts. The authors have previously presented examples of how to perform such predictions for additively manufactured (AM) Ti-6Al-4V; wire arc additively manufactured (WAAM) 18Ni 250 Maraging steel; and Boeing Space, Intelligence and Weapon Systems laser bed powder fusion (LPBF) Scalmalloy^®, which is an additively manufactured Aluminium-Scandium-Mg alloy, using the Hartman-Schijve crack growth equation. In these studies, the constants used were as determined from ASTM E647 standard tests on long cracks, and the fatigue threshold term in the Hartman-Schijve equation was set to a small value (namely, 0.1 MPa √m). This paper illustrates how this approach can also be used to predict the growth of naturally occurring three-dimensional cracks in WAAM CP-Ti (commercially pure titanium) specimens built by Solvus Global as well as in WAAM-built Inconel 718. As in the prior studies mentioned above, the constants used in this analysis were taken from prior studies into the growth of long cracks in conventionally manufactured CP-Ti and in AM Inconel 718, and the fatigue threshold term in these analyses was set to 0.1 MPa √m. These studies are complemented via a prediction of the growth of naturally occurring three-dimensional cracks in conventionally built M300 steel. Full article

This study focuses on assessing the physical growth of cities and the land-cover changes resulting from it, which play a crucial role in understanding the environmental impacts and managing phenomena such as the Daytime Urban Surface Heat Island Intensity (DSUHII). Predicting the trends of these changes for the future provides valuable insights for urban planning and mitigating thermal effects in arid environments. This research aims to evaluate the spatial and temporal changes in the intensity of urban surface heat islands in cities under different climatic conditions, resulting from land-cover changes in the past, and to predict future trends. For this purpose, Landsat satellite data products, including Surface Reflectance with a 30-m resolution and Land Surface Temperature (LST) originally at a 100 (120)-meter resolution for Landsat 8 (Landsat 5) (resampled to 30 m for compatibility), along with a database of underlying criteria affecting urban growth, were used to analyze land-cover and LST changes. The land-cover classification was carried out using the Support Vector Machine (SVM) algorithm, and its accuracy was assessed. Spatial and temporal changes in LST and land-cover classes were quantified using cross-tabulation models and subtraction operators. Subsequently, the impact of land-cover changes on LST in different climates was analyzed, and the trends of land-cover and DUSHII changes were simulated for the future using the CA–Markov model. The results showed that in the humid climate (Babol and Rasht), built-up areas increased by over 100% from 1990 to 2023 and are projected to grow further by 2055, while green spaces significantly decreased. In the cold–dry climate (Mashhad), urban development increased dramatically, and green spaces nearly halved. In the hot–dry climate (Yazd and Kerman), built-up areas tripled, and the reduction of green spaces will continue. Additionally, in cities with hot and dry climates, a significant area of barren land was converted into built-up areas, and this trend is predicted to continue in the future. DSUHII in Babol increased from 2.5 °C in 1990 to 5.4 °C in 2023 and is projected to rise to 7.8 °C by 2055. In Rasht, this value increased from 2.9 °C to 5.5 °C, and is expected to reach 7.6 °C. In Mashhad, the DSUHII was negative, decreasing from −1.1 °C in 1990 to −1.5 °C in 2023, and is projected to decline to −1.9 °C by 2055. In Yazd, DSUHII also remained negative, decreasing from −2.5 °C in 1990 to −3.3 °C in 2023, with an expected drop to −6.4 °C by 2055. Similarly, in Kerman, the intensity of DSUHII decreased from −2.8 °C to −5.1 °C, and it is expected to reach −7.1 °C by 2055. Overall, the conclusions highlight that in humid climates, DSUHII has significantly increased, while green spaces have decreased. In moderate, cold, and dry climates, a gradual reduction in DSUHII is observed. In the hot–dry climate, the most substantial decrease in DSUHII is evident, indicating the varying impacts of land-cover changes on DSUHII across these regions. Full article

As many areas of the world continue to grow, it is important to detect areas that are urbanizing at paces above the norm and predict future urban areas, so that optimal city planning can occur. However, methods to detect rapid urbanization are currently absent. Additionally, methods that predict future urban areas often rely on deep learning algorithms, which can be computationally expensive and require a large data volume. Furthermore, prediction methods are typically developed in a single location and are not evaluated across diverse geographies. In this study, rapid and future urbanization algorithms are developed, which are based on methods that use an ensemble of built-up spectral indices and a random forest classifier to detect built-up land cover in Sentinel-2 imagery, across ten sites that vary in their climate and population. Results show that the rapid urbanization algorithm can highlight anomalous urban growth. The future urbanization algorithm had an average overall accuracy of 0.66 (±0.11) and an average F1-score of 0.46 (±0.23). However, the method performed well in areas without seasonal vegetation changes and bare ground surroundings with overall accuracy values and F1-scores near or over 0.80. Overall, these methods provide an automated global approach to identifying rapid and future urban areas with minimal data and computational resources needed, which can enable urban planners to obtain information quickly so that decision making for city planning can be completed faster. Full article

The integration of cloud computing, IoT (Internet of Things), and artificial intelligence (AI) is transforming precision agriculture by enabling real-time monitoring, data analytics, and dynamic control of environmental factors. This study develops a cloud-driven data analytics pipeline for indoor agriculture, using lettuce as a test crop due to its suitability for controlled environments. Built with Apache NiFi (Niagara Files), the pipeline facilitates real-time ingestion, processing, and storage of IoT sensor data measuring light, moisture, and nutrient levels. Machine learning models, including SVM (Support Vector Machine), Gradient Boosting, and DNN (Deep Neural Networks), analyzed 12 weeks of sensor data to predict growth trends and optimize thresholds. Random Forest analysis identified light intensity as the most influential factor (importance: 0.7), while multivariate regression highlighted phosphorus (0.54) and temperature (0.23) as key contributors to plant growth. Nitrogen exhibited a strong positive correlation (0.85) with growth, whereas excessive moisture (–0.78) and slightly elevated temperatures (–0.24) negatively impacted plant development. To enhance resource efficiency, this study introduces the Integrated Agricultural Efficiency Metric (IAEM), a novel framework that synthesizes key factors, including resource usage, alert accuracy, data latency, and cloud availability, leading to a 32% improvement in resource efficiency. Unlike traditional productivity metrics, IAEM incorporates real-time data processing and cloud infrastructure to address the specific demands of modern indoor farming. The combined approach of scalable ETL (Extract, Transform, Load) pipelines with predictive analytics reduced light use by 25%, water by 30%, and nutrients by 40% while simultaneously improving crop productivity and sustainability. These findings underscore the transformative potential of integrating IoT, AI, and cloud-based analytics in precision agriculture, paving the way for more resource-efficient and sustainable farming practices. Full article

Creating a suitable growing environment is necessary to ensure good plant growth in a plant factory, which requires wireless sensor networks (WSNs) to monitor the environment in real time. However, existing WSN clustered routing methods hardly take into account the network unreliability caused by varying link quality among nodes, resulting in reduced stability and accuracy of environmental monitoring. This study proposes a wireless sensor network system strategy for improving network reliability in large-scale reliable wireless sensor networks suitable for plant factory scenarios. Firstly, a hybrid wireless sensor network was designed and built based on Wi-Fi and ZigBee communication protocols. Secondly, a nonlinear link quality prediction model for plant factory scenarios was developed using a function fitting method, taking into account the interference and attenuation caused by the dense concentration of agricultural facilities and plants in plant factories on the wireless signal propagation. Finally, a network coverage optimization scheme was designed by combining a particle swarm optimization (PSO) algorithm and link quality prediction model, and a reliable cluster routing protocol was designed by combining K-means algorithm. The results indicated that the coefficient of determination (R²) for the prediction model was 0.9962. The impact of agricultural facilities and vegetation on link quality was most significant when the node height was 0.7 m. Under the optimal node deployment, the number of nodes was 33, and the network coverage rate (CR) reached 97.512%. Compared with the traditional clustered routing method, the wireless sensor network designed in this study is more applicable to the field of plant factories; it further enhances data transmission effectiveness and link quality, improves the reliability of the network, and realizes the load balancing of the internal transmission of the network, which in turn ensures the accuracy of environmental monitoring and the stability of the system. Full article

Rapid urbanization and the consequent alteration in land use and land cover (LULC) significantly change the natural landscape and adversely affect hydrological cycles, biological systems, and various ecosystem services, especially in the developing world. Thus, it is vital to study the environmental conditions of a region to mitigate the negative impacts of urbanization. Out of a wide array of parameters, the Environmental Criticality Index (ECI), a relatively new concept, was used in this study, which was conducted over the Kolkata Metropolitan Area (KMA). It was derived using Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to quantify heat-related impact. An increase in the percentage of land area under high ECI categories, from 23.93% in 2000 to 32.37% in 2020, indicated a progressive increase in criticality. The Spatio-temporal Thermal-based Environmental Criticality Consistency Index (STTECCI) and hotspot analysis identified the urban and industrial areas in KMA as criticality hotspots, consistently recording higher ECI. The correlation analysis between ECI and LULC features revealed that there exists a negative correlation between ECI and natural vegetation and agriculture, while built-up areas and ECI are positively correlated. Bare lands, despite being positively correlated with ECI, have an insignificant relationship with it. Also, the designed built-up index extracted the built-up areas with an accuracy of 89.5% (kappa = 0.78). The future scenario of ECI in KMA was predicted using Modules for Land Use Change Evaluation (MOLUSCE) with an accuracy level above 90%. The percentage of land area under low ECI categories is expected to decline from 50.02% in 2000 to 35.6% in 2040, while the percentage of land area under high ECI categories is expected to increase from 23.93% in 2000 to 36.56% in 2040. This study can contribute towards the development of tailored management strategies that foster sustainable growth, resilience, and alignment with the Sustainable Development Goals, ensuring a balance between economic development and environmental preservation. Full article

Global trends in health, climate, and population growth drive the demand for high-nutrient plants like spinach, which thrive under controlled conditions with minimal resources. Despite technological advances in agriculture, current systems often rely on traditional methods and need robust computational models for precise plant growth forecasting. Optimizing vegetable growth using advanced agricultural and computational techniques, addressing challenges in food security, and obtaining efficient resource utilization within urban agriculture systems are open problems for humanity. Considering the above, this paper presents an enclosed agriculture system for growth and modeling spinach of the Viroflay (Spinacia oleracea L.) species. It encompasses a methodology combining data science, machine learning, and mathematical modeling. The growth system was built using LED lighting, automated irrigation, temperature control with fans, and sensors to monitor environmental variables. Data were collected over 60 days, recording temperature, humidity, substrate moisture, and light spectra information. The experimental results demonstrate the effectiveness of polynomial regression models in predicting spinach growth patterns. The best-fitting polynomial models for leaf length achieved a minimum Mean Squared Error (MSE) of 0.158, while the highest MSE observed was 1.2153, highlighting variability across different leaf pairs. Leaf width models exhibited improved predictability, with MSE values ranging from 0.0741 to 0.822. Similarly, leaf stem length models showed high accuracy, with the lowest MSE recorded at 0.0312 and the highest at 0.3907. Full article

Land use conflicts represent an increasing challenge to sustainable development, particularly in regions undergoing rapid urbanization. This study investigated the spatiotemporal dynamics of land use conflicts and their ecological implications in Tianshui City from 1980 to 2020. The main objectives were to identify patterns of spatial heterogeneity, explore the driving factors behind these conflicts, and analyze their relationship with the ecological risks. The results indicate the following findings. In terms of spatiotemporal heterogeneity, early land use changes were primarily driven by structural factors, such as topography and climate, with a Nugget/Still ratio of <0.30 observed from 1980 to 2000. After 2000, however, stochastic factors, including an average annual urbanization rate increase of 5.2% and a GDP growth rate of 9.1%, emerged as the dominant drivers, as reflected in a Nugget/Still ratio > 0.36. Regarding conflict intensity, high-conflict areas expanded by approximately 1110 square kilometers between 1980 and 2020, predominantly concentrated in fertile agricultural regions such as the Weihe River Basin and urban core areas. Conversely, non-conflict zones decreased by 38.7%. In terms of ecological risk correlation, bivariate LISA cluster analysis revealed a significant spatial autocorrelation between severe land use conflicts and ecological risks (Moran’s I = 0.62, p < 0.01). High-risk clusters in areas transitioning from arable land to built-up land increased by 23% after 2000. Predictions based on the future land-use simulation (FLUS) model suggest that by 2030, high-intensity conflict areas will expand by an additional 16%, leading to intensified competition for land resources. Therefore, incorporating ecological safety thresholds into land spatial planning policies is essential for reconciling the conflicts between development and conservation, thereby promoting sustainable land use transitions. Full article

Live-streaming platforms such as TikTok have been recently experiencing exponential growth, attracting millions of daily viewers. This surge in network traffic often results in increased latency, even on resource-rich nodes during peak times, leading to the downgrade of Quality of Experience (QoE) for users. This study aims to predict QoE downgrade events by leveraging cross-layer device data through real-time predictions and monitoring. We propose a Real-time Multi-level Transformer (RMT) model to predict the QoE of live streaming by integrating time-series data from multiple network layers. Unlike existing approaches, which primarily assess the immediate impact of network conditions on video quality, our method introduces a device-mask pretraining (DMP) technique that applies pretraining on cross-layer device data to capture the correlations among devices, thereby improving the accuracy of QoE predictions. To facilitate the training of RMT, we further built a Live Stream Quality of Experience (LSQE) dataset by collecting 5,000,000 records from over 300,000 users in a 7-day period. By analyzing the temporal evolution of network conditions in real-time, the RMT model provides more accurate predictions of user experience. The experimental results demonstrate that the proposed pretraining task significantly enhances the model’s prediction accuracy, and the overall method outperforms baseline approaches. Full article

The growth, development, and differentiation of multicellular organisms are primarily driven by intercellular communication, which coordinates the activities of diverse cell types. This cell-to-cell signaling is typically mediated by various types of protein–protein interactions, including ligand–receptor; receptor–receptor, and extracellular matrix–receptor interactions. Currently, computational methods for inferring ligand–receptor communication primarily depend on gene expression data of ligand–receptor pairs and spatial information of cells. Some approaches integrate protein complexes; transcription factors; or pathway information to construct cell communication networks. However, few methods consider the critical role of protein–protein interactions (PPIs) in intercellular communication networks, especially when predicting communication between different cell types in the absence of cell type information. These methods often rely on ligand–receptor pairs that lack PPI evidence, potentially compromising the accuracy of their predictions. To address this issue, we propose CellGAT, a framework that infers intercellular communication by integrating gene expression data of ligand–receptor pairs, PPI information, protein complex data, and experimentally validated pathway information. CellGAT not only builds a priori models but also uses node embedding algorithms and graph attention networks to build cell communication networks based on scRNA-seq (single-cell RNA sequencing) datasets and includes a built-in cell clustering algorithm. Through comparisons with various methods, CellGAT accurately predicts cell–cell communication (CCC) and analyzes its impact on downstream pathways; neighboring cells; and drug interventions. Full article

Urban areas serve as critical habitats for numerous plant species. Existing studies suggest that, due to human-mediated introductions, urban environments often harbor a greater variety of plant species compared to suburban areas, potentially becoming focal points for biodiversity. Consequently, investigating the driving forces and complex mechanisms by which urban environmental factors influence plant species distribution is essential for establishing the theoretical foundation for urban biodiversity conservation and future urban planning and management. Solar radiation, among these factors, is a critical determinant of plant growth, development, and reproduction. However, there is a notable lack of research on how this factor affects the distribution of urban plant species and influences species’ richness and composition within plant communities. We present for the first time an analysis of how solar radiation drives the spatial distribution of plant species within the built-up areas of Nanchang City, China. Based on three years of monitoring and survey data from experimental sites, this study employs three evaluation models—Species Richness Index (R), Simpson’s Diversity Index (D), and Shannon–Wiener Index (H)—to analyze and validate the survey results. Additionally, MATLAB and ArcGIS Pro software are utilized for the numerical simulation and visualization of spatial data. Our study shows that areas with low solar radiation exhibit higher plant species richness, while plots with high plant diversity are primarily concentrated in regions with strong solar radiation. Moreover, the Diversity Index D proves to be more sensitive than the Shannon–Wiener Index (H) in evaluating the spatial distribution of plant species, making it a more suitable metric for studying urban plant diversity in our study area. Among the 18 plant species analyzed, Mulberry and Dandelion are predominantly dispersed by birds and wind, showing no significant correlation with solar radiation. This finding indicates that the spatial distribution of urban plant species is influenced by multiple interacting factors beyond solar radiation, highlighting the critical need for long-term observation, monitoring, and analysis. This study also suggests that shaded urban areas may serve as hubs of high species richness, while regions with relatively strong solar radiation can sustain greater plant diversity. These findings underscore the practical significance of this research, offering essential insights to guide urban planning and management strategies. Additionally, this study offers valuable insights for the future predictions of plant species distribution and potential areas of high plant diversity in various urban settings by integrating computational models, building data, Digital Elevation Models (DEMs), and land cover data. Full article

In the past two decades, Islamabad has experienced significant urbanization. As a result of inadequate urban planning and spatial distribution, it has significantly influenced land use–land cover (LULC) changes and green areas. To assess these changes, there is an increasing need for reliable and appropriate information about urbanization. Landsat imagery is categorized into four thematic classes using a supervised classification method called the support vector machine (SVM): built-up, bareland, vegetation, and water. The results of the change detection of post-classification show that the city region increased from 6.37% (58.09 km²) in 2000 to 28.18% (256.49 km²) in 2020, while vegetation decreased from 46.97% (428.28 km²) to 34.77% (316.53 km²) and bareland decreased from 45.45% (414.37 km²) to 35.87% (326.49 km²). Utilizing a land change modeler (LCM), forecasts of the future conditions in 2025, 2030, and 2035 are predicted. The artificial neural network (ANN) model embedded in IDRISI software 18.0v based on a well-defined backpropagation (BP) algorithm was used to simulate future urban sprawl considering the historical pattern for 2015–2020. Selected landscape morphological measures were used to quantify and analyze changes in spatial structure patterns. According to the data, the urban area grew at a pace of 4.84% between 2015 and 2020 and will grow at a rate of 1.47% between 2020 and 2035. This growth in the metropolitan area will encroach further into vegetation and bareland. If the existing patterns of change persist over the next ten years, a drop in the mean Euclidian Nearest Neighbor Distance (ENN) of vegetation patches is anticipated (from 104.57 m to 101.46 m over 2020–2035), indicating an accelerated transformation of the landscape. Future urban prediction modeling revealed that there would be a huge increase of 49% in urban areas until the year 2035 compared to the year 2000. The results show that in rapidly urbanizing areas, there is an urgent need to enhance land use laws and policies to ensure the sustainability of the ecosystem, urban development, and the preservation of natural resources. Full article

Residual stress (RS) in laser powder bed fusion (LPBF) additive manufactured structures can significantly affect mechanical performance, potentially leading to premature failure. The complex distribution of residual stresses, combined with the limitations of full-field measurement techniques, presents a substantial challenge in conducting damage tolerance analyses of aircraft structures. To address these challenges, this study developed a comprehensive simulation framework to analyze the 3D distribution of residual stresses and fatigue crack growth in LPBF parts. The 3D residual stress profiles of as-built samples in 15° and 75° build directions were computed and compared to experimental data. The fatigue crack propagation behavior of the 75° sample, considering 3D residual stress, was predicted, and the effects of residual stress redistribution under cyclic loading were discussed. It shows that the anisotropy of residual stress, influenced by the build direction, can lead to mixed-mode fracture and subsequent crack deflection. Tensile residual stress in the near-surface region and compressive stress in the inner region can cause an inverted elliptical crack front and accelerate fatigue crack growth. Full article

Oxide phases such as $\alpha$-Fe₂O₃ (hematite) and $\alpha$-Al₂O₃ (corundum) are highly insoluble in water; however, subcritical crack growth has been observed in humidity nonetheless. Chemically induced bond breaking at the crack tip appears unlikely due to sterically hindered molecular transport. The molecular mechanics of a crack in corundum with a reactive force field reveal minimal lattice trapping, leading to bond breaking before sufficient space opens for water transport. To address this, we model a pre-built blunt crack with space for H₂O molecule adsorption at the tip and show that it reduces fracture toughness by lowering the critical J-integral. Then, we explore stress-enhanced dissolution to understand the mechanism of crack tip blunting in the oxide/water system. Density functional theory combined with metadynamics was employed to describe atomic dissolution from flat hematite and corundum surfaces in pure water. Strain accelerates dissolution, stabilizing intermediate states with broken bonds before full atom detachment, while the free energy profile of unstrained surfaces is almost monotonic. The atomistic calculations provided input for a kinetic model, predicting the shape evolution of a blunt crack tip, which displays three distinct regimes: (i) dissolution primarily away from the tip, (ii) enhanced blunting near but not at the apex, and (iii) sharpening near the apex. The transition between regimes occurs at a low strain, highlighting the critical role of water in the subcritical crack growth of oxide scales, with dissolution as the fundamental microscopic mechanism behind this process. Full article