Search Results (242)

The main bearing of a tunnel boring machine (TBM) is a critical component of the main driving system that enables continuous excavation, and its performance is crucial for ensuring the safe operation of the TBM. Currently, there are few testing technologies for TBM main bearings, and a comprehensive testing and evaluation system has yet to be established. This study presents an experimental investigation using a self-developed, full-scale TBM main bearing test bench. Based on a representative load spectrum, both operational condition tests and life cycle tests are conducted alternately, during which the signals of the main bearing are collected. The observed vibration signals are weak, with significant vibration attenuation occurring in the large structural components. Compared with the test bearing, which reaches a vibration amplitude of 10 g in scale tests, the difference is several orders of magnitude smaller. To effectively utilize the selected evaluation indicators, the entropy weight method is employed to assign weights to the indicators, and a comprehensive analysis is conducted using grey relational analysis. This strategy results in the development of a comprehensive evaluation method based on entropy weighting and grey relational analysis. The main bearing performance is evaluated under various working conditions and the same working conditions in different time periods. The results show that the greater the bearing load, the lower the comprehensive evaluation coefficient of bearing performance. A multistage evaluation method is adopted to evaluate the performance and condition of the main bearing across multiple working scenarios. With the increase of the test duration, the bearing performance exhibits gradual degradation, aligning with the expected outcomes. The findings demonstrate that the proposed performance evaluation method can effectively and accurately evaluate the performance of TBM main bearings, providing theoretical and technical support for the safe operation of TBMs. Full article

There has been a great need for replacing combustion-powered vehicles with electric vehicles (EV), and fully electric cars are meant to replace combustion engine cars. This has led to considerable research into improving the performance of EVs, especially via electric motor voltage control. A wide range of optimization algorithms have been used as traditional approaches, but the dynamic parameters of electric motors, impacted by temperature and different driving cycles, continue to be a problem. This study introduces an improved version of the Parrot Optimizer (IPO) aimed at enhancing voltage regulation in EVs. The algorithm can intelligently adjust certain motor parameters for adaptive management to maintain performance based on different situations. To ensure a stable and sustainable power supply for the powertrain of the EV, a photovoltaic (PV) system is used with energy storage batteries. Such an arrangement seeks to deliver permanent electric energy, a solution to traditional grid electricity reliance. This demonstrates the effectiveness of IPO, with the resultant motor performance remaining optimal despite parameter changes. It is also illustrated that energy production, by integrating PV systems, prevents excessive voltage line drops and thus voltage imbalances. The proposed intelligent controller is verified based on multiple simulations, demonstrating and ensuring significant improvements in EV efficiency and reliability. Full article

Climate change intensifies hydrological cycles, leading to an increased variability in terrestrial water storage anomalies (TWSAs) and a heightened drought risk. Understanding the spatiotemporal dynamics of TWSAs and their driving factors is crucial for sustainable water management. While previous studies have primarily attributed TWSAs to regional factors, this study employs wavelet coherence, partial correlation analysis, and multiple linear regression to comprehensively analyze TWSA dynamics and their drivers in the Hengduan Mountains (HDM) region from 2003 to 2022, incorporating both regional and global influences. Additionally, dry–wet variations were quantified using the GRACE-based Drought Severity Index (GRACE-DSI). Key findings include the following: The annual mean TWSA showed a non-significant decreasing trend (−2.83 mm/y, p > 0.05), accompanied by increased interannual variability. Notably, approximately 36.22% of the pixels in the western HDM region exhibited a significantly decreasing trend. The Nujiang River Basin (NRB) (−17.17 mm/y, p < 0.01) and the Lancang (−17.17 mm/y, p < 0.01) River Basin experienced the most pronounced declines. Regional factors—particularly precipitation (PRE)—drove TWSA in 59% of the HDM region, followed by potential evapotranspiration (PET, 28%) and vegetation dynamics (13%). Among global factors, the North Atlantic Oscillation showed a weak correlation with TWSAs (r = −0.19), indirectly affecting it via winter PET (r = −0.56, p < 0.05). The decline in TWSAs corresponds to an elevated drought risk, notably in the NRB, which recorded the largest GRACE-DSI decline (slope = −0.011, p < 0.05). This study links TWSAs to climate drivers and drought risk, offering a framework for improving water resource management and drought preparedness in climate-sensitive mountain regions. Full article

The spotted wing drosophila, Drosophila suzukii is an injurious polyphagous pest threatening worldwide soft fruit production. Its high adaptability to new colonized environments, short life cycle, and wide host range are supporting its rapid spread. The most common techniques to reduce its significant economic damage are based on multiple insecticides applications per season, even prior to the harvest, which reduces agroecosystem biodiversity and affects human and animal health. Environmental concerns and regulatory restrictions on insecticide use are driving the need for studies on alternative biological control strategies. This study aimed to assess the effect of T. drosphilae in controlling D. suzukii infestations and its interaction with P. vindemiae, a secondary parasitoid naturally present in Apulia (South Italy). Field experiments were carried out in organic cherry orchards in Gioia del Colle (Bari, Italy) to test the efficacy and adaptability of T. drosphilae following weekly releases of artificially reared individuals. Additionally, the interaction between P. vindemiae and T. drosphilae was studied under laboratory conditions. Results from field experiments showed that D. suzukii populations were significantly lower when both parasitoids were present. However, T. drosophilae was less prone to adaptation, so its presence and parasitism were limited to the post-release period. Laboratory experiments, instead, confirmed the high reduction of D. suzukii populations when both parasitoids are present. However, the co-existence of the two parasitoids resulted in a reduced parasitism rate and offspring production, notably for T. drosophilae. This competitive disadvantage may explain its poor establishment in field conditions. These findings suggest that the field release of the two natural enemies should be carried out with reference to their natural population abundance to not generate competition effects. Full article

Based on ascending and descending orbit SAR data from 2017–2025, this study analyzes the long time-series deformation monitoring and slip pattern of an active-layer detachment thaw slump, a typical active-layer detachment thaw slump in the permafrost zone of the Qinghai-Tibetan Plateau, by using the small baseline subset InSAR (SBAS-InSAR) technique. In addition, a three-dimensional displacement deformation field was constructed with the help of ascending and descending orbit data fusion technology to reveal the transportation characteristics of the thaw slump. The results show that the thaw slump shows an overall trend of “south to north” movement, and that the cumulative surface deformation is mainly characterized by subsidence, with deformation ranging from −199.5 mm to 55.9 mm. The deformation shows significant spatial heterogeneity, with its magnitudes generally decreasing from the headwall area (southern part) towards the depositional toe (northern part). In addition, the multifactorial driving mechanism of the thaw slump was further explored by combining geological investigation and geotechnical tests. The analysis reveals that the thaw slump’s evolution is primarily driven by temperature, with precipitation acting as a conditional co-factor, its influence being modulated by the slump’s developmental stage and local soil properties. The active layer thickness constitutes the basic geological condition of instability, and its spatial heterogeneity contributes to differential settlement patterns. Freeze–thaw cycles affect the shear strength of soils in the permafrost zone through multiple pathways, and thus trigger the occurrence of thaw slumps. Unlike single sudden landslides in non-permafrost zones, thaw slump is a continuous development process that occurs until the ice content is obviously reduced or disappears in the lower part. This study systematically elucidates the spatiotemporal deformation patterns and driving mechanisms of an active-layer detachment thaw slump by integrating multi-temporal InSAR remote sensing with geological and geotechnical data, offering valuable insights for understanding and monitoring thaw-induced hazards in permafrost regions. Full article

The drive towards carbon neutrality has prompted the worldwide expansion of utility-scale solar facilities. Previous studies have reported the positive effects of solar facilities’ installation on pasture productivity and biodiversity in arid regions. However, our understanding of how solar facilities influence a wide range of ecosystem functions simultaneously, and the relative contributions of soil microbial attributes, remains incomplete. To address this gap, we assessed the changes in ecosystem multifunctionality following solar facility installation in an alpine desert grassland in the Qinghai–Tibet plateau by measuring twenty-three ecosystem function indicators comprising primary production, the soil nutrient pool, carbon cycling, nitrogen cycling, phosphorus cycling and oxidation–reduction. Furthermore, we estimated the soil microbial diversity, microbial indicator taxa and microbial activity to identify the primary driving factors. The results showed that solar facilities had positive effects on ecosystem multifunctionality; the positive effect size was higher in the initial installation period (31.4%) than in the constant running period (3.5%). The enhancements in ecosystem multifunctionality were mainly due to enhanced nutrient cycling induced by the increased abundance of fungal indicator taxa and microbial activity. Moreover, the structural equation model revealed distinct regulatory paths between the two periods and a transition in the primary driving factors of ecosystem multifunctionality from microbial indicator taxa to microbial activity. In conclusion, our study demonstrates the positive influence of solar facilities on multiple ecosystem functions, emphasizing the critical role of soil microbial mechanisms in regulating ecosystem multifunctionality. These findings provide valuable insights into soil biota-driven processes that could inform strategies aimed at enhancing soil health and ecosystem functionality in arid grasslands under human-managed systems. Full article

This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller. Full article

In cold-region rock engineering, freeze–thaw cycle-induced crack propagation in fractured rock masses serves as a major cause of disasters such as slope instability. Existing studies primarily focus on the influence of individual fissure parameters, yet lack a systematic analysis of the crack propagation mechanisms under the coupled action of multiple parameters. To address this, we establish three groups of slope models with different rock bridge distances (d), rock bridge angles (α), and fissure angles (β) based on the PFC2D discrete element method. Frost heave loads are simulated by incorporating the volumetric expansion during water–ice phase change. The Parallel Bond Model (PBM) is used to capture the mechanical behavior between particles and the bond fracture process. This reveals the crack evolution laws under freeze–thaw cycles. The results show that, at a short rock bridge distance of d = 60 m, stress concentrates in the fracture zone. This easily leads to the rapid penetration of main cracks and triggers sudden instability. At a long rock bridge distance where d ≥ 100 m, the degree of stress concentration decreases. Meanwhile, the stress distribution range expands, promoting multiple crack initiation points and the development of branch cracks. The number of cracks increases as the rock bridge distance grows. In cases where the rock bridge angle is α ≤ 60°, stress is more likely to concentrate in the fracture zone. The crack propagation exhibits strong synergy, easily forming a penetration surface. When α = 75°, the stress concentration areas become dispersed and their distribution range expands. Cracks initiate earliest at this angle, with the largest number of cracks forming. Cumulative damage is significant under this condition. When the fissure angle is β = 60°, stress concentration areas gather around the fissures. Their distribution range expands, making cracks easier to propagate. Crack propagation becomes more dispersed in this case. When β = 30°, the main crack rapidly penetrates due to stress concentration, inhibiting the development of branch cracks, and the number of cracks is the smallest after freeze–thaw cycles. When β = 75°, the freeze–thaw stress dispersion leads to insufficient driving force, and the number of cracks is 623. The research findings provide a theoretical foundation for assessing freeze–thaw damage in fractured rock masses of cold regions and for guiding engineering stability control from a multi-parameter perspective. Full article

Three-dimensional object detection has emerged as a focal point of increasing interest among researchers, driven by advancements in and widespread adoption of autonomous driving technologies. However, this field still faces inherent challenges in single-modal approaches that rely solely on point cloud data for 3D object detection, such as the difficulty in effectively extracting features from sparse point clouds and the lack of critical texture information in the captured representations. To overcome these limitations, we introduce PomageNet, a fusion approach that combines point cloud and image data for 3D object detection. First, initial detection results from the two different kinds of data were applied as inputs, and joint encoding was performed. The encoded joint tensor is then fed into fusion layers. In the fusion stage, multiple 1 × 1 2D convolutions are employed to extract joint high-dimensional features. To enhance feature extraction, a parallel dual-branch framework was designed, and a multidimensional joint encoding mechanism tailored to the network was proposed to better capture contextual information. Experiments show that the capacity of our model is comparable to state-of-the-art (SOTA) methods on KITTI, which was achieved by the proposed network. Results were delivered by the method in detecting small objects, a key challenge in 3D object detection. An average precision (AP) of 67.87% and 60.40% was reached on the cyclist and pedestrian splits of KITTI. Compared to CLOCs, significant improvements were achieved by PomageNet, with 1.28%, 8.40%, and 3.64% increases in the result of detection achieved on the car, cycle, and pedestrian splits of the KITTI dataset. Full article

To implement the Kigali Amendment to the Montreal Protocol, the global academic community has intensified its research on environmentally friendly refrigerant substitutes. This effort aims to effectively reduce greenhouse gas emissions and facilitate the achievement of carbon neutrality goals. In this study, 14 key influencing factors were identified through the Delphi method, and the Decision-making Trial and Evaluation Laboratory (DEMATEL) approach was innovatively applied to systematically analyze the interrelationships among these factors. The results indicate that technological innovation related to refrigerant substitution ranks first with a centrality score of 5.429, confirming it as the core driving factor for refrigerant substitution. Subsequently, through the integration of Interpretive Structural Modeling (ISM) and Cross-impact Matrix Multiplication Applied to Classification (MICMAC), a hierarchical structure of influencing factors was further developed. This clarified high-driving factors such as government policies and life-cycle costs, as well as highly interrelated factors including climate conditions, greenhouse gas emissions, and performance coefficients. The key contribution of this paper is its success in overcoming the limitations of single-factor analysis by integrating multiple dimensions of influencing factors to construct a hierarchical classification. This innovative and systematic theoretical framework not only offers a scientific basis and decision-making support for refrigerant substitution but also possesses substantial theoretical value and practical guidance. Furthermore, it serves as an essential reference for advancing the development of low-carbon refrigeration technologies. Full article

Recently, the electrification and automation of heavy-duty trucks has gained significant attention from both industry and academia, driven by new legislation introduced by the European Union. During a typical drive cycle, the mass of an urban service truck can vary substantially as waste is collected, yet most existing studies rely on a single controller with fixed gains. This limits the ability to adapt to mass changes and results in suboptimal energy usage. Within the framework of the EU-funded OBELICS and ESCALATE projects, this study proposes a novel control strategy for a semi-autonomous refuse truck. The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. The main objectives of the proposed method are to (i) optimize controller parameters, (ii) reduce overall energy consumption, and (iii) minimize speed tracking error. A cost function addressing these objectives is formulated for both autonomous and manual driving modes. The strategy is evaluated using a real-world drive cycle from Eskişehir City, Turkiye. Simulation results show that the proposed MMC-based method improves vehicle performance by $5.19\%$ in autonomous mode and $0.534\%$ in manual mode compared to traditional fixed-gain approaches. Full article

This paper is devoted to the study of the dynamical behaviors of a socioeconomic mathematical model with a discrete time delay. The model includes economic growth, corruption, and unemployment, which are some of the main factors driving the economy of a nation. Due to the complex nonlinear nature of the relationships between the variables, our aim is to explore stable steady states, bifurcations, and limit cycles in the parameter space. We prove the existence of multiple limit cycles arising from Hopf bifurcations. In particular, we establish conditions for the existence of Hopf bifurcations and the appearance of economic limit cycles. We find threshold values for the delay in which these Hopf bifurcations occur. We provide additional support to the theoretical findings by performing numerical simulations. Various interesting socioeconomic scenarios are displayed in which limit cycles occur. The discussion and future directions of the research are presented. Full article

Life cycle assessment (LCA) is an excellent tool for developing energy saving and emission reduction strategies. However, inconsistencies in the summary calculation methods in LCA can significantly affect the reliability of LCA reports, such as the allocation of environmental loads related to solid waste. Essentially, allocation methods are used to allocate responsibility for environmental loads in situations where boundaries are unclear, and therefore, they are susceptible to regional, industry, and regulatory influences. For a long time, there has been controversy over the selection of allocation methods. This study is based on actual production data from a typical cement plant in South China. Multiple parallel LCA cases were carried out using different allocation methods, and different allocation methods were analyzed. Concepts such as driving force, active/passive environmental load, Valorized Solid Waste (VSW), and Non-Valorized Solid Waste (NVSW) were introduced. Analysis shows that the choice of allocation method directly affects the effectiveness of energy saving and emission reduction plans in the LCA report. For VSW, the economic allocation method has been proven to have high universality, effectively capturing the driving forces of economic factors. For NVSW, based on the “polluter pays principle” and active/passive environmental load, we introduced the Collaborative Disposal Allocation Method (CD method). In this study, the environmental benefits of domestic waste collaborative disposal were calculated using the CD method, resulting in a 2.25% reduction in global warming potential (GWP) and a 45.39% reduction in respiratory inorganics (RIs). Full article

The photochemical production of tropospheric ozone (O₃) is very closely linked to seasonal cycles and peaks in solar radiation occurring during warm seasons. In the Mediterranean Basin, which is a hotspot for climate and air mass transport mechanisms, boreal warm seasons cause a notable increase in tropospheric O₃, which unlike stratospheric O₃ is not beneficial for the environment. At the Lamezia Terme (code: LMT) World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) station located in Calabria, Southern Italy, peaks of tropospheric O₃ were observed during boreal summer and spring seasons, and were consequently linked to specific wind patterns compatible with increased photochemical activity in the Tyrrhenian Sea. The finding resulted in the introduction of a correction factor for O₃ in the O₃/NO_x (ozone to nitrogen oxides) ratio “Proximity” methodology for the assessment of air mass aging. However, some of the mechanisms driving O₃ patterns and their correlation with other parameters at the LMT site remain unknown, despite the environmental and health hazards posed by tropospheric O₃ in the area. In general, the issue of ozone photochemical pollution in the region of Calabria, Italy, is understudied. In this study, the behavior of O₃ at the site is assessed with remarkable detail using nine years (2015–2023) of data and correlations with surface temperature and solar radiation. The evaluations demonstrate non-negligible correlations between environmental factors, such as temperature and solar radiation, and O₃ concentrations, driven by peculiar patterns in local wind circulation. The northeastern sector of LMT, partly neglected in previous works, yielded higher statistical correlations with O₃ than expected. The findings of this study also indicate, for central Calabria, the possibility of heterogeneities in O₃ exposure due to local geomorphology and wind patterns. A case study of very high O₃ concentrations reported during the 2015 summer season is also reported by analyzing the tendencies observed during the period with additional methodologies and highlighting drivers of photochemical pollution on larger scales, also demonstrating that near-surface concentrations result from specific combinations of multiple factors. Full article

While Battery Electric Vehicles (BEVs) offer environmental benefits by reducing carbon emissions during use, their range remains limited compared to conventionally fuelled vehicles. This paper focuses on identifying factors that directly influence BEV range and explores strategies to mitigate range anxiety among potential users. Specifically, it reviews the impact of battery cell characteristics and vehicle lightweighting. Using the WLTP Class 3B drive cycle, energy consumption and Depth of Discharge (DoD) were evaluated across various battery capacities. Multiple Lithium-Ion battery models were simulated to analyse discharge behaviour, while vehicle mass composition was examined to assess the effectiveness of lightweighting in extending driving range. A lower initial State of Charge (SoC) and a standard discharge rate were used to estimate the remaining range, highlighting an approximate gain of up to 6 km at lower DoD levels. This work aims to accurately demonstrate how battery technology and structural weight impact energy consumption and usable range in BEVs. Current modelling approaches often overlook the relationship between driver discomfort and battery performance metrics. The main contribution is to address the gap by integrating Li-ion discharge modelling with vehicle dynamics to estimate range and compare cell characteristics. The ultimate goal is to support cost-effective strategies for increasing BEV usability, aligning them more closely with conventional vehicle expectations and enhancing journey flexibility. Full article