Search Results (2,213)

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data measurements were collected from a cultivator shank in the northeast of Kansas using the AVDAQ system. The test field soils were Reading silt loam and Eudora–Bismarck Grove silt loams. The relationship between shank vibrations, soil moisture (measured by a Hydrosense II soil–water sensor), and soil compaction (measured by a cone penetrometer) was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. Overall, the results demonstrate that combining vibration data with soil moisture data as model inputs enables accurate estimation of soil compaction, providing a foundation for future in situ soil sensing, reduced tillage intensity, and more sustainable cultivation practices. Full article

Driven by “Dual Carbon” goals, advancing the green development of oil and gas resources is imperative. The Shaximiao Formation tight gas reservoirs in the Sichuan Basin suffer from wellbore instability, impairing drilling efficiency and elevating energy use and emissions. This study integrates mineralogy, mechanics, drilling fluid optimization, and geostress modeling to address instability mechanisms and support low-carbon drilling. XRD shows that clay content decreases with depth (11–48%), while quartz and plagioclase dominate (45–80%). Synthetic-based drilling fluids fully inhibit clay swelling (0% expansion), outperforming calcium-based (2.4–3.1%) and water-based systems (5.4%). Synthetic and calcium-based fluids also reduce waste treatment difficulty and carbon intensity. Rolling recovery reaches 98.12% for synthetic-based vs. 78.18% for water-based. Strength tests reveal a 36.9% reduction after 14-day immersion in synthetic-based fluid, whereas water-based systems with nano-plugging agents show self-recovery, cutting energy use per foot by ~15%. Geostress modeling indicates a maximum horizontal stress of 90.08 MPa (NE114° ± 13°) and minimum of 67.2 MPa (NE24° ± 13°). Collapse pressure (48–60 MPa) varies azimuthally, requiring higher density (58–60 MPa) along the min. horizontal stress direction. A low-carbon mitigation strategy is proposed: prioritize synthetic or calcium-based drilling fluids, and optimize well trajectory using geostress models. This reduces fluid loss risk by >20%, limits methane emissions, shortens drilling cycles, and enhances efficiency while lowering carbon footprint. These insights support green and efficient natural gas development through intelligent drilling and eco-material applications. Full article

This paper presents the design and analysis of a forklift hydraulic system utilizing an open-center configuration equipped with unloading (safety-overflow) valves and an emergency lowering mechanism. The hydraulic system includes an external gear pump, double-acting power cylinders, hydraulic distributors, and control valves. A comprehensive approach is undertaken to select system components based on catalog data and to model the flow rate, required torque, and power characteristics of the pump, along with load handling performance as a function of cylinder dimensions and hydraulic pressure. System behavior under various operating conditions is simulated using Automation Studio, enabling performance optimization and fault response assessment. The inclusion of unloading valves and an emergency button enhances system safety by enabling controlled pressure relief and emergency actuation. The impact of thermal effects, filter efficiency, and reservoir design on hydraulic fluid integrity is also addressed. This study aims to improve reliability, efficiency, and safety in hydraulic forklift systems while supporting informed design decisions using simulation-driven methodologies. Full article

In urban underground construction, Earth Pressure Balance (EPB) tunneling faces complex geological uncertainties and dynamic operational risks. Traditional safety management approaches often struggle under such conditions. This paper proposes an integrated safety resilience assessment framework for EPB tunneling that combines an entropy-weighted TOPSIS method, the Analytic Network Process (ANP), and an extension cloud model to capture interdependencies and uncertainties. A hierarchical indicator system with four primary dimensions (stability, redundancy, efficiency, and fitness) is constructed. The entropy-TOPSIS algorithm provides objective initial weights and scenario ranking, while ANP models the feedback relationships among criteria. The extension cloud model quantifies fuzziness in expert judgments and converts qualitative assessments into probabilistic resilience ratings. The methodology is applied to a case study of the EPB shield tunnel section of Jinan Metro Line 6 (China). The section’s resilience is classified as a medium level, which agrees with expert evaluation. The results demonstrate that the proposed approach yields accurate and robust safety resilience evaluations, supporting data-driven decision-making. This framework offers a quantitative tool for resilience-based safety management of shield tunneling projects, providing guidance for shifting from traditional risk control toward a resilience-enhancement strategy. Full article

The Hunga-Tonga volcanic eruption on 15 January 2022, produced significant atmospheric and ionospheric disturbances that may degrade global navigation satellite system (GNSS) and precise point positioning (PPP) accuracy. Using data from the GEONET GNSS network and Soratena barometric pressure sensors across Japan, we analyzed the eruption’s effects through the gradient ionospheric index (GIX) and the rate of TEC index (ROTI) to characterize the propagation and effects of these disturbances on ionospheric total electron content (TEC) gradients. Our analysis identified two separate ionospheric disturbance events. The first event, coinciding with the arrival of atmospheric Lamb waves, was characterized by wave-like pressure anomalies, differential TEC (dTEC) fluctuations, and modest horizontal gradients of vertical TEC (VTEC). In contrast, the second, more pronounced disturbance was driven by equatorial plasma bubbles (EPBs), which generated severe ionospheric irregularities and large TEC gradients. Further analysis revealed that these two disturbances had markedly different impacts on GNSS positioning accuracy. The Lamb wave–induced disturbance mainly caused moderate TEC fluctuations with limited effects on positioning accuracy, and mid-latitude stations maintained both average and 95th percentile positioning ($\mathrm{p}\mathrm{p}\mathrm{p},{\mathrm{P}}_{95}$) errors below 0.1 m throughout the event. In contrast, the EPB-driven disturbance had a substantial impact on low-latitude regions, where the average horizontal PPP error peaked at 0.5 m and the horizontal and vertical $\mathrm{p}\mathrm{p}\mathrm{p},{\mathrm{P}}_{95}$ errors exceeded 1 m. Our findings reveal two episodes of spatial-gradient enhancement and successfully estimate the propagation speed and direction of the Lamb waves, supporting the potential application of ionospheric gradient monitoring in forecasting GNSS performance degradation. Full article

Urban tree diversity plays a crucial role in enhancing the resilience of cities by contributing to ecosystem services such as mitigating the effects of land degradation, combating urban heat islands, improving air quality, and fostering biodiversity habitats. A diverse tree population enhances resilience to vulnerabilities related to climatic stress, disease, and habitat loss by promoting stability, adaptability, and efficiency within the ecosystem. Little is known about urban tree diversity in Serbia; therefore, this study examines the diversity of tree species in the City of Niš, Serbia, to assess its implications for urban resilience and biodiversity preservation in the context of land-use change. Using the Shannon Diversity Index, we quantify species richness and evenness across both central and suburban zones of the city. The results are benchmarked against similar indices in five other European cities to assess how patterns of urban tree distribution vary under different urbanisation pressures. The study reveals that tree diversity is markedly lower in the city centre than in peripheral areas, highlighting spatial inequalities in green infrastructure that may accelerate biodiversity loss due to compact urban development. These findings demonstrate how urban expansion and infrastructure density contribute to ecological fragmentation, potentially leading to long-term effects on ecosystem services. This study emphasises the strategic importance of integrating greenery diversity into urban and landscape planning, particularly in rapidly growing urban centres in Southeastern Europe. This research contributes to the existing body of literature, providing a deeper understanding of the interdependencies between urban tree diversity, land degradation, and biodiversity loss, offering data-driven insights. This enables urban planners, landscape architects, and policy advisors to make informed decisions about street tree diversity and green city infrastructure, contributing to the development of sustainable cities. Full article

Conventional static fixation for distal radius fractures (DRF) is clinically challenging, with methods often leading to complications such as malunion and pressure-related injuries. These issues stem from uncontrolled pressure and a lack of real-time biomechanical feedback, resulting in suboptimal functional recovery. To overcome these limitations, we engineered an intelligent, adaptive orthopedic device. The system is built on a patient-specific, 3D-printed architecture for a lightweight, personalized fit. It embeds an array of thin-film pressure sensors at critical anatomical sites to continuously quantify biomechanical forces. This data is transmitted via an Internet of Things (IoT) module to a cloud platform, enabling real-time remote monitoring by clinicians. The core innovation is a closed-loop feedback controller governed by a robust Interval Type-2 Fuzzy Logic (IT2-FLC) algorithm. This system autonomously adjusts servo-driven straps to dynamically regulate fixation pressure, adapting to changes in limb swelling. In a preliminary clinical evaluation, the group receiving the integrated treatment protocol, which included the smart splint and TCM herbal therapy, demonstrated superior anatomical restoration and functional recovery, evidenced by higher Cooney scores (91.65 vs. 83.15) and lower VAS pain scores. This proof-of-concept study validates a new paradigm for adaptive orthopedic devices, showing high potential for clinical translation. Full article

Data-driven models for complex thermal systems face two main challenges: a heavy dependence on high-quality training datasets and a “black-box” nature that makes it difficult to align model predictions with fundamental physical laws. To address these issues, this study introduces a novel physics-informed variational autoencoder (PI-VAE) framework for modeling thermal systems. The framework formalizes the mechanistic relationships among state parameters and establishes mathematical formulations for multi-level physical constraints. These constraints are integrated into the training loss function of the VAE as physical inconsistency losses, steering the model to comply with the system’s underlying physical principles. Additionally, a synthetic sample-generation strategy using latent variable sampling is introduced to improve the representation of physical constraints. The effectiveness of the proposed framework is validated through numerical simulations and an engineering case study. Simulation results indicate that as the complexity of embedded physical constraints increases, the test accuracy of the PI-VAE progressively improves, with R² increasing from 0.902 (standard VAE) to 0.976. In modeling a high-pressure feedwater heater system in a thermal power plant, the PI-VAE model achieves high prediction accuracy while maintaining physical consistency under previously unseen operating conditions, thereby demonstrating superior generalization capability and interpretability. Full article

The periphery of Lomami National Park in the Democratic Republic of the Congo (DR Congo) is experiencing intense and increasing hunting pressure, driven by both local subsistence needs and growing urban demand for bushmeat. This situation poses a serious challenge to sustainable natural resource management and underscores the need to realign protected-area policies with the realities faced by surrounding communities. In the absence of comprehensive ecological monitoring, this study used hunters’ perceptions to assess the current availability of mammalian wildlife around the park. From October to December 2023, surveys were conducted using a snowball sampling method with 60 hunters from nine villages bordering the park. Results show that hunting is a male-dominated activity, mainly practiced by individuals aged 30–40 years, with firearms as the primary tools. It occurs both in the park’s buffer zones and, alarmingly, within its core protected area. This practice has contributed to the local disappearance of key species such as African forest elephant (Loxodonta cyclotis), African buffalo (Syncerus caffer), and African leopard (Panthera pardus pardus), and to the marked decline of several Cephalophus species. These patterns of overexploitation reveal critical weaknesses in current conservation strategies and point to the urgent need for integrated, community-based resource management approaches. Strengthening law enforcement, improving ranger support, and enhancing participatory governance mechanisms are essential. Equally important is the promotion of sustainable alternative livelihoods—including livestock farming, aquaculture, and agroforestry—to reduce hunting dependence and build long-term resilience for both biodiversity and local communities. Full article

In response to mounting regulatory and environmental pressures, the maritime sector must urgently improve energy efficiency and reduce greenhouse gas emissions. However, conventional operational interfaces often fail to deliver real-time, actionable insights needed for informed decision-making onboard. This work presents an innovative Augmented Reality (AR) interface integrated with an established shipboard data collection system to enhance real-time monitoring and operational decision-making on commercial vessels. The baseline data acquisition infrastructure is currently installed on over 800 vessels across various ship types, providing a robust foundation for this development. To validate the AR interface’s feasibility and performance, a field trial was conducted on a representative dry bulk carrier. Through hands-free AR smart glasses, crew members access real-time overlays of key performance indicators, such as fuel consumption, engine status, emissions levels, and energy load balancing, directly within their field of view. Field evaluations and scenario-based workshops demonstrate significant gains in energy efficiency (up to 28% faster decision-making), predictive maintenance accuracy, and emissions awareness. The system addresses human–machine interaction challenges in high-pressure maritime settings, bridging the gap between complex sensor data and crew responsiveness. By contextualizing IoT data within the physical environment, the AR-IoT platform transforms traditional workflows into proactive, data-driven practices. This study contributes to the emerging paradigm of digitally enabled sustainable operations and offers practical insights for scaling AR-IoT solutions across global fleets. Findings suggest that such convergence of AR and IoT not only enhances vessel performance but also accelerates compliance with decarbonization targets set by the International Maritime Organization (IMO). Full article

Urban economic development together with the concentration of population acts as a major stimulus for changes in land-use configurations, thereby reshaping local ecosystems and influencing habitat quality. Conducting a rigorous evaluation of the temporal–spatial dynamics and the mechanisms underlying these changes is crucial for refining spatial management strategies, improving urban livability, and steering cities toward sustainable pathways. In this research, we established a comprehensive analytical framework that integrates the PLUS model, the InVEST model, and the GeoDetector model to examine shifts in land-use patterns and habitat quality in Busan Metropolitan City during 1988–2019 to pinpoint the principal influencing factors and to project possible trajectories for 2029–2049 under multiple climate change scenarios. The key findings can be summarized as follows: (1) during the last thirty years, the city’s land-use structure underwent substantial transformation, with forested areas and built-up zones becoming the primary categories, indicating continuous urban encroachment and the reduction in ecological land; (2) the average habitat quality dropped by 18.23%, displaying a distinct spatial gradient from low values in plains and coastal areas to higher values in mountainous and inland zones; (3) results from the GeoDetector revealed that variations in land-use type and NDVI exerted the greatest influence on habitat quality differences, reflecting the combined impacts of environmental conditions and socio-economic pressures; (4) scenario projections show that the SSP1-2.6 pathway supports ecological land growth and leads to a notable improvement in habitat quality, while SSP5-8.5 causes ongoing deterioration driven by the expansion of construction land. The SSP2-4.5 pathway demonstrates a relatively moderate pattern, balancing urban development needs with ecological preservation and thus is more consistent with the long-term sustainability objectives of Busan. This study provides a robust scientific basis for understanding historical and projected changes in land cover and habitat quality in Busan and offers theoretical guidance for optimizing land-use structures, strengthening ecological protection, and fostering sustainable development in Busan and other coastal mountainous cities. Full article

Designing efficient nanoparticle-enhanced CO₂ capture systems is challenging due to the diversity of nanoparticles, solvent formulations, reactor configurations, and operating conditions. This study presents the first ANN-based meta-analysis framework developed to predict CO₂ absorption enhancement across multiple reactor systems, including batch reactors, packed columns, and membrane contactors. A curated dataset of 312 experimental data points was compiled from literature, and an artificial neural network (ANN) model was trained using six input variables: nanoparticle type, concentration, system configuration, base fluid, pressure, and temperature. The proposed model achieved high predictive accuracy (R² > 0.92; RMSE: 4.2%; MAE: 3.1%) and successfully captured complex nonlinear interactions. Feature importance analysis revealed nanoparticle concentration (28.3%) and system configuration (22.1%) as the most influential factors, with functionalized nanoparticles such as Fe₃O₄@SiO₂-NH₂ showing superior performance. The model further predicted up to 130% enhancement for ZnO in optimized membrane contactors. This AI-driven tool provides quantitative insights and a scalable decision-support framework for designing advanced nanoparticle–solvent systems, reducing experimental workload, and accelerating the development of sustainable CO₂ capture technologies. Full article

Extensive radar investigations, observed spectral signatures, geomorphological, and paleoclimate modeling support the presence of mid- to low-latitude ground ice on Mars. The presence of near-surface ice and glacial features has been proposed in Ismenius Lacus, but the ice composition and age remain unconstrained. Our high-resolution stereoscopic analysis reveals distinctive landforms, including sharp-edged polyhedra, chevron patterns, and en-echelon open fractures, indicative of plastic glacial deformation. Current climatic conditions may support year-round ice stability, while sharp-edged polyhedra, open fractures, and the absence of superposed craters suggest active glaciation. The Ariguani delta system lacks fluvial signatures but aligns with glacial erosional and depositional processes. Unlike terrestrial glaciers, ice accumulation here is likely driven by escarpment-fed melt from seasonal permafrost thawing under lithostatic pressure, generating neo-glacial flows that sustain the glacial tongue. This mechanism can also explain regional features, including U-shaped valley subsidence, gravitational slides, flow of low-viscosity material lobes, and ring-mold craters. Thus, we propose sharp-edged polyhedra as diagnostic markers for identifying ongoing ice dynamics on Mars, enabling future automated detection of active glacial environments. Full article

Proper estimation of flowing bottomhole pressure at coiled tubing depth (BHP-CTD) is crucial in optimization of nitrogen lifting operations in oil wells. Conventional estimation techniques such as empirical correlations and mechanistic models may be characterized by poor generalizability, low accuracy, and inapplicability in real time. This study overcomes these shortcomings by developing and comparing sixteen machine learning (ML) regression models, such as neural networks and genetic programming-based symbolic regression, in order to predict BHP-CTD with field data collected on 518 oil wells. Operational parameters that were used to train the models included fluid flow rate, gas–oil ratio, coiled tubing depth, and nitrogen rate. The best performance was obtained with the neural network with the L-BFGS optimizer (R² = 0.987) and the low error metrics (RMSE = 0.014, MAE = 0.011). An interpretable equation with R² = 0.94 was also obtained through a symbolic regression model. The robustness of the model was confirmed by both k-fold and random sampling validation, and generalizability was also confirmed using blind validation on data collected on 29 wells not included in the training set. The ML models proved to be more accurate, adaptable, and real-time applicable as compared to empirical correlations such as Hagedorn and Brown, Beggs and Brill, and Orkiszewski. This study does not only provide a cost-efficient alternative to downhole pressure gauges but also adds an interpretable, data-driven framework to increase the efficiency of nitrogen lifting in various operational conditions. Full article